Heat Load Index Forecast Season 2015-2016s3-ap-southeast-2.amazonaws.com/katestone-chlt-uploads/wp... · 2016-09-29 · Katestone Environmental Pty Ltd D14079-12 Meat and Livestock

Heat Load Index Forecast Season 2015-

2016

Prepared for:

Meat and Livestock Australia

September 2016

Final

Prepared by:

Katestone Environmental Pty Ltd

ABN 92 097 270 276

Ground Floor, 16 Marie Street | PO Box 2217

Milton, Brisbane, Queensland, 4064, Australia

www.katestone.com.au

[email protected]

Ph +61 7 3369 3699

Fax +61 7 3369 1966

Disclaimer

http://katestone.com.au/disclaimer/

Copyright

This document, electronic files or software are the copyright property of Katestone Environmental Pty. Ltd. and the information contained therein is solely for the use of the authorised recipient and may not be used, copied or reproduced in whole or part for any purpose without the prior written authority of Katestone Environmental Pty. Ltd. Katestone Environmental Pty. Ltd. makes no representation, undertakes no duty and accepts no responsibility to any third party who may use or rely upon this document, electronic files or software or the information contained therein.

Copyright Katestone Environmental Pty. Ltd.

Document Control

Deliverable #: D14079-12

Title: Heat Load Index Forecast Season 2015-2016

Version: 1.0 (Final)

Client: Meat and Livestock Australia

Document reference: D14079-12 CHLT2015-2016v1.0.docx

Prepared by: Andrew Wiebe, Frank Quintarelli, Ella Castillo and Christine Killip

Reviewed by: Christine Killip

Approved by:

Christine Killip

23 June 2016

http://katestone.com.au/disclaimer/

Katestone Environmental Pty Ltd D14079-12 Meat and Livestock Australia – Heat Load Index Forecast Season 2015-2016 – Final

29 September 2016

Page i

Contents

Executive Summary ............................................................................................................................................... iv 1. Project objectives ................................................................................................................................... 1 2. Service use ............................................................................................................................................... 2

3. Methodology ........................................................................................................................................... 4 3.1 Overview ..................................................................................................................................................... 4 3.2 The weather model ................................................................................................................................... 5 3.3 Heat Load ................................................................................................................................................... 6 3.4 Delivery ........................................................................................................................................................ 8

3.4.1 Forecast generation ................................................................................................................. 8 3.4.2 Website and database administration ................................................................................. 9 3.4.3 Onsite AWS integration .......................................................................................................... 10 3.4.4 Alerts ......................................................................................................................................... 11 3.4.5 Tools ........................................................................................................................................... 11

4. Season overview ................................................................................................................................... 12 4.1 Climate ...................................................................................................................................................... 12

4.1.1 ENSO ......................................................................................................................................... 12 4.1.2 Tropical Cyclones ................................................................................................................... 12

4.2 Heat Load ................................................................................................................................................. 12 4.2.1 Heat Advisories ........................................................................................................................ 15 4.2.2 Automated alerts .................................................................................................................... 15

4.3 Web site statistics ..................................................................................................................................... 16 5. Service performance ........................................................................................................................... 19

5.1 Benchmark locations .............................................................................................................................. 19 5.2 Forecast performance evaluation ....................................................................................................... 20 5.3 Performance of 2015/16 season ........................................................................................................... 20

5.3.1 Heat Load Index ..................................................................................................................... 20 5.3.2 Accumulated Heat Load Units ............................................................................................. 24

6. Subscriber survey................................................................................................................................... 32

7. Discussion ............................................................................................................................................... 33 7.1 Future improvements ................................................................................................................................. 33

8. Conclusion ............................................................................................................................................. 34

9. Bibliography ........................................................................................................................................... 35 Appendix A - Evaluation parameters ............................................................................................................... 36

A1 Performance measures for HLI, BGT, T, RH and WS at benchmark sites - Day 1 ............................ 38 A2 Performance measures for HLI, BGT, T, RH and WS at benchmark sites - Day 2 ............................ 40 A3 Performance measures for HLI, BGT, T, RH and WS at benchmark sites - Day 3 ............................ 42 A4 Performance measures for HLI, BGT, T, RH and WS at benchmark sites - Day 4 ............................ 44 A5 Performance measures for HLI, BGT, T, RH and WS at benchmark sites - Day 5 ............................ 46 A6 Performance measures for HLI, BGT, T, RH and WS at benchmark sites - Day 6 ............................ 48 A7 Performance measures for HLI, BGT, T, RH and WS at benchmark sites - Day 7 ............................ 50

Appendix B - AHLU contingency tables day one to seven .......................................................................... 52 Appendix C - Survey Results ............................................................................................................................. 188

Tables

Table 1 Southern Oscillation Index 90 day rolling average................................................................................. 12 Table 2 Difference in monthly average daily maximum HLI from 2015/16 and 2014/15 ............................... 14 Table 3 Monthly average daily maximum HLI during 2015/16 ........................................................................... 14 Table 4 Web site traffic for 2012-13 to 2015-16 season ........................................................................................ 16 Table 5 Forecast benchmark locations .................................................................................................................. 19 Table 6 1 and 2 day forecast AHLU contingency tables for all locations for the forecast season (forecast is on

the vertical and observed on the horizontal) ......................................................................................... 25 Table 7 3 and 4 day forecast AHLU contingency tables for all locations for the forecast season............... 26 Table 8 5 and 6 day forecast AHLU contingency tables for all locations for the forecast season............... 28 Table 9 7 day forecast AHLU contingency tables for all locations for the forecast season .......................... 30


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Table 10 State and feedlot size (#SCU) distribution of respondents ................................................................... 32

Figures

Figure 1 Site Summary Page the Cattle Heat Load Toolbox ................................................................................. 1 Figure 2 Uptake of the CHLT service since its launch in 2010/11 ........................................................................... 2 Figure 3 Location of subscriber feedlots, colour coded by number of head of cattle .................................... 3 Figure 4 Location of feedlots uploading to the HLDN, colour coded by head of cattle ................................. 3 Figure 5 Schematic of the CHLT early warning system ........................................................................................... 4 Figure 6 K-WRF forecast domain ................................................................................................................................ 5 Figure 7 Schematic of the land surface scheme used in the K-WRF system ....................................................... 6 Figure 8 Overview of the current process to deliver a forecast to the CHLT ...................................................... 9 Figure 9 Example HLI Threshold Calculator and Alert level .................................................................................. 11 Figure 10 Difference in mean daily maximum HLI for 2015/16 and 2014/15 by month aggregated for the 17

benchmark locations .................................................................................................................................. 13 Figure 11 Breakdown of alerts sent by month during forecast period ................................................................. 15 Figure 12 Total number of alerts issued by type and month .................................................................................. 16 Figure 13 Web site visits as a function of time of the day....................................................................................... 17 Figure 14 Web site visits as a function of day of the week ..................................................................................... 17 Figure 15 Web site visits as a function of State ......................................................................................................... 18 Figure 16 Benchmark locations .................................................................................................................................. 20 Figure 17 Mean HLI RMSE for the 17 benchmark sites since 2006 ......................................................................... 21 Figure 18 Minimum, maximum and mean root mean square error aggregated for the 17 benchmark locations

........................................................................................................................................................................ 22 Figure 19 Minimum, maximum and mean MAE aggregated for the 17 benchmark locations....................... 23 Figure 20 Minimum, maximum and mean bias aggregated for the 17 benchmark locations ....................... 23 Figure 21 Minimum, maximum and mean IOA aggregated for the 17 benchmark locations ........................ 24


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Glossary

Term Definition ACCESS Australian Community Climate and Earth-System Simulator

AFWA Air Force Weather Agency AHLU Accumulated Heat Load Unit ARW Advanced Research and Weather AWS Automatic Weather Stations BOM Bureau of Meteorology

CAPS Center for Analysis and Prediction of Storms CHTL Cattle Heat Load Toolbox

DFI Digital Filter Initialisation ECMWF European Centre for Medium-Range Weather Forecasts

ENSO The El Niño/Southern Oscillation ESRL Earth System Research Laboratory

FAA Federal Aviation Administration GASP Global Analysis and Prediction model

GFS Global Forecasting System HLI Heat Load Index

HPC High performance computing IOA Index of Agreement km kilometre

K-WRF Katestone Weather Research and Forecasting Model LAPS Local Area Prediction System LSM Land Surface Model

M metre m/s metres per second

MAE mean absolute error MJO Madden-Julian Oscillation

MMM Mesoscale and Microscale Meteorology MOS Model output statistic

NCAR National Centre for Atmospheric Research NCEP National Centre for Environmental Prediction NMM Non-Hydrostatic Mesoscale Model

NOAA National Oceanic and Atmospheric Administration NRL Naval Research Laboratory

NWP Numerical weather prediction NWP Numerical weather prediction

ºC degrees Celsius RMSE root mean squared error

SOI Southern Oscillation Index SST sea surface temperature

TC Tropical Cyclones VAR variational data-assimilation

WMO World Meteorological Organisation WPS WRF Pre-processing System WRF Weather Research and Forecasting WSF WRF System Framework


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EXECUTIVE SUMMARY

Heat stress in feedlot cattle can have a deleterious effect on cattle performance and in extreme cases lead to

cattle death. The National Feedlot Accreditation Scheme requires that feedlots have a heat stress management

plan in place to cope with weather events that can lead to excessive heat loads. The Cattle Heat Load Toolbox,

developed by Katestone, alerts feedlot operators of impending adverse weather conditions that could lead to

excessive heat load in feedlot cattle.

The toolbox is web based and provides access to weather and heat load forecasts out one week and risk

assessment programs. The service is underpinned by over 15 years of research into cattle heat load funded by

Meat and Livestock Australia (MLA). The Cattle Heat Load Toolbox brings all this research together and uses a

world class weather forecasting system to generate accurate forecasts across Australia. This service provides

useful and practical information to help feedlot operators manage heat stress in cattle through advanced warning

of adverse conditions. Thus allowing operators time to undertake appropriate actions to mitigate the risk of heat

stress when alerted.

Katestone has been providing this service for over ten years and in that time we have extended the service from

16 to 319 forecast locations (including 90 public sites). The forecast for the service is generated using the

Weather Research & Forecasting model. The system is monitored by Katestone scientists throughout the

summer season and assessed for performance in predicting the location, magnitude and duration of heat load

events. The system has proven to accurately predict these key features and alert the relevant operators of the

impending situation.

There are currently 440 subscribers, 228 user sites (221 feedlots and 7 abattoirs) registered to use the

forecasting service, covering nearly a million head of feedlot cattle across Australia. This season saw 45 new

feedlots register for the service.

Feedlot Operators subscribe to the service free of charge and request a forecast for their feedlot. This year, the

forecast service was extended to include abattoirs. Subscribers also define risk alert levels suitable to their

feedlot management and cattle type and condition through the Risk Assessment Program. Alerts are sent daily

by email or SMS to designated recipients (e.g. site managers, veterinarians).

The service provides early warning of potential major heat load events and rapid changes in the HLI through the

automated alerts system and the written Heat Load Advisory service. Warnings are issued at least 3 to 4 days in

advance. A total of 28 targeted email alerts were issued for the period October 2015 to March 2016 covering all

the major heat load events across Australia.

Overall the system performed well in predicting the HLI on an hour by hour basis for the majority of the forecast

period. Feedback from the end of season survey indicates that there is a high level of satisfaction with the

forecast accuracy, from most respondents in the survey. A number of participants have also expressed interest in

participating in a trial version of the Daily Monitoring App..


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1. PROJECT OBJECTIVES

The Cattle Heat Load Toolbox (Figure 1) was developed to assist in warning feedlot operators of impending

adverse weather conditions that could lead to excessive heat loads (and potential mortality) for feedlot cattle. The objectives of the project are to:

Provide heat load forecasts for feedlot operators across Australia

Deliver warnings of impending heat load events

Provide access to forecasts and the Risk Analysis Program over the internet

Provide guidance to feedlot operators to minimise losses due to heat load

Figure 1 Site Summary Page the Cattle Heat Load Toolbox

http://chlt.katestone.com.au/


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2. SERVICE USE

A total of 440 subscribers, 228 user sites (221 feedlots and 7 abattoirs) are currently registered for the CHLT

(Figure 2Error! Reference source not found.). The 2015-16 season saw the number of subscribers increase by

83%, the number of feedlots subscribed to the service increased by 25% and for the first time seven abattoirs

subscribed to the service. The CHLT service has experienced consistent growth in the subscriber base and

feedlots utilising the service (Figure 2). The addition of abattoirs to the service provides new opportunities to

manage heat stress throughout the supply chain and improve the overall efficiency of the feedlot industry.

Figure 2 Uptake of the CHLT service since its launch in 2010/11

The service is provided to any NFAS accredited feedlot and caters to large feedlots with greater than 10,000

head of cattle to smaller operations with less than 1,000 (Figure 3). There are 30 Feedlots participating in the

Heat Load Data Network (HLDN) (Figure 4). The HLDN integrates the onsite calculated AHLU’s into the CHLT

system every day before the alerts are sent; initialising the predicted AHLU from the measured data. HLDN data

is also displayed on the Feedlots CHLT My Site page.

The 2015/16 service saw some changes including:

National Heat Load Outlook generated by a Katestone forecaster updated twice weekly

Katestone forecaster issued targeted alerts and updates to subscribers in regions that were most at risk based on the National Heat Load Outlook

Transition to cloud based computing and hosting of the forecasting, database and web servers


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Figure 3 Location of subscriber feedlots, colour coded by number of head of cattle

Figure 4 Location of feedlots uploading to the HLDN, colour coded by head of cattle


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3. METHODOLOGY

3.1 Overview

There are three parts to a successful early warning system:

1. Accurate weather forecast

2. Appropriate triggers that are relevant to the local climate and represent conditions that are conducive to

heat stress in lot fed cattle

3. Communication of the warnings via an appropriate media

The following schematic presents an overview of the CHLT system (Figure 5). The blue areas represent the

global input from weather stations and models. These data are not gathered or generated directly by Katestone.

The purple represents the local weather forecast, generated by Katestone every day. The red box indicates the

areas of research that need to go into developing a robust system. The grey box represents the input from feedlot

weather stations (HLDN). And finally the delivery of the information is represented in green and shows the web

site and alerts.

Figure 5 Schematic of the CHLT early warning system


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3.2 The weather model

The Weather Research and Forecasting – Advanced Research and Weather (WRF-ARW) model, or simply WRF

is a numerical weather prediction (NWP) system used the world over by national weather service’s and militaries

for mission critical applications.

Katestone’s implementation of the WRF model (K-WRF) is initialised daily producing a 7 day forecast at an hourly

time step. The modelling domain extends from 105°E to 160°E and 8°S to 45°S encompassing a significant

portion of the oceans to better resolve the generation of tropical weather systems (Figure 6). The resolution of the

model is 12 km, meaning that data is generated at 12 km spacing over a 23,436,000 km2 area. K-WRF receives

its initial conditions from the GFS model, which already contains data assimilated from the Australian WMO

monitoring sites, as well as satellite and upper air soundings. The model also incorporates a detailed land surface

model that accounts for soil type, moisture content, porosity and vegetation type and density (Figure 7).

Figure 6 K-WRF forecast domain


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Figure 7 Schematic of the land surface scheme used in the K-WRF system

3.3 Heat Load

There are a number of climatic conditions that may predispose feedlot cattle to high body heat loads, including:

A recent rain event

A high ongoing minimum and maximum ambient temperature

A high ongoing relative humidity

An absence of cloud cover with a high solar radiation level

Minimal air movement over an extended period (4-5 days)

A sudden change to adverse climatic conditions

It is usually a combination of some or all of these conditions that leads to an excessive heat load event, which

may result in cattle deaths if conditions persist for a number of days.

The calculation of HLI requires Relative Humidity (RH) expressed as a percentage, Wind Speed (WS) in m/s and

Black Globe Temperature (BGT) in °C. HLI is calculated as a composite of HLILOW and HLIHIGH, with a weighting

factor determined as a function of the difference in the calculated BGT and a threshold of 25 °C. A blending

function was introduced as a result of an analysis of data over time, wherein it was evident that large jumps in

HLI could occur under the some circumstances when the BGT passes through 25°C – for example from 24.9°C

to 25.1°C.

In equation form, HLILOW and HLIHIGH are calculated as follows, noting that exp is the exponentiation function:

The weighting factor is calculated and used as:


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It is also worth noting that if any calculation of HLI yields a value less than 50, this value must be set to 50, as the

dissipation of heat does not increase below this point.

The use of BGT in calculating the HLI, rather than ambient temperature, takes into account radiation effects as

well as air temperature. Although sensors for measuring BGT exist, these are not included as part of the standard

weather station and must be ordered from a suitable supplier. In the absence of measured BGT, a quantified

relationship between BGT, ambient temperature (T) and solar radiation (SR) can be used. Here solar radiation

can either be a measured value or a calculated value.

BGT can be calculated from T and SR using the following equation (noting that log is the logarithm function using

base-10):

Accumulated Heat Load Units (AHLU) has been developed to give some indication of the amount of heat that is

accumulated by an animal when it is exposed to environmental conditions that are above its ability to maintain

thermo-neutral conditions.

For every hour that an animal is above its threshold HLI value, it will gain heat. This additional heat load

accumulates over time and is reflected as an increase in body temperature. It is a normal physiological response

for animals to gain heat during the day and dissipate this accumulated heat to the environment at night. If the

animal cannot dissipate this accumulated heat overnight, the animal carries a heat load into the following day.

This makes the animal more susceptible to the effects of subsequent heat load. The three aspects that determine

the potential for excessive heat load in feedlot cattle include time, intensity, and the opportunity to dissipate heat.

In order to calculate the AHLU, the HLI, upper (UL) and lower (LL) limit of the thermal neutral zones, and interval

(in hours) between successive HLI estimates (∆t) are required. LL is fixed at 77, while UL is a variable dependent

on the HLI value at which stock begins to accumulate heat. This depends on the stock characteristics, location,

and management practices including mitigation measures.

The equation for calculating AHLU is as follows:

If the HLI is less than LL (77), then the heat is dissipated at half the rate of accumulation (the difference between

HLI and LL). If the HLI falls between the LL and UL, then heat is neither dissipated nor accumulated. If the HLI is

greater than UL, heat is accumulated.

http://chlt.katestone.com.au/wp-admin/post.php?post=1164&action=edit#bgt-def


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In equation form, the AHLUCURRENT is calculated as:

AHLU values do not go below zero. If any calculation results in an AHLU value below zero, it is set to zero.

Sites connected to the HLDN are initialised from AHLU calculated from data collected at local AWS, which

theoretically would result in a more accurate AHLU forecast. The same holds true for BOM sites. Sites which do

not have an integrated AWS are initialised from the previous day’s AHLU forecast.

3.4 Delivery

3.4.1 Forecast generation

The sequence of steps that must be completed for the forecast to be delivered (as outlined in Figure 8); from data

retrieval and pre-processing to forecast computation and post processing is currently monitored by Katestone

between the hours of 5 am and 9 pm, 7 days a week. In the event there is a system failure between these hours

Katestone will work through the issue until it is resolved.


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Figure 8 Overview of the current process to deliver a forecast to the CHLT

Once the forecast is generated a daily checklist is completed by Katestone’s meteorologist. These checks include

but are not limited to:

1. K-WRF, GFS, ACCESS, ECMWF comparison

2. Weather systems analysis and comparison to satellite observations

3. Hot spot identification and analysis

4. Performance analysis versus observations (magnitude and timing)

Weekly Heat Load Advisories are issued by a Katestone meteorologist on Monday and Wednesday. Under

extreme situations the advisories are ramped up to daily updates. The Advisories are posted on the CHLT

website and emailed to subscribers in effected States

3.4.2 Website and database administration

The CHLT system is administered and maintained by a Katestone system administrator. The system

administrator maintains the integrity and security of the HPC facility within the data centre. There are three

nodes within the HPC facility that require administration and maintenance:

1. Computational node - Core activities are data retrieval, pre-processing and forecast computation

2. Database node - Core activities are post processing, data storage and data availability to the web server

3. Web node - Core activities are website delivery, user information management, web security


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In February the entire CHLT infrastructure was migrated to the Amazon Web Services platform. The

structure of the service remains the same with compute, database and web nodes, however the stability

and reliability of the service is now more robust.

The system administrator also maintains the CHLT website and includes:

Registering new subscribers

o checks their coordinates are valid

o configures site specific forecast in the model

Maintenance of the Cattle Heat Load Toolbox web site and associated databases

E-mail and SMS alert functions

Daily monitoring and maintenance of computer systems including weekends and holidays

Online and phone support for registered users during regular office hours (9 am to 5 pm)

Maintenance and update of the FAQ page

3.4.3 Onsite AWS integration

The Heat Load Data Network (HLDN) allows feedlots to send in their weather station data to Katestone's servers

and include these data in their site specific forecast for the AHLU. To date 28 sites are operational.

The AWS integration requires continuous monitoring of data quality, as spurious data entering the system can

adversely impact the prediction of risk and degrade confidence in the system. The integration step involves

calculating the AHLU for all thresholds from the onsite data and initialising the predicted AHLU from the last

available time step in the observations.

Katestone provides an automated data quality check at the integration step that flags spurious data and issues

an internal alert to manually quality assure the offending dataset. Our experience indicates that the spurious data

is either due to damage to the sensor, i.e. lightning strike or changes to the data format following a system

update by the AWS provider.

Katestone custom builds the data integration step for each sites specific AWS configuration, changes or updates

to this configuration can impact the integration step and cause severe data quality issues further down the line. In

the event spurious data is flagged Katestone will initiate the following process:

1. Lead meteorologist/Senior Data Analyst alerted

2. Data extracted for manually QA

3. AWS owner notified of issue and solution if known

4. If solution is unknown

5. AWS taken offline

6. AWS owner contacted and solution identified

7. If solution is still unknown

8. AWS supplier contacted and solution indentified

9. AWS reinstated to operations


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3.4.4 Alerts

The alerts, for a user selected HLI Threshold value, used in the system are:

AHLU event today: AHLU >50 units for today

AHLU event tomorrow: AHLU >50 for tomorrow and AHLU = 0 for less than 6 hours

Extended AHLU event: AHLU >50 units for more than 3 consecutive days

Incomplete night time recovery: AHLU = 0 for less than 6 hours for more than 3 consecutive days in 7 day forecast period

Rapid HLI change: change in HLI >40 units over 4 hours

3.4.5 Tools

A “Tools” page at the CHLT website provides users with access to previous reports, pre-summer checklists, the

Managing Summer Heat Workbook and various other resources.

The “HLI Threshold Calculator” appears in the sidebar of all pages. The Calculator allows users to quickly

calculate the HLI threshold for quick review of the correct forecast, based on the current status of their cattle and

site. This is different from the RAP as it only takes into account the cattle and site details as these variables can

change on a daily basis.

For example as the cattle’s days on feed increases, their risk of experiencing heat load also increases and their

alert threshold should therefore decrease overtime. The calculator allows the user to determine whether their

current alert threshold is too high or too low for a given cattle population. To assist with this process the current

alert level is displayed on the sidebar for each site (Figure 9).

Figure 9 Example HLI Threshold Calculator and Alert level


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4. SEASON OVERVIEW

4.1 Climate

2015-16 was the sixth hottest summer on record for Australia; national maximum temperatures were the tenth-

warmest overall and minimum temperatures the fourth-warmest. Minimum temperatures were very much above

average for more than 40% of Australia.

Mean temperatures were also very much above average for coastal northern Australia, inland western

Queensland and New South Wales, most of Victoria and South Australia. No state recorded a mean, minimum or

maximum that was below its long-term average. Rainfall was average for all states, except South Australia where

it was below average. Localised areas of above average rainfall were recorded in central New South Wales and

inland Queensland.

October 2015 was the warmest October on record and set new records across southern Australia. Average

maximum temperatures in Victoria where 5.94°C above average. The average October temperature in South

Australia, Victoria, New South Wales and parts of Western Australia was closer to an average December

temperature for these states. December 2015 was also a record setting month, while the maximum temperatures

were above average it was the minimum temperatures that set records across much of Victoria and parts of

South Australia and southern New South Wales.

Late February and early March 2016 saw record high temperatures set for inland Queensland, weak monsoon

activity and below average rainfall contributed to the buildup of heat across the region.

4.1.1 ENSO

The Southern Oscillation Index (SOI) remained was in a strong negative (El Niňo) phase over the 2015/16

summer season (Table 1). The El Niňo phase is generally associated with higher temperatures and reduced

rainfall across much of southern and eastern Australia.

Table 1 Southern Oscillation Index 90 day rolling average

Date Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16

SOI -20.2 -19.6 -13.0 -12.7 -12.2 -15.3

Source: Bureau of Meteorology

4.1.2 Tropical Cyclones

The 2015-16 tropical cyclone season was the least active in the satellite era (since.1969), with only three named

tropical cyclones. Tropical Cyclone Stan was the only system to make landfall, near Port Hedland in Western

Australia on January 31 2016. TC Uriah and TC Tatiana formed in the Australian region but did not pass within

400 km of the east coast.

4.2 Heat Load

The difference in the daily maximum HLI derived from observations at the 17 benchmark locations for the

2014/15 and 2015/16 summer forecast season observations is shown in Figure 10. Detailed breakdown for each


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of the sites is shown in Table 2. Negative values, shaded in grey, represent areas where in the monthly average

daily maximum for 2015/16 is lower than 2014/15.

On average, the daily maximum HLI for the 2015/16 summer season is higher than the 2014/15 summer season

for all months except for December. Hay, Griffith, Yanco, Albury, Clare, and Kataning were the only sites where

the daily maximum HLI during 2015/16 were higher than 2014/15. There were a few sites where the daily

maximum HLI during 2015/16 were higher than 2014/15. The differences are most notable during October,

February, and March.

The monthly average daily maximum HLI derived from observations for all sites is presented in Table 3. For most

of the sites, HLI peaks between January and February. The only exceptions are Hay and Tamworth, where peak

HLI values occurred during December.

Figure 10 Difference in mean daily maximum HLI for 2015/16 and 2014/15 by month aggregated for the 17 benchmark locations


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Table 2 Difference in monthly average daily maximum HLI from 2015/16 and 2014/15

Benchmark Site Monthly Average Daily Maximum HLI (2015/16 – 2014/15)

Oct Nov Dec Jan Feb Mar

Hay 6.6 3.2 2.5 1.4 -2.0 6.7

Moree 2.6 -1.1 -2.3 0.7 1.5 0.3

Griffith 7.5 2.0 2.3 < -0.1 -0.6 6.7

Yanco 7.8 1.7 1.6 1.1 -1.4 6.8

Tamworth 5.1 -0.1 -3.4 -1.0 2.6 0.3

Cessnock 6.2 2.4 -0.8 0.4 1.4 2.6

Armidale 4.6 1.3 -2.8 -0.3 1.7 0.6

Albury 7.1 1.9 0.8 1.4 -1.0 7.1

Emerald 0.8 1.8 -2.1 -0.2 6.2 1.2

Roma 1.7 0.7 -2.2 0.5 2.4 0.0

Oakey 1.0 2.4 -2.4 -0.6 3.9 0.3

Warwick 2.2 0.8 -3.9 -1.4 3.5 -2.8

Amberley 2.3 2.1 -2.3 -4.0 2.1 -2.5

Miles 2.1 3.1 -1.4 0.1 4.1 -0.2

Clare 4.6 1.3 4.3 1.3 -0.2 8.4

Charlton 4.7 0.9 -1.1 4.8 -2.5 7.3

Katanning 3.0 1.9 2.5 1.7 -1.4 2.0

Table 3 Monthly average daily maximum HLI during 2015/16

Benchmark Site Monthly Average Maximum HLI

Oct Nov Dec Jan Feb Mar

Hay 75 77 79 82 80 79

Moree 78 83 83 85 85 83

Griffith 76 76 79 81 81 79

Yanco 77 77 80 83 83 80

Tamworth 76 80 80 82 82 80

Cessnock 79 82 83 87 86 85

Armidale 68 74 73 76 76 74

Albury 76 77 79 82 82 79

Emerald 80 87 86 90 92 89

Roma 78 84 83 86 87 84

Oakey 75 84 81 84 84 82

Warwick 77 84 82 85 86 84

Amberley 80 87 87 89 89 89


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Miles 79 87 86 88 88 85

Clare 71 71 76 77 77 77

Charlton 72 72 76 78 77 77

Katanning 70 71 73 77 75 74

4.2.1 Heat Advisories

This season Katestone introduced a regional heat load advisory service. The advisory was updated twice weekly

by one of Katestones forecasters and increased to daily under extreme or rapidly changing situations. Regions

that were at risk of excessive heat loads, rapidly changing conditions or extended periods of adverse conditions

were identified and posted on the CHLT website. The forecaster also drafted an email explaining the situation

with a link to the advisory and send it to those states at risk. The email is state based as the location information

of the feedlots refers to state, further refinement to region is a possibility in the upcoming 2016/17 season.

4.2.2 Automated alerts

A total of 3,229 emails and 2,292 SMS alerts were issue during the 2015/16 summer forecast period, with a peak

number of email and sms alerts sent in February (Figure 11). The breakdown of alerts by type for each month is

shown in Figure 12. Extended heat events and alerts for today/tomorrow comprise most of the alerts. For the

months of October and November, rapid change in HLI also contributes significantly to the volume of alerts sent.

Figure 11 Breakdown of alerts sent by month during forecast period


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Figure 12 Total number of alerts issued by type and month

4.3 Web site statistics

Statistics for the CHLT website from 2012-13 to 2015-16 are summarised in Table 4. The web site was well

visited with 11,962 sessions from 2,437 users over the six month period from October 2015 to March 2016.

Traffic increased by 7% from last season and the number of site users increased by 3%. There was a slight

decrease in the number of users accessing the website from a mobile device (-10%). This may be because the

site is not optimised for mobile use. This issue should be addressed moving forward.

Table 4 Web site traffic for 2012-13 to 2015-16 season

Season Page

Views

Site

Users

Sessions Mobiles

Sessions

Average time on site

(mins)

2012-13 21,784 1,296 7,143 161 2.38

2013-14 28,350 1,683 8,515 524 2.57

2014-15 42,019 2,362 11,134 2,368 2.54

2015-16 43,638 2,437 11,962 2,124 2.58

The following figures present the breakdown of the web site visits as a function of time of day (Figure 13) and day

of the week (Figure 14) and State (Figure 15). The time of day and day of week breakdowns indicate that the site


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is most frequently used in the morning 6-11 am, 7 days a week. There is a slight drop in visits on the weekends,

but this is probably due to the reduced number of staff onsite.

Queensland is by far the most active state with 63% of visits followed by NSW (16%) and Victoria (5%), WA

(3%), SA (2%) and the NT (1%). The remaining 10% of page visits are from overseas.

Figure 13 Web site visits as a function of time of the day

Figure 14 Web site visits as a function of day of the week


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Figure 15 Web site visits as a function of State


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5. SERVICE PERFORMANCE

5.1 Benchmark locations

The performance of the forecasting service has been assessed against 17 benchmark locations. Most of these

sites have been included in the forecast service since its inception and provide a good measure of the forecasts

performance over the years. Figure 16 and Table 5 describe the benchmark locations.

Table 5 Forecast benchmark locations

Site Name Lat Lon WMO code State

Hay -34.54 144.83 94702 NSW

Moree -29.48 149.84 95527 NSW

Griffith -34.24 146.06 95704 NSW

Yanco -34.62 146.43 95705 NSW

Tamworth -31.07 150.83 95762 NSW

Cessnock -32.78 151.33 95771 NSW

Armidale -30.52 151.61 95773 NSW

Albury Airport -36.07 146.95 95896 NSW

Emerald -23.56 148.17 94363 QLD

Roma -26.54 148.77 94515 QLD

Oakey -27.4 151.74 94552 QLD

Warwick -28.2 152.1 94555 QLD

RAAF Amberley -27.62 152.71 94568 QLD

Miles -26.65 150.18 95529 QLD

Clare High School -33.82 138.59 95667 SA

Charlton -36.28 143.33 94839 VIC

Katanning -33.68 117.6 94641 WA


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Figure 16 Benchmark locations

5.2 Forecast performance evaluation

The performance of the forecast is reviewed at the AWS sites identified above. These locations are Bureau of

Meteorology sites and as such do not have Black Globe Temperature (BGT) measurements or solar radiation

measurements. Temperature, humidity and wind speed are reviewed for forecasting ability as is the HLI (and

AHLUs) calculated assuming the clear skies assumption for solar radiation. The HLI and AHLU performance

analysis is presented here. The performance analysis of the meteorological variables that are used to derive the

HLI and AHLU values are presented in Appendix A.

We have received some feedback from sites this summer indicating that BGT observations at site can be

significantly different to those calculated using solar radiation assumptions. These issues have been taken into

consideration in the future development path of the service, but unfortunately, cannot be addressed here.

5.3 Performance of 2015/16 season

5.3.1 Heat Load Index

Figure 17 shows the progression of the forecasts performance since 2006 for the 17 benchmark locations. The

parameters that are used to assess the performance of the system are in Appendix A. The root mean square

error (RMSE) for the one day out forecast of HLI is 3.24; the RMSE for the three day out forecast of HLI is 3.98,

well below the benchmark RMSE of 5. This is lowest compared to previous years, and shows that the forecast for

2015-16 out-performed all other years.


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Figure 17 Mean HLI RMSE for the 17 benchmark sites since 2006

The RMSE is a good benchmark for assessing the performance of the forecast as it places a higher weighting on

large errors. The result is a statistic that defines the largest error that can be expected on average. Figure 18

shows the aggregated RMSE for the 17 benchmark locations for the forecast period 1 October 2015 to 31 March

2016 for a 7 day forecast horizon.

The average RMSE remains below 5 until the 6th

day of the forecast and remains below 6 for the 6th

and 7th

day.

The minimum and maximum values are the sites that had the lowest and highest RMSE for that forecast day.

The distance between the crossbars in Figure 18Error! Reference source not found. remain relatively the

same throughout each day of the forecast, indicating that the errors in the forecast are internally consistent and

most likely the result of the sensitivity of the equations to small variations in the input parameters rather than the

omission or misrepresentation of environmental conditions.


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Figure 18 Minimum, maximum and mean root mean square error aggregated for the 17 benchmark locations

The Mean Absolute Error (MAE) and Bias are also useful statistics when used in conjunction with the RMSE.

Where the RMSE emphasises the few large errors in a dataset the MAE defines magnitude of the average

deviation of the prediction from the observed. This is shown in Figure 19 where the average error in the HLI is

below 3 units for the first 4 days of the forecast, only just reaching above 4 by the 7th

day.

The closer the MAE and the RMSE are to each other indicates a similar magnitude error in the forecast. In this

case the RMSE is higher than the MAE by about 1 to 1.5 HLI units. This indicates there are a small proportion of

errors in the data.


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Figure 19 Minimum, maximum and mean MAE aggregated for the 17 benchmark locations

The Bias defines whether average errors in the forecast are positive (over predictions) or negative (under

predictions). This is shown in Figure 20, where it is evident that on average the forecast has negative bias and

tends to under predict the HLI at the 17 benchmark locations.

Figure 20 Minimum, maximum and mean bias aggregated for the 17 benchmark locations

The IOA is a good measure of model performance in that it compares each individual hour against the mean of

the observations, such that if the predicted and observed values vary at different scales or rates a poor

agreement will result (< 0.6), where an IOA of 1 indicates perfect agreement. Figure 21 shows the IOA


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aggregated for the 17 benchmark locations. The IOA shows good agreement on average across all 7 forecast

days; with the minimum IOA remaining above 0.90 even on the 7th

day of the forecast.

Figure 21 Minimum, maximum and mean IOA aggregated for the 17 benchmark locations

Overall the system performed well in predicting the HLI on an hour by hour basis for the majority of the forecast

period. Feedback from the end of season survey (Section 6) indicates that there is a high level of satisfaction with

the forecast accuracy, from most respondents in the survey.

It is also worth noting that as the data is paired in time the forecast can be an hour or two behind or ahead of the

environment, causing a disparity in the dataset where the observed HLI is higher than predicted at any given

hour. This can be caused by the movement of weather features, such as a trough, across the monitoring point.

Where the model may move the trough through a region at 7 am, where as in reality the trough crossed that point

at 9 am. These small variations at the hourly scale can cause large variations in the HLI and this is then

displayed in the statistics on reanalysis. A review of daily AHLU via the contingency tables (as presented in the

following section) overcomes some of the minor discrepancies by interpreting hourly data.

5.3.2 Accumulated Heat Load Units

Table 6, Table 7, Table 8 and Table 9 show the one through to seven day forecast AHLU contingency tables for

all locations combined for the forecast season. Appendix B contains AHLU contingency tables for each

benchmark location. In the contingency tables, the vertical represents the forecast and the horizontal represents

the observed AHLU category. The tables summarise the accuracy of the forecast in predicting the correct AHLU

risk level as well as the frequency each risk level was predicted.

For example in Table 6 for day 1 of the forecast the Extreme AHLU 80 level was observed on average 4.6% of

the time and was correctly predicted on average 2% of the time. The forecast period covers 185 data points, so a

frequency of 4.6% translates to roughly 8.5 days and 4.1% to 3.7 days. The remaining 2.6% or 4.8 days is spread

over the predictions for High, Medium and Low AHLU 80 risk levels.


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As expected the error in the forecast gradually increases the further out the forecast goes. Such that by day 7 the

frequency of predicting a Low AHLU 80 risk level when an Extreme AHLU 80 risk level was observed has

increased significantly (from 1.3% to 2.9%). Overall the service correctly predicted the AHLU risk level the

majority of the time.

Table 6 1 and 2 day forecast AHLU contingency tables for all locations for the forecast season (forecast is on the vertical and observed on the horizontal)

1 Day

2 Day

AHLU 80 AHLU 80

Extreme 0.0 0.0 0.0 2.5 Extreme 0.0 0.0 0.0 2.2

High 0.1 0.3 0.9 0.8 High 0.0 0.3 0.9 0.8

Medium 0.9 2.3 1.7 0.5 Medium 0.9 2.2 1.5 0.6

Low 83.0 5.2 1.1 0.7 Low 82.8 5.4 1.4 1.1

Low Medium High Extreme Low Medium High Extreme

AHLU 83 AHLU 83

Extreme 0.0 0.0 0.0 1.4 Extreme 0.0 0.0 0.0 1.4

High 0.0 0.1 0.2 0.3 High 0.0 0.2 0.2 0.2

Medium 0.6 1.1 0.5 0.2 Medium 0.6 1.0 0.4 0.2

Low 91.4 2.8 0.4 1.0 Low 91.3 2.9 0.6 1.1


AHLU 86 AHLU 86

Extreme 0.0 0.0 0.0 0.6 Extreme 0.0 0.0 0.0 0.5

High 0.0 0.0 0.0 0.1 High 0.0 0.0 0.0 0.1

Medium 0.3 0.3 0.2 0.1 Medium 0.3 0.2 0.1 0.1

Low 96.8 1.0 0.3 0.3 Low 96.7 1.0 0.4 0.4


AHLU 89 AHLU 89


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Extreme 0.0 0.0 0.0 0.2 Extreme 0.0 0.0 0.0 0.2

High 0.0 0.0 0.0 0.1 High 0.0 0.0 0.0 0.1

Medium 0.1 0.0 0.1 0.0 Medium 0.1 0.0 0.0 0.0

Low 99.0 0.3 0.1 0.0 Low 99.0 0.3 0.1 0.1


AHLU 92 AHLU 92

Extreme 0.0 0.0 0.0 0.0 Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0 High 0.0 0.0 0.0 0.0

Medium 0.0 0.0 0.0 0.0 Medium 0.0 0.0 0.0 0.0

Low 99.6 0.2 0.1 0.0 Low 99.6 0.2 0.1 0.0


AHLU 92 AHLU 92

Extreme 0.0 0.0 0.0 0.0 Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0 High 0.0 0.0 0.0 0.0

Medium 0.0 0.0 0.0 0.0 Medium 0.0 0.0 0.0 0.0

Low 99.9 0.1 0.0 0.0 Low 99.9 0.1 0.0 0.0


Table 7 3 and 4 day forecast AHLU contingency tables for all locations for the forecast season

Day 3

Day 4

AHLU 80 AHLU 80

Extreme 0.0 0.0 0.1 2.2 Extreme 0.0 0.0 0.0 2.1

High 0.1 0.3 0.9 0.6 High 0.1 0.3 0.7 0.6

Medium 1.1 2.0 1.2 0.5 Medium 1.0 1.7 1.2 0.5

Low 82.5 5.5 1.6 1.3 Low 82.5 5.9 1.8 1.5


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AHLU 83 AHLU 83

Extreme 0.0 0.0 0.1 1.4 Extreme 0.0 0.0 0.0 1.3

High 0.0 0.1 0.2 0.2 High 0.0 0.2 0.2 0.2

Medium 0.6 1.0 0.4 0.2 Medium 0.6 0.8 0.2 0.2

Low 91.2 3.0 0.6 1.1 Low 91.2 3.0 0.7 1.2


AHLU 86 AHLU 86

Extreme 0.0 0.0 0.0 0.5 Extreme 0.0 0.0 0.0 0.4

High 0.0 0.0 0.0 0.1 High 0.0 0.0 0.0 0.1

Medium 0.3 0.2 0.1 0.1 Medium 0.3 0.2 0.1 0.1

Low 96.7 1.0 0.4 0.4 Low 96.7 1.0 0.4 0.5


AHLU 89 AHLU 89

Extreme 0.0 0.0 0.0 0.2 Extreme 0.0 0.0 0.0 0.1

High 0.0 0.0 0.0 0.1 High 0.0 0.0 0.0 0.1

Medium 0.1 0.1 0.0 0.1 Medium 0.1 0.0 0.0 0.0

Low 98.9 0.3 0.1 0.1 Low 99.0 0.3 0.1 0.2


AHLU 92 AHLU 92

Extreme 0.0 0.0 0.0 0.0 Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0 High 0.0 0.0 0.0 0.0

Medium 0.0 0.0 0.0 0.0 Medium 0.0 0.0 0.0 0.0

Low 99.6 0.2 0.1 0.1 Low 99.6 0.2 0.1 0.1


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AHLU 95 AHLU 95

Extreme 0.0 0.0 0.0 0.0 Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0 High 0.0 0.0 0.0 0.0

Medium 0.0 0.0 0.0 0.0 Medium 0.0 0.0 0.0 0.0

Low 99.9 0.1 0.0 0.0 Low 99.9 0.1 0.0 0.0


Table 8 5 and 6 day forecast AHLU contingency tables for all locations for the forecast season

Day 5

Day 6

AHLU 80 AHLU 80

Extreme 0.0 0.0 0.1 2.0 Extreme 0.0 0.0 0.1 1.7

High 0.1 0.3 0.6 0.6 High 0.3 0.3 0.5 0.5

Medium 1.1 1.5 1.2 0.6 Medium 1.3 1.5 1.0 0.6

Low 82.3 6.1 1.9 1.6 Low 81.8 6.2 2.3 1.9


AHLU 83 AHLU 83

Extreme 0.0 0.0 0.0 1.3 Extreme 0.0 0.0 0.0 1.2

High 0.1 0.1 0.1 0.1 High 0.0 0.1 0.1 0.1

Medium 0.6 0.8 0.2 0.2 Medium 0.7 0.6 0.3 0.2

Low 91.1 3.2 0.8 1.4 Low 91.0 3.4 0.9 1.4


AHLU 86 AHLU 86

Extreme 0.0 0.0 0.0 0.4 Extreme 0.0 0.0 0.0 0.4


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High 0.0 0.0 0.0 0.1 High 0.0 0.0 0.0 0.1

Medium 0.3 0.2 0.1 0.1 Medium 0.3 0.1 0.1 0.0

Low 96.7 1.1 0.4 0.6 Low 96.7 1.2 0.5 0.7


AHLU 89 AHLU 89

Extreme 0.0 0.0 0.0 0.1 Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0 High 0.0 0.0 0.0 0.0

Medium 0.1 0.1 0.0 0.0 Medium 0.0 0.0 0.0 0.0

Low 99.0 0.3 0.1 0.2 Low 99.0 0.3 0.1 0.3


AHLU 92 AHLU 92

Extreme 0.0 0.0 0.0 0.0 Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0 High 0.0 0.0 0.0 0.0

Medium 0.0 0.0 0.0 0.0 Medium 0.0 0.0 0.0 0.0

Low 99.6 0.2 0.1 0.1 Low 99.6 0.1 0.1 0.1


AHLU 95 AHLU 95

Extreme 0.0 0.0 0.0 0.0 Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0 High 0.0 0.0 0.0 0.0

Medium 0.0 0.0 0.0 0.0 Medium 0.0 0.0 0.0 0.0

Low 99.9 0.1 0.0 0.0 Low 99.9 0.1 0.0 0.0



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Table 9 7 day forecast AHLU contingency tables for all locations for the forecast season

7 Day

AHLU 80

Extreme 0.0 0.0 0.1 1.6

High 0.3 0.3 0.4 0.5

Medium 1.6 1.4 1.0 0.6

Low 81.3 6.4 2.4 2.1

Low Medium High Extreme

AHLU 83

Extreme 0.0 0.0 0.0 1.1

High 0.1 0.1 0.1 0.1

Medium 0.7 0.4 0.2 0.2

Low 90.9 3.6 0.9 1.5


AHLU 86

Extreme 0.0 0.0 0.0 0.3

High 0.0 0.0 0.0 0.0

Medium 0.3 0.1 0.0 0.1

Low 96.7 1.2 0.5 0.7


AHLU 89

Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0

Medium 0.1 0.0 0.0 0.0


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Low 99.0 0.3 0.1 0.4


AHLU 92

Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0

Medium 0.0 0.0 0.0 0.0

Low 99.6 0.1 0.1 0.1


AHLU 95

Extreme 0.0 0.0 0.0 0.0

High 0.0 0.0 0.0 0.0

Medium 0.1 0.0 0.0 0.0

Low 99.9 0.1 0.0 0.0



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6. SUBSCRIBER SURVEY

At the end of this season, a survey was sent out to all CHLT subscribers. The subscribers were invited to rate the

service. In addition, the survey was also used to gauge the subscribers’ interest in participating in a Daily

Monitoring App. Detailed results are shown in Appendix C.

The survey received a total of 61 responses, representing 50 feedlots. Users such as nutritionists or veterinarians

may be associated with more than one site.

The respondents were comprised of feedlot managers (41%), person responsible for monitoring the heat

situation at their site (33%), person responsible for the feed (12%), nutritionist (6%), live stock manager (4%),

researcher (1%) and vet (1%).

The spatial distribution of the respondents, categorised by estimated size of the feedlots is summarised in Table

10.

Table 10 State and feedlot size (#SCU) distribution of respondents

State No cattle

data 1 - 1000

1000 -

5000

5000 -

10000 > 10000 Total

QLD 7 9 1 4 4 25

NSW 2 3 1 3 4 13

SA 4 3 7

VIC 1 1 2

WA 1 2 3

Total 15 14 5 7 9 50

Approximately 96% of respondents were happy with the level of service this season. The same percentage of

users found the regular heat load outlook on the website, and the additional emails sent useful. However, only

57% take action to reduce the risk of a heat event based on the heat load outlooks all of the time; while an

additional 26% took action sometimes.

Approximately 43% of respondents experienced heat events (cattle with Panting Score 2-4). None of the sites

experienced multiple events. Some respondents believe that there were circumstances which may have

increased the risk (e.g. light winds and the cleanliness of the pen).

Forty three percent (43%) of respondents believe that the onsite AWS did not indicate that the cattle were at risk

of a heat event. On the other hand, 89% of all respondents believe that the forecast provided adequate warning

of the event.

Forty eight percent (48%) of respondents were interested in a better understanding of the risk of heat stress. Fifty

five percent (55%) of respondents have indicated their interest in participating in a trial group of the Daily

Monitoring App.


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7. DISCUSSION

The CHLT service has become an integral part of heat load management at Australian Feedlots. The number of

subscribers and feedlots that are registering for the service continues to grow every year. Overall the user base is

satisfied with the delivery and performance of the service and will be using the service in the future.

Katestone has already taken steps to ensure maximum uptime of the service, by transferring the CHLT

infrastructure the Amazon Web Services platform, a cloud based computing service. This move has significantly

increased the reliability and robustness of the service. Since the move there has been no downtime or data

transfer lag time. Meaning that the web site is always available, the alerts are issued and the forecast is

displayed by 6 am every day. It is important to note that during the peak season the service is monitored between

5am and 10pm every day; weekends and public holidays alike. While the majority of the system is automated, the

heat load advisories are generated by a human forecaster who interprets all the observations and predictions into

a plain English outlook for the week ahead.

Overall the 2015/16 season was uneventful, while there was a few high heat load days, no major incidents were

reported to Katestone. The weekly outlooks and heat load advisories were well received by the subscriber base

and proved to be a very productive avenue for communication between Katestone and individual feedlots. See

comments received from subscribers below.

"The hot weather over the past couple of weeks has certainly very trying. Thank you for the forecasts it's very helpful in regards to changing feed rations prior to the hot weather conditions arriving."

"Thanks for all the sound advice, has been working well in helping us make our decisions about the cattle. The cattle acted according to the your predictions made over the last week down here at Mathoura, I thought the higher temps might have had the HLI higher. Keep up the good work as makes us more confident in our strategies"

7.1 Future improvements

The current service has been well received and regarded by the subscriber base. No immediate suggestions

from the subscriber base for improvement were mentioned in the end of season survey. Katestone believes that

there is always room for improvement and our internal review of the service has identified the following areas for

improvement:

Heat load advisory emails should be targeted by region, i.e. Warrego and Maranoa, Northwest Slopes and Plains, in lieu of an all QLD and NSW email.

Further investigations into the BGT calculation and its measurement on site

Reassess the AHLU and its use and its application, taking into account the influence of different climatic regions

Allow feedlots to set their own alerts and alert thresholds (i.e. not all AHLU = 50)

Further development of a daily monitoring tool that integrates with the CHLT service

Make the CHLT web site more tablet and mobile friendly

Review onsite weather station data with on site observations of cattle heat stress

The last point is vital as 43% of the feedlots surveyed identified that their onsite weather station was telling them

that they were having a heat load event when their own observations contradicted the data. This implies that the

interpretation of AHLU from the measured onsite parameters is not indicating the true risk of a heat event in

feedlot cattle.


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8. CONCLUSION

A heat load forecast service was provided for the 2015/16 summer period. The service delivered 2,792

automated alerts via sms and e-mail. The numerical weather prediction system, K-WRF, performed well in

simulating the meteorological conditions that drive heat load events and in predicting the HLI and AHLU risk

levels. However, the HLI and AHLU must be reassessed; while the measured and predicted data show good

agreement, human observations of the cattle behaviour do not always agree with the data. This discrepancy

between the data and what the operators are witnessing is degrading subscriber confidence in the system.


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9. BIBLIOGRAPHY

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/weather-forecasting

http://www.bom.gov.au/australia/charts/about/about_access.shtml

http://www.ecmwf.int/products/forecasts/guide/The_ECMWF_global_atmospheric_model.html

http://www.emc.ncep.noaa.gov/index.php?branch=GFS

EA Systems (2004), “FLOT. 327 – Development of a Heat Load Risk Assessment Process for the Australian

feedlot industry”.

Gaughan J, Goopy J and Spark J (2002), “Excessive Heat Load Index for Feedlot Cattle”, University of

Queensland.

Liu, Y. and co-authors, 2008: The operational mesogammascale analysis and forecast system of the U.S. Army

Test and Evaluation Command. Part I: Overview of the modeling system, the forecast products, and how the

products are used. J. of Appl. Meteor. and Clim., 47, 1077-1092.

Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M.G. Duda, X-Y. Huang, W. Wang and J. G.

Powers, 2005: A description of the Advanced Research WRF Version 3. NCAR Technical Note, NCAR/TN-

475+(STR), National Center for Atmospheric Research, Boulder, CO, 125 pp.

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/weather-forecasting

http://www.bom.gov.au/australia/charts/about/about_access.shtml

http://www.ecmwf.int/products/forecasts/guide/The_ECMWF_global_atmospheric_model.html

http://www.emc.ncep.noaa.gov/index.php?branch=GFS


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APPENDIX A - EVALUATION PARAMETERS

The measures used to evaluate performance are the Index of Agreement (IOA), the Mean Absolute Error (MAE),

Bias and the Root Mean Square Error (RMSE). For example:

an IOA of 1 implies a perfect forecast with no difference between the observed and the predicted at all points in time

an MAE of 2 for temperature signifies an average error of 2°C over the entire period of the analysis

a Bias of -1 for temperature indicates that on average the model under predicts the temperature over the entire period of the analysis

a RMSE of 2 for temperature signifies an average squared error of 2°C, the statistic exposes large errors and should be considered in relation to the MAE

the closer the RMSE is the to the MAE indicates that most of the errors of the same magnitude and that they are consistent, this is preferable.

The IOA is the ratio of the total RMSE to the sum of two differences: the difference between each prediction and

the observed mean and the difference between each observation and observed mean. The IOA is a good

measure of model performance in that it compares each individual hour against the mean of the observations,

such that if the predicted and observed values vary at different scales or rates a poor agreement will result (<

0.6). Where an IOA of 1 indicates perfect agreement or both the predicted and observed vary about the mean at

the same scale and rate. The equation for the IOA is:

2

1

2

1

|)||(|

)(

1IOA

meani

N

i

meani

N

i

ii

OOOP

OP

The root mean squared error (RMSE) can be described as the standard deviation of the difference for hourly

prediction and observation pairings at a specific point which measures the average magnitude of the error. The

difference between forecast and corresponding observed values are each squared and then averaged over the

sample. Finally, the square root of the average is taken. Since the errors are squared before they are averaged,

the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors

are particularly undesirable. Overall, the RSME is a good overall measure of model performance. The equation

for the RMSE is:

2

1

)(1

RMSE

N

i

ii OPN

The mean absolute error (MAE) measures the average magnitude of the errors in a set of forecasts, without

considering their direction. It measures accuracy for continuous variables. The equation is given in the library

references. Expressed in words, the MAE is the average over the verification sample of the absolute values of

the differences between forecast and the corresponding observation. The MAE is a linear score which means

that all the individual differences are weighted equally in the average. The equation for the MAE is:


29 September 2016

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)(MAE1

OP i

N

ii

abs

The MAE and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts. The

RMSE will always be larger or equal to the MAE; the greater difference between them, the greater the variance in

the individual errors in the sample. If the RMSE=MAE, then all the errors are of the same magnitude.

The Bias is simply the mean difference between the observed and the predicted. It provides a measure of the

average sign and magnitude of the error; such that a negative value is an under prediction and a positive value

an over prediction. The equation for Bias is:

)(BIAS1

OP i

N

ii

There are no defined standards for NWP model performance; statistical scores simply provide a means to

quantify the magnitude of the difference between predictions and observations. Table A1 is a useful guide to

performance benchmarks of what should be expected from a model. These values are guidelines and not

absolute values of pass or fail. They are based on hind cast model evaluation techniques; where a hind cast is

initialised by re-analysis data and corrected observations are assimilated (Emery et al 2001).

Table A1 Performance benchmark guidelines

Measure HLI T RH WS

IOA >0.9 >0.7 >0.7 >0.6

MAE <6 <2 <10 <2

BIAS ±2 ±1 ±5 ±1

RMSE <6 <2 <15 <2


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A1 PERFORMANCE MEASURES FOR HLI, BGT, T, RH AND WS AT BENCHMARK SITES - DAY 1

HLI Hay Moree Griffith Yanco Tamworth Cessnock Armidale Albury Emerald Roma Oakey Warwick Amberley Miles Clare Charlton Katanning

IOA 0.98 0.99 0.99 0.98 0.99 0.98 0.98 0.98 0.97 0.98 0.99 0.98 0.98 0.97 0.98 0.98 0.99

MAE 2.24 2.18 1.85 2.26 1.94 2.47 1.67 2.42 3.04 2.57 2.06 2.79 2.45 3.62 1.85 1.67 1.38

BIAS -1.10 0.12 -0.48 -1.55 0.54 -1.44 -0.16 -1.51 -2.10 -1.33 -1.20 -2.17 -0.80 -3.07 -1.37 -0.65 0.34

RMSE 3.33 3.11 2.86 3.27 2.89 3.49 2.59 3.56 4.07 3.53 2.83 3.66 3.40 4.68 2.91 2.73 2.22

BGT Hay Moree Griffith Yanco Tamworth Cessnock Armidale Albury Emerald Roma Oakey Warwick Amberley Miles Clare Charlton Katanning

IOA 0.98 0.98 0.99 0.99 0.98 0.98 0.99 0.99 0.98 0.97 0.99 0.99 0.99 0.97 0.99 0.98 0.99

MAE 2.26 2.02 1.77 1.63 1.94 1.99 1.57 1.80 2.13 2.33 1.47 1.48 1.47 2.38 1.47 1.88 1.57

BIAS -0.96 -1.10 -0.40 -0.30 -1.12 -1.08 -0.88 -0.37 -1.20 -0.66 -0.40 -0.36 -0.22 -1.19 0.10 -1.06 -0.56

RMSE 2.77 2.48 2.32 2.24 2.40 2.47 2.06 2.27 2.63 2.88 1.95 1.95 1.93 2.86 1.94 2.50 2.04

T Hay Moree Griffith Yanco Tamworth Cessnock Armidale Albury Emerald Roma Oakey Warwick Amberley Miles Clare Charlton Katanning

IOA 0.96 0.95 0.97 0.97 0.96 0.95 0.96 0.97 0.93 0.93 0.96 0.97 0.97 0.93 0.98 0.97 0.97


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MAE 2.14 1.90 1.69 1.56 1.86 1.92 1.57 1.73 2.00 2.19 1.40 1.42 1.40 2.25 1.44 1.82 1.55

BIAS -0.89 -1.03 -0.38 -0.30 -1.06 -1.04 -0.89 -0.34 -1.13 -0.61 -0.38 -0.34 -0.22 -1.13 0.11 -1.03 -0.58

RMSE 2.61 2.33 2.21 2.14 2.28 2.40 2.05 2.17 2.46 2.70 1.85 1.87 1.84 2.70 1.89 2.42 2.01

RH Hay Moree Griffith Yanco Tamworth Cessnock Armidale Albury Emerald Roma Oakey Warwick Amberley Miles Clare Charlton Katanning

IOA 0.93 0.91 0.92 0.93 0.90 0.89 0.92 0.91 0.90 0.86 0.93 0.93 0.94 0.88 0.95 0.93 0.93

MAE 7.99 9.15 8.28 7.66 10.68 9.63 9.32 8.84 10.71 11.83 8.42 7.96 7.68 11.84 7.59 8.27 9.96

BIAS 2.11 4.22 -0.53 -2.68 6.69 1.16 1.41 -4.74 -0.06 -0.21 0.83 -0.38 0.50 2.64 -2.07 4.26 6.90

RMSE 10.97 12.20 11.86 11.26 13.11 13.38 13.31 12.10 14.37 15.60 11.41 11.04 10.49 15.46 10.88 11.41 12.63

WS Hay Moree Griffith Yanco Tamworth Cessnock Armidale Albury Emerald Roma Oakey Warwick Amberley Miles Clare Charlton Katanning

IOA 0.82 0.75 0.78 0.76 0.78 0.76 0.80 0.74 0.73 0.69 0.71 0.61 0.83 0.51 0.61 0.60 0.84

MAE 1.01 1.11 1.03 1.00 1.03 0.85 0.92 1.01 1.00 1.17 1.39 1.88 0.98 1.84 1.79 1.61 0.84

BIAS 0.03 -0.41 -0.06 0.53 -0.39 0.13 -0.33 0.16 0.00 0.53 0.72 1.58 0.30 1.69 1.61 0.35 0.31

RMSE 1.37 1.44 1.36 1.34 1.31 1.12 1.17 1.27 1.31 1.54 1.77 2.28 1.27 2.20 2.16 2.41 1.14


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IOA 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.97 0.97 0.98 0.97 0.98 0.95 0.98 0.98 0.98

MAE 2.35 2.48 2.07 2.47 2.18 2.60 1.89 2.45 3.38 3.19 2.32 2.96 2.54 4.05 2.01 1.82 1.69

BIAS -1.17 -0.20 -0.57 -1.61 0.49 -1.42 -0.37 -1.39 -2.39 -1.86 -1.34 -2.24 -0.67 -3.42 -1.39 -0.77 0.48

RMSE 3.48 3.52 3.15 3.54 3.22 3.70 2.93 3.54 4.58 4.54 3.31 3.95 3.57 5.39 3.15 2.98 2.67


IOA 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.97 0.99 0.98 0.99 0.97 0.99 0.98 0.99

MAE 2.40 2.09 1.97 1.84 1.96 2.02 1.66 1.95 1.96 2.43 1.53 1.63 1.50 2.27 1.74 2.03 1.67

BIAS -0.76 -0.88 -0.30 -0.23 -0.83 -0.95 -0.83 0.07 -0.65 -0.48 -0.20 -0.12 0.06 -0.78 -0.03 -1.04 -0.17

RMSE 2.93 2.63 2.56 2.47 2.46 2.54 2.17 2.42 2.58 2.99 2.11 2.19 2.01 2.81 2.26 2.73 2.22


IOA 0.96 0.94 0.97 0.97 0.96 0.95 0.95 0.96 0.93 0.92 0.96 0.95 0.96 0.93 0.97 0.97 0.97


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MAE 2.28 1.96 1.88 1.76 1.86 1.95 1.66 1.87 1.82 2.28 1.46 1.56 1.43 2.14 1.69 1.95 1.63

BIAS -0.69 -0.82 -0.28 -0.23 -0.78 -0.91 -0.84 0.08 -0.61 -0.44 -0.19 -0.11 0.04 -0.73 -0.01 -1.00 -0.18

RMSE 2.76 2.46 2.44 2.36 2.34 2.45 2.16 2.32 2.40 2.80 2.00 2.10 1.90 2.64 2.18 2.61 2.15


IOA 0.91 0.90 0.90 0.90 0.90 0.88 0.91 0.88 0.87 0.83 0.90 0.90 0.92 0.85 0.93 0.92 0.92

MAE 8.53 9.20 8.89 8.58 10.63 10.27 9.93 9.86 11.42 12.49 9.48 9.11 8.29 12.78 8.87 9.23 10.26

BIAS 0.73 1.25 -1.77 -3.81 5.09 0.80 0.52 -6.25 -3.69 -4.07 -0.69 -1.49 -0.38 -1.18 -1.71 4.34 5.33

RMSE 11.81 12.51 12.83 12.58 13.16 14.34 14.12 13.50 16.10 17.20 13.19 13.21 11.37 17.36 12.50 12.36 13.19


IOA 0.78 0.70 0.75 0.75 0.75 0.72 0.75 0.72 0.67 0.68 0.68 0.58 0.79 0.50 0.60 0.61 0.81

MAE 1.10 1.21 1.10 1.01 1.08 0.91 1.01 1.04 1.12 1.13 1.43 2.01 1.07 1.80 1.70 1.59 0.92

BIAS -0.02 -0.56 -0.14 0.44 -0.37 0.14 -0.34 0.21 0.20 0.30 0.71 1.68 0.36 1.60 1.45 0.35 0.28

RMSE 1.48 1.57 1.42 1.35 1.40 1.21 1.29 1.30 1.45 1.47 1.83 2.40 1.39 2.16 2.05 2.36 1.24


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IOA 0.97 0.97 0.98 0.97 0.98 0.98 0.97 0.97 0.96 0.96 0.98 0.97 0.98 0.95 0.97 0.98 0.98

MAE 2.61 2.82 2.31 2.73 2.42 2.75 2.11 2.64 3.70 3.54 2.72 3.40 2.70 4.41 2.27 1.98 1.94

BIAS -1.30 -0.44 -0.86 -1.79 0.11 -1.44 -0.64 -1.53 -2.59 -1.89 -1.66 -2.58 -0.85 -3.58 -1.44 -0.91 0.52

RMSE 3.83 4.08 3.53 3.97 3.60 3.93 3.25 3.84 5.02 4.99 3.85 4.53 3.73 5.86 3.49 3.14 3.09


IOA 0.97 0.97 0.98 0.98 0.98 0.98 0.98 0.98 0.97 0.97 0.98 0.98 0.99 0.97 0.98 0.98 0.98

MAE 2.52 2.24 2.21 2.07 2.08 2.09 1.89 2.08 2.03 2.63 1.60 1.68 1.55 2.32 2.03 2.23 1.84

BIAS -0.84 -0.92 -0.50 -0.40 -0.92 -0.98 -1.00 0.03 -0.58 -0.45 -0.17 -0.10 0.06 -0.70 -0.05 -1.15 -0.10

RMSE 3.08 2.80 2.85 2.75 2.61 2.62 2.42 2.59 2.73 3.23 2.18 2.26 2.15 2.94 2.64 2.92 2.41


IOA 0.95 0.93 0.96 0.96 0.95 0.94 0.94 0.96 0.92 0.91 0.95 0.95 0.96 0.92 0.96 0.96 0.96


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MAE 2.39 2.10 2.11 1.97 1.98 2.02 1.88 1.99 1.90 2.47 1.52 1.62 1.47 2.19 1.97 2.15 1.80

BIAS -0.76 -0.86 -0.47 -0.38 -0.87 -0.93 -1.00 0.05 -0.54 -0.42 -0.17 -0.09 0.05 -0.65 -0.03 -1.11 -0.10

RMSE 2.90 2.62 2.71 2.63 2.48 2.52 2.41 2.48 2.54 3.03 2.07 2.17 2.03 2.76 2.54 2.81 2.34


IOA 0.90 0.89 0.88 0.88 0.88 0.87 0.90 0.86 0.84 0.80 0.88 0.87 0.91 0.83 0.92 0.91 0.92

MAE 8.76 9.47 9.75 9.43 11.08 10.78 10.69 10.47 12.42 13.54 10.24 10.23 8.63 13.36 9.74 9.65 10.73

BIAS 0.17 0.03 -2.19 -3.91 4.24 0.88 0.35 -6.60 -5.03 -5.41 -1.80 -2.87 -0.82 -2.78 -1.94 3.99 4.54

RMSE 12.24 13.41 14.11 13.73 13.99 14.83 15.59 14.27 17.68 18.80 14.74 15.36 12.45 18.81 13.62 13.09 13.82


IOA 0.75 0.64 0.74 0.73 0.73 0.72 0.74 0.69 0.64 0.66 0.68 0.55 0.79 0.49 0.58 0.59 0.78

MAE 1.18 1.32 1.10 1.02 1.11 0.90 1.03 1.07 1.14 1.16 1.45 2.11 1.09 1.73 1.68 1.63 0.98

BIAS -0.17 -0.69 -0.25 0.32 -0.42 0.07 -0.42 0.16 0.15 0.12 0.73 1.72 0.37 1.48 1.40 0.23 0.20

RMSE 1.55 1.70 1.41 1.35 1.41 1.19 1.31 1.33 1.50 1.49 1.84 2.51 1.40 2.11 2.06 2.32 1.33


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IOA 0.97 0.97 0.97 0.97 0.98 0.97 0.96 0.97 0.95 0.95 0.97 0.96 0.97 0.94 0.96 0.97 0.97

MAE 2.91 3.05 2.62 3.00 2.52 2.94 2.31 2.79 4.07 3.83 2.96 3.58 3.00 4.60 2.67 2.29 2.20

BIAS -1.38 -0.77 -0.88 -1.76 0.02 -1.49 -0.78 -1.34 -2.93 -2.13 -1.91 -2.83 -0.99 -3.83 -1.69 -0.92 0.68

RMSE 4.33 4.36 3.95 4.35 3.76 4.14 3.61 4.05 5.38 5.37 4.26 4.86 4.21 6.20 4.09 3.61 3.57


IOA 0.97 0.97 0.97 0.97 0.98 0.97 0.98 0.97 0.97 0.96 0.98 0.98 0.98 0.97 0.97 0.97 0.98

MAE 2.74 2.36 2.44 2.37 2.23 2.30 2.05 2.30 2.13 2.69 1.69 1.81 1.62 2.39 2.44 2.44 2.06

BIAS -0.80 -0.95 -0.50 -0.35 -1.04 -1.11 -1.12 0.06 -0.61 -0.54 -0.25 -0.18 0.07 -0.71 -0.29 -1.14 0.11

RMSE 3.46 2.98 3.17 3.12 2.84 2.89 2.60 2.93 2.90 3.32 2.29 2.43 2.19 3.03 3.21 3.23 2.76


IOA 0.94 0.92 0.95 0.95 0.94 0.93 0.93 0.94 0.91 0.90 0.95 0.94 0.95 0.92 0.95 0.95 0.95


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MAE 2.59 2.22 2.32 2.25 2.13 2.21 2.04 2.20 1.99 2.53 1.61 1.74 1.54 2.25 2.36 2.36 2.01

BIAS -0.72 -0.89 -0.47 -0.34 -0.98 -1.06 -1.12 0.08 -0.57 -0.50 -0.24 -0.17 0.06 -0.67 -0.26 -1.09 0.10

RMSE 3.26 2.80 3.01 2.96 2.70 2.77 2.58 2.79 2.70 3.12 2.18 2.32 2.07 2.85 3.07 3.10 2.66


IOA 0.87 0.85 0.86 0.86 0.87 0.86 0.88 0.83 0.82 0.78 0.86 0.86 0.90 0.80 0.89 0.90 0.91

MAE 9.74 10.46 10.60 10.41 11.64 11.28 11.48 11.24 13.10 14.29 11.30 10.85 9.16 14.08 11.40 10.21 11.04

BIAS -0.51 -0.94 -2.36 -4.12 4.29 1.52 0.32 -6.55 -5.88 -5.77 -2.27 -3.11 -1.46 -3.48 -1.70 3.99 3.68

RMSE 13.86 15.00 15.28 15.05 14.77 15.43 16.38 15.54 18.94 19.79 15.95 16.06 13.15 19.87 15.63 13.91 14.18


IOA 0.69 0.63 0.71 0.70 0.71 0.69 0.72 0.63 0.61 0.63 0.65 0.55 0.79 0.49 0.56 0.55 0.76

MAE 1.32 1.34 1.15 1.07 1.16 0.94 1.06 1.13 1.19 1.22 1.50 2.09 1.10 1.73 1.70 1.71 1.03

BIAS -0.18 -0.68 -0.26 0.32 -0.43 0.07 -0.38 0.09 0.15 0.16 0.69 1.73 0.36 1.48 1.38 0.20 0.19

RMSE 1.72 1.71 1.47 1.41 1.47 1.24 1.37 1.41 1.55 1.56 1.91 2.52 1.42 2.09 2.08 2.43 1.37


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IOA 0.96 0.97 0.97 0.96 0.98 0.97 0.96 0.96 0.94 0.96 0.97 0.96 0.97 0.94 0.95 0.96 0.97

MAE 3.30 3.18 2.96 3.50 2.65 3.32 2.36 3.15 4.53 3.83 3.14 3.74 3.20 4.68 3.04 2.82 2.51

BIAS -1.53 -0.63 -1.06 -1.91 0.28 -1.30 -0.61 -1.51 -3.37 -2.14 -1.95 -2.78 -0.99 -3.82 -1.97 -1.10 0.78

RMSE 4.88 4.47 4.39 4.96 3.91 4.52 3.63 4.57 6.04 5.34 4.46 5.02 4.47 6.19 4.71 4.36 3.87


IOA 0.96 0.97 0.96 0.97 0.97 0.97 0.97 0.97 0.97 0.96 0.98 0.98 0.98 0.97 0.96 0.96 0.98

MAE 3.01 2.48 2.84 2.68 2.34 2.40 2.23 2.50 2.18 2.74 1.85 1.89 1.77 2.51 2.86 2.91 2.27

BIAS -0.95 -0.89 -0.56 -0.38 -0.94 -1.05 -1.05 0.05 -0.70 -0.65 -0.35 -0.27 0.03 -0.83 -0.46 -1.38 0.14

RMSE 3.79 3.18 3.65 3.52 2.97 3.07 2.83 3.20 2.92 3.40 2.50 2.54 2.44 3.19 3.73 3.84 2.97


IOA 0.92 0.91 0.93 0.93 0.94 0.92 0.92 0.93 0.91 0.90 0.94 0.94 0.94 0.91 0.92 0.93 0.95


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MAE 2.85 2.33 2.70 2.55 2.23 2.31 2.22 2.39 2.03 2.58 1.77 1.82 1.68 2.36 2.77 2.81 2.21

BIAS -0.86 -0.83 -0.53 -0.36 -0.89 -1.00 -1.05 0.07 -0.66 -0.61 -0.33 -0.25 0.02 -0.78 -0.42 -1.33 0.13

RMSE 3.56 2.98 3.47 3.35 2.82 2.94 2.81 3.05 2.71 3.20 2.38 2.43 2.30 2.99 3.58 3.69 2.87


IOA 0.85 0.83 0.83 0.83 0.85 0.84 0.86 0.81 0.80 0.77 0.84 0.86 0.89 0.80 0.87 0.87 0.89

MAE 10.77 11.32 11.72 11.60 12.23 11.74 12.48 11.86 13.83 14.43 11.99 10.81 9.59 14.48 12.58 11.36 12.26

BIAS 0.13 -0.63 -2.43 -4.23 4.29 1.84 0.21 -6.49 -6.75 -5.62 -1.94 -2.55 -1.32 -3.03 -1.75 4.55 3.83

RMSE 15.02 15.94 16.76 16.59 15.47 15.96 17.69 16.33 19.85 19.87 16.87 15.78 13.72 20.02 17.24 15.80 15.86


IOA 0.63 0.62 0.67 0.67 0.69 0.68 0.70 0.63 0.60 0.60 0.62 0.53 0.77 0.46 0.55 0.51 0.68

MAE 1.46 1.36 1.25 1.19 1.17 0.96 1.11 1.17 1.21 1.25 1.53 2.10 1.14 1.75 1.71 1.80 1.17

BIAS -0.07 -0.67 -0.22 0.39 -0.44 0.09 -0.36 0.21 0.14 0.07 0.67 1.71 0.36 1.48 1.36 0.17 0.15

RMSE 1.89 1.75 1.60 1.54 1.51 1.27 1.40 1.47 1.60 1.60 1.97 2.56 1.48 2.16 2.08 2.48 1.55


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IOA 0.94 0.96 0.96 0.95 0.97 0.96 0.96 0.96 0.93 0.95 0.96 0.96 0.96 0.93 0.92 0.94 0.96

MAE 3.80 3.39 3.31 3.71 2.89 3.54 2.56 3.30 4.80 4.01 3.33 3.80 3.56 4.80 3.68 3.33 2.69

BIAS -1.77 -0.63 -1.24 -2.11 0.20 -1.49 -0.66 -1.68 -3.52 -2.21 -1.94 -2.71 -1.02 -3.94 -2.16 -1.23 0.71

RMSE 5.62 4.87 4.87 5.29 4.22 4.85 3.95 4.86 6.39 5.57 4.68 5.07 4.83 6.38 5.77 5.33 4.28


IOA 0.95 0.96 0.96 0.96 0.97 0.97 0.97 0.96 0.97 0.96 0.98 0.98 0.98 0.96 0.95 0.95 0.97

MAE 3.34 2.57 3.08 2.94 2.49 2.47 2.32 2.73 2.23 2.84 1.94 1.94 1.87 2.58 3.32 3.29 2.56

BIAS -0.95 -0.91 -0.65 -0.47 -1.00 -1.08 -1.10 -0.05 -0.66 -0.66 -0.29 -0.19 0.08 -0.81 -0.68 -1.29 0.04

RMSE 4.27 3.32 4.04 3.91 3.24 3.12 2.97 3.58 3.01 3.59 2.61 2.56 2.53 3.31 4.30 4.35 3.41


IOA 0.90 0.90 0.91 0.91 0.92 0.91 0.91 0.91 0.90 0.89 0.93 0.94 0.94 0.90 0.90 0.91 0.92


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MAE 3.16 2.42 2.93 2.80 2.37 2.38 2.31 2.61 2.07 2.67 1.85 1.86 1.78 2.43 3.21 3.17 2.49

BIAS -0.86 -0.85 -0.62 -0.45 -0.94 -1.03 -1.09 -0.02 -0.62 -0.62 -0.28 -0.17 0.06 -0.76 -0.63 -1.23 0.04

RMSE 4.02 3.11 3.84 3.71 3.07 2.99 2.94 3.41 2.79 3.37 2.48 2.45 2.39 3.11 4.13 4.16 3.28


IOA 0.81 0.82 0.80 0.80 0.83 0.83 0.84 0.79 0.78 0.74 0.83 0.85 0.88 0.78 0.84 0.84 0.88

MAE 11.94 11.74 12.61 12.44 13.04 12.01 13.19 12.61 14.53 15.76 12.47 11.04 10.01 15.06 13.98 12.79 12.52

BIAS -0.60 -0.76 -2.40 -4.01 4.60 1.21 0.14 -6.45 -7.74 -5.57 -2.36 -3.05 -1.66 -3.48 -1.48 4.24 4.38

RMSE 16.46 16.19 17.91 17.57 16.79 16.40 18.65 16.88 21.05 21.32 17.56 16.08 14.17 20.74 18.68 17.42 16.16


IOA 0.64 0.56 0.67 0.63 0.65 0.67 0.63 0.57 0.57 0.57 0.61 0.52 0.73 0.45 0.52 0.48 0.65

MAE 1.45 1.45 1.28 1.26 1.23 0.96 1.22 1.26 1.25 1.30 1.54 2.07 1.19 1.70 1.69 1.83 1.23

BIAS -0.06 -0.67 -0.18 0.42 -0.46 0.09 -0.44 0.21 0.11 0.09 0.65 1.63 0.37 1.40 1.26 0.30 0.21

RMSE 1.88 1.85 1.63 1.65 1.59 1.26 1.55 1.57 1.70 1.66 1.97 2.54 1.60 2.11 2.08 2.63 1.63


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IOA 0.93 0.96 0.94 0.93 0.97 0.96 0.95 0.94 0.92 0.95 0.95 0.95 0.95 0.92 0.91 0.92 0.95

MAE 4.32 3.62 3.97 4.40 3.17 3.77 2.80 4.02 4.99 4.20 3.70 4.11 3.94 5.02 4.00 3.74 2.88

BIAS -1.65 -0.33 -1.20 -2.01 0.46 -1.09 -0.49 -1.73 -3.58 -1.88 -1.91 -2.65 -1.00 -3.81 -2.25 -1.36 0.63

RMSE 6.39 5.12 5.92 6.36 4.56 5.07 4.17 5.87 6.90 5.76 5.20 5.55 5.38 6.83 6.19 5.88 4.59


IOA 0.94 0.96 0.95 0.94 0.97 0.96 0.97 0.95 0.97 0.96 0.97 0.98 0.98 0.96 0.94 0.94 0.96

MAE 3.61 2.61 3.40 3.31 2.61 2.78 2.43 2.98 2.26 2.81 2.04 2.06 1.97 2.54 3.71 3.53 2.75

BIAS -0.88 -0.79 -0.67 -0.47 -0.92 -1.08 -1.08 -0.04 -0.66 -0.56 -0.35 -0.22 0.12 -0.80 -0.75 -1.36 -0.01

RMSE 4.65 3.39 4.47 4.38 3.41 3.67 3.15 3.90 3.03 3.52 2.73 2.72 2.60 3.24 4.79 4.66 3.55


IOA 0.88 0.90 0.88 0.88 0.91 0.88 0.89 0.89 0.90 0.89 0.92 0.93 0.93 0.90 0.86 0.89 0.92


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MAE 3.41 2.45 3.24 3.14 2.48 2.67 2.42 2.85 2.11 2.64 1.94 1.97 1.87 2.39 3.60 3.40 2.67

BIAS -0.79 -0.73 -0.62 -0.44 -0.87 -1.03 -1.07 -0.01 -0.62 -0.52 -0.33 -0.20 0.10 -0.75 -0.69 -1.30 -0.01

RMSE 4.38 3.18 4.24 4.16 3.23 3.49 3.12 3.71 2.81 3.30 2.60 2.61 2.46 3.05 4.62 4.46 3.42


IOA 0.81 0.81 0.78 0.79 0.84 0.79 0.83 0.79 0.74 0.74 0.81 0.84 0.86 0.77 0.81 0.77 0.88

MAE 11.95 12.05 12.99 12.60 12.76 12.77 13.69 12.62 15.95 15.62 13.22 11.82 10.75 15.43 15.00 15.00 12.91

BIAS -0.64 -0.09 -2.36 -4.02 4.68 2.12 0.62 -6.73 -8.13 -5.53 -2.30 -2.98 -2.06 -3.64 -1.55 -0.23 4.31

RMSE 16.48 16.43 18.54 17.95 16.21 17.73 19.14 16.73 22.90 21.16 18.21 17.04 15.34 21.18 20.29 21.59 16.30


IOA 0.59 0.52 0.61 0.58 0.59 0.61 0.57 0.56 0.54 0.53 0.55 0.50 0.73 0.42 0.49 0.48 0.61

MAE 1.55 1.52 1.35 1.29 1.35 1.07 1.29 1.24 1.31 1.37 1.65 2.00 1.20 1.75 1.77 1.95 1.30

BIAS -0.20 -0.64 -0.27 0.31 -0.53 0.05 -0.48 0.15 0.09 0.02 0.56 1.51 0.30 1.37 1.31 -0.13 0.22

RMSE 1.96 1.92 1.71 1.67 1.75 1.38 1.64 1.55 1.76 1.77 2.10 2.49 1.55 2.18 2.17 2.72 1.69


29 September 2016

Page 52

APPENDIX B - AHLU CONTINGENCY TABLES DAY ONE TO SEVEN

Table B1 AHLU contingency table for Hay. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0.1 0.6 0

High 0 0.1 1.1 0

Medium 1.2 1.6 1.8 0

Medium 0.6 1.8 1.3 0

Low 89.1 5 0.5 0

Low 89.7 4.9 0.6 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0.1 0

High 0 0 0 0

High 0.1 0.4 0.9 0

Medium 0.3 0.7 0.3 0

Medium 1.1 1.3 0.9 0

Low 95.7 2.8 0.1 0

Low 89.1 5.1 1 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0 0

Medium 0.2 0.1 0 0

Low 99.2 0.8 0 0

Low 98.9 0.7 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0


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High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0.2 0 0 0

Low 100 0 0 0

Low 99.8 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 54

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0.1 0

Extreme 0 0 0 0

High 0.1 0.4 0.9 0

High 0 0 0.6 0

Medium 1.1 1.3 0.9 0

Medium 0.7 1.2 1.3 0

Low 89.1 5.1 1 0

Low 89.5 5.6 1.1 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.3 0 0

High 0 0 0 0

Medium 0.4 0.7 0.3 0

Medium 0.2 0.7 0.3 0

Low 95.5 2.6 0.1 0

Low 95.8 2.9 0.2 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.5 0.3 0 0

Medium 0.1 0.1 0 0

Low 98.7 0.6 0 0

Low 99.1 0.7 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0 0 0 0

Low 99.9 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0.4 0

High 0.3 0 0.3 0

Medium 0.3 0.8 1 0

Medium 1.2 0.7 0.9 0

Low 89.8 6 1.6 0

Low 88.6 6.2 1.8 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0.1 0

High 0 0 0 0

Medium 0.3 0.2 0.1 0

Medium 0.3 0.3 0.2 0

Low 95.6 3.3 0.3 0

Low 95.6 3.3 0.3 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0.1 0 0

Medium 0 0 0 0

Low 98.9 0.7 0 0

Low 99.1 0.9 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 7

AHLU 80

Extreme 0 0 0 0

High 0 0.1 0 0

Medium 1.3 0.6 0.8 0

Low 88.7 6.3 2.2 0


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.3 0 0.1 0

Low 95.6 3.6 0.4 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.9 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 60

Table B2 AHLU contingency table for Moree. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0.1 0.5

Extreme 0 0 0 0.3

High 0 0.8 0.8 0.2

High 0.1 1.1 1.4 0.3

Medium 2.8 3.9 1.6 0

Medium 2.4 3.5 1 0

Low 85.4 3.9 0.1 0

Low 85.7 4 0.2 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0.2 0.1 0

High 0 0 0.4 0.1

High 0.3 0.9 1 0.3

Medium 1.5 1.1 0.3 0

Medium 3.3 3.3 0.8 0.3

Low 95.1 1.5 0 0

Low 84.5 4.2 0.7 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0.1 0

High 0 0 0 0

Medium 0.3 0.2 0 0

Medium 0.3 0.4 0.1 0

Low 98.8 0.4 0 0

Low 98.9 0.4 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0.2 0.1 0 0

Medium 0 0 0 0

Low 99.7 0 0 0

Low 99.9 0.1 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0 0 0

Medium 0 0 0 0

Low 99.8 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0.2 0.1 0

Extreme 0.1 0.3 0 0

High 0.3 0.9 1 0.3

High 0.6 0.8 0.7 0

Medium 3.3 3.3 0.8 0.3

Medium 2.5 2.3 0.9 0.3

Low 84.5 4.2 0.7 0

Low 84.9 5.2 1 0.3



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.2 0.2 0 0

High 0.3 0.1 0 0

Medium 1.3 0.9 0.4 0

Medium 1.5 1 0.1 0

Low 95 1.5 0.3 0.1

Low 94.8 1.6 0.6 0.1



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.7 0 0.1 0

Medium 0.8 0 0 0

Low 98.4 0.7 0 0

Low 98.3 0.8 0.1 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0 0 0

Medium 0.1 0 0 0

Low 99.7 0.1 0 0

Low 99.8 0.1 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 64

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0.2 0.1 0

Extreme 0 0 0 0

High 0.5 0.9 0.5 0

High 0.6 0.5 0.4 0

Medium 3.3 2.5 0.9 0.5

Medium 4 2.9 0.9 0.3

Low 84.2 5.3 1.1 0.2

Low 83.3 5.4 1.3 0.4



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0.1 0 0

High 0 0.2 0 0

Medium 1 0.7 0.1 0

Medium 1.6 0.5 0 0

Low 95.4 1.8 0.7 0.1

Low 94.9 2 0.8 0.1



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0 0 0

High 0 0 0 0

Medium 0.4 0 0 0

Medium 0.5 0 0 0

Low 98.6 0.8 0.1 0

Low 98.6 0.8 0.1 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.4 0 0 0

Medium 0.2 0 0 0

Low 99.5 0.1 0 0

Low 99.7 0.1 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0 0 0 0

Low 99.9 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 66

Day 7

AHLU 80

Extreme 0 0 0 0

High 0.7 0.3 0.3 0

Medium 4.5 2.5 1.1 0.1

Low 82.7 6.1 1.2 0.6


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 1.6 0.7 0 0

Low 94.9 2 0.8 0.1


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.8 0.1 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 68

Table B3 AHLU contingency table for Griffith. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0.4 0.7 0

High 0 0.3 0.6 0

Medium 1.2 2.4 0.8 0

Medium 0.9 2.4 0.9 0

Low 90.8 3.6 0 0

Low 91 3.7 0.1 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0.1 0.5 0.7 0

Medium 0.5 1.1 0.1 0

Medium 0.8 2.2 0.7 0

Low 96.9 1.3 0 0

Low 91.1 3.8 0.2 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0 0

Medium 0.1 0.2 0 0

Low 99.5 0.3 0 0

Low 99.4 0.3 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 70

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0.5 0.7 0

High 0 0.1 0.4 0

Medium 0.8 2.2 0.7 0

Medium 1.5 2.1 0.7 0

Low 91.1 3.8 0.2 0

Low 90.4 4.2 0.6 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0 0 0

High 0 0 0 0

Medium 0.6 1.1 0.1 0

Medium 0.2 0.5 0 0

Low 96.7 1.3 0 0

Low 97.2 1.9 0.1 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0.1 0 0

Medium 0.1 0.1 0 0

Low 99.4 0.3 0 0

Low 99.5 0.3 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0.4 0

High 0.2 0.1 0.3 0

Medium 0.5 1.5 0.6 0

Medium 0.9 1.2 0.5 0

Low 91.3 4.9 0.6 0

Low 90.7 5.2 0.9 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.3 0.5 0 0

Medium 0.5 0.4 0 0

Low 97.1 2 0.1 0

Low 96.9 2.1 0.1 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0 0 0 0

Low 99.5 0.4 0 0

Low 99.5 0.4 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 7

AHLU 80

Extreme 0 0 0 0

High 0 0 0.1 0

Medium 1.5 1.3 0.4 0

Low 90.3 5.3 1.2 0


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0 0

Low 97.3 2.4 0.1 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.6 0.4 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


29 September 2016

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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 76

Table B4 AHLU contingency table for Yanco. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0.8 0

High 0 0.1 0.7 0

Medium 0.3 2.3 2 0

Medium 0.4 1.8 1.7 0

Low 87.8 6.3 0.4 0

Low 87.6 6.8 0.8 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0.2 1.2 0

Medium 0.1 1.1 0.1 0

Medium 0.6 2.4 1.5 0

Low 95.7 2.8 0.2 0

Low 87.4 6.2 0.6 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.2 0 0

Medium 0 0.1 0 0

Low 99.1 0.6 0 0

Low 99.1 0.7 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.2 1.2 0

High 0 0.2 0.4 0

Medium 0.6 2.4 1.5 0

Medium 0.5 2.1 1.5 0

Low 87.4 6.2 0.6 0

Low 87.3 6.6 1.3 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.2 0 0

High 0 0 0 0

Medium 0.2 1.3 0.2 0

Medium 0.4 0.6 0 0

Low 95.5 2.5 0 0

Low 95.3 3.3 0.3 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.2 0 0

Medium 0 0.1 0 0

Low 99.1 0.6 0 0

Low 99.1 0.7 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0.3 0

High 0 0.2 0.1 0

Medium 0.6 1.5 1 0

Medium 0.5 1.1 1.1 0

Low 87.2 7.5 1.9 0

Low 87.2 7.6 2.1 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0.6 0 0

Medium 0.5 0.2 0.1 0

Low 95.5 3.4 0.3 0

Low 95.1 3.9 0.2 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0.1 0 0 0

Low 99.1 0.9 0 0

Low 99 0.9 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




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Day 7

AHLU 80

Extreme 0 0 0 0

High 0 0.1 0.1 0

Medium 0.9 1.1 0.7 0

Low 86.8 7.8 2.5 0


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.3 0.1 0 0

Low 95.3 3.9 0.3 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.9 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 84

Table B5 AHLU contingency table for Tamworth. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0.1 0 0

Extreme 0 0 0 0

High 0.4 0.3 0.3 0

High 0.3 0.1 0.1 0

Medium 3.3 1.6 0.2 0

Medium 2.7 1.9 0.4 0

Low 92.5 1.4 0 0

Low 93.1 1.3 0 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0 0

High 0.2 0.3 0.1 0

Medium 0.8 0.4 0 0

Medium 2.8 1.2 0.2 0

Low 98.3 0.3 0 0

Low 93.1 2 0.1 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.3 0 0 0

Medium 0.2 0 0 0

Low 99.7 0 0 0

Low 99.8 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


29 September 2016

Page 85

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 86

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.2 0.3 0.1 0

High 0.2 0.3 0.2 0

Medium 2.8 1.2 0.2 0

Medium 2.7 1.3 0.2 0

Low 93.1 2 0.1 0

Low 93.2 1.9 0.1 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0.1 0 0 0

Medium 0.7 0.2 0 0

Medium 0.9 0.3 0 0

Low 98.5 0.7 0 0

Low 98.2 0.6 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0.2 0 0 0

Low 99.9 0 0 0

Low 99.8 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 87



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 88

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.6 0.1 0.1 0

High 0.3 0.1 0 0

Medium 3 1.1 0.2 0

Medium 3.2 1.1 0.1 0

Low 92.5 2.2 0.1 0

Low 92.6 2.2 0.4 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0 0 0

High 0 0 0 0

Medium 1.3 0.2 0 0

Medium 0.9 0 0 0

Low 97.7 0.6 0 0

Low 98.3 0.9 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.4 0 0 0

Medium 0 0 0 0

Low 99.6 0 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 89



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 90

Day 7

AHLU 80

Extreme 0 0 0 0

High 0.2 0.4 0.1 0

Medium 3 0.9 0.2 0

Low 92.9 2.2 0.2 0


AHLU 83

Extreme 0 0 0 0

High 0.1 0 0 0

Medium 0.9 0.1 0 0

Low 98.2 0.8 0 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Low 99.9 0 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


29 September 2016

Page 91


AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 92

Table B6 AHLU contingency table for Cessnock. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 1.9

Extreme 0 0 0 0.9

High 0 0.3 2 1.9

High 0 0.3 1.4 2.5

Medium 0.9 3.9 4 0.2

Medium 0.9 4 3.7 0.4

Low 75.3 8 1.5 0.1

Low 75.2 8 2.3 0.3



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0.6

High 0 0.1 0.6 0.4

High 0 0.1 1.9 1

Medium 0.5 2.4 1.1 0.3

Medium 1 3.6 2.1 1.1

Low 88.8 5.3 0.4 0.1

Low 75 8.6 3.5 1.4



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0.2 0 0

Medium 0.5 0.7 0.4 0

Medium 0.4 0.3 0.1 0

Low 96.5 1.5 0.4 0.1

Low 96.6 1.7 0.7 0.1



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


29 September 2016

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Medium 0.1 0 0 0

Medium 0.2 0 0 0

Low 99.3 0.3 0.2 0

Low 99.3 0.3 0.2 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.7 0.3 0 0

Low 99.7 0.3 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 94

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0.6

Extreme 0 0 0 0.5

High 0 0.1 1.9 1

High 0 0.4 1.5 1

Medium 1 3.6 2.1 1.1

Medium 1.2 3.7 2.7 1

Low 75 8.6 3.5 1.4

Low 74.7 8.3 3.3 1.7



AHLU 83

AHLU 83

Extreme 0 0 0.1 0

Extreme 0 0 0 0

High 0 0.3 0.3 0.1

High 0 0.2 0.3 0.2

Medium 0.2 2.2 0.6 0.3

Medium 0.7 2.1 0.5 0.4

Low 89 5.4 1.2 0.4

Low 88.4 5.6 1.3 0.3



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.2 0 0

High 0 0.1 0 0

Medium 0.3 0.1 0 0

Medium 0.2 0.3 0.1 0

Low 96.6 1.8 0.8 0.1

Low 96.7 1.8 0.7 0.1



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.3 0 0 0

Medium 0.2 0 0 0

Low 99.1 0.3 0.2 0

Low 99.3 0.3 0.2 0


29 September 2016

Page 95



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.7 0.3 0 0

Low 99.7 0.3 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 96

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0.2

Extreme 0 0 0 0.2

High 0.3 0.7 1.1 0.8

High 0.3 1 1.5 0.7

Medium 1.7 3.7 3.5 1.7

Medium 1.6 3.6 2.8 1

Low 73.8 8 3 1.4

Low 73.8 8 3.3 2.3



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.2 0 0.1

High 0 0.2 0 0.2

Medium 1.8 1.7 0.5 0.3

Medium 1.2 2 0.5 0.1

Low 87.3 6 1.6 0.4

Low 87.7 5.7 1.6 0.5



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.4 0 0.1 0

Medium 0.7 0.1 0.1 0

Low 96.5 2.2 0.7 0.1

Low 96.2 2.1 0.7 0.1



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.4 0.4 0.2 0

Low 99.4 0.4 0.2 0


29 September 2016

Page 97



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.7 0.3 0 0

Low 99.7 0.3 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 98

Day 7

AHLU 80

Extreme 0 0 0.1 0

High 0.6 1.2 1.2 0.6

Medium 2.2 3.2 2.5 1.5

Low 72.6 8.2 3.9 2.1


AHLU 83

Extreme 0 0 0 0

High 0.5 0.6 0 0

Medium 1.5 1.3 0.5 0.1

Low 86.9 6.1 1.7 0.7


AHLU 86

Extreme 0 0 0 0

High 0.1 0 0 0

Medium 1.4 0.1 0 0

Low 95.4 2.1 0.8 0.1


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.2 0 0 0

Low 99.2 0.4 0.2 0


29 September 2016

Page 99


AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.7 0.3 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 100

Table B7 AHLU contingency table for Armidale. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 99.9 0.1 0 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 99.9 0.1 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


29 September 2016

Page 101

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 102

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 99.9 0.1 0 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 103



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 104

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 99.9 0.1 0 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 105



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 106

Day 7

AHLU 80

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


29 September 2016

Page 107


AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 108

Table B8 AHLU contingency table for Albury. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0.4 0

High 0 0.2 0.5 0

Medium 0.5 2.6 0.7 0

Medium 0.7 2.5 0.5 0

Low 91.5 3.9 0.3 0

Low 91.2 4 0.4 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0.1 0.6 0

Medium 0.3 0.7 0 0

Medium 0.7 2.1 0.6 0

Low 97.8 1.2 0 0

Low 91.3 4.4 0.2 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0.1 0 0 0

Low 99.8 0.1 0 0

Low 99.7 0.1 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


29 September 2016

Page 109

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 110

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0.6 0

High 0.1 0.1 0.4 0

Medium 0.7 2.1 0.6 0

Medium 0.9 2.2 0.6 0

Low 91.3 4.4 0.2 0

Low 90.9 4.4 0.4 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.4 0.7 0 0

Medium 0.5 0.6 0 0

Low 97.6 1.2 0 0

Low 97.5 1.3 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0.1 0 0 0

Low 99.7 0.1 0 0

Low 99.8 0.1 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 111



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 112

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0.1 0.3 0

High 0.1 0.1 0.4 0

Medium 0.6 1.5 0.8 0

Medium 0.9 1.7 0.5 0

Low 91.1 5.2 0.4 0

Low 90.8 5 0.6 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.4 0.5 0 0

Medium 0.4 0.7 0 0

Low 97.7 1.4 0 0

Low 97.7 1.3 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Medium 0 0 0 0

Low 99.8 0.1 0 0

Low 99.9 0.1 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 113



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 114

Day 7

AHLU 80

Extreme 0 0 0 0

High 0.1 0 0 0

Medium 1.1 1.3 0.6 0

Low 90.6 5.5 0.8 0


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.2 0.1 0 0

Low 97.8 1.9 0 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


29 September 2016

Page 115


AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 116

Table B9 AHLU contingency table for Emerald. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 22.3

Extreme 0 0 0.3 21.8

High 0 0.5 2.6 5.5

High 0 0.2 2.2 3.8

Medium 0.3 3.8 3.4 5.6

Medium 0.4 3.3 3.2 5.4

Low 36.1 7.5 2.2 9.9

Low 35.7 8.5 2.6 12.7



AHLU 83

AHLU 83

Extreme 0 0 0 18.7

Extreme 0 0 0.3 22.7

High 0.2 0.2 0.7 1.6

High 0 0.3 2.1 3.7

Medium 0.8 2.5 1.3 2.1

Medium 0.7 3.1 2.6 4.1

Low 51.1 5.3 0.8 14.9

Low 35.1 8.6 3.4 13.3



AHLU 86

AHLU 86

Extreme 0 0 0 9.4

Extreme 0 0 0 9.1

High 0 0.2 0.2 1.2

High 0 0.2 0.1 0.7

Medium 1 1 0.9 1.6

Medium 1 0.5 0.9 0.6

Low 74.7 2.8 2.5 4.5

Low 74.6 3.3 2.6 6.4



AHLU 89

AHLU 89

Extreme 0 0 0 4.2

Extreme 0 0 0 3.6

High 0 0 0.1 1.4

High 0 0 0.1 1.7


29 September 2016

Page 117

Medium 0.4 0.5 0.7 0.6

Medium 0.4 0.4 0.3 0.7

Low 88.8 1.9 0.6 0.7

Low 88.7 2 1 1.1



AHLU 92

AHLU 92

Extreme 0 0 0 0.1

Extreme 0 0 0 0

High 0 0 0 0.8

High 0 0 0 0.5

Medium 0.2 0.1 0.2 0.7

Medium 0.1 0 0 0.6

Low 95.3 1.5 0.9 0.2

Low 95.3 1.5 1.1 0.8



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0.1 0

Medium 0 0 0 0

Low 98.7 1.2 0 0

Low 98.6 1.3 0.1 0




29 September 2016

Page 118

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0.3 22.7

Extreme 0 0 0.1 23.3

High 0 0.3 2.1 3.7

High 0 0.4 2.4 2.5

Medium 0.7 3.1 2.6 4.1

Medium 1.3 1.8 2.3 3.6

Low 35.1 8.6 3.4 13.3

Low 34 9.9 3.5 14.7



AHLU 83

AHLU 83

Extreme 0 0 0.3 19.9

Extreme 0 0 0.3 20.3

High 0.1 0.7 0.6 0.4

High 0 0.9 0.5 0.5

Medium 0.6 2.4 0.8 2.1

Medium 0.8 2 0.6 1.1

Low 50.8 5 1.1 15.2

Low 50.4 5.1 1.4 16



AHLU 86

AHLU 86

Extreme 0 0 0 7.9

Extreme 0 0 0 7.2

High 0.2 0.1 0.2 1.1

High 0 0 0.3 1.3

Medium 0.9 0.7 0.6 1

Medium 0.6 0.8 0.6 0.7

Low 74.3 3.2 2.8 6.8

Low 74.7 3.2 2.8 7.8



AHLU 89

AHLU 89

Extreme 0 0 0 2.7

Extreme 0 0 0 2.3

High 0 0 0 1.6

High 0 0 0 1

Medium 0.6 0.7 0 1

Medium 0 0.3 0 0.2

Low 88.5 1.7 1.5 1.8

Low 89 2.1 1.5 3.6


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0.2

High 0 0 0 0.2

Medium 0 0 0 0.2

Medium 0 0 0 0.6

Low 95.5 1.5 1.1 1.5

Low 95.4 1.6 1.1 1.1



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 98.6 1.3 0.1 0

Low 98.6 1.3 0.1 0




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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0.1 23.4

Extreme 0 0.2 0.1 23.8

High 0.3 0.7 1.7 2.2

High 1.1 0.8 1 1.7

Medium 1.3 1.9 2.5 3.3

Medium 2.1 2 1.7 3.5

Low 33.5 9.6 4.1 15.3

Low 31.5 9.2 5.7 15.6



AHLU 83

AHLU 83

Extreme 0 0 0 21

Extreme 0 0 0 20.4

High 0.5 0.7 0.4 0.3

High 0.3 0.1 0 1.4

Medium 0.9 1.3 0.4 1.1

Medium 2.2 1.1 1 1

Low 49.5 6.1 2 15.8

Low 48.2 6.9 1.9 15.5



AHLU 86

AHLU 86

Extreme 0 0 0 6.7

Extreme 0 0 0 6.1

High 0 0 0.3 1.4

High 0 0 0.2 1.3

Medium 0.9 0.3 0.3 1

Medium 1.1 0 0.2 0.5

Low 74.3 3.8 3.1 7.9

Low 73.9 4 3.3 9.3



AHLU 89

AHLU 89

Extreme 0 0.2 0 1.9

Extreme 0.2 0.5 0.2 0.8

High 0 0.1 0 0.5

High 0 0 0 0.7

Medium 0.5 0.5 0 0.7

Medium 0.2 0.2 0 0.3

Low 88.4 1.6 1.5 4

Low 88.5 1.8 1.3 5.4


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.2 0 0 0.1

High 0.7 0.2 0 0

Medium 0.2 0 0 0.7

Medium 0 0 0 0.8

Low 95 1.6 1.1 1.1

Low 94.7 1.4 1.1 1.1



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0.3 0 0 0

Medium 0.3 0 0 0

Medium 0.6 0 0 0

Low 98.3 1.3 0.1 0

Low 97.7 1.3 0.1 0




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Day 7

AHLU 80

Extreme 0.1 0.2 0 23.8

High 0.9 0.8 1.5 2.5

Medium 2.4 2.3 2.4 4

Low 30.9 9 4.7 14.5


AHLU 83

Extreme 0 0 0 18.5

High 0.3 0.4 0.2 1.7

Medium 1.5 1.5 0.3 2.1

Low 48.6 6.3 2.3 16.2


AHLU 86

Extreme 0 0 0 4.6

High 0 0 0 0.6

Medium 1.3 0.3 0 1.7

Low 73.6 3.8 3.7 10.5


AHLU 89

Extreme 0.2 0.2 0.6 0.7

High 0 0 0 0.5

Medium 0.2 0 0 0.1

Low 88.5 2.2 0.9 6


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AHLU 92

Extreme 0 0 0 0

High 0.6 0.5 0 0

Medium 0 0 0 0

Low 94.7 1.1 1.1 1.9


AHLU 95

Extreme 0 0 0 0

High 0.2 0 0 0

Medium 0.9 0 0 0

Low 97.6 1.3 0.1 0



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Table B10 AHLU contingency table for Roma. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0.3

Extreme 0 0 0.2 0.2

High 0.3 0.3 0.9 1.3

High 0.1 0.5 0.7 0.5

Medium 0.6 3.6 2 0.4

Medium 1.1 2.6 1.5 0.7

Low 80.9 8 1.4 0.1

Low 80.5 8.8 2 0.8



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0.1 0.4 0.6

High 0 0 0.1 0.1

High 0.4 0.7 1.1 0.5

Medium 0.7 1 0.7 0.2

Medium 1.8 2.6 0.9 0.3

Low 93.1 3.5 0.6 0

Low 79.3 8.5 2.1 0.8



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0.2 0

Medium 0.1 0.3 0.2 0

Medium 0.6 0.1 0 0

Low 98.3 1.1 0.1 0

Low 97.8 1.3 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0 0 0 0

Medium 0 0.2 0 0

Low 99.7 0.3 0 0

Low 99.7 0.1 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0.2 0 0 0

Low 100 0 0 0

Low 99.8 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




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Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0.1 0.4 0.6

Extreme 0 0.1 0.5 1.2

High 0.4 0.7 1.1 0.5

High 0.5 0.6 1 0.6

Medium 1.8 2.6 0.9 0.3

Medium 1.6 1.6 1 0.2

Low 79.3 8.5 2.1 0.8

Low 79.3 9.6 2 0.1



AHLU 83

AHLU 83

Extreme 0 0 0.3 0.1

Extreme 0 0 0.1 0

High 0 0.4 0.3 0.1

High 0 0.7 0.5 0.1

Medium 1.4 1.1 0.4 0

Medium 0.9 0.9 0.3 0.1

Low 92.3 3.1 0.5 0

Low 92.8 3 0.4 0



AHLU 86

AHLU 86

Extreme 0 0.3 0 0

Extreme 0 0 0 0

High 0.1 0.2 0 0

High 0.1 0 0 0

Medium 0.7 0.2 0.2 0

Medium 0.6 0.5 0.1 0

Low 97.5 0.9 0 0

Low 97.7 0.9 0.1 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.3 0 0 0

High 0 0 0 0

Medium 0.3 0 0 0

Medium 0 0 0 0

Low 99.2 0.3 0 0

Low 99.7 0.3 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.4 0 0 0

Medium 0 0 0 0

Low 99.6 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0.2 0.3 0.4 0.2

Extreme 0.1 0.2 0.3 0

High 0.3 0.6 0.8 0.9

High 0.5 0.1 0.6 0.4

Medium 1.7 1.3 1.4 0.9

Medium 1.4 1.6 1 0.5

Low 79.1 9.8 1.8 0.1

Low 79.2 10.1 2.7 1.3



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0.1 0.1

High 0 0 0 0

Medium 0.6 1.1 0.6 0.1

Medium 0.7 0.9 0.2 0.1

Low 93 3.5 0.7 0

Low 93 3.7 1.2 0.2



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.4 0.3 0 0

Medium 0.1 0 0 0

Low 97.9 1.2 0.2 0

Low 98.2 1.5 0.2 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.7 0.3 0 0

Low 99.7 0.3 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




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Day 7

AHLU 80

Extreme 0 0.1 0.4 0.3

High 0.3 0.4 0.4 0.2

Medium 2 1.9 0.6 0.2

Low 78.8 9.9 3.2 1.5


AHLU 83

Extreme 0 0 0 0

High 0.1 0.1 0.2 0

Medium 0.7 0.5 0.2 0.1

Low 92.9 4.1 1 0.1


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.1 0.2 0.1 0

Low 98.2 1.3 0.2 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.7 0.3 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 132

Table B11 AHLU contingency table for Oakey. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0.2

Extreme 0 0 0 0.3

High 0 0.3 0.3 0.4

High 0.1 0.5 0.3 0.2

Medium 0.7 2.4 1.3 0.1

Medium 1.3 2.5 1 0.1

Low 86.3 6.2 1.7 0.1

Low 85.5 6.1 2.1 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0.2 0

High 0.1 0.8 0.1 0.2

Medium 0.7 0.5 0.5 0

Medium 1.2 1.9 1.1 0.4

Low 95 3 0.1 0

Low 85.6 6.4 2.2 0.1



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.2 0 0

Medium 0 0 0 0

Low 99.2 0.6 0 0

Low 99.1 0.8 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0.2

High 0.1 0.8 0.1 0.2

High 0 0.4 0.2 0.2

Medium 1.2 1.9 1.1 0.4

Medium 0.6 1.7 0.6 0.1

Low 85.6 6.4 2.2 0.1

Low 86.1 7 2.6 0.2



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0 0 0

High 0 0 0.1 0.1

Medium 1.2 0.1 0.3 0

Medium 0.7 0.3 0.2 0

Low 94.3 3.4 0.5 0.1

Low 95 3.2 0.5 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.3 0 0 0

Medium 0.3 0.2 0 0

Low 98.8 0.8 0 0

Low 98.8 0.6 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0.3 0.2 0.2

High 0 0 0 0.1

Medium 0.6 1 0.6 0.1

Medium 1.1 1.1 0.6 0.2

Low 85.9 8 2.6 0.4

Low 85.5 8.1 2.8 0.4



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.6 0.2 0.1 0

Medium 0.2 0 0 0.1

Low 95 3.3 0.6 0

Low 95.4 3.5 0.8 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0 0

Medium 0 0 0 0

Low 99.1 0.7 0 0

Low 99.1 0.8 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 7

AHLU 80

Extreme 0 0 0 0

High 0.3 0.1 0.1 0.1

Medium 1.5 0.9 0.4 0.1

Low 84.7 8.3 2.9 0.5


AHLU 83

Extreme 0 0 0 0

High 0.1 0 0 0

Medium 0.6 0 0.3 0

Low 94.9 3.5 0.5 0.1


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Low 99 0.9 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 140

Table B12 AHLU contingency table for Warwick. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.3 1 0.1

High 0.1 0.3 0.7 0

Medium 0.2 1.8 1.5 0

Medium 1.1 2.3 1.5 0.1

Low 85.5 8.1 1.5 0

Low 84.4 7.6 1.8 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0.3 0

High 0 0.2 0.5 0

Medium 0.5 0.8 0.3 0

Medium 1.3 1.7 1.2 0.1

Low 94.1 4 0.1 0

Low 84.2 8.4 2.4 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0.3 0 0

Medium 0.4 0.1 0 0

Low 99 0.5 0 0

Low 98.8 0.7 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.2 0.5 0

High 0 0.2 0.5 0

Medium 1.3 1.7 1.2 0.1

Medium 0.7 1.7 1.1 0

Low 84.2 8.4 2.4 0

Low 84.7 8.5 2.4 0.1



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0.1 0 0

Medium 0.4 0.5 0.1 0

Medium 0.3 0.6 0.1 0

Low 94.1 4.4 0.5 0

Low 94.2 4.2 0.5 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.1 0 0

Medium 0.2 0 0 0

Low 99.1 0.7 0 0

Low 99 0.8 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.3 0.5 0

High 0 0.2 0 0

Medium 0.9 1.4 1 0

Medium 1.2 1.4 1.1 0.1

Low 84.4 8.7 2.5 0.1

Low 84 8.8 3 0.1



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0.1 0 0

High 0 0 0 0

Medium 0.8 0.5 0.2 0

Medium 0.5 0.2 0 0

Low 93.7 4.3 0.4 0

Low 94 4.7 0.6 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0 0 0

Medium 0 0 0 0

Low 98.9 0.8 0 0

Low 99.2 0.8 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 7

AHLU 80

Extreme 0 0 0 0

High 0.1 0.1 0.1 0

Medium 1.7 1.7 1.5 0.1

Low 83.4 8.7 2.6 0.1


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.6 0.1 0.1 0

Low 93.9 4.9 0.5 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Low 99.1 0.8 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 148

Table B13 AHLU contingency table for Amberley. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 11

Extreme 0 0 0.1 10.4

High 0 1 3.7 2.7

High 0 1.3 4 3.3

Medium 1.6 6.3 5.9 1.4

Medium 1.7 5 6.1 1.5

Low 53.2 8.9 3.7 0.7

Low 52.7 9.9 3.1 0.7



AHLU 83

AHLU 83

Extreme 0 0 0 4.8

Extreme 0 0 0.1 10.2

High 0 0.6 1 1.3

High 0 1 3.7 3.2

Medium 2.1 4.5 3.2 0.4

Medium 2.2 5.6 5.7 1.6

Low 72.1 7.9 1.6 0.5

Low 52 9.7 4 1



AHLU 86

AHLU 86

Extreme 0 0 0 0.2

Extreme 0 0 0 0.2

High 0 0.2 0.3 0.3

High 0 0.2 0.5 0.3

Medium 1.8 1 1 0.3

Medium 2 1.6 0.9 0.5

Low 88 4.6 1.8 0.5

Low 87.7 4 1.8 0.3



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0.8 0.1 0 0

Medium 0.8 0.2 0 0

Low 97.3 1.7 0 0

Low 97.3 1.7 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

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Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0.1 10.2

Extreme 0 0 0 8.9

High 0 1 3.7 3.2

High 0.2 1.6 3.9 3.8

Medium 2.2 5.6 5.7 1.6

Medium 2.1 5.1 5.5 1.4

Low 52 9.7 4 1

Low 51.7 9.8 4.2 2



AHLU 83

AHLU 83

Extreme 0 0 0.3 2.8

Extreme 0 0 0.3 0.7

High 0 0.3 1.2 2.5

High 0.2 1.2 1.3 2.1

Medium 2.1 3.5 2.3 0.7

Medium 2.4 3.2 1.7 2.1

Low 71.8 9.4 2.2 1

Low 71.2 8.8 2.6 2.1



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0.3

High 0.2 0.3 0.1 0.5

High 0 0.1 0.1 0.1

Medium 1.1 1.4 1 0.3

Medium 2.6 1.4 0.6 0

Low 88.4 4.1 2.1 0.5

Low 87 4.3 2.5 1



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.2 0 0 0

High 0.1 0.2 0 0

Medium 1 0.4 0 0

Medium 1 0.2 0 0

Low 97 1.5 0 0

Low 97 1.5 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0.1 0 0 0

Medium 0.2 0 0 0

Medium 0.4 0 0 0

Low 99.8 0 0 0

Low 99.5 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0.2 0 0 0

Low 100 0 0 0

Low 99.8 0 0 0




29 September 2016

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Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0.1 0.5 8.3

Extreme 0.4 0.4 0.7 4.9

High 0.2 1.3 3.7 3.1

High 0.8 1.4 3.9 4.3

Medium 2.4 5.4 4.9 1.8

Medium 3.4 5 3.8 3.1

Low 51.1 9.7 4.5 2.9

Low 49 9.7 5.3 3.9



AHLU 83

AHLU 83

Extreme 0.2 0 0 0.5

Extreme 0 0.1 0.1 0

High 0.2 0.6 1.4 0.9

High 0.5 0.8 0.7 0.8

Medium 2.2 4.3 1.4 1.2

Medium 2.8 3.8 2 1.4

Low 71.2 8.4 3.1 4.4

Low 70.2 8.6 3.2 4.8



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.2 0.1 0.4 0.3

High 0 0 0 0.1

Medium 1.3 1.7 0.8 0.3

Medium 2.3 1.8 0.6 0

Low 88 4.1 2 0.7

Low 87.2 4.2 2.6 1.2



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0 0 0

High 0 0 0 0

Medium 0.5 0.6 0 0

Medium 0.1 0.1 0 0

Low 97.4 1.3 0 0

Low 97.9 1.8 0 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0 0 0

Medium 0 0 0 0

Low 99.8 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 154

Day 7

AHLU 80

Extreme 0.2 0.3 0.9 3.4

High 1.6 1.5 2.9 3.5

Medium 4.2 4.2 3.9 2.8

Low 47.3 10.8 6.1 6.7


AHLU 83

Extreme 0.1 0 0 0.2

High 0.9 1.2 0.4 0.8

Medium 3 2 1.9 0.9

Low 69.4 10.3 3.8 5.3


AHLU 86

Extreme 0 0 0 0

High 0.4 0 0 0.2

Medium 2.2 0.9 0.7 0.1

Low 86.8 5 2.6 1


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 1 0.2 0 0

Low 97.1 1.7 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.2 0 0 0

Low 99.8 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 156

Table B14 AHLU contingency table for Miles. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 5.9

Extreme 0 0 0 4.1

High 0.1 0.1 1.2 2.3

High 0 0.1 0.7 2.2

Medium 0.4 1.4 3.1 1.3

Medium 0.7 1.6 2.4 1.6

Low 64.4 12.8 5.3 1.8

Low 63.9 12.6 6.6 3.5



AHLU 83

AHLU 83

Extreme 0 0 0 0.8

Extreme 0 0 0.1 3

High 0 0 0.2 0.9

High 0.1 0.3 1.1 1.7

Medium 0.5 1.4 1.2 0.5

Medium 0.8 2 1.9 1.4

Low 82.6 8 2.9 1

Low 63.6 12.1 6.6 5.3



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0.4

High 0 0 0 0

Medium 0.2 0.3 0.1 0.3

Medium 0.3 0.1 0 0.2

Low 94.6 3.2 0.8 0.1

Low 94.6 3.3 0.9 0.6



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0 0 0.5 0

Medium 0 0 0 0

Low 98.2 1 0.3 0

Low 98.2 1 0.8 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.8 0.1 0

Low 99.1 0.8 0.1 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 158

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0.1 3

Extreme 0 0 0 1.6

High 0.1 0.3 1.1 1.7

High 0 0.2 0.4 1.7

Medium 0.8 2 1.9 1.4

Medium 0.6 1.6 2.4 2.5

Low 63.6 12.1 6.6 5.3

Low 63.7 12.7 6.9 5.7



AHLU 83

AHLU 83

Extreme 0 0 0 0.3

Extreme 0 0 0 0.4

High 0 0.1 0.3 0.4

High 0 0.1 0.7 0.3

Medium 0.6 1.4 0.6 0.1

Medium 0.4 1.1 0.4 0.3

Low 82.3 8.1 3.5 2.4

Low 82.4 8.5 3.3 2.2



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0.3

High 0 0 0 0.4

Medium 0.3 0.5 0 0.4

Medium 0 0.6 0 0.3

Low 94.5 3 0.9 0.1

Low 94.7 2.8 0.9 0.2



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0.4 0

Medium 0.1 0 0.4 0

Low 98.2 1 0.4 0

Low 98.1 1 0.4 0


29 September 2016

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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.8 0.1 0

Low 99.1 0.8 0.1 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 160

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 1.1

Extreme 0 0 0 0.2

High 0 0 0.7 2.2

High 0.1 0 0.5 1.3

Medium 0.7 1.2 2 1.4

Medium 0.8 0.8 1.7 1.5

Low 63.4 13.4 7.1 6.8

Low 63 13.9 7.7 8.7



AHLU 83

AHLU 83

Extreme 0 0 0 0.1

Extreme 0 0 0 0

High 0 0 0.2 0.4

High 0 0 0.2 0

Medium 0.3 1.3 0.7 0.2

Medium 0.4 0.7 0.5 0.2

Low 82.4 8.5 3.4 2.5

Low 82.3 9 3.6 3.1



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.2 0.5 0 0

Medium 0.2 0.3 0 0

Low 94.5 3 0.9 0.8

Low 94.5 3.2 0.9 0.9



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 98.2 1 0.8 0

Low 98.2 1 0.8 0


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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.8 0.1 0

Low 99.1 0.8 0.1 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 162

Day 7

AHLU 80

Extreme 0 0 0 0.3

High 0.1 0.1 0.4 0.8

Medium 0.9 1.6 1.4 1.2

Low 62.7 13 8.1 9.4


AHLU 83

Extreme 0 0 0 0

High 0 0 0.3 0

Medium 0.7 0.6 0.2 0.2

Low 81.8 9.2 3.9 3.1


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.1 0.4 0 0

Low 94.6 3.2 0.9 0.9


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 98.2 1 0.9 0


29 September 2016

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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.9 0.1 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 164

Table B15 AHLU contingency table for Clare. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.8 0.2 0.1

Medium 0 0.7 0.1 0

Low 96.3 2.4 0.1 0

Low 96.3 2.5 0.3 0.1



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0 0

Medium 0.1 0.6 0.1 0

Low 99.1 0.6 0.2 0

Low 96.2 2.6 0.3 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.8 0.2 0 0

Low 99.8 0.2 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


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Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 99.9 0.1 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 166

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.6 0.1 0

Medium 0.2 0.1 0 0

Low 96.2 2.6 0.3 0

Low 96.1 3.1 0.4 0.1



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0 0

Medium 0 0 0 0

Low 99.1 0.7 0.2 0

Low 99.1 0.7 0.2 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.8 0.2 0 0

Low 99.8 0.2 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 99.9 0.1 0 0


29 September 2016

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AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 168

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.4 0 0

Medium 0 0.2 0 0

Low 96.2 2.9 0.4 0.1

Low 96.2 3.1 0.4 0.1



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.7 0.2 0

Low 99.1 0.8 0.2 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.8 0.2 0 0

Low 99.8 0.2 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 99.9 0.1 0 0


29 September 2016

Page 169



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 170

Day 7

AHLU 80

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.3 0 0 0

Low 96 3.3 0.4 0.1


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.1 0.8 0.2 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.8 0.2 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0


29 September 2016

Page 171


AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 172

Table B16 AHLU contingency table for Charlton. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0.1 0 0 0

High 0 0 0.1 0

Medium 0.7 1 0.6 0

Medium 0.7 0.7 0.6 0

Low 96.2 1.3 0.1 0

Low 96.1 1.6 0.2 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0.1 0

Medium 0.3 0.2 0 0

Medium 0.7 0.6 0.5 0

Low 98.7 0.8 0 0

Low 96.1 1.7 0.3 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0.1 0 0 0

Low 100 0 0 0

Low 99.8 0.1 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


29 September 2016

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Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 174

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0.1 0

High 0 0 0.1 0

Medium 0.7 0.6 0.5 0

Medium 0.4 0.3 0.3 0

Low 96.1 1.7 0.3 0

Low 96.4 2.1 0.3 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.3 0 0

Medium 0 0.2 0 0

Low 98.7 0.9 0 0

Low 99 0.8 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 175



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 176

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.3 0.4 0.3 0

Medium 0.5 0.3 0.3 0

Low 96.2 2.3 0.4 0

Low 95.8 2.5 0.6 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 98.9 1 0 0

Low 98.7 1.3 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 99.9 0.1 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 177



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 178

Day 7

AHLU 80

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.3 0.3 0.2 0

Low 95.3 3.1 0.7 0


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 98.5 1.5 0 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


29 September 2016

Page 179


AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



29 September 2016

Page 180

Table B17 AHLU contingency table for Katanning. Predictions are on the vertical axis and Observations are on the horizontal

Day1

Day 2

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.4 0 0

Medium 0 0.2 0 0

Low 99 0.3 0 0

Low 99.1 0.6 0 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0.1 0.1 0 0

Medium 0 0.2 0 0

Low 99.8 0 0 0

Low 99.2 0.6 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0


29 September 2016

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Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 182

Day 3

Day 4

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.2 0 0

Medium 0.2 0 0 0

Low 99.2 0.6 0 0

Low 99 0.8 0 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0.1 0 0

Medium 0.1 0 0 0

Low 99.9 0 0 0

Low 99.8 0.1 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 183



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




29 September 2016

Page 184

Day 5

Day 6

AHLU 80

AHLU 80

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.2 0.8 0 0

Low 99.1 0.8 0 0



AHLU 83

AHLU 83

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0

Low 99.9 0.1 0 0



AHLU 86

AHLU 86

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 89

AHLU 89

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0


29 September 2016

Page 185



AHLU 92

AHLU 92

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0



AHLU 95

AHLU 95

Extreme 0 0 0 0

Extreme 0 0 0 0

High 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0

Low 100 0 0 0




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Day 7

AHLU 80

Extreme 0 0 0 0

High 0 0 0 0

Medium 0.1 0 0 0

Low 99.1 0.8 0 0


AHLU 83

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 99.9 0.1 0 0


AHLU 86

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 89

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


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AHLU 92

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0


AHLU 95

Extreme 0 0 0 0

High 0 0 0 0

Medium 0 0 0 0

Low 100 0 0 0



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APPENDIX C - SURVEY RESULTS

Table C1 This year we have made some major changes to the CHLT service and you probably can't even tell the difference! It's all in the back end running the models and housing the databases and the web site. Following the disruptions to the service last year we decided to make the move to a cloud based system. The technical support from Amazon Web Services has been great and the process happened without a hitch (well almost). Are you happy with the level of service this season?

Yes 51

No 2

Most of the Time 8

Total Responses 61

Comments:

There were some discrepancies in some instances with site forecast vs local forecasts & what actually occurred. The web based forecast tool is good from a PC but not as user friendly from Smart Phone or Tablet, an example is when you hover your curser over the AHLU trend to see the actual forecast AHLU you cannot hover with a smart phone or tablet. Once the user lists the site trigger point it should remove the rest of the AHLU lines from the table making it much less busy, also the triangle can be difficult for people that are colour blind to interpret. Another suggestion is to be able to access the on-site weather station info online so we can look at it remotely from a mobile device.

This year there were no surprises. The forecast predicted the hot weather events well. Need to remind feedlots it is a forecast and severity at specific site will be variable. Encourage operators to make use of cattle observations to fine tune mitigation strategies.

Yes, quite happy, however it would be really good if we were able to receive sms alerts based on our own criteria (eg if the forecast heat load for the day exceeds 20 AHLU)

The outage at the end of the previous season could have proved disastrous.

We had enabled SMS function as well as email notification, we only received emails and no SMS were sent out. Also sometimes the website would state that we would have a huge heatload event for the whole yard when our weather and weather reports would be completely different to Katestone Events this happened about 3 times

Totally useless for our area


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Table C2 This season we added a regular heat load outlook on the web site and if a particular area were at risk an e-mail was also sent out. Did you find the outlooks useful?

Yes 53

No 2

Sometimes 6

Total Responses 61

Comments:

this was a useful prompt about possible impending adverse weather

it is useful so that we can observe the cattle's from the day w/c at high level of heat load

The outlooks were useful, but I generally gleen the same information from regular checks of the BOM 7 day weather forecasts & the 7 day CHLT heat load forecasts

Very helpful, alert emails assisted planning around unexpected changes

Fantastic tool!

Did not apply to our area

Table C3 Did you take action to reduce the risk of a heat event at your site based on the heat load outlooks?


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Yes 35

No 10

Sometimes 16

Total Responses 62

Table C4 Did you have any heat events at your site (cattle with Panting Score 2-4)?

Yes 26

No 35

Total Responses 61


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Table C5 Getting detailed information about actual heat load events that occurred at a site will provide invaluable information and allow us to refine and improve the service. If you experienced heat load events at your site please fill in the details on the following page. If you would like us to contact you to discuss the details of these events or any other weather related event please let us know. If you did not experience any heat or weather related events at your site and do not wish to be contacted please select the appropriate response below and skip the following 2 sections.

My site did experience heat load events, please contact me for details 5

My site did experience heat load events, please do not contact me for details 14

My site did not experience heat load events, please do not contact me 27

I would prefer to discuss this directly with Katestone 5

Other weather related events 2

Total Responses 53

Comments:

We did have conditions that would have created a HLE but at the time we only had bos indicus cattle that were under 30 days on feed.


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Table C6 Did your site experience multiple events?

Yes, please contact me for more information 0

No 15

Total Responses 15

Table C7 What type of cattle were impacted?

New arrivals <14 days on feed 2

Sick or recovering 1

Cattle ready for market 1

Bos Taurus 3

Wagyu 0

European 0

Bos Indicus 2


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Table C8 Were the pens...?

Wet 0

Moist 5

Dry 3

Total Responses 8

Table C9 Were there any other circumstances that you think may have increased the risk? e.g. winds were really light, the solar radiation was really intense, pens were not very clean or the cattle had been processed in the 2 days prior to the event?

Comments:

no breeze

After storms and light winds

For non shade pens, wind movement is critical

Pens were in need of cleaning

Total Responses 4


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Table C10 Did the onsite AWS indicate that the cattle were at risk of a heat event??

Yes 4

No 5

Total Responses 9

Table C11 Did the forecast provide adequate warning of the event??

Yes 9

No 1

Total Responses 10


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Table C12 During the season we tested a new Daily Monitoring App at a number of sites (thank you to those who contributed). The aim of the trial was twofold. Firstly we wanted to test the use of an App to undertake daily monitoring and deliver a status level for each cattle population monitored. The second aim was to collect information about activities on site and the behaviour of the cattle and link the weather station data and the forecast to provide valuable data to improve the algorithms that define an heat event and our understanding of the risks. Would you be interested in being part of the App trial group for next season?

Yes 27

No 22

Total Responses 49


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Table C13 Would you like to better understand the risk of heat stress at your processing plant?

Yes 22

No 24

Total Responses 46

Table C14 A bit about you: (check those that most describe you)Are you...?

person responsible for monitoring the heat situation at your site 17

feedlot manager 21

live stock manager 2


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pen rider 0

responsible for the feed 6

researcher 1

vet 1

nutritionist 3

Total Responses 51

Documents

Heat Load Index Forecast Season 2015-2016s3-ap-southeast-2.amazonaws.com/katestone-chlt-uploads/wp... · 2016-09-29 · Katestone Environmental Pty Ltd D14079-12 Meat and Livestock