Potential Benefits and Challenges of Integrating Gridded Weather Data in IPM Applications: A...

Potential Benefits and Challenges of Integrating Gridded Weather Data in

IPM Applications: A Preliminary Assessment in Michigan

Michael T. Kiefer and Jeffrey A. Andresen Michigan State University, Department of Geography, East Lansing, MI

• Why use gridded weather analyses?• High-spatial and temporal resolution representation of

near-surface weather conditions• A variety of intended uses

• creation and verification of gridded forecasts• coastal zone and fire management• dispersion modeling for the transport of hazardous

materials• aviation and surface transportation management• impact studies of climate change on the regional scale.

• Increasing use in agricultural sector

Background

2

Introduction

Motivation

• Uses for gridded analyses in agriculture• Fill in gaps between weather stations• Proxy for an observation if point observation is

missing• Improve situational awareness (e.g., contoured

maps of temperature depicting frontal boundary)• Specific application: Enviro-weather (EW)

automated weather network.• 79 automated weather stations (and growing!)

Introduction

3

Enviro-weather Automated Weather Network

Interactive information system linking real-time weather data, forecasts, and biological and other process-based models for assistance in operational decision-making and risk management associated with Michigan’s agriculture and natural resource industries.

July 2014

Gridded Datasets

• Real Time Mesoscale Analysis (RTMA)– Generated at the National Centers for Environmental

Prediction (NCEP), a division of the National Weather Service (NWS)

– First guess (i.e., background): 1-hr forecast from • Rapid Update Cycle (RUC) / Rapid Refresh (RAP) models

– Large number of observations assimilated (ASOS*, mesonet, satellite wind, etc.)

– Includes precipitation analysis (Stage II)– Grid spacing: 2.5 km (5 km recently phased out)– Temporal frequency: hourly

Introduction

* Automated Surface Observing System

Gridded Datasets

• Stage IV precipitation analysis (aka MPE)– 1-hour precipitation estimates from NWS Doppler radar

combined with rain gauge observations (~3000 more gauges than Stage II)

– Regional analyses generated at individual river forecast centers (RFCs), sent to NCEP, and merged

– Manual quality control performed at each RFC– Grid spacing: 4 km– Temporal frequency: hourly, but manual QC process and

transmittal to NCEP delays availability (i.e., not real-time). 6- and 24-hour analyses also available.

Introduction

Study Questions

• How do nearest-grid-point RTMA temperature, dewpoint and relative humidity (derived) differ from point observations?

• Are precipitation differences smaller with Stage IV than Stage II? If so, how much smaller?

• Overall, are differences larger at EW stations than ASOS stations? If so, how much larger?

• How do differences impact the output of plant pest and disease models?

Introduction

7

Study Parameters

• Five years (1 Aug 2008 – 31 Jul 2013) • 12 stations (6 ASOS, 6 EW)• Variables extracted at nearest grid point

– Temperature, dewpoint, wind speed, wind direction, hourly precipitation

• Gross error check used to reject obviously erroneous observations

• Timescales: hourly, daily, diurnal, seasonal

8

Methodology

Observation Sites

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ASOS networkKLAN: LansingKGRR: Grand RapidsKDTW: Detroit MetroKTVC: Traverse CityKAPN: AlpenaKIMT: Iron Mountain

EW networkEITH: IthacaESAN: SanduskyECOL: ColdwaterEENT: EntricanEARL: ArleneESTE: Stephenson

Methodology

RTMA analysis: OverviewResults (hourly)

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Temperature, Dewpoint, Relative humidity

6-station median

RMSE BIAS RMSE BIAS RMSE BIAS

RTMA analysis: Bias histograms

11

Results (hourly)

Relative humidity bias (%)

Stage II vs IV precipitation

12

Results (hourly)

ASOS

(False alarm)

(Miss)Larger percent correct

Stage II vs IV precipitation

13

Results (hourly)

EW*

* warm season (1 Apr-30 Sep) only

(False alarm)

(Miss)Larger percent correct

Max & Min T, Growing Degree DaysResults (daily)

14

Base 10 C*Baskerville-Emin method

6-station median

RMSE BIAS RMSE BIAS RMSE BIAS

Plant disease and pest models

• Fire blight– Inputs: Degree days, degree hours, 24-hr mean

and maximum temperature (also need information on wetting event or trauma)

• Codling moth– Input: Degree day

• Apple scab (primary infection model)– Inputs: Degree day, precipitation, 1-hr mean

temperature, mean RH, leaf wetness proportion

15

Apple scab primary infection model

• Fungus (Venturia inaequalis)• Rain of at least 0.01” needed to soak

overwintering leaves and release ascospores• Wetting period begins with 0.01”+

– may be extended with additional rain, RH >= 90% (dew), or leaf wetness proportion >= 25% (r/d)

– Progress to infection a function of temperature– Dry period of less than 8 hours stalls progress to

infection but does not eliminate risk

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(as applied at Enviro-weather)

Apple scab wetting periodsResults (apple scab)

17

RTMA5 STAGEIV

ASOS 2.70 3.01

EW 2.32 2.28

RTMA5 STAGEIV

ASOS -2 5.5

EW -14.5 6.5

6-station median: ANL-OBS

6-station median: ANL-OBS

ASOS EW

ASOS EW

* Mean event duration

*

5-year period

Apple scab infection eventsResults (apple scab)

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RTMA5 STAGEIV

ASOS 2.76 2.56

EW 1.42 1.18

RTMA5 STAGEIV

ASOS 7.50 9.00

EW 4.50 11.00

6-station median: ANL-OBS

6-station median: ANL-OBS

ASOS EW

ASOS EW

5-year period

* Mean event duration

*

1-2 more per year

Apple scab: Interpretation

• Wetting period count sensitive to choice of Stage II or Stage IV. Duration less sensitive. (Number of wetting periods is a function of precipitation only)

• Infection events (number and duration) sensitive to choice of Stage II/IV, especially sensitive to RTMA temperature & RH errors

• Considerable station-to-station and year-to-year variability (not shown)

19

Results (apple scab)

Gridded Analysis Summary

• Gridded analyses have promise as a source of weather data for IPM applications in Michigan

• However, we must proceed with caution:• Disease models with multiple weather inputs pose a challenge

for RTMA/STAGEIV; also: long-duration degree day accumulations (aggregate errors)

• Considerable station-to-station variation in errors• Errors generally larger at EW sites than ASOS sites

• Temperature/dewpoint analysis suggests that bias correction has promise, but would need to be site-specific

Conclusions

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Current/Future Directions

• Develop gridded leaf wetness duration proxy• Work toward integration of:

– mesonet observations with gridded analyses– historical climate data with gridded analyses and forecasts

• Look at additional IPM applications to further evaluate applicability of gridded data– Special focus: assess feasibility of using gridded precipitation

analyses and forecasts in IPM applications

• Explore spatial variability of gridded product error

21

22

Acknowledgements

• Enviro-weather supported by MI Project GREEEN, MI AgBioResearch, MSU Extension, external grants, corporate/individual sponsorships, and grower contributions

• Special thanks go to Tracy Aichele for assistance with plant disease/pest models

www.enviroweather.msu.edu

Questions?

NDFD evaluation

• National Digital Forecast Database (NDFD)– consists of gridded forecasts of sensible weather

elements (e.g., cloud cover, maximum temperature)

– seamless mosaic of digital forecasts from NWS field offices working in collaboration with the National Centers for Environmental Prediction (NCEP)

– 7 Days: Day 1-3 forecasts (updated hourly) and day 4-7 forecasts (updated four times per day)

23

Gridded Forecasts

NDFD: Growing Degree Days*

24

00 UTC forecast *Baskerville-Emin method

Gridded Forecasts

Backup slides

RTMA analysis: Bias histograms

26

Results (hourly)

2 m temperature (K)

RTMA analysis: Bias histograms

27

Results (hourly)

2 m dewpoint temperature (K)

T bias: Diurnal trends

6-station median

TD bias: Diurnal trends

6-station median

RH bias: Diurnal trends

6-station median

Applescab: Infection Severity(Percentage of total infection hours)

Applescab: Infection Severity(Percentage of total infection hours)

Codling moth: Difference in # of days to milestones

Accumulated GDD: 2009 vs. 2011

ST2/ST4: Performance measures

37

ASOS EW

A word about RTMA 2.5 km…

38

ASOS

6-station median