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Visualising Seasonal Climate Forecasts

Rachel Lowe - rl263@exeter.ac.uk - EUROBRISA workshop - 17 Mar 2008

In collaboration with David Stephenson (University of Exeter), Caio Coelho (CPTEC), Richard Graham (Met Office), Aidan Slingsby and Jason Dykes (City University)

Exeter Climate Systems (XCS)

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Plan of talk

Overview of current seasonal climate forecast visual products.

Limitations of existing products.

New visualisation techniques by City University informatics team.

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EUROBRISA

Forecast products

1-month lead South America precipitation forecasts for a three month season.

A forecast issued in February is valid for the following March-April-May (MAM) season.

A EURO-BRazilian Initiative for improving South American seasonal forecasts.

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IRI

ECMWF

The Met Office

Web productsProbability of most likely tercile

Categorical

Prob. precipitation <lower tercile

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Graphical products available

Mean forecast anomalyProbability of lower tercileProbability of upper tercileCategoricalProbability of most likely tercile

Probability of above averageProbability of lower quintileProbability of upper quintileVariety of verification products

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Observed and forecast precipitation anomaly for Dec-Jan-Feb 2005-06

Observed anomalies (Y25) Forecast anomalies (X25)

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Binary indicator for below, near and above average categories

b25(1) b25

(2) b25(3)

Fo

reca

stO

bse

rved

bo25(1) bo25

(2) bo25(3)

0

1tb otherwise

kzt

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Categories for forecast and observed precipitation anomalies

for Dec-Jan-Feb 2005-06 Forecast categories (zt = 1,2,3)Observed categories (zot = 1,2,3)

max

1

)(k

k

ktt kbz

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Probabilistic forecasts

Future: inherently uncertain. Communicate uncertainty via forecasts –allow users to make optimal decisions.

Issue probability statements to quantify uncertainty about future observable outcomes.

The probability of below normal pt(1), near normal pt

(2) and above normal pt

(3) precipitation gives an idea of how rainfall is expected to differ from the long-term average over the forthcoming period (baseline: pt

(1)= pt(2)= pt

(3)= 0.33).

Example: if pt(1) = 0.7, pt

(2) = 0.2 and pt(3) =0.1, below

average rainfall more likely for the following season.

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Probability of below, near and above normal precipitation. Issued Nov 2005. Valid for Dec-Jan-Feb 2005-06

Prob. below normal pt(1) Prob. near normal pt

(2) Prob. above normal pt(3)

33.3% baseline

1max

1

)(

k

k

ktp

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Probability of most likely tercile Issued Nov 2005Valid for DJF 2005-06

lower tercile -33.3%White = central tercile most likely 33.3 % upper tercile

0max

max

max p

p

qif pt

(1) = pmax and pt(3) ≠ pmax

if pt(3) = pmax and pt

(1) ≠ pmax

otherwise

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Combined categorical forecast

Using forecast probability values for the lower pt(1), middle pt

(2), and upper pt

(3) (tercile categories), five forecast categories are displayed according to the following:

c = 1 c = 2 c = 3 c = 4 c = 5

5

4

3

2

1

c

pt(1) ≥ 2/5 and pt

(2) ≤ 1/3 and pt(3) ≤ 1/3

pt(1) ≥ 2/5 and pt

(2) ≥ 1/3 or pt(1) ≥ 1/3 and pt

(2) ≥ 2/5

pt(1) ≤ 1/3 and pt

(2) ≥ 2/5 and pt(3) ≤ 1/3

pt(3) ≥ 2/5 and pt

(2) ≥ 1/3 or pt(3) ≥ 1/3 and pt

(2) ≥ 2/5

pt(1) ≤ 1/3 and pt

(2) ≤ 1/3 and pt(3) ≥ 2/5

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Combined categorical forecast Issued Nov 2005Valid for DJF 2005-06

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Forecast Validation: Brier Skill Score Brier Score (BS): Mean squared error of a probabilistic forecast.n - number of realisations of forecast process over which validation is performed, here n=25).

For each realisation t, pt is the forecast probability of the occurrence of the event.

bt =1 if event occurred, bt=0 if not.

0<BS<1. Perfect system: pt=bt for all t.Brier Skill Score (BSS) – referenced to low-skill climatology, here pref = 1/3.BSS = 1 for perfect system, skillful values positive. BSS = 0 (negative) for a system that performs like (poorer) than reference system.

2

1

)(1

t

n

tt bp

nBS

refBS

BSBSS 1

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Brier Skill Score of below, near and above normal precipitation (1981-2005). Issued Nov Valid for Dec-Jan-Feb

BSS(1). precip. below normal BSS(2). precip. near normal BSS(3). precip. above normal

Perfect forecastNo better than climatology

ref

kk

BS

BSBSS

)()( 1

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Issues with existing products

Limited information availableUnderstanding of probability/risk varies from person to person. Helpful to have access to historical observation and hindcast data visually.

Information lost using categorical/probability of most likely tercile forecastBinning of probabilities for categorical forecast does not account for all possible combinations of probabilities.User may require probability of all 3 categories to make optimum decisions.

Use of colour alone can be limitingCertain colour combinations can be misleading and problematic especially for colour-blind users.

Need to refer to a separate map to judge the accuracy of the forecastIs it possible to combine a verification skill score with a seasonal forecast?

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City UniversityTime series data animation

Multi-part glyphs

Symbol size to represent observed

and mean forecast anomalies

Google EarthInteractive timeline

(stepped/animated)

Turn layers on/off

Zoom tool

Elevation

Country borders

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Scalable Vector Graphics (SVG)RGB colour composites

Red – (255,0,0) 100% probability of below average rainfall

colour (pt(1)

x 255, pt(2) x 255, pt

(3) x 255)

Green – (0,255,0) 100% probability of near average rainfall

Blue – (255,0,0) 100% probability of above average rainfall

bimodal

Wet or average

Dry or average

pt(1)

=pt(2)=pt

(3)

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Visualising anomaliesSymbol size used to represent observed (left) and mean forecast (right) precipitation anomalies

Circles help to make spatial comparisons and recognise model errors

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Multivariable Glyphsp1 p3

p2

Climatology pt(1)=pt

(2)=pt(3) = 1/3

p1

p3

p2

p1

p3

p2

RED

GREEN

BLUE

Below normal rainfall most likely pt

(1) > pt(2) > pt

(3)

Above normal rainfall most likely pt

(3) > pt(2) > pt

(1)

Colour and glyphs - double encoding draws attention to particular trends and characteristics.

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Google EarthObserved precip. anomaly Yt

pt(1) and two-way glyphs

pt(3) colour scale and raw data

Brier Skill Score BSS(3)

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1.21.0

1.0233121

r

ppppppr

Probability of most likely tercile

Issued Nov 1997Valid for DJF 1997-98

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Benefits of new visual productsGoogle Earth – Display past observation/hindcast data, deterministic and probabilistic forecast, raw data and verification information.

Multiple layers -viewed together or separately.

RGB colour composite – info provided for each grid point unlike existing categorical maps.

All tercile probabilities displayed within one map using multivariable glyphs. Symbol size used to represent magnitude of probability of each tercile.

Still to consider….The probability within one grid point -uniform. Approximation to more locally varying field of probability. Danger of users zooming in on a specific location and placing more confidence in the forecast than is justified.

The requirements and level of knowledge of the decision makers needs to be fully understood to prescribe the most useful and accurate information.

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SummaryImprovements to existing products using interactive visual techniques.

Work in progress. Input from climate scientists and forecast users needed to further develop ideas.

Future ideas: include layers for prediction of climatic scenarios that impact the spread of infectious disease or cause crop failure, floods and droughts.

Use as a risk tool for health risk, agriculture and hydropower production planning.

EUROBRISAtemperature and

precipitation forecastand hincast data

Visualisation techniques. Risk tool for South America

Use climate data to spatially and temporally model disease patterns

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Web referencesThe Met Officehttp://www.metoffice.gov.uk/

European Centre for Medium Range Weather Forecasts (ECMWF)http://www.ecmwf.int/

International Research Institute (IRI)http://portal.iri.columbia.edu/

EUROBRISAhttp://www6.cptec.inpe.br/eurobrisa/

City Universityhttp://www.city.ac.uk/

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Further ReadingCoelho C.A.S., S. Pezzulli, M. Balmaseda, F. J. Doblas-Reyes and D. B. Stephenson, 2004:“Forecast Calibration and Combination: A Simple Bayesian Approach for ENSO”. J. Climate, 17, 1504-1516.

Coelho C.A.S., D. B. Stephenson, M. Balmaseda, F. J. Doblas-Reyes and G. J. van Oldenborgh, 2006: Towards an integrated seasonal forecasting system for South America. J. Climate , 19, 3704-3721.

Jolliffe, I. T. and D. B. Stephenson, 2003. Forecast Verification: A practitioner’s guide in atmospheric science. Wiley and Sons. First edition. 240 pp.

Stephenson, D. B., Coelho, C. A. S., Doblas-Reyes, F.J. and Balmaseda, M., 2005: “Forecast Assimilation: A Unified Framework for the Combination of Multi-Model Weather and Climate Predictions.” Tellus A, Vol. 57, 253-264.

Troccoli A, Harrison M, Anderson DLT and Mason SJ 2008 (eds) Seasonal Climate: Forecasting and Managing Risk. NATO Science Series, Springer Academic Publishers, In Press .

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Single grid pointy = (n ×1) vector of observed precipitation anomalies. = (y1,y2,…,yt,…,yn)’ where t = 1,2,…,n.

x = (n ×1) vector of ensemble mean forecast precipitation anomalies. = (x1,x2,…,xt,…,xn)’ where t = 1,2,…,n.

z = (n ×1) vector indicating within which category the forecast ensemble mean precipitation falls.

= (z1,z2,…,zt,…,zn)’ where t = 1,2,…,n.

zt = 1,2,3 for tercile categories.

n = 25

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Where u1 and u2 denote the lower and upper tercile boundaries respectively. In general, zt = k if xt (uk-1, uk) where k = 1,2,…,kmax for kmax categories.

Time series for a single grid point

Probability of precipitation above upper tercile (p(3))

Observed precipitation anomaly (y)

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Mean forecast precipitation anomaly (x)

u1

u2

u1

u2

3

2

1

tztxu 1

21 uxu t

2uxt