1. 1 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)

2. 2 Plan of talk Overview of current seasonal climate forecast visual products. Limitations of existing products. New visualisation techniques by City University informatics team.

3. 3 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.

4. 4 IRI ECMWF The Met Office Web products Probability of most likely tercile Categorical Prob. precipitation <lower tercile

5. 5 Graphical products available Mean forecast anomaly Probability of lower tercile Probability of upper tercile Categorical Probability of most likely tercile Probability of above average Probability of lower quintile Probability of upper quintile Variety of verification products

6. 6 Observed and forecast precipitation anomaly for Dec-Jan-Feb 2005-06 Observed anomalies (Y 25 )Forecast anomalies (X 25 )

7. 7 Binary indicator for below, near and above average categories b 25 (1) b 25 (2) b 25 (3) Forecast Observed bo 25 (1) bo 25 (2) bo 25 (3)

8. 8 Categories for forecast and observed precipitation anomalies for Dec-Jan-Feb 2005-06 Forecast categories (z t = 1,2,3)Observed categories (zo t = 1,2,3)

9. 9 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 p t (1), near normal p t (2) and above normal p t (3) precipitation gives an idea of how rainfall is expected to differ from the long-term average over the forthcoming period (baseline: p t (1) = p t (2) = p t (3) = 0.33). Example: if p t (1) = 0.7, p t (2) = 0.2 and p t (3) =0.1, below average rainfall more likely for the following season.

10. 10 Probability of below, near and above normal precipitation. Issued Nov 2005. Valid for Dec-Jan-Feb 2005-06 Prob. below normal p t (1) Prob. near normal p t (2) Prob. above normal p t (3) 33.3% baseline

11. 11 Probability of most likely tercile Issued Nov 2005 Valid for DJF 2005-06 lower tercile -33.3% White = central tercile most likely 33.3 % upper tercile if p t (1) = p max and p t (3) ≠ p max if p t (3) = p max and p t (1) ≠ p max otherwise

12. 12 Combined categorical forecast Using forecast probability values for the lower p t (1), middle p t (2), and upper p t (3) (tercile categories), five forecast categories are displayed according to the following: c = 1c = 2c = 3c = 4c = 5 p t (1) ≥ 2/5 and p t (2) ≤ 1/3 and p t (3) ≤ 1/3 p t (1) ≥ 2/5 and p t (2) ≥ 1/3 or p t (1) ≥ 1/3 and p t (2) ≥ 2/5 p t (1) ≤ 1/3 and p t (2) ≥ 2/5 and p t (3) ≤ 1/3 p t (3) ≥ 2/5 and p t (2) ≥ 1/3 or p t (3) ≥ 1/3 and p t (2) ≥ 2/5 p t (1) ≤ 1/3 and p t (2) ≤ 1/3 and p t (3) ≥ 2/5

13. 13 Combined categorical forecast Issued Nov 2005 Valid for DJF 2005-06

14. 14 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, p t is the forecast probability of the occurrence of the event. b t =1 if event occurred, b t =0 if not. 0<BS<1. Perfect system: p t =b t for all t. Brier Skill Score (BSS) – referenced to low-skill climatology, here p ref = 1/3. BSS = 1 for perfect system, skillful values positive. BSS = 0 (negative) for a system that performs like (poorer) than reference system.

15. 15 Brier Skill Score of below, near and above normal precipitation (1981-2005). Issued Nov Valid for Dec-Jan-Feb BSS (1). precip. below normalBSS (2). precip. near normalBSS (3). precip. above normal Perfect forecast No better than climatology

16. 16 Issues with existing products Limited information available Understanding 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 forecast Binning 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 limiting Certain 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 forecast Is it possible to combine a verification skill score with a seasonal forecast?

17. 17 City University Time series data animation Multi-part glyphs Symbol size to represent observed and mean forecast anomalies Google Earth Interactive timeline (stepped/animated) Turn layers on/off Zoom tool Elevation Country borders

18. 18 Scalable Vector Graphics (SVG) RGB colour composites Red – (255,0,0) 100% probability of below average rainfall colour (p t (1) x 255, p t (2) x 255, p t (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 p t (1) = p t (2) = p t (3)

19. 19 Visualising anomalies Symbol size used to represent observed (left) and mean forecast (right) precipitation anomalies Circles help to make spatial comparisons and recognise model errors

20. 20 Multivariable Glyphs p1p1 p3p3 p2p2 Climatology p t (1) =p t (2) =p t (3) = 1/3 p1p1 p3p3 p2p2 p1p1 p3p3 p2p2 RED GREEN BLUE Below normal rainfall most likely p t (1) > p t (2) > p t (3) Above normal rainfall most likely p t (3) > p t (2) > p t (1) Colour and glyphs - double encoding draws attention to particular trends and characteristics.

21. 21 Google Earth Observed precip. anomaly Y t p t (1) and two-way glyphs p t (3) colour scale and raw data Brier Skill Score BSS (3)

22. 22 Probability of most likely tercile Issued Nov 1997 Valid for DJF 1997-98

23. 23 Benefits of new visual products Google 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.

24. 24 Summary Improvements 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. EUROBRISA temperature and precipitation forecast and hincast data Visualisation techniques. Risk tool for South America Use climate data to spatially and temporally model disease patterns

25. 25 Web references The Met Office http://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/ EUROBRISA http://www6.cptec.inpe.br/eurobrisa/ City University http://www.city.ac.uk/

26. 26 Further Reading Coelho 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.

27. 27 Single grid point y = (n ×1) vector of observed precipitation anomalies. = (y 1,y 2,…,y t,…,y n )’ where t = 1,2,…,n. x = (n ×1) vector of ensemble mean forecast precipitation anomalies. = (x 1,x 2,…,x t,…,x n )’ where t = 1,2,…,n. z = (n ×1) vector indicating within which category the forecast ensemble mean precipitation falls. = (z 1,z 2,…,z t,…,z n )’ wheret = 1,2,…,n. z t = 1,2,3 for tercile categories. n = 25

28. 28 Where u 1 and u 2 denote the lower and upper tercile boundaries respectively. In general, z t = k if x t (u k-1, u k ) where k = 1,2,…,k max for k max categories. Time series for a single grid point Probability of precipitation above upper tercile (p (3) ) Observed precipitation anomaly (y) 2 Mean forecast precipitation anomaly (x) u1u1 u2u2 u1u1 u2u2