Communicating the value of probabilistic forecasts with weather roulette

Meteorological Applications

Volumn 16, Issue 2, 2009, Pages 143-155

  Hagedorn, Renate ^a   Smith, Leonard A ^b  

^a EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTS   (United Kingdom)

^b LONDON SCHOOL OF ECONOMICS   (United Kingdom)

Author keywords

Communication; Diagnostic; Forecast evaluation; Probability forecast; Proper; Score; Value

Indexed keywords

COMMUNICATION;

DIAGNOSTIC; FORECAST EVALUATION; PROBABILITY FORECASTS; PROPER; SCORE; VALUE;

WEATHER FORECASTING;

CONCEPTUAL FRAMEWORK; DATA ACQUISITION; PERFORMANCE ASSESSMENT; PREDICTION; PROBABILITY; PUBLIC SERVICE; WEATHER FORECASTING;

EID: 67650876188     PISSN: 13504827     EISSN: 14698080     Source Type: Journal    
DOI: 10.1002/met.92     Document Type: Article

References (24)

1. Bröocker J, Smith LA. 2007. Scoring probabilistic forecasts: on the importance of being proper. Weather and Forecasting 22: 382-388.
2. Bröcker J, Smith LA. 2008. From ensemble forecasts to predictive distribution functions. Tellus 60(4): 663-678.
3. Doswell CA, Brooks HE. 1998. Budget-cutting and the value of weather forecasting. Weather and Forecasting 13: 206-212.
4. Freebairn JW, Zillman JW. 2002a. Economic benefits of meteorological services. Meteorological Applications 9: 33-44.
5. Freebairn JW, Zillman JW. 2002b. Funding meteorological services. Meteorological Applications 9: 45-54.
6. Gneiting T, Raftery AE. 2007. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102(477): 359-378.
7. Good IJ. 1952. Rational decisions. Journal of the Royal Statistical Society 14: 107-114.
8. Judd K. 2008a. Forecasting with imperfect models. Physica D 237(2): 216-232.
9. Judd K. 2008b. Non-probabilistic odds and forecasting with imperfect models. Physica D submitted.
10. Kelly J. 1956. A new interpretation of information rate. Bell Systems Technical Journal 35: 916-926.
11. Mailier PJ, Jolliffe IT, Stephenson DB. 2006. Quality of weather forecasts. Online publication of the Royal Meteorological Society, available at: http://www.subscriptions.rmets.org/pdf/fqp_report.pdf.
12. Raftery AE, Gneiting T, Balabdaoui F, Polakowski M. 2005. Using bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review 133: 1155-1174.
13. Richardson DS. 2000. Skill and economic value of the ECMWF ensemble prediction system. Quarterly Journal of the Royal Meteorological Society 126: 649-668.
14. Rodwell MJ. 2005. Comparing and combining deterministic and ensemble forecasts: how to predict rainfall occurrence better. ECMWF Newsletter 106: 17-23.
15. Roebber PJ, Bosart LF. 1996. The complex relationship between forecast skill and forecast value: a real-world comparison. Weather and Forecasting 11: 544-559.
16. Rosenfeld J. 2000. Do we need the national weather service? Scientific American Presents: Weather 11(1): 28-31.
17. Roulston MS, Kaplan DT, Hardenberg J, Smith LA. 2003. Using medium-range weather forecasts to improve the value of wind energy production. Renewable Energy 28(4): 585-602.
18. Roulston MS, Smith LA. 2002. Evaluating probabilistic forecasts using information theory. Monthly Weather Review 130: 1653-1660.
19. Roulston MS, Smith LA. 2003. Combining dynamical and statistical ensembles. Tellus Series A-Dynamic Meteorology and Oceanography 55: 16-30.
20. Smith LA. 2007. Chaos, A Very Short Introduction. Oxford University Press: Oxford; 180.
21. White PW (ed.). 2003. IFS documentation, Part VI: Technical and Computational Procedures (CY25R1), Available online: http://www.ecmwf.int/research/ifsdocs/CY25r1/pdf_files/Technical.pdf.
22. Wilks D. 2006a. Statistical Methods in the Atmospheric Sciences, 2nd edn. Academic Press: New York, 2nd edition, 627, see chapter 7 (255-332).
23. Wilks D. 2006b. Comparison of ensemble-MOS methods in the Lorenz '96 setting. Meteorological Applications 13: 243-256.
24. Ziehmann C, Smith LA, Kuths J. 2000. Prediction of probability. Phys. Lett. A. 271(4): 237-251.