Spatially adaptive post-processing of ensemble forecasts for temperature

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 63, Issue 3, 2014, Pages 405-422

  Scheuerer, Michael ^a   Büermann, Luca ^a  

^a NONE

Author keywords

Continuous ranked probability score; Ensemble prediction system; Gaussian process; Non homogeneous gaussian regression; Predictive distribution

Indexed keywords

EID: 84897653033     PISSN: 00359254     EISSN: 14679876     Source Type: Journal    
DOI: 10.1111/rssc.12040     Document Type: Article

References (33)

1. Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M. and Reinhardt, T. (2011) Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities. Mnthly Weath. Rev., 139, 3887-3905.
2. Berrocal, V. J., Raftery, A. E. and Gneiting, T. (2007) Combining spatial statistical and ensemble information in probabilistic weather forecasts. Mnthly Weath. Rev., 135, 1386-1402.
3. Chilès, J.-P. and Delfiner, P. (1999) Geostatistics: Modeling Spatial Uncertainty. New York: Wiley.
4. Dawid, A. P. (1984) Statistical theory: the prequential approach (with discussion). J. R. Statist. Soc. A, 147, 278-292.
5. Di Narzo, A. F. and Cocchi, D. (2010) A Bayesian hierarchical approach to ensemble weather forecasting. Appl. Statist., 59, 405-422.
6. Doms, G. and Schättler, U. (2002) A description of the nonhydrostatic regional model LM: dynamics and numerics. Technical Report. Deutscher Wetterdienst, Offenbach.
7. Fraley, C., Raftery, A. E., Gneiting, T., Sloughter, J. M. and Berrocal, V. J. (2011) Probabilistic weather forecasting in R. R J., 3, 55-63.
8. Gebhardt, C., Theis, S. E., Paulat, M. and Ben Bouallègue, Z. (2011) Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variations of lateral boundaries. Atmosph. Res., 100, 168-177.
9. Gel, Y., Raftery, A. E. and Gneiting, T. (2004) Calibrated probabilistic mesoscale weather field forecasting: the geostatistical output perturbation (GOP) method (with discussion). J. Am. Statist. Ass., 99, 575-590.
10. Glahn, B., Gilbert, K., Cosgrove, R., Ruth, D. P. and Sheets, K. (2009) The gridding of MOS. Weath. Forecast., 24, 520-529.
11. Glahn, H. R. and Lowry, D. A. (1972) The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 1203-1211.
12. Gneiting, T., Balabdaoui, F. and Raftery, A. E. (2007) Probabilistic forecasts, calibration and sharpness. J. R. Statist. Soc. B, 69, 243-268.
13. Gneiting, T. and Raftery, A. E. (2005) Weather forecasting with ensemble methods. Science, 310, 248-249.
14. Gneiting, T. and Raftery, A. E. (2007) Strictly proper scoring rules, prediction, and estimation. J. Am. Statist. Ass., 102, 359-378.
15. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T. (2005) Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mnthly Weath. Rev., 133, 1098-1118.
16. Hacker, J. P. and Rife, D. L. (2007) A practical approach to sequential estimation of systematic error on near-surface mesoscale grids. Weath. Forecast., 22, 1257-1273.
17. Hamill, T. M. (2001) Interpretation of rank histograms for verifying ensemble forecasts. Mnthly Weath. Rev., 129, 550-560.
18. Hamill, T. M. and Colucci, S. J. (1997) Verification of Eta-RSM short-range ensemble forecasts. Mnthly Weath. Rev., 125, 1312-1327.
19. Hersbach, H. (2000) Decomposition of the continuous ranked probability score for ensemble prediction systems. Weath. Forecast., 15, 559-570.
20. Iversen, T., Deckmyn, A., Santos, C., Sattler, K., Bremnes, J. B., Feddersen, H. and Frogner, I.-L. (2011) Evaluation of 'GLAMEPS'-a proposed multimodel EPS for short range forecasting. Tellus A, 63, 513-530.
21. Kleiber, W., Raftery, A. E., Baars, J., Gneiting, T., Mass, C. and Grimit, E. P. (2011a) Locally calibrated probabilistic temperature foreasting using geostatistical model averaging and local Bayesian model averaging. Mnthly Weath. Rev., 139, 2630-2649.
22. Kleiber, W., Raftery, A. E. and Gneiting, T. (2011b). Geostatistical model averaging for locally calibrated probabilistic quantitative precipitation forecasting. J. Am. Statist. Ass., 106, 1291-1303.
23. Mass, C. F., Baars, J., Wedam, G., Grimit, E. and Steed, R. (2008) Removal of systematic model bias on a model grid. Weath. Forecast., 23, 438-459.
24. Matheron, G. (1973) The intrinsic random functions and their applications. Adv. Appl. Probab., 5, 439-468.
25. Palmer, T. N. (2002) The economic value of ensemble forecasts as a tool for risk assessment: from days to decades. Q. J. R. Meteor. Soc., 128, 747-774.
26. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M. (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mnthly Weath. Rev., 133, 1155-1174.
27. Rippa, S. (1999) An algorithm for selecting a good value for the parameter c in radial basis function interpolation. Adv. Comput. Math., 11, 193-210.
28. Scherrer, S. C., Appenzeller, C., Eckert, P. and Cattani, D. (2004) Analysis of the spread-skill relations using the ecmwf ensemble prediction system over Europe. Weath. Forecast., 19, 552-565.
29. Scheuerer, M. (2013) Probabilistic quantitative precipitation forecasting using ensemble model output statistics. Q. J. R. Meteor. Soc., to be published, doi: 10.1002/9j-2183.
30. Scheuerer, M., Schaback, R. and Schlather, M. (2013) Interpolation of spatial data-a stochastic or a deterministic problem? Eur. J. Appl. Math., 24, 601-629.
31. Stein, M. L. (1999) Interpolation of Spatial Data. Heidelberg: Springer.
32. Thorarinsdottir, T. L. and Gneiting, T. (2010) Probabilistic forecasts of wind speed: ensemble model output statistics using heteroscedastic censored regression. J. R. Statist. Soc. A, 173, 371-388.
33. Toth, Z., Zhu, Y. and Marchock, T. (2001) The use of ensembles to identify forecasts with small and large uncertainty. Weath. Forecast., 16, 463-477.