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Abstract:

Different techniques for obtaining probabilistic quantitative precipitation forecasts (PQPFs) over South America are tested during the 2002-2003 warm season. They have been applied to a regional ensemble system which uses the breeding technique to generate initial and boundary conditions perturbations. This comparison involves seven algorithms and also includes experiments to select an adequate size for the training period. Results show that the sensitivity to different calibration strategies is small with the exception of the rank histogram algorithm. The inclusion of the ensemble spread or the use of different ensemble members for the computation of probabilities shows almost no improvement with respect to probabilistic forecasts computed using the ensemble mean. This is basically due to the strong relationship between precipitation error and its amount. © 2011 Royal Meteorological Society.

Registro:

Documento: Artículo
Título:How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part I: Sensitivity to calibration methods
Autor:Ruiz, J.J.; Saulo, C.
Filiación:Departamento de Ciencias de la Atmósfera y los Océanos, Universidad de Buenos Aires, Buenos Aires 1428, Argentina
Centro de Investigaciones del Mar y de la Atmósfera (CONICET-UBA), Buenos Aires 1428, Argentina
Palabras clave:Ensemble forecasting; Ensemble generation; Probabilistic quantitative precipitation forecasts
Año:2012
Volumen:19
Número:3
Página de inicio:302
Página de fin:313
DOI: http://dx.doi.org/10.1002/met.286
Título revista:Meteorological Applications
Título revista abreviado:Meteorol. Appl.
ISSN:13504827
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13504827_v19_n3_p302_Ruiz

Referencias:

  • Applequist, S., Gahrs, E.G., Pfeffer, R.L., Niu, X.-F., Comparison of methodologies for probabilistic quantitative precipitation forecasting (2002) Weather and Forecasting, 17, pp. 783-799
  • Brier, G.W., Verification of forecasts expressed in terms of probability (1950) Monthly Weather Review, 78, pp. 1-3
  • Ebert, E.E., Ability of a poor man's ensemble to predict the probability and distribution of precipitation (2001) Monthly Weather Review, 129, pp. 2461-2480
  • Ebert, E.E., Damrath, U., Wergen, W., Baldwin, M.E., The WGNE assessment of short-term quantitative precipitation forecasts (2003) Bulletin of the American Meteorological Society, 84, pp. 481-492
  • Ebert, E.E., Janowiak, J.E., Kidd, C., Comparison of near-real-time precipitation estimates from satellite observations and numerical models (2007) Bulletin of the American Meteorological Society, 88, pp. 47-64
  • Eckel, F.A., Walters, M.K., Calibrated probabilistic quantitative precipitation forecasts based on the MRF ensemble (1998) Weather and Forecasting, 13, pp. 1132-1147
  • Epstein, E.S., Stochastic-dynamic prediction (1969) Tellus, 21, pp. 739-759
  • Fraley, C., Raftery, A.E., Gneiting, T., Calibrating multimodel forecast ensembles with exchangeable and missing members using bayesian model averaging (2010) Monthly Weather Review, 138, pp. 190-202
  • Gahrs, G.E., Applequist, S., Pfeffer, R.L., Niu, X., Improved results for probabilistic quantitative precipitation forecasting (2003) Weather and Forecasting, 18, pp. 879-890
  • Gallus, W.A., Baldwin, M.E., Elmore, K.L., Evaluation of probabilistic precipitation forecasts determined from ETA and AVN forecasted amounts (2007) Weather and Forecasting, 22, pp. 207-215
  • Gallus, W.A., Seagal, M., Does increased predicted warm-season rainfall indicate enhanced likelihood of rain occurrence? (2004) Weather and Forecasting, 19, pp. 1127-1135
  • Gneiting, T., Raftery, A.E., Westveld, A.H., Goldman, T., Calibrated probabilistic forecasts using ensemble model output statistics and minimum CRPS estimation (2005) Monthly Weather Review, 133, pp. 1098-1118. , III
  • Hamill, T., Colucci, S.J., Verification of Eta-RSM short-range ensemble forecasts (1997) Monthly Weather Review, 125, pp. 1312-1327
  • Hamill, T., Colucci, S.J., Evaluation of Eta-RSME ensemble probabilistic precipitation forecasts (1998) Monthly Weather Review, 126, pp. 711-724
  • Hamill, T., Whitaker, J.S., Probabilistic quantitative precipitation forecasts based on reforecast analogs: theory and application (2006) Monthly Weather Review, 134, pp. 3209-3229
  • Hamill, T., Whitaker, J.S., Wei, X., Ensemble re-forecasting: improving medium-range forecast skill using retrospective forecasts (2004) Monthly Weather Review, 132, pp. 1434-1447
  • Hamill, T., Whitaker, J.S., Probabilistic Quantitative Precipitation Forecasts based on Reforecast Analogs: Theory and Application (2006) Monthly Weather Review, 134, pp. 3209-3229
  • Hersbach, H., Decomposition of the continuous ranked probability score for ensemble prediction systems (2000) Weather and Forecasting, 15, pp. 559-570
  • Hong, S., Pan, H., Nonlocal boundary layer vertical diffusion in a medium-range forecast model (1996) Monthly Weather Review, 10, pp. 2322-2339
  • Janowiak, J.E., Kousky, V.E., Joyce, R.J., Diurnal cycle of precipitation determined from the CMORPH high spatial and temporal resolution global precipitation analyses (2005) Journal of Geophysical Research, 110, p. 18. , pp. DOI: 10.1029/2005JD006156
  • Joyce, R.J., Janowiak, J.E., Arkin, P.A., Xie, P., CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution (2004) Journal of Hydrometeorology, 5, pp. 487-503
  • Kain, J.S., The kain-fritsch convective parameterization: an update (2004) Journal of Applied Meteorology, 43, pp. 170-181
  • McLean Sloughter, J., Raftery, A., Gneiting, T., Fraley, C., Probabilistic quantitative precipitation forecasting using bayesian model averaging (2007) Monthly Weather Review, 135, pp. 3209-3220
  • Palmer, T.N., Buizza, R., Leutbecher, M., Hagedorn, R., Jung, T., Rodwell, M., Vitart, F., Gilmour, I., (2007), p. 53. , The Ensemble Prediction System-Recent and Ongoing Developments. ECMWF Technical Memoranda N°430, October 2007, pp; Raftery, A., Gneiting, T., Balabdaoui, F., Polakowski, M., Using Bayesian model averaging to calibrate forecast ensembles (2005) Monthly Weather Review, 133, pp. 1155-1174
  • Ruiz, J., CMORPH precipitation estimates calibration and verification over South America (2009) Revista Brasileira de Meteorologia, 24, pp. 474-488. , Spanish)
  • Ruiz, J., Saulo, C., Kalnay, E., Comparison of methods to generate probabilistic quantitative precipitation forecasts over South America (2009) Weather and Forecasting, 24, pp. 319-336
  • Ruiz, J.J., Saulo, C., Nogués-Paegle, J., WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface Variables (2010) Mon. Wea. Rev., 138, pp. 3342-3355. , DOI: 10.1175/2010MWR3358.1
  • Ruiz, J.J., Saulo, C., Kalnay, E., How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part II: Sensitivity to ensemble generation methods (2011) Meteorological Applications, , DOI: 10.1002/met.262
  • Salio, P., Nicolini, M., Zipser, E., Mesoscale convective systems in Southeastern South America and their relationship with the South American Low Level Jet (2007) Monthly Weather Review, 135, pp. 1290-1309
  • Schaffer, C.J., Gallus, A.G., Segal, M., Improving probabilistic ensemble forecasts of convection through the application of QPF-POP relationships (2011) Weather and Forecasting, 26, pp. 319-336
  • Schmeits, M.J., Kok, K.J., A comparison between raw ensemble output, (modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts (2010) Monthly Weather Review, 138, pp. 4199-4211
  • Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., Powers, J.G., (2005) A Description of the Advanced Research WRF Version 2, p. 100. , Technical Note TN-468 + STR. NCAR: Boulder, CO;
  • Stensrud, D., Yussouf, N., Reliable probabilistic quantitative precipitation forecasts from a short-range ensemble forecasting system (2007) Weather and Forecasting, 22, pp. 3-17
  • Toth, Z., Kalnay, E., Ensemble forecasting at NMC: the generation of perturbations (1993) Bulletin of the American Meteorological Society, 74, pp. 2317-2330
  • Stephenson, D.B., Coelho, C.A.S., Jolliffe, I.T., Two Extra Components in the Brier Score Decomposition (2008) Weather and Forecasting, 23, pp. 752-757
  • Wilks, D.S., (2006) Statistical Methods in the Atmospheric Sciences, p. 627. , International Geophysics Series, Vol. 91. Academic Press: London;
  • Wilks, D.S., Comparison of ensemble'MOS methods in the Lorenz'96 system (2006) Quarterly Journal of the Royal Meteorological Society, 131, pp. 238-407
  • Wilks, D.S., Extending logistic regression to provide full' probability distribution MOS forecasts (2009) Meteorological Applications, 16, pp. 361-368
  • Wilks, D.S., Hamill, T., Comparison of ensemble-MOS methods using GFS reforecasts (2007) Monthly Weather Review, 135, pp. 2379-2390
  • Zhu, Y., Toth, Z., Wobus, R., Richardson, D., Mylne, K., The economic value of ensemble-based weather forecast (2002) Bulletin of the American Meteorological Society, 83, pp. 73-83

Citas:

---------- APA ----------
Ruiz, J.J. & Saulo, C. (2012) . How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part I: Sensitivity to calibration methods. Meteorological Applications, 19(3), 302-313.
http://dx.doi.org/10.1002/met.286
---------- CHICAGO ----------
Ruiz, J.J., Saulo, C. "How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part I: Sensitivity to calibration methods" . Meteorological Applications 19, no. 3 (2012) : 302-313.
http://dx.doi.org/10.1002/met.286
---------- MLA ----------
Ruiz, J.J., Saulo, C. "How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part I: Sensitivity to calibration methods" . Meteorological Applications, vol. 19, no. 3, 2012, pp. 302-313.
http://dx.doi.org/10.1002/met.286
---------- VANCOUVER ----------
Ruiz, J.J., Saulo, C. How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part I: Sensitivity to calibration methods. Meteorol. Appl. 2012;19(3):302-313.
http://dx.doi.org/10.1002/met.286