Open AccessHow sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part II: sensitivity to ensemble generation method
Author(s) -
Ruiz Juan J.,
Saulo Celeste,
Kalnay Eugenia
Publication year - 2012
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.262
Subject(s) - sensitivity (control systems) , probabilistic logic , brier score , precipitation , ensemble forecasting , calibration , computer science , environmental science , algorithm , statistics , meteorology , mathematics , machine learning , artificial intelligence , physics , electronic engineering , engineering
In this work the sensitivity of summer Probabilistic Quantitative Precipitation Forecasts (PQPF) to alternative ensemble generation methods over southeastern South America is examined. A perturbed initial condition ensemble using the breeding technique, a multimodel ensemble and a pragmatic ensemble based on spatial shifts of the forecast fields have been used to generate calibrated PQPF over the selected region and the results were evaluated using the Brier Skill Score. Results show that calibrated PQPF quality exhibits sensitivity to the ensemble system used and this sensitivity is mainly related with the resolution component of the Brier Skill Score. For the 24 h lead time, the pragmatic approach shows surprisingly good results while for the 48 h lead time, the best results are obtained with the multimodel ensemble. The combination of the spatial shift technique with the multimodel and with the perturbed initial conditions ensemble has also been evaluated and resulted in an increase of the PQPF skill at all lead times. Copyright © 2011 Royal Meteorological Society