Addressing Challenges Associated with Training Sample Sizes for Effective Calibration of NWP Precipitation Forecasts

Abstract

Effective calibration of precipitation forecasts produced by numerical weather prediction (NWP) models faces challenges associated with the training sample size. Newly operationalized NWP models may only accumulate a small number of forecasts and thus may limit robust parameter inference in forecast calibration. It is necessary to investigate how the performance of forecast calibration changes with the amount of training data, to determine an effective training sample size. In this study, we thoroughly investigate the impacts of training sample size on precipitation forecast calibration based on the seasonally coherent calibration (SCC) model across Australia. Overall, the performance of the..