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 model tends to stabilize in most parts of Australia when raw forecasts of 10 months or longer are used for parameter inference. Whether the training dataset cover wet months substantially affects forecast calibration. The findings of this study are critical for understanding the impacts of training sample size on forecast calibration and will provide implications for future forecast calibration and the generation of hindcasts. Significance Statement Numerical weather prediction (NWP) models have been widely used to predict future precipitation. The predictions often need to be improved to correct systematic errors, which can be achieved by training the predictions on previous forecasts. How much training data are needed for effective error correction remains unclear. We investigate how training sample sizes affect error correction and validate that the seasonally coherent calibration model could correct weather prediction errors using a small amount of training data in Australia, effectively addressing challenges related to training sample size in the postprocessing of NWP forecasts.