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.
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.
Abstract
Western central Africa is atypical of the equatorial domain as the main dry season is cloudier than the rainy seasons. To understand this cloud cover’s diurnal evolution, we set up an infrared camera and acquired measurements of the total cloud cover fraction (TCF) and cloud optical depth at Bambidie, Gabon (0°44′30.5″S, 12°58′12.4″E), from May to October 2022. Diurnal variations in TCF can be summarized into four types, mostly discretized through the timing and duration of clouds clearing in the afternoon [early afternoon clearing (EaC), late afternoon clearing (LaC), and clear night (CNi)], while one type [no clearing (NoC)] shows overcast conditions all day long. Meteorological measurements show that NoC days record 50 W m−2 less shortwave incoming surface radiation, resulting in daytime temperatures 1°C lower than the seasonal norm, but 20% more diffuse light and 0.5 mm day−1 less ETo. Conversely, EaC days record 50 W m−2 more shortwave incoming surface radiation, leading to temperatures 1.5°C higher than the seasonal norm, but 40% more direct light. The larger water demand (0.5 mm day−1 more ETo) is partly compensated by more frequent rainfall at nighttime. The satellite estimates of Satellite Application Facilities for supporting Nowcasting and very short-range Forecasting (SAFNWC) capture the TCF variations for most of the four types well. They confirm that TCF is dominated by very low and low clouds whose dissipation in the afternoon and evolution into fractional and cumuliform convective clouds explains the clearings on EaC and LaC days. Satellite estimates also show that the four types of days extracted at Bambidie are representative of a larger-scale cloud cover evolution in western central Africa, with a west–east gradient in the timing of afternoon cloud dissipation.
Abstract
Western central Africa is atypical of the equatorial domain as the main dry season is cloudier than the rainy seasons. To understand this cloud cover’s diurnal evolution, we set up an infrared camera and acquired measurements of the total cloud cover fraction (TCF) and cloud optical depth at Bambidie, Gabon (0°44′30.5″S, 12°58′12.4″E), from May to October 2022. Diurnal variations in TCF can be summarized into four types, mostly discretized through the timing and duration of clouds clearing in the afternoon [early afternoon clearing (EaC), late afternoon clearing (LaC), and clear night (CNi)], while one type [no clearing (NoC)] shows overcast conditions all day long. Meteorological measurements show that NoC days record 50 W m−2 less shortwave incoming surface radiation, resulting in daytime temperatures 1°C lower than the seasonal norm, but 20% more diffuse light and 0.5 mm day−1 less ETo. Conversely, EaC days record 50 W m−2 more shortwave incoming surface radiation, leading to temperatures 1.5°C higher than the seasonal norm, but 40% more direct light. The larger water demand (0.5 mm day−1 more ETo) is partly compensated by more frequent rainfall at nighttime. The satellite estimates of Satellite Application Facilities for supporting Nowcasting and very short-range Forecasting (SAFNWC) capture the TCF variations for most of the four types well. They confirm that TCF is dominated by very low and low clouds whose dissipation in the afternoon and evolution into fractional and cumuliform convective clouds explains the clearings on EaC and LaC days. Satellite estimates also show that the four types of days extracted at Bambidie are representative of a larger-scale cloud cover evolution in western central Africa, with a west–east gradient in the timing of afternoon cloud dissipation.
