Abstract
Accurate and timely precipitation forecasts are a key factor for improving hydrological forecasts. Therefore, it is fundamental to evaluate the skill of Numerical Weather Prediction (NWP) for precipitation forecasting. In this study, the Global Environmental Multi-scale (GEM) model, which is widely used around Canada, was chosen as the high-resolution medium-term prediction model. Based on the forecast precipitation with the resolution of 0.24° and taking regional differences into consideration, the study explored the forecasting skill of GEM in nine drought sub-regions around China. Spatially, GEM performs better in East and South China than in the inland areas. Temporally, the model is able to produce more precise precipitation during flood periods (summer and autumn) compared with the non-flood season (winter and spring). The forecasting skill variability differs with regions, lead time and season. For different precipitation categories, GEM for trace rainfall and little rainfall performs much better than moderate rainfall and above. Overall, compared with other prediction systems, GEM is applicable for the 0–96 h forecast, especially for the East and South China in flood season, but improvement for the prediction of heavy and storm rainfall and for the inland areas should be focused on as well.
Highlights
And skillful precipitation forecasts are important for decision-making when dealing with meteorological and hydrological hazards such as floods and droughts
Probabilistic Quantitative Precipitation Forecast (PQPF) based on probabilistic prediction has been developed and it offers many advantages [1] over QPF as it allows the total uncertainty related to the occurrence of a future event to be quantified
We can see that threat score (TS) in different sub-regions decreases with the increase of lead time and the increase of precipitation intensity, while bias score (BS) increases instead and the variation characteristics of these two metrics correspond to previous studies [16,40,48,49,50]
Summary
And skillful precipitation forecasts are important for decision-making when dealing with meteorological and hydrological hazards such as floods and droughts. Reservoir operators can benefit from skillful precipitation forecasts for effective flood control, while during droughts farmers and water resources managers can utilize precipitation forecasts to determine irrigation schedules for more effective drought mitigation. To this end, Quantitative Precipitation Forecast (QPF) in the short-term (up to 72 h in lead time) and medium-range (up to 15 days) are available from a number of Numerical Weather Prediction (NWP) models around the world. Probabilistic Quantitative Precipitation Forecast (PQPF) based on probabilistic prediction has been developed and it offers many advantages [1] over QPF as it allows the total uncertainty related to the occurrence of a future event to be quantified. Stochastic models have been proposed for precipitation forecasting [7,8,9,10] but for flood prediction and drought detection
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