Abstract
The prediction of the short-term quantitative precipitation nowcasting (QPN) from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN usingimages of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F) which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC); the Horn-Schunck optical-flow scheme (PHS); and the Pyramid Lucas-Kanade Optical Flow method (PPLK), which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6). The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.
Highlights
The extrapolation-based short-term Quantitative Precipitation Nowcasting (QPN), which involves forecasting future precipitation in a notably short time (e.g., 0~2 hr) based on extracting information from current observations, is important for numerous hydro-meteorological applications [1, 2]
The results show that the critical success index (CSI) and Corr of QPN decreases with coarser spatial resolutions, except for the spatial resolution of 0.1–0.2t, and that the Corr slightly increased with coarser spatial resolution
The factors affecting the predictability of QPN ware analyzed using different QPN methods, precipitation characters and spatial resolution
Summary
The extrapolation-based short-term Quantitative Precipitation Nowcasting (QPN), which involves forecasting future precipitation in a notably short time (e.g., 0~2 hr) based on extracting information from current observations (e.g., radar and satellite imageries), is important for numerous hydro-meteorological applications [1, 2]. QPN can play a complementary role for Numerical Weather Prediction (NWP) models in quantitative precipitation forecasting [1] for capability of producing reliable nowcasting precipitation data, for the analysis of a few hours [3,4,5,6,7]. In contrast to numerous studies on the radar, much less effort has been devoted to geostationary satellite, satellite-based QPN can provide data globally, for regions lacking in situ observational systems such as rain gauge networks. Smoother spatial characteristics of satellite products tend to make it harder to track cloud movement in the overlap region of two consecutive images for lack of obvious tracking signs compared to an equivalent terrestrial radar product
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