title: Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite.
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 using images 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.
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Liu
authors.affiliation: The PLA Information Engineering University, Zhengzhou, China; Naval Institute of Hydrographic Surveying and Charting, Tianjin, China.
authors.forename: Du-Gang
authors.initials: DG
authors.lastname: Xi
authors.affiliation: Key Laboratory of Agri-informatics, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China; ICube, UdS, CNRS, 300 boulevard Sebastien Brant, CS 10413, 67412, Illkirch, France.
authors.forename: Zhao-Liang
authors.initials: ZL
authors.lastname: Li
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D005544:Forecasting; D015233:Models, Statistical; D011891:Rain; D063809:Satellite Imagery; D018531:Spacecraft