Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite

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.

基于连续时序卫星图像开展短期定量降水临近预报（quantitative precipitation nowcasting, QPN），对水文气象建模与预报具有重要的指导意义。
然而，当前针对QPN可预报性的系统性分析仍较为有限。
本研究旨在利用中国风云二号F星（Fengyun-2F, FY-2F）的卫星图像，评估预报模型、降水特性以及卫星分辨率对QPN可预报性的影响；该卫星自发射以来覆盖了全部密集观测任务，尽管总观测时长仅约10天。
首先，本研究对比了三种方法以评测QPN方法的性能：基于最大相关法的像素级QPN（PMC）、Horn-Schunck光流方案（PHS），以及本文首次提出的金字塔Lucas-Kanade光流法（PPLK）。
随后，借助8个降水时段的2338幅卫星图像，分析了降水系统对QPN可预报性的影响。
进而，通过分析六种空间分辨率（0.1空间分辨率、0.3、0.4空间分辨率及0.6）下的QPN结果，验证了分辨率依赖性。
结果表明，相较于其余对比方法，PPLK的性能更优异，可有效提升QPN的可预报性。
QPN的可预报性显著受降水系统影响，而卫星较低的空间分辨率会降低QPN的可预报性。