Name
Title | ||
|---|---|---|
Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018. |
Abstract | ||
|---|---|---|
Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of 0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11242992 | REMOTE SENSING |
Keywords | DocType | Volume |
precipitation,evaluation,error analysis,Fengyun,quantitative precipitation estimates,GPM,IMERG | Journal | 11 |
Issue | Citations | PageRank |
24 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jintao Xu | 1 | 0 | 0.34 |
| Ziqiang Ma | 2 | 0 | 3.38 |
| Guoqiang Tang | 3 | 0 | 0.68 |
| Qingwen Ji | 4 | 0 | 0.34 |
| Xiaoxiao Min | 5 | 0 | 0.34 |
| Wei Wan | 6 | 0 | 0.34 |
| Zhou Shi | 7 | 11 | 12.06 |