Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
3. Methods
3.1. Data Processing
3.2. Evaluation Index System
4. Results
4.1. Evaluation of Precipitation Products at Annual and Monthly Scales
4.2. Daily Precipitation Capturing Capacity and Distribution
4.3. Regional Differences and Proportion of Evaluation Points with Different Precisions
5. Conclusions and Discussion
- For all three products, the correlation was always higher at the monthly scale than at the annual scale, with the correlation in December being the lowest among all months. The correlations at the annual scale, at the monthly scale and in December of GPM were higher than those of the other products. In general, GPM performed best at both the annual and monthly scales, followed by TRMM and CMORPH;
- GPM had the best detection accuracy for both precipitation and non-precipitation events, while TRMM and CMORPH had comparable capturing capabilities. The three products overestimated 0.1~1 mm/day precipitation, which occurred most frequently; underestimated precipitation at 10~20 mm/day, which contributed the largest proportion for precipitation of the TP; and underestimate the annual maximum daily precipitation;
- Compared with TRMM and CMORPH, GPM had higher CC in the southeast, higher PODrain in the south, higher HSS in the southern and central–eastern regions, and lower MAR in the southern part of the TP. The mean values of CC, BIAS, PODrain, PODnorain, FARrain, FARnorain, POFD, MAR and HSS of all stations showed that GPM had the best performance among the three products.
Author Contributions
Funding
Conflicts of Interest
References
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Evaluation Index | Equation | Number |
---|---|---|
Correlation coefficient (CC) | (1) | |
Relative bias (BIAS) | (2) | |
Root-mean-square error (RMSE) | (3) | |
Standard deviation (SD) | (4) | |
Probability of detection (POD) | (5) | |
(6) | ||
False alarm ratio (FAR) | (7) | |
(8) | ||
Probability of false detection (POFD) | (9) | |
Missing alarm ratio (MAR) | (10) | |
Heidke skill score (HSS) | (11) | |
Probability distribution function by occurrence (PDFc) | (12) | |
Probability distribution function by volume (PDFv) | (13) |
Index | Division of Index Intervals | |||
---|---|---|---|---|
I | II | III | IV | |
CC | <0.15 | 0.15~0.30 | 0.30~0.45 | >0.45 |
BIAS | <0 | 0~1.5 | 1.5~3 | >3 |
RMSE | <2.5 | 2.5~5 | 5~7.5 | >7.5 |
PODrain | <0.25 | 0.25~0.50 | 0.50~0.75 | >0.75 |
PODnorain | <0.7 | 0.7~0.8 | 0.8~0.9 | >0.9 |
FARrain | <0.4 | 0.4~0.6 | 0.6~0.8 | >0.8 |
FARnorain | <0.1 | 0.1~0.2 | 0.2~0.3 | >0.3 |
POFD | <0.1 | 0.1~0.2 | 0.2~0.3 | >0.3 |
MAR | <0.25 | 0.25~0.50 | 0.50~0.75 | >0.75 |
HSS | <0.08 | 0.08~0.16 | 0.16~0.24 | >0.24 |
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Yu, S.; Lu, F.; Zhou, Y.; Wang, X.; Wang, K.; Song, X.; Zhang, M. Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau. Water 2022, 14, 2169. https://doi.org/10.3390/w14142169
Yu S, Lu F, Zhou Y, Wang X, Wang K, Song X, Zhang M. Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau. Water. 2022; 14(14):2169. https://doi.org/10.3390/w14142169
Chicago/Turabian StyleYu, Songbin, Fan Lu, Yuyan Zhou, Xiaoyu Wang, Kangming Wang, Xinyi Song, and Ming Zhang. 2022. "Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau" Water 14, no. 14: 2169. https://doi.org/10.3390/w14142169
APA StyleYu, S., Lu, F., Zhou, Y., Wang, X., Wang, K., Song, X., & Zhang, M. (2022). Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau. Water, 14(14), 2169. https://doi.org/10.3390/w14142169