Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Ground Precipitation Data
2.3. Satellite Precipitation Products
2.3.1. IMERG Products
2.3.2. GSMaP Products
3. Methodology
3.1. Statistical Metrics
3.2. Confusion Matrix for Daily Precipitation Evaluation
4. Results
4.1. Statistical Evaluation of IMERG SPPs
4.1.1. Statistical Indices at Multiple Temporal Scales
4.1.2. Precipitation Error Distribution
4.1.3. Precipitation Frequency Distribution
4.2. Statistical Evaluation of GSMaP SPPs
4.2.1. Statistical Indices at Different Temporal Scales
4.2.2. Precipitation Error Distribution
4.2.3. Precipitation Frequency Distribution
4.3. IMERG SPPs Versus GSMaP SPPs
4.3.1. Temporal Variation of SPP-based Precipitation Estimates
4.3.2. Spatial Pattern of Statistical Metrics
4.3.3. Confusion Matrix
5. Discussion
6. Conclusions
- (1)
- Owing to the gauge-based calibration with the GPCC dataset, IMERG-F generally performed better than IMERG-E and IMERG-L, presenting considerably lower systematic biases. IMERG-E and IMERG-L underestimated the occurrences of the no-rain and light-rain events but overestimated moderate and heavy rain.
- (2)
- Regarding the performance of the three GSMaP SPPs, GSMaP-Gauge outperformed the other four GSMaP SPPs in all statistical metrics, although numerous false precipitation detections were incurred at the hourly scale. However, GSMaP-Gauge excessively underestimated the precipitation under 0.1 mm and overestimated the precipitation ranging from 0.1 to 1 mm. GSMaP-Gauge-NRT was ranked as the second after GSMaP-Gauge with evident improvement over GSMaP-NRT. GSMaP-MVK and GSMaP-NRT showed significant overestimations, and GSMaP-NOW notably underestimated total precipitation.
- (3)
- By comparing the performance of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT presented better characteristics of the diurnal and monthly cycles most of the time among all real- and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability for the quantification of daily precipitation events, whereas IMERG-F resulted in the best precipitation estimates at the monthly scale. Considering the estimation accuracy of SPPs in complex elevation and topography, the evaluation metrics (BIAS and CC) at the hourly scale presented distinct spatial pattern. The BIAS and CC were higher in the southeastern part (the lowlands) of the basin than in the northwestern part (the highlands). In the application of confusion matrix, GSMaP-Gauge performed the best and showed the most stable quality results among all post-real-time SPPs, followed by IMERG-F.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Products | Resolution | Latency | Time Period |
---|---|---|---|
IMERG-E | 0.1°, 0.5 h | 4 h | 1 June 2014 to 31 December, 2018 |
IMERG-L | 0.1°, 0.5 h | 14 h | 1 June 2014 to 31 December, 2018 |
IMERG-F | 0.1°, 0.5 h | 3.5 months | 1 January 2014 to 31 December, 2018 |
GSMaP-NOW | 0.1°, 0.5 h | Real time | 29 March 2017 to 31 December, 2018 |
GSMaP-NRT | 0.1°, 1 h | 4 h | 17 January 2017 to 31 December, 2018 |
GSMaP-Gauge-NRT | 0.1°, 1 h | 4 h | 17 January 2017 to 31 December, 2018 |
GSMaP-MVK | 0.1°, 1 h | 3 days | 1 March 2014 to 31 December, 2018 |
GSMaP-Gauge | 0.1°, 1 h | 3 days | 1 March 2014 to 31 December, 2018 |
Categories | Statistic Metrics | Equations | Perfect Value |
---|---|---|---|
1 | Pearson correlation coefficient (CC) | 1 | |
2 | Mean error (ME) | 0 | |
Mean absolute error (MAE) | 0 | ||
Root-mean-squared error (RMSE) | 0 | ||
Relative BIAS (BIAS) | 0 | ||
3 | Probability of detection (POD) | 1 | |
False alarm ratio (FAR) | 0 | ||
Critical success index (CSI) | 1 | ||
4 | Coefficient of skewness (SK) | 0 |
Time Scale | SPPs | CC | ME (mm) | MAE (mm) | RMSE (mm) | BIAS (%) | POD | FAR | CSI |
---|---|---|---|---|---|---|---|---|---|
Hourly | IMERG-E | 0.24 | −0.024 | 0.093 | 0.55 | −33.17 | 0.69 | 0.27 | 0.55 |
IMERG-L | 0.22 | −0.025 | 0.094 | 0.56 | −34.45 | 0.69 | 0.27 | 0.55 | |
IMERG-F | 0.24 | 0.001 | 0.111 | 0.60 | 2.04 | 0.71 | 0.32 | 0.53 | |
Daily | IMERG-E | 0.53 | −0.558 | 1.499 | 3.93 | −33.17 | 0.77 | 0.25 | 0.61 |
IMERG-L | 0.52 | −0.581 | 1.487 | 4.09 | −34.45 | 0.74 | 0.22 | 0.61 | |
IMERG-F | 0.60 | 0.044 | 1.606 | 4.12 | 2.04 | 0.80 | 0.26 | 0.63 | |
Monthly | IMERG-E | 0.82 | −16.744 | 23.306 | 33.76 | −33.17 | - | - | - |
IMERG-L | 0.82 | −17.414 | 23.512 | 34.39 | −34.45 | - | - | - | |
IMERG-F | 0.95 | 1.449 | 10.289 | 15.83 | 2.04 | - | - | - | |
Seasonal | IMERG-E | 0.88 | −50.716 | 55.521 | 75.41 | −33.17 | - | - | - |
IMERG-L | 0.88 | −52.745 | 57.422 | 77.03 | −34.45 | - | - | - | |
IMERG-F | 0.97 | 4.529 | 21.069 | 30.12 | 2.04 | - | - | - |
Time Scales | SPPs | SK |
---|---|---|
Hourly | IMERG-E | −0.26 |
IMERG-L | −0.27 | |
IMERG-F | 0.02 | |
Daily | IMERG-E | −0.68 |
IMERG-L | −0.70 | |
IMERG-F | −0.10 | |
Monthly | IMERG-E | −1.62 |
IMERG-L | −1.78 | |
IMERG-F | 0.28 | |
Seasonal | IMERG-E | −1.56 |
IMERG-L | −1.50 | |
IMERG-F | 0.46 |
Time Scales | SPPs | CC | ME (mm) | MAE (mm) | RMSE (mm) | BIAS (%) | POD | FAR | CSI |
---|---|---|---|---|---|---|---|---|---|
Hourly | GSMaP-NOW | 0.26 | −0.019 | 0.115 | 0.58 | −23.14 | 0.25 | 0.62 | 0.17 |
GSMaP-NRT | 0.16 | 0.040 | 0.171 | 1.25 | 48.29 | 0.23 | 0.64 | 0.16 | |
GSMaP-Gauge-NRT | 0.21 | 0.009 | 0.143 | 0.81 | 11.24 | 0.24 | 0.67 | 0.16 | |
GSMaP-MVK | 0.19 | 0.049 | 0.175 | 0.98 | 58.77 | 0.29 | 0.67 | 0.18 | |
GSMaP-Gauge | 0.31 | 0.002 | 0.125 | 0.54 | 2.88 | 0.53 | 0.71 | 0.23 | |
Daily | GSMaP-NOW | 0.53 | −0.464 | 1.895 | 4.37 | −23.14 | 0.59 | 0.24 | 0.50 |
GSMaP-NRT | 0.46 | 0.968 | 2.702 | 8.63 | 48.29 | 0.67 | 0.25 | 0.55 | |
GSMaP-Gauge-NRT | 0.54 | 0.225 | 2.151 | 5.67 | 11.24 | 0.67 | 0.24 | 0.55 | |
GSMaP-MVK | 0.54 | 1.178 | 2.642 | 6.84 | 58.77 | 0.78 | 0.28 | 0.60 | |
GSMaP-Gauge | 0.73 | 0.058 | 1.321 | 3.19 | 2.88 | 0.82 | 0.19 | 0.68 | |
Monthly | GSMaP-NOW | 0.75 | −14.480 | 27.866 | 42.33 | −23.14 | - | - | - |
GSMaP-NRT | 0.78 | 29.425 | 41.294 | 67.99 | 48.29 | - | - | - | |
GSMaP-Gauge-NRT | 0.84 | 6.857 | 26.003 | 41.58 | 11.24 | - | - | - | |
GSMaP-MVK | 0.79 | 35.635 | 43.343 | 64.03 | 58.77 | - | - | - | |
GSMaP-Gauge | 0.92 | 1.861 | 12.816 | 22.17 | 2.88 | - | - | - | |
Seasonal | GSMaP-NOW | 0.78 | −43.440 | 27.866 | 94.68 | −23.14 | - | - | - |
GSMaP-NRT | 0.85 | 88.276 | 41.294 | 142.32 | 48.29 | - | - | - | |
GSMaP-Gauge-NRT | 0.89 | 20.571 | 26.003 | 78.20 | 11.24 | - | - | - | |
GSMaP-MVK | 0.87 | 106.906 | 43.343 | 141.50 | 58.77 | - | - | - | |
GSMaP-Gauge | 0.96 | 5.584 | 25.414 | 38.75 | 2.88 | - | - | - |
Time Scale | SPPs | SK |
---|---|---|
Hourly | GSMaP-NOW | −0.17 |
GSMaP-NRT | 0.27 | |
GSMaP-Gauge-NRT | 0.07 | |
GSMaP-MVK | 0.32 | |
GSMaP-Gauge | 0.02 | |
Daily | GSMaP-NOW | −0.31 |
GSMaP-NRT | 0.49 | |
GSMaP-Gauge-NRT | 0.15 | |
GSMaP-MVK | 0.68 | |
GSMaP-Gauge | 0.05 | |
Monthly | GSMaP-NOW | −1.02 |
GSMaP-NRT | 1.64 | |
GSMaP-Gauge-NRT | 0.58 | |
GSMaP-MVK | 1.93 | |
GSMaP-Gauge | 0.23 | |
Seasonal | GSMaP-NOW | −1.15 |
GSMaP-NRT | 1.60 | |
GSMaP-Gauge-NRT | 0.72 | |
GSMaP-MVK | 1.82 | |
GSMaP-Gauge | 0.30 |
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Shi, J.; Yuan, F.; Shi, C.; Zhao, C.; Zhang, L.; Ren, L.; Zhu, Y.; Jiang, S.; Liu, Y. Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region. Water 2020, 12, 1006. https://doi.org/10.3390/w12041006
Shi J, Yuan F, Shi C, Zhao C, Zhang L, Ren L, Zhu Y, Jiang S, Liu Y. Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region. Water. 2020; 12(4):1006. https://doi.org/10.3390/w12041006
Chicago/Turabian StyleShi, Jiayong, Fei Yuan, Chunxiang Shi, Chongxu Zhao, Limin Zhang, Liliang Ren, Yonghua Zhu, Shanhu Jiang, and Yi Liu. 2020. "Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region" Water 12, no. 4: 1006. https://doi.org/10.3390/w12041006