Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Dataset
2.2.1. Gauged Precipitation Data
2.2.2. Satellite-Based and Reanalysis Precipitation Products
2.2.3. Digital Elevation Model Data
2.3. Evaluation Indices
2.4. Evaluation Experimental Design
3. Results and Discussion
3.1. Basic Statistics and Distribution of the Daily Precipitation
3.2. Evaluation of the Precipitation Products at the Different Time Scales
3.2.1. Evaluation at the Daily Scale
3.2.2. Evaluation at Monthly and Seasonal Scales
3.3. Evaluation of the Products at the Different Precipitation Grades and Elevation Levels
3.3.1. Evaluation at the Different Precipitation Grades
3.3.2. Evaluation at the Different Elevation Levels
4. Conclusions
- (1)
- At the daily scale, CMORPH outperforms the other products in terms of both quantitative accuracy and rainfall detection capability, while CHIRPS performs the worst.
- (2)
- At the monthly and seasonal scales, ERA5 produces the highest CCs in all months. Among all products, CCs are the highest in autumn, MAE and RMSE increase first and then decrease throughout the year, and peak values occur in July. The PODs of all products except for CHIRPS increase first from January to April, then remain stable for some months and finally decrease. The FARs and BIASs of the products first decrease and then increase, and the FARs in summer are the lowest, followed by those in autumn, spring, and winter. MAE, RMSE, POD, and ETS exhibit consistent monthly and seasonal rainfall amounts, and FAR and BIAS show the opposite trends to that of the rainfall amount. The summer months (e.g., June and July) with large rainfall amounts result in a low continuous accuracy and high rainfall detection capability.
- (3)
- Spatially, at the daily, monthly, and seasonal scales, the MAE, RMSE, POD, and ETS increase from northwest to southeast, which is consistent with the precipitation pattern. The precipitation amount in the humid areas of the source region and middle reaches is likely to be overestimated, and that in the arid areas is likely to be underestimated.
- (4)
- The CC, ME, MAE, RMSE, POD and BIAS generally exhibit an increasing trend with increasing daily precipitation. All products slightly overestimate precipitation at low precipitation grades and underestimate it at high precipitation grades. Regarding rainfall detection capability, CHIRPS exhibits the best capability to detect no-rain events, and ERA5 performs the worst. Regarding the rainy days, ERA5 misses the fewest rain events, while CHIRPS misses the most.
- (5)
- Most evaluation indices show a negative correlation with elevation. The RMSE, FAR, BIAS, and ETS decrease with increasing elevation. CMORPH performs the best for all elevation levels, while CHIRPS performs the worst.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Products | Spatial Coverage and Resolution | Temporal Coverage and Resolution | Unit | Date Source | References |
---|---|---|---|---|---|
CMORPH V 1.0 BLD | 0.125E–359.875E, 9.875N–59.875S, 0.25° × 0.25° | 1 January 1998–31 December 2019, 1 day | mm/h | PMW, IR, gauge data | Xie et al. [55] |
PERSIANN-CDR | 180W–180E, 60S–60N, 0.25° × 0.25° | 1 January 1983–30 September 2019, 1 day | mm/day | PMW, IR, GPCP monthly 2.5° V2.2 | Ashouri et al. [56] |
IMERG V6 Final | 180W–180E, 90S–90N, 0.1° × 0.1° | 1 June 2000–1 August 2020, 1 day | mm/h | PMW, IR, gauge data | Huffman et al. [57] |
ERA5-Land | 180W–180E, 90S–90N, 0.1° × 0.1° | 1 January 1981–present, 1 h | m | Gauge data | Muñoz Sabater [58] |
Gauge-adjusted GSMaP-NRT V6 | 180W–180E, 60S–60N, 0.1° × 0.1° | 1 April 2000–present, 1 h | mm/day | PMW, IR | Kubota et al. [21] |
CHIRPS V 2.0 | 180W–180E, 50S–50N, 0.05° × 0.05° | 1 January 1981–present, 1 day | mm/day | CCD, TMPA 3B42, CFS precipitation, gauge data | Funk et al. [19] |
Evaluation Indices | Equation | Range of Values | Perfect Value |
---|---|---|---|
Correlation coefficient (CC) | 1 | ||
Mean error (ME) | 0 | ||
Mean absolute error (MAE) | 0 | ||
Root mean square error (RMSE) | 0 | ||
Probability of detection (POD) | 1 | ||
False alarm ratio (FAR) | 0 | ||
BIAS | 1 | ||
Equitable threat score (ETS) | 1 |
Satellite Products | |||
---|---|---|---|
Rain | No-Rain | ||
Observation | Rain | hit (H) | miss (M) |
No-Rain | false (F) | correct negative (C) |
Sources | Minimum | Median | Mean | Maximum | Standard Deviation |
---|---|---|---|---|---|
Gauge stations | 0.38 | 1.35 | 1.31 | 2.91 | 0.43 |
CHIRPS | 0.33 | 1.40 | 1.35 | 3.65 | 0.44 |
CMORPH | 0.39 | 1.36 | 1.33 | 2.87 | 0.43 |
ERA5 | 0.41 | 1.66 | 1.66 | 4.16 | 0.58 |
GSMaP | 0.38 | 1.32 | 1.32 | 3.14 | 0.51 |
IMERG | 0.41 | 1.31 | 1.30 | 2.33 | 0.40 |
PERSIANN | 0.38 | 1.29 | 1.32 | 3.02 | 0.49 |
Product | CC | ME | MAE | RMSE | POD | FAR | BIAS | ETS |
---|---|---|---|---|---|---|---|---|
CHIRPS | 0.27 | 0.10 | 2.06 | 7.06 | 0.27 | 0.54 | 0.59 | 0.08 |
CMORPH | 0.47 | 0.08 | 1.52 | 4.96 | 0.72 | 0.42 | 1.24 | 0.30 |
ERA5 | 0.48 | 0.35 | 1.66 | 4.96 | 0.98 | 0.68 | 3.04 | 0.03 |
GSMaP | 0.35 | 0.09 | 1.78 | 5.55 | 0.58 | 0.42 | 1.00 | 0.24 |
IMERG | 0.41 | 0.05 | 1.66 | 5.29 | 0.75 | 0.55 | 1.65 | 0.18 |
PERSIANN | 0.30 | 0.07 | 1.88 | 5.31 | 0.69 | 0.58 | 1.62 | 0.13 |
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An, Y.; Zhao, W.; Li, C.; Liu, Y. Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China. Atmosphere 2020, 11, 1223. https://doi.org/10.3390/atmos11111223
An Y, Zhao W, Li C, Liu Y. Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China. Atmosphere. 2020; 11(11):1223. https://doi.org/10.3390/atmos11111223
Chicago/Turabian StyleAn, Yiming, Wenwu Zhao, Changjia Li, and Yanxu Liu. 2020. "Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China" Atmosphere 11, no. 11: 1223. https://doi.org/10.3390/atmos11111223