Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China
Abstract
:1. Introduction
2. Methodology and Datasets
2.1. Study Area
2.2. Observation and Satellite Retrieval Precipitation Production
2.3. Methodology for Evaluation
2.3.1. Statistical Metrics
2.3.2. Evaluation Indices
3. Results
3.1. Evaluation of Gridded Observation (CN05.1) with In Situ Point Observation
3.2. Evaluation of CHIRPS with the Gridded Observation (CN05.1)
3.2.1. Evaluation on Monthly Scale
3.2.2. Evaluation of CHIRPS in Sub-Regions
3.3. Evaluation of CHIRPS with In Situ Point Observation
3.3.1. Evaluation on Monthly Scale
3.3.2. Evaluation of Technical Skill Indices
3.4. Interannual Variation of Statistic Metrics
3.5. Evaluation of Precipitation Intensity
3.6. Evaluations with Typical Cases
3.6.1. Snowfall
3.6.2. Typhoon Events
4. Discussion and Future Work
5. Conclusions
- Limited to the resolution, the gridded observations do not completely agree with the data from high-density rain gauge networks across mainland, China, especially in complex terrain areas (e.g., XN). Overall, the gridded observation, CN05.1, has an r value above 0.90 over the course of a seasonal cycle. However, the evaluation results are quite different when comparing point evaluation and grid evaluation.
- The CHIRPS precipitation is mainly based on the statistical model based on IR data and TRMM 3B42’s precipitation in pentad time. In mainland China, the CHIRPS QPEs have better performance in DN and PR, in southern China, and poor performance in XB, YR, SHJ, LH, and HAH. The ME and RSME exhibits significant variation with seasonal change, which are caused by the limitations of TRMM, which is suitable only for tropical regions, not middle-latitude regions. Thus, CHIRPS exhibits good performance in southern China.
- CHIRPS also exhibits better performance for areas that experience large amounts of precipitation (e.g., southern China) as compared to areas characterized by arid and semi-arid land (e.g., northwest China). In addition, the change in good performance zones change is strongly related to monsoon movement.
- Generally, the accuracy of CHIRPS is better in warm seasons than in Winter. It has limited ability to detect snowfall of any intensity.
- CHIRPS is moderately sensitive to the precipitation from typhoon weather systems.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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QPEs | Temporal Coverage | Temporal and Spatial Resolution | Websites |
---|---|---|---|
TRMM 3B42 | 1998-present | 0.25°/3 h | https://pmm.nasa.gov/data-access/downloads/TRMM |
CMORPH | 1998-present | 0.07°/30 min | ftp://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/RAW/8km-30min/ |
PERSIANN-CRT | 1983-present | 0.25°/1 d | ftp://persiann.eng.uci.edu/CHRSdata/PERSIANN-CDR/daily/ |
GsMaP | 2000-present | 0.10°/30 min | ftp://hokusai.eorc.jaxa.jp/reanalysis/v6/ |
GPM IMERGE | 2014-present | 0.10°/30 min | https://pmm.nasa.gov/data-access/downloads/gpm |
CHIRPS | 1981-present | 0.05°/1 d | ftp://ftp.chg.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/global_daily/netcdf/p05/ |
Basin | Spring | Summer | Autumn | Winter | Annual | |||||
---|---|---|---|---|---|---|---|---|---|---|
r | ME | r | ME | r | ME | r | ME | r | ME | |
SHJ | 0.302 | −0.044 | 0.249 | 0.085 | 0.332 | −0.024 | 0.226 | −0.039 | 0.277 | −0.006 |
LH | 0.320 | −0.048 | 0.288 | 0.324 | 0.354 | 0.024 | 0.158 | 0.051 | 0.280 | 0.088 |
XB | 0.145 | 0.018 | 0.160 | −0.138 | 0.182 | −0.040 | 0.111 | 0.023 | 0.150 | −0.034 |
HAH | 0.299 | −0.068 | 0.310 | 0.071 | 0.304 | −0.111 | 0.166 | 0.013 | 0.270 | −0.024 |
YR | 0.269 | −0.003 | 0.268 | 0.051 | 0.326 | −0.014 | 0.206 | −0.018 | 0.267 | 0.004 |
YZR | 0.278 | 0.035 | 0.336 | 0.078 | 0.339 | 0.011 | 0.271 | −0.034 | 0.306 | 0.023 |
HUH | 0.336 | 0.053 | 0.382 | 0.081 | 0.327 | −0.092 | 0.296 | 0.057 | 0.335 | 0.025 |
DN | 0.352 | 0.460 | 0.512 | 0.802 | 0.415 | 0.204 | 0.362 | 0.013 | 0.410 | 0.370 |
XN | 0.219 | 0.100 | 0.272 | 0.551 | 0.348 | 0.136 | 0.248 | −0.017 | 0.272 | 0.193 |
PR | 0.400 | 0.211 | 0.472 | 0.407 | 0.418 | −0.055 | 0.353 | −0.104 | 0.411 | 0.115 |
Regions | DN | HAH | HUH | LH | SHJ | YR | YZR | XN | PR | XB |
---|---|---|---|---|---|---|---|---|---|---|
0.05° | 0.44 | 0.34 | 0.39 | 0.34 | 0.33 | 0.32 | 0.37 | 0.37 | 0.46 | 0.21 |
0.25° | 0.41 | 0.27 | 0.34 | 0.28 | 0.28 | 0.27 | 0.31 | 0.27 | 0.41 | 0.15 |
Difference | 0.03 | 0.07 | 0.05 | 0.06 | 0.06 | 0.05 | 0.06 | 0.10 | 0.05 | 0.06 |
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Bai, L.; Shi, C.; Li, L.; Yang, Y.; Wu, J. Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China. Remote Sens. 2018, 10, 362. https://doi.org/10.3390/rs10030362
Bai L, Shi C, Li L, Yang Y, Wu J. Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China. Remote Sensing. 2018; 10(3):362. https://doi.org/10.3390/rs10030362
Chicago/Turabian StyleBai, Lei, Chunxiang Shi, Lanhai Li, Yanfen Yang, and Jing Wu. 2018. "Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China" Remote Sensing 10, no. 3: 362. https://doi.org/10.3390/rs10030362