Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India

JOURNAL OF HYDROLOGY

卷:571

Article

Prakash, Satya¹ 

[1] Indian Inst Sci, Divecha Ctr Climate Change, Bengaluru 560012, India

关键词: Precipitation;    Multi-satellite;    Rain gauge;    Soil moisture;    Error decomposition;   

DOI  :  10.1016/j.jhydrol.2019.01.036

来源: Elsevier

【 摘 要 】

Accurate long-term estimates of precipitation at fine spatiotemporal resolution are vital for several applications ranging from hydrometeorology to climatology. The availability of a good network of rain gauges, and high precipitation variability associated with two annual monsoon systems and complex topography make India a suitable test-bed to assess the performance of any satellite-based precipitation product This study assesses the performance of latest versions of four multi-satellite precipitation products: (i) Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), (ii) Multi-Source Weighted-Ensemble Precipitation (MSWEP), (iii) SM2RAIN-Climate Change Initiative (SM2RAIN-CCI), and (iv) TRMM Multisatellite Precipitation Analysis (TMPA) across India using gauge-based observations for the period of 1998-2015 at monthly scale. These four multi-satellite precipitation products are essentially based on different algorithms and input data sets. Among these multi-satellite precipitation products, SM2RAIN-CCI is the only product that does not use rain gauge observations for bias adjustment. Results indicate that CHIRPS and TMPA are comparable to gauge-based precipitation estimates at all-India and sub-regional scales followed by MSWEP estimates. However, SM2RAIN-CCI largely underestimates precipitation across the country as compared to gauge-based observations. The systematic error component in SM2RAIN-CCI dominates as compared to random error component, which suggests the need of a suitable bias correction to SM2RAIN-CCI before integrating it in any application. The overall results indicate that CHIRPS data set could be used for long-term precipitation analyses with rather higher confidence.

【 授权许可】

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