Abstract
As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, the latest GPM-era satellite-based precipitation estimations, including Global Satellite Mapping of Precipitation (GSMaP) and Integrated Multi-satellitE Retrievals for the GPM (IMERG), have been released. However, few studies have systematically evaluated these products over mainland China, although this is very important for both the end users and data developers. To these ends, the final-run uncalibrated IMERG V05 (V05UC), gauge-calibrated IMERG V05 (V05C) and IMERG V04 (V04C), and latest gauge-calibrated GSMaP V7 (GSMaP) are systematically evaluated and mutually compared against a merged product obtained from the China Meteorological Data Service Center via continuous statistical indices and an error decomposition analysis technology suite over mainland China from April 2014 to December 2016 at a 3 hourly scale and 0.1° × 0.1° resolution. The results show that, irrespective of the slight overestimation in the southeast and underestimation in the northern Tibetan Plateau, all four GSPEs could generally capture the spatial patterns of precipitation over mainland China. Meanwhile, the overall quality of the GSMaP is slightly superior to the IMERG products in east and south China; however, it also suffers from an overestimation of light rain and an underestimation of heavy rain. Such overestimation and underestimation are primarily from a large false precipitation in light rain and a negative hit bias in heavy rain, respectively. The latest IMERG V05 products have not shown significant improvement over the earlier version (V04C) in east and south China, but the calibrated V05C can best reproduce the probability density function in terms of precipitation intensity. Furthermore, V04C shows remarkable underestimation over the Tibetan Plateau, while this shortcoming has been resolved significantly in V05C. Alternately, the effects of the gauge calibration algorithm (GCA) used in IMERG are examined by comparison of V05UC and V05C. The results indicate that GCA cannot reduce the missed precipitation, and even enlarges the false precipitation over some regions. This reveals that GCA cannot effectively alleviate the bias resulting from the rain areas’ delineation and raining or not-raining detection. In addition, all of the products’ performance can be improved, particularly in the dry climate and high-latitude regions. This is a systematic estimation for GSPEs, providing deep insight into the characteristics and sources of error, and it could be valuable as a reference for both algorithm developers and data users, as well as for associated global products and various applications.
Highlights
In China, floods and droughts are two primary frequently occurring and disastrous natural hazards that have caused tremendous loss of life and property over past decades [1,2]
The objectives of this study are threefold: (1) evaluating the quality of the four GPM-era SPEs (GSPEs) over the entirety of mainland China at 3 hourly (3-h), 0.1◦ × 0.1◦ resolution against an hourly merged product obtained from the China Meteorological Date Service Center (CMDSC) over mainland China with the conventional statistical approaches and the error-component analysis technique; (2) performing an intercomparison between variant Integrated Multi-satellitE Retrievals for the GPM (IMERG) products to explore the improvement of IMERG version upgrades and the performance boost achieved by the gauge-calibrated process; and (3) intercomparing IMERG and Global Satellite Mapping of Precipitation (GSMaP) products to explore the similarities and differences between the two products using different retrieval algorithms
The calibrated V04C, compared to other estimations, shows obvious underestimates in the Tibetan Plateau, but holds a higher level over southeast China, while V05UC has the lowest precipitation of the multiple GSPEs over southeast China
Summary
In China, floods and droughts are two primary frequently occurring and disastrous natural hazards that have caused tremendous loss of life and property over past decades [1,2]. Spatial interpolation and geostatistical analysis using gauge-based observations are primary approaches to obtain the spatial precipitation distribution for different regions. For sufficient accuracy, these approaches require an adequate and dense rainfall observation network. Ground-based weather radar, which can provide real-time high spatiotemporal-resolution monitoring, is another important source for acquiring localized precipitation information. The distribution of the weather radars is much more sparse than rain gauges, which means that precipitation estimations based on weather radars cannot be widely used in China. The only practical way to achieve the comprehensive estimation of precipitation on regional and national scales relies on earth observation satellites [3,7] due to their extensive spatial coverage and consistent measurements. Satellite-based precipitation estimations (SPEs) may not be precise to within a single pixel, some of them can provide near real-time data and even accurate information on precipitation occurrence, amount, and distribution in area-averaged estimations over sub-basins [8]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
