Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period

Yuan

et al. 2020

Water

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.

Section: Introductionmentioning

confidence: 99%

Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region

Yuan

et al. 2020

Water

Intl Journal of Climatology

“…The study is especially important for India considering enormous spatial variability in climate and terrain. Although there have been few studies on evaluation of precipitation fidelity in India (Prakash et al ., 2015; 2016; Rana et al ., 2015; Sunilkumar et al ., 2015; Shah and Mishra, 2016; Nair and Indu, 2017; Mondal et al ., 2018; Gupta et al ., 2019; Prakash, 2019; Reddy et al ., 2019; Shukla et al ., 2019; Singh et al ., 2019; Thakur et al ., 2019), comprehensive evaluation of all the products available on GEE is lacking. Various sections of this paper are structured as follows; section 2 discusses various dataset used in the study along with the applied methodology; section 3 comprises of evaluation results over the country and across various elevation bands and climate zones; finally, section 4 presents the summary of the entire study.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of precipitation datasets available on Google earth engine over India

Dubey

Gupta

Goyal

et al. 2021

Monthly mean precipitation estimates of seven products (TerraClimate, TRMM, CHIRPS, PERSIANN‐CDR, GPM‐IMERG, ERA5 and CFSR) available on Google earth engine (GEE) are evaluated against gridded gauge‐based precipitation product available from Indian Meteorological Department (IMD) for their skills and presence of systematic biases (during 2001–2018). All these products represent the climatological features reasonably well. Presence of systematic biases in these products is also observed from their evaluation. Biases across the periphery of the country are relatively on the higher side in comparison to the central regions. The magnitude of spatial variability is represented better for winter precipitation in comparison to summer precipitation. During both winter and summer, ensemble mean of various products outperforms individual products in terms of both RMSE and correlation. Performance of these products is also assessed across various Indian states, elevation bands and climate zones. The ability of these products to represent the seasonality was observed to be highest for the states with mid‐ranged peaks (10–20 mm·day−1) which tend to decrease with both increasing and decreasing peaks. Ability of the precipitation products to resemble the annual cycle does not vary with the amount of precipitation, although individual disparity among the products exists. Additionally, an alternative approach for data evaluation using Multiple Triple Collocation (MTC) was performed for the period 2001–2015 using an additional dataset obtained from soil‐moisture‐based rainfall estimates (SM2RAIN). Results from MTC convey that ERA5 performs relatively poor in comparison to the other products for central India followed by CFSR. In brief, the comprehensive evaluation of precipitation products reported herein will act a valuable reference for the researchers as well as decision makers to select the optimal product for their intended application and will inform the users about the various uncertainties in the foundations and specification of these products.

Earth and Space Science

“…IMERG products have not been widely applied in meteorological or hydrological fields, and presently, several studies tend to concentrate on evaluating data reliability. Many studies have evaluated the application of IMERG‐F at different spatiotemporal scales (Asong et al, 2017; H. Guo et al, 2016; Mahmoud et al, 2019; Reddy et al, 2019; Tang et al, 2016; Wei et al, 2018) under different topographic and climate characteristics (Kim et al, 2017; C. Zhang, Chen, et al, 2018; Lu et al, 2019, 2018; Anjum et al, 2019), and in the hydrological field (Jiang et al, 2018; Ma et al, 2019; N. Li, Tang, et al, 2017; Z. Wang et al, 2017). According to reference standards such as ground rain gauges or weather radar inversion products (e.g., quantitative precipitation estimation) (N. Li, Wang, et al, 2017), these studies evaluate IMERG‐F solely or in conjunction with other satellite‐based precipitation data analysis products (e.g., TMPA, CMORPH, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks).…”

Section: Introductionmentioning

confidence: 99%

Can the GPM IMERG Hourly Products Replicate the Variation in Precipitation During the Wet Season Over the Sichuan Basin, China?

Wang

Wang²,

et al. 2020

Two near-real-time and one post-processing products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (GPM IMERG) were evaluated during the period from 2016 to 2018 in the wet seasons (June-November) over the Sichuan Basin, China. Results indicated the following: (1) The three products could generally replicate strong precipitation well; however, significantly large biases were observed when detecting weak precipitation. (2) All three products replicated summer precipitation more accurately than autumn precipitation. The IMERG "early run" product (IMERG-E) largely underestimated precipitation in the wet season, while the "latter run" (IMERG-L) and "final run" products (IMERG-F) could counteract this to a certain degree. (3) IMERG-F captured weak, moderate, and strong precipitation well during the wet season, and IMERG-E showed excellent potential in detecting different precipitation intensities. All the three products could replicate the diurnal cycle of the wet season precipitation. The findings of this study can facilitate the application of IMERG products in regions with complex topography and have also highlighted the potential of IMERG-E for rapid early warning and forecast systems.