Evaluation of GPM IMERG precipitation products with the point rain gauge records over Sichuan, China

Yang, Manshu; Liu, Guodong; Chen, Ting; Chen, Yu; Xia, Chengcheng

doi:10.1016/j.atmosres.2020.105101

Cited by 59 publications

(53 citation statements)

References 36 publications

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“…However, regions with complex terrain have exhibited poor performance because of the high spatial and temporal variability in precipitation and the lack of rain gauge observations [33,34]. The bias of the real-time version of TMPA (TMPA-RT) and PERSIANN over the Tibetan Plateau depends on topography, and can be explained mostly by the variability of elevation and surface roughness [35].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China

Zhao

et al. 2020

Atmosphere

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Satellite-based and reanalysis products are precipitation data sources with high potential, which may exhibit high uncertainties over areas with a complex climate and terrain. This study aimed to evaluate the accuracy of the latest versions of six precipitation products (i.e., Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) V2.0, gauge-satellite blended (BLD) Climate Prediction Center Morphing technique (CMORPH) V1.0, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V6 Final, Global Satellite Mapping of Precipitation (GSMaP) near-real-time product (NRT) V6, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-CDR) over the Yellow River Basin, China. The daily precipitation amounts determined by these products were evaluated against gauge observations using continuous and categorical indices to reflect their quantitative accuracy and capability to detect rainfall events, respectively. The evaluation was first performed at different time scales (i.e., daily, monthly, and seasonal scales), and indices were then calculated at different precipitation grades and elevation levels. The results show that CMORPH outperforms the other products in terms of the quantitative accuracy and rainfall detection capability, while CHIRPS performs the worst. The mean absolute error (MAE), root mean square error (RMSE), probability of detection (POD), and equitable threat score (ETS) increase from northwest to southeast, which is similar to the spatial pattern of precipitation amount. The correlation coefficient (CC) exhibits a decreasing trend with increasing precipitation, and the mean error (ME), MAE, RMSE, POD and BIAS reveal an increasing trend. CHIRPS demonstrates the highest capability to detect no-rain events and the lowest capability to detect rain events, while ERA5 has the opposite performance. This study suggests that CMORPH is the most reliable among the six precipitation products over the Yellow River Basin considering both the quantitative accuracy and rainfall detection capability. ME, MAE, RMSE, POD (except for ERA5) and BIAS (except for ERA5) increase with the daily precipitation grade, and CC, RMSE, POD, false alarm ratio (FAR), BIAS, and ETS exhibit a negative correlation with elevation. The results of this study could be beneficial for both developers and users of satellite and reanalysis precipitation products in regions with a complex climate and terrain.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China

Zhao

et al. 2020

Atmosphere

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“…Li et al [39] found that IMERG-F performed better than IMERG-E and IMERG-L in an extreme heavy rainfall process over Nanjing city in China. Yang et al [40] compared the three IMERG precipitation products and found that the IMERG-F may be regarded as a reliable data source. However, Tang et al [23] found that the IMERG-F was not always superior to IMERG-E and IMERG-L in several evaluations.…”

Section: Discussionmentioning

confidence: 99%

“…However, Tang et al [23] found that the IMERG-F was not always superior to IMERG-E and IMERG-L in several evaluations. Yang et al [40] evaluated the three IMERG precipitation products in extreme precipitation events over Sichuan province in China and found that the level of the precipitation detection capability of the IMERG precipitation products depended on the rainfall type and topography. Thus, the influence of topography on extreme precipitation events must be determined.…”

Section: Discussionmentioning

confidence: 99%

“…The Expert Team on Climate Change Detection, Monitoring, and Indices (ETCCDMI) recommends a total of 27 core indexes with a primary focus on extremes derived from daily station data [41]. Yang et al [40] used extreme indicators to evaluate the three IMERG precipitation products in extreme precipitation events while considering the influence of topography and rain type. In accordance with Camarasa et al [42], who used eight rainfall indicators (including 1, 6, and 12 h rainfall intensity) to estimate flood risk thresholds, we selected two extreme rainfall indexes: rainfall intensity and accumulated rainfall.…”

Section: Discussionmentioning

confidence: 99%

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Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events

Zhou

Gao

et al. 2021

Remote Sensing

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The monitoring of extreme precipitation events is an important task in environmental research, but the ability of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation products to monitor extreme precipitation events remains poorly understood. In this study, three precipitation products for IMERG version 6, early-, late-, and final-run products (IMERG-E, IMERG-L, and IMERG-F, respectively), were used to capture extreme precipitation, and their applicability to monitor extreme precipitation events over Hubei province in China was evaluated. We found that the accuracy of the three IMERG precipitation products is inconsistent in areas of complex and less complex topography. Compared with gauge-based precipitation data, the results reveal the following: (1) All products can accurately capture the spatiotemporal variation patterns in precipitation during extreme precipitation events. (2) The ability of IMERG-F was good in areas of complex topography, followed by IMERG-E and IMERG-L. In areas of less complex topography, IMERG-E and IMERG-L produced outcomes that were consistent with those of IMERG-F. (3) The three IMERG precipitation products can capture the actual hourly precipitation tendencies of extreme precipitation events. (4) In areas of complex topography, the rainfall intensity estimation ability of IMERG-F is better than those of IMERG-E and IMERG-L.

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“…However, they have rarely used the latest version 06 release of IMERG. These earlier studies are based on IMERG precipitation products that span over only a few years [42,43]. A comprehensive assessment of satellite-based precipitation products at the scale of a megacity is lacking [44].…”

Section: Introductionmentioning

confidence: 99%

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Ren

et al. 2021

Remote Sensing

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In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

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Evaluation of GPM IMERG precipitation products with the point rain gauge records over Sichuan, China

Cited by 59 publications

References 36 publications

Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China

Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China

Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

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