Errors of five satellite precipitation products for different rainfall intensities

Chen, Hanqing; Wen, Duancheng; Du, Yumin; Xiong, Luyun; Wang, Leyang

doi:10.1016/j.atmosres.2023.106622

Cited by 12 publications

(7 citation statements)

References 50 publications

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“…The ability of SPEs to capture hourly scale precipitation events in different regions was evaluated based on categorical indicators (i.e., POD, FAR, and CSI) (Figure 6). Overall, SPEs have a limited ability to accurately capture actual precipitation events from satellite precipitation data on an hourly scale, generally ranging from 0.22 to 0.77, a finding that is consistent with the results of previous studies [5,28,49,50]. Meanwhile, there are more precipitation misreporting phenomena, and the FAR values are all greater than 0.43.…”

Section: The Ability To Capture Hourly Scale Precipitation Eventssupporting

confidence: 87%

“…To further improve the precipitation accuracy, the MVK-C product is obtained by correcting precipitation errors using the global-scale CPC daily precipitation product provided by the NOAA [21]. By incorporating ground observation data into the precipitation estimation process, the MVK-C product can better capture spatial and temporal changes in ground precipitation [28].…”

Section: Satellite-based Gsmap Productsmentioning

confidence: 99%

“…Liu et al [26] compared the performance of NRT-R and NOW-R in captur-ing the Henan rainstorm event and found that NRT-R performed better than NOW-R. Mega et al [21] compared NRT-R, MVK-R, and MVK-C in Japan and found that the RMSE of these three products gradually decreased. Hsu et al [27] compared NRT-R and NRT-C in Taiwan, and Chen et al [28] validated MVK-R and MVK-C in China and confirmed that the gauge correction algorithm used in the MVK-C system effectively reduced the systematic bias of MVK-R under various terrain conditions. However, on the hourly scale, few studies have focused on the intercomparison between the in three GSMaP versions with different latency times.…”

Section: Introductionmentioning

confidence: 97%

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Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

Lv,

Guo,

Tian

et al. 2024

Remote Sensing

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A thorough evaluation of the recently released Global Satellite Mapping of Precipitation (GSMaP) is critical for both end-users and algorithm developers. In this study, six products from three versions of GSMaP version 8, including real time (NOW-R and NOW-C), near real time (NRT-R and NRT-C), and post-real time (MVK-R and MVK-C), are systematically and quantitatively evaluated based on time-by-time observations from 2167 stations in mainland China. Among each version, both products with and without gauge correction are adopted to detect the gauge correction effect. Error quantification is carried out on an hourly timescale. Three common statistical indices (i.e., correlation coefficient (CC), relative bias (RB), and root mean square error (RMSE)) and three event detection capability indices (i.e., probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI)) were adopted to analyze the inversion errors in precipitation amount and precipitation event frequency across the various products. Additionally, in this study, we examine the dependence of GSMaP errors on rainfall intensity and elevation. The following main results can be concluded: (1) MVK-C exhibits the best ability to retrieve rainfall on the hourly timescale, with higher CC values (0.31 in XJ to 0.47 in SC), smaller RMSE values (0.14 mm/h in XJ to 0.99 mm/h in SC), and lower RB values (−4.78% in XJ to 16.03% in NC). (2) Among these three versions, the gauge correction procedure plays a crucial role in reducing errors, especially in the post-real-time version. After being corrected, MVK-C demonstrates an obvious CC value improvement (>0.3 on the hourly timescale) in various sub-regions, increasing the percentage of sites with CC values above 0.5 from 0.03% (MVK-R) to 28.47% (MVK-C). (3) GSMaP products generally exhibit error dependencies on precipitation intensity and elevation, particularly in areas with drastic elevation changes (such as 1200–1500 m and 3000–3300 m), where the accuracy of satellite precipitation estimates is significantly affected. (4) CC values decreased with an increasing rainfall intensity; RB and RMSE values increased with an increasing rainfall intensity. The results of this study may be helpful for algorithm developers and end-users and provide a scientific reference for different hydrological applications and disaster risk reduction.

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Section: The Ability To Capture Hourly Scale Precipitation Eventssupporting

confidence: 87%

Section: Satellite-based Gsmap Productsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

Lv,

Guo,

Tian

et al. 2024

Remote Sensing

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“…IMERG and other satellite rainfall datasets have lower accuracy in topographically complex settings, and worse performance of IMERG in capturing intense rainfall in the mountainous parts of Italy and the Carpathians may be one of the reasons for the underestimation of the IMERG-based erosivity estimates, although further research and comparison with ground-based gauges is certainly warranted. Recent research has shown that satellite rainfall datasets, including IMERG, may consistently underestimate the total amounts of heavy and storm rainfall (Marc et al, 2022;Chen et al, 2023).…”

Section: Comparison With Ground-based Datamentioning

confidence: 99%

Dynamic rainfall erosivity estimates derived from IMERG data

Emberson

2023

Hydrol. Earth Syst. Sci.

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Abstract. Soil degradation is a critical threat to agriculture and food security around the world. Understanding the processes that drive soil erosion is necessary to support sustainable management practices and to reduce eutrophication of water systems from fertilizer runoff. The erosivity of precipitation is a primary control on the rate of soil erosion, but to calculate erosivity high-frequency precipitation data are required. Prior global-scale analysis has almost exclusively used ground-based rainfall gauges to calculate erosivity, but the advent of high-frequency satellite rainfall data provides an opportunity to estimate erosivity using globally consistent gridded satellite rainfall. In this study, I have tested the use of IMERG (Integrated Multi-satellitE Retrievals for GPM, Global Precipitation Mission) rainfall data to calculate global rainfall erosivity. I have tested three different approaches to assess whether simplification of IMERG data allows for robust calculation of erosivity, finding that the highest-frequency 30 min data are needed to best replicate gauge-based estimates. I also find that in areas where ground-based gauges are sparse, there is more disparity between the IMERG-derived estimates and the ground-based results, suggesting that IMERG may allow for improved erosivity estimates in data-poor areas. The global extent and accessibility of IMERG data allow for regular calculation of erosivity in a month-to-month time frame, permitting improved dynamic characterization of rainfall erosivity across the world in near-real time. These results demonstrate the value of satellite data to assess the impact of rainfall on soil erosion and may benefit practitioners of sustainable land management planning.

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“…Additionally, improved computational efficiency of numerical model is also becoming and effective way of precipitation estimation (Xin et al., 2022). Nevertheless, both being the indirect method, have their limitations; satellite suffers from ambiguity in the bias correction algorithm (H. Chen et al., 2023), retrieval methods and sensor used (Liu et al., 2022; Sunilkumar et al., 2019) while complexities of atmospheric processes and uncertainties in their representation limits numerical models (Moalafhi et al., 2020; Y. Chen et al., 2021). Still, efforts are being made to produce improved precipitation datasets: merging multi‐satellite precipitation estimates (Huffman et al., 2007, 2015; Sadeghi et al., 2021), blending gauge observation, satellites, and reanalysis datasets (Beck et al., 2019; Funk et al., 2015; Jiang et al., 2023), and dynamical downscaling of reanalysis datasets (X. Wang, Tolksdorf, et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Nepal,

Bao,

et al. 2024

JGR Atmospheres

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Sub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

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Errors of five satellite precipitation products for different rainfall intensities

Cited by 12 publications

References 50 publications

Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

Dynamic rainfall erosivity estimates derived from IMERG data

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

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