Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

Zad, Siti Najja Mohd; Zulkafli, Zed; Muharam, Farrah Melissa

doi:10.3390/rs10030388

Cited by 33 publications

(14 citation statements)

References 38 publications

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“…However, the SRPs showed poor performance in simulating daily rainfall. The authors of [72] also found that by applying bias correction on SRPs, the error discrepancy and correlation between TRMM and rain gauge can be reduced by 18.38% and up to 16.28% respectively. Improving the accuracy of SRPs by considering bias correction is of great importance to ensure their applicability for daily and/or hourly application in hydrological and water resources modelling.…”

Section: Resultsmentioning

confidence: 96%

“…It is unanticipated, in particular, the GSMaP-NRT data, as studies found that GSMaP had a significant discrepancy with observed data in rainfall estimation [68][69][70] and runoff simulation [71]. In Malaysia, rainfall estimates using SRPs have been carried out in several studies [19,20,72]. It has been reported that all SRPs are able to produce good results in estimating annual and monthly precipitation.…”

Section: Resultsmentioning

confidence: 99%

“…In fact, NRT products do not take gauge-correction into account [80]. More studies on the bias correction of satellite rainfall products are conducted [72,[85][86][87]. In addition to enhancing the reliability of rainfall estimates, the streamflow simulation model has also improved [83,85].…”

Section: Resultsmentioning

confidence: 99%

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Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

et al. 2019

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The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with −12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between −23.3% and −31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

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Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

et al. 2019

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“…Despite this adjustment, Delahaye [8] showed many qualitative (hit or miss of rainy events) and quantitative estimation errors in 3B42 version 6 in the Brazilian Legal Amazon. These errors partially arise due to a lower capacity of multi-satellite based algorithms-such as those used by TRMM-to detect warm top stratiform cloud systems [11][12][13] particularly in mountainous regions, where precipitations tend to be underestimated [14,15]. Still, 3B42 generally shows high performance, if not the best, compared to other remote sensing products [8,13,15,16].…”

Section: Introductionmentioning

confidence: 99%

Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

et al. 2018

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Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

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“…In the past few years, numerous studies have been carried out to validate the TRMM precipitation product in different regions [15][16][17][18][19][20]. Tong et al evaluated four widely used global high-resolution satellite precipitation products against gauge streamflow observations over the Tibetan Plateau [21].…”

Section: Introductionmentioning

confidence: 99%

Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Wang

Ding

Zhao

et al. 2018

Water

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Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009-2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0-1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009-2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

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Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

Cited by 33 publications

References 38 publications

Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

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