Assessment of the correlation between TMPA satellite-based and rain gauge rainfall (original) (raw)
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Satellite and ground data rainfall characterization in Malaysia
2011
This work presents the validation of ground based rainfall measurement with TRMM products and GPCC data. The bias error between ground and satellite rainfall measurements was found to be ± 15 %. The TRMM 3B43 agreed with rain gauge rainfall measurement with correlation coefficient of 0.835. Consequently, for sea and remote areas where rain gauge cannot cover, TRMM 3B43 V6 is recommended for use in lieu of ground measurement in Malaysia and its environs.
Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2011
Near real time rainfall information is necessary for early warning of rainfall triggered hazard such as floods and landslides. Remote sensing based rainfall estimation has been considered to be used to fulfill that purpose. This research is addressed to use geostationary based rainfall estimation by using Multi Transport Satellite (MTSAT) data which is blended with Tropical Rainfall Measuring Mission (TRMM) 2A12 datasets in order to provide near real time rainfall information, especially for hazard study purposes over Java Island, Indonesia. Comparison to TRMM Multi Precipitation Analysis (TMPA) datasets is performed. Spatial and temporal validation of those rainfall estimations is conducted by validating them with available rain gauge data during a rainy season in December 2007. Temporal validation result shows that TMPA demonstrated better statistical performance than MTSAT blended. However for the spatial correlation, MTSAT blended shows relatively better performance than TMPA.
Comparison between Satellite-derived Rainfall and Rain Gauge Observation over Peninsular Malaysia
Sains Malaysiana, 2022
Validation of the bias-corrected product of National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre Morphing Technique CMORPH-CRT was conducted using gridded rain gauge dataset of Wong et al. (2011) and rain gauge data from meteorological stations throughout Peninsular Malaysia. The CMORPH-CRT was compared for four contrasting topographic sub-regions of Peninsular Malaysia, i.e. west coast (WC), foothills of Titiwangsa range (FT), inland-valley (IN) and east coast (EC). CMORPH-CRT product with grid resolution of 8 km × 8 km at temporal resolution of 1-hour from 00Z January 1998 to 23Z December 2018 was utilized. The results show that CMORPH-CRT are in agreement with the rain gauge data. The CMORPH-CRT performed best over coastal sub-regions but it underestimated over FT sub-region and overestimated at IN. CMORPH-CRT tend to perform better in moderate rather than heavy rainfall events. For extreme weather events, the CMORPH-CRT had shown capability in observing the formation and decay of low-pressure system in Penang during 4th November 2017 and it is in agreement with rain gauge based SPI index i.e. drought conditions over Peninsular Malaysia.
Remote Sensing, 2018
The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction.
NATIONAL CONFERENCE ON PHYSICS AND CHEMISTRY OF MATERIALS: NCPCM2020, 2021
GSMaP and TRMM data validation was carried out with rainfall data in West Borneo. This study aims to evaluate the performance of GSMAP and TRMM in estimating daily rainfall in seven watersheds, namely Ambawang (AB), Kakap (KP), Pontianak (PT), Rasar (RS), Mandor (MD), Sebadu (SB), and Segedong. (SG). The analysis was performed using statistical analysis, error calculation, and rain event presentation. The results of the GSMaP satellite data validation against the observation data showed very high correlation values in all observation locations (R> 0.75). The similar result is also found in the TRMM data. In general, all of locations show a very small RMSE value. This indicates that the GSMaP and TRMM data tend to have a good performance in estimating rainfall in West Borneo. The relative bias values show pretty good results (<15%) except for MD both GSMaP and TRMM data. The results of the estimation of rainfall data show a tendency that GSMaP is overestimated compared to TRMM data in AB, KP, PT, RS, SB, and SG while TRMM is more representative of the actual conditions. Both GSMaP and TRMM data tend not to be able to detect rains smaller than 1 mm. Overall, the three rainfall data indicate that the percentage of the frequency of rainfall events based on six rainfall classifications in all observation locations often occurs at rainfall values of 1-10 mm.
Ground validation of space-borne satellite rainfall products in Malaysia
Advances in Space Research, 2012
Precipitation studies globally is not only important to hydrological cycling, but also very crucial to satellite and terrestrial communication system designs. This work presents the validation of ground based rainfall measurement with TRMM products and GPCC data. The result shows that an error bias of ±15 % exists between the ground and satellite rainfall measurements. The study also shows that the TRMM 3B43 V6 is in good agreement with the rain gauge rainfall measurement with highest and lowest correlation coefficient of 0.9721 and 0.7791 respectively. Therefore, TRMM 3B43 V6 is recommended for use in lieu of the ground measurement in Malaysia and its environs, most importantly for sea and remote areas where rain gauge cannot cover.
Distribution of one-minute rain rate in Malaysia derived from TRMM satellite data
Annales Geophysicae, 2013
Total rainfall accumulation, as well as convective and stratiform rainfall rate data from the Tropical Rainfall Measuring Mission (TRMM) satellite sensors have been used to derive the thunderstorm ratio and one-minute rainfall rates, R 0.01 , for 57 stations in Malaysia for exceedance probabilities of 0.001-1 % for an average year, for the period 1998-2010. The results of the rain accumulations from the TRMM satellite were validated with the data collected from different ground data sources from the National Oceanic and Atmospheric Administration (NOAA) global summary of the day (1949-2010), Global Precipitation Climatology Centre (GPCC) (1986-2010), and NASA (1950-1999). The correlation coefficient and the average bias error between TRMM and GPCC for Malaysia were found to be 0.79-0.89 and ±50 mm, respectively. The deduced one-minute rainfall rates correlated fairly well with those obtained from the previous work carried out in Malaysia, with correlation coefficients of 0.7 in all the 57 locations. The inferred mean annual oneminute rainfall rates were found to be highest in the eastern Malaysia, with values between 84.7 and 153.9 mm h −1 for 0.01 % exceedance, and in western Malaysia with values between 81.8 and 143.8 mm h −1. The present results will be useful for satellite rain attenuation modeling in tropical and subtropical stations around the world.
Alexandria Engineering Journal, 2020
In several research domains, the precipitation products produced based on the Satellite images are used because of their advantage of high spatiotemporal resolution and near-global coverage. And still the products applications are limited according to the uncertainty. To facilitate using these products, it's vital to identify and quantify their error characteristics. The objective of this study is to evaluate the two satellite-based precipitation products; RainFall Estimate (RFE) and Tropical Rainfall Measuring Mission (TRMM) using ground gauge-based rainfall measurements throughout the Blue Nile River sub-basin in the Sudan for the period (2001-2016). The importance of this study comes from the discontinuity of the ground data due to some difficulties such as the accessibility of the region's topography, in addition to the poorly gauged areas. Therefore, estimating the amount of rain from verified satellites is useful to obtain the pattern of rainfall that could be used in hydrological models to provide river discharges forecasts and to specify areas of flood hazard. For a quantified evaluation of satellite-based precipitation products, continuous Mean Error (ME), and Correlation Coefficient (CC) were used. The results show that RFE and TRMM products performing well in estimating and observing rainfall over the study area.
2010
This study investigates the potential of the calibrated satellite-based rainfall estimates data product from Tropical Rainfall Measuring Mission (TRMM) satellites, known as TRMM 3B43 Version 6 or TRMM Multi-Satellites Precipitation Analysis (TMPA) water run-off modeling at tropical forested watershed scale in Peninsular Malaysia. The calibrated satellite-based rainfall is used as the main input in the Thornthwaite water balance equation to estimate the water run-off (yield) at monthly basis from 1998 to 2007. Four forested watershed which located in each main region in the Peninsular Malaysia are used as sample to evaluate the model performance. Preliminary assessment of the calibrated satellite-based rainfall at the selected forested watershed indicates good agreement with the ground rainfall measurement with average bias of ±43.35mm per month. The monthly estimated water run-off for all forested watershed in all region also shows good agreement and comparable with the simulated run-off using the rain gauged data. The average bias for the satellite-based derived water run-off for all forested watershed in all Peninsular Malaysia region is 22.7mm per month except for east region where higher values are recorded which reach 55.5mm per month.
Hydrology
A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physical terrain complexity were found to be linked with low accuracy. Accuracy was slightly better in dry seasons or low rain rate classes. The errors increased exponentially with the increase in rain rates. CHIPRS and PERSIANN tend to slightly underestimate at lower rain rates, but do show a consistently better performance, with an NRMSE of 6–12%. CHRIPS and PERSIANN also exhibit better estimates o...