Flood modelling with global precipitation measurement (GPM) satellite rainfall data: a case study of Dehradun, Uttarakhand, India

2016 ◽  
Author(s):  
Sai Krishna V. V. ◽  
Anil Kumar Dikshit ◽  
Kamal Pandey
Keyword(s):  
Rainfall Data ◽  
Flood Modelling ◽  
Precipitation Measurement ◽  
Satellite Rainfall ◽  
Global Precipitation Measurement ◽  
Global Precipitation

scholarly journals How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey

2018 ◽  
Vol 10 (7) ◽  
pp. 1150 ◽  
Author(s):  
Ehsan Omranian ◽  
Hatim Sharif ◽  
Ahmad Tavakoly
Keyword(s):  
The United States ◽  
Reference Dataset ◽  
Precipitation Measurement ◽  
Short Period ◽  
Effective Reduction ◽  
Global Precipitation Mission ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Reconstructed Precipitation

Hurricanes and other severe coastal storms have become more frequent and destructive during recent years. Hurricane Harvey, one of the most extreme events in recent history, advanced as a category IV storm and brought devastating rainfall to the Houston, TX, region during 25–29 August 2017. It inflicted damage of more than $125 billion to the state of Texas infrastructure and caused multiple fatalities in a very short period of time. Rainfall totals from Harvey during the 5-day period were among the highest ever recorded in the United States. Study of this historical devastating event can lead to better preparation and effective reduction of far-reaching consequences of similar events. Precipitation products based on satellites observations can provide valuable information needed to understand the evolution of such devastating storms. In this study, the ability of recent Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM-IMERG) final-run product to capture the magnitudes and spatial (0.1° × 0.1°)/temporal (hourly) patterns of rainfall resulting from hurricane Harvey was evaluated. Hourly gridded rainfall estimates by ground radar (4 × 4 km) were used as a reference dataset. Basic and probabilistic statistical indices of the satellite rainfall products were examined. The results indicated that the performance of IMERG product was satisfactory in detecting the spatial variability of the storm. It reconstructed precipitation with nearly 62% accuracy, although it systematically under-represented rainfall in coastal areas and over-represented rainfall over the high-intensity regions. Moreover, while the correlation between IMERG and radar products was generally high, it decreased significantly at and around the storm core.

scholarly journals Performance Evaluation and Comparison of Satellite-Derived Rainfall Datasets over the Ziway Lake Basin, Ethiopia

Climate ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 113
Author(s):  
Aster Tesfaye Hordofa ◽  
Olkeba Tolessa Leta ◽  
Tena Alamirew ◽  
Nafyad Serre Kawo ◽  
Abebe Demissie Chukalla
Keyword(s):  
Time Scales ◽  
Hydrological Modeling ◽  
Evaluation Criteria ◽  
Rainfall Data ◽  
Lake Basin ◽  
Climate Trend ◽  
Precipitation Measurement ◽  
Basin Scale ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Consistent time series rainfall datasets are important in performing climate trend analyses and agro-hydrological modeling. However, temporally consistent ground-based and long-term observed rainfall data are usually lacking for such analyses, especially in mountainous and developing countries. In the absence of such data, satellite-derived rainfall products, such as the Climate Hazard Infrared Precipitations with Stations (CHIRPS) and Global Precipitation Measurement Integrated Multi-SatellitE Retrieval (GPM-IMERG) can be used. However, as their performance varies from region to region, it is of interest to evaluate the accuracy of satellite-derived rainfall products at the basin scale using ground-based observations. In this study, we evaluated and demonstrated the performance of the three-run GPM-IMERG (early, late, and final) and CHIRPS rainfall datasets against the ground-based observations over the Ziway Lake Basin in Ethiopia. We performed the analysis at monthly and seasonal time scales from 2000 to 2014, using multiple statistical evaluation criteria and graphical methods. While both GPM-IMERG and CHIRPS showed good agreement with ground-observed rainfall data at monthly and seasonal time scales, the CHIRPS products slightly outperformed the GPM-IMERG products. The study thus concluded that CHIRPS or GPM-IMERG rainfall data can be used as a surrogate in the absence of ground-based observed rainfall data for monthly or seasonal agro-hydrological studies.

scholarly journals PEMETAAN POTENSI BAHAYA BANJIR DI KABUPATEN MANOKWARI MELALUI PEMANFAATAN DATA GLOBAL PRECIPITATION MEASUREMENT (GPM) DAN ANALISIS BENTANG LAHAN

2020 ◽  
Vol 9 (2) ◽  
pp. 96
Author(s):  
Arif Faisol ◽  
Indarto Indarto ◽  
Elida Novita ◽  
Budiyono Budiyono
Keyword(s):  
Main Parameter ◽  
Flood Hazard ◽  
Rainfall Data ◽  
Landscape Analysis ◽  
Hazard Mapping ◽  
Precipitation Measurement ◽  
Flood Analysis ◽  
Almost All ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Rainfall data is the main parameter in flood analysis. The limited number of climate stations and rain stations in Manokwari due to low spatial representativeness of rainfall. This study aims to utilize Global Precipitation Measurement (GPM) as a satellite-based rainfall observer to analyze and floods hazard mapping in Manokwari. The method used in this research is landscape analysis. Research showed that almost all areas in Manokwari had high levels of flood hazard at any period except Tanah Rubuh district.

Preliminary Assessment of GPM Satellite Rainfall over Myanmar

2018 ◽  
Vol 13 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Muhammad Mohsan ◽  
Ralph Allen Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Heavy Rain ◽  
Probability Of Detection ◽  
Light Rain ◽  
Precipitation Measurement ◽  
Observation Network ◽  
Satellite Rainfall ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Statistical Metrics

Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting systems. This study investigates the utility of these GPM satellite estimates for representing the daily rainfall for 25 rain gauges over Myanmar. Statistical metrics were used to understand the characteristic performance of the GPM satellite estimates. Daily rainfall estimates from GPM show a range of 29.3% to 81.1% probability of detection (POD). The satellite estimates show a capability of detecting no-rain days between 61.4 and 93.5%. For different rainfall intensities, the satellite estimates have a 12.9 to 39.1% POD for light rain (1–10 mm/day), 11.1 to 49% POD for moderate rain (10–50 mm/day), a maximum of 36% for heavy rain (50–150 mm/day), and a maximum of 12.5% for extreme rain (=150 mm/day). However, the correlation coefficient (CC) only ranges from 0.064 to 0.581, which is considered low, and is not uniform for all the stations. The highest CC scores and POD scores tend to be located in the northern part and deltaic region extending to the southern coasts in Myanmar, indicating a dependency of the statistical metrics on rainfall magnitude. The high POD scores indicate the utility of the estimates without correction for early warning purposes, but the estimates have low reliability for rainfall intensity. The satellite estimates can be used for forecasting and modeling purposes in the region, but the estimates require bias-correction before application.

PERBANDINGAN SEBARAN CURAH HUJAN HASIL OBSERVASI SATELIT GPM DENGAN MODEL CUACA WRF (Studi Kasus: Siklon Tropis Cempaka Dan Dahlia, 27 Nov – 3 Des 2017)

2019 ◽  
Vol 3 ◽  
pp. 1063
Author(s):  
Fatkhuroyan Fatkhuroyan
Keyword(s):  
Weather Research And Forecasting ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Japan Aerospace Exploration Agency ◽  
Weather Research

Satelit GPM (Global Precipitation Measurement) merupakan proyek kerjasama antara NASA (National Aeronautics and Space Administration) dan JAXA (Japan Aerospace Exploration Agency) serta lembaga internasional lainnya untuk membuat satelit generasi terbaru dalam rangka pengamatan curah hujan di bumi sejak 2014. Model Cuaca WRF (Weather Research and Forecasting) merupakan model cuaca numerik yang telah dipakai oleh BMKG (Badan Meteorologi Klimatologi dan Geofisika) untuk pelayan prediksi cuaca harian kepada masyarakat. Pada tanggal 27 November – 3 Desember 2017 telah terjadi bencana alam siklon tropis Cempaka dan Dahlia di samudra Hindia sebelah selatan pulau Jawa. Tujuan Penelitian ialah untuk mengetahui sebaran akumulasi curah hujan antara observasi satelit GPM dan model cuaca WRF, serta keakuratan model WRF terhadap observasi satelit GPM saat terjadinya bencana alam tersebut. Metode yang dipakai ialah dengan melakukan analisa meteorologi pertumbuhan terjadinya siklon tropis tersebut hingga terjadinya hujan sangat lebat secara temporal maupun spasial. Dari hasil analisa disimpulkan bahwa satelit GPM memiliki luasan sebaran curah hujan yang lebih kecil daripada sebaran hujan model cuaca WRF pada saat siklon tropis Cempaka dan Dahlia. Bias akumulasi sebaran hujan model cuaca WRF juga cukup bagus terhadap satelit GPM sehingga dapat dilakukan antisipasi dampak hujan lebat yang terjadi.

scholarly journals Validation of Satellite-Based Precipitation Products from TRMM to GPM

2021 ◽  
Vol 13 (9) ◽  
pp. 1745
Author(s):  
Jianxin Wang ◽  
Walter A. Petersen ◽  
David B. Wolff
Keyword(s):  
Regional Differences ◽  
Tropical Rainfall Measuring Mission ◽  
Decomposition Approach ◽  
Precipitation Product ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Error Decomposition ◽  
Improved Accuracy ◽  
Global Precipitation ◽  
Statistical Metrics

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

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