Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multisatellite sensors

Mishra, Anoop Kumar; Gairola, R. M.; Varma, A. K.; Agarwal, Vijay K.

doi:10.1029/2009jd012157

Cited by 47 publications

(43 citation statements)

References 28 publications

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“…These results differ from the findings of Jamandre and Narisma [48] in the Philippines, who found that SPPs (in their case CMORPH and TRMM) performed better during the SWM. By contrast, Mishra et al [49], in India, showed that TRMM 3B42V6 had a lower RMSE, bias, and FAR for NEM compared to SWM. These differences may be explained by the origin of precipitation with convective weather systems (which dominate during the NEM) being more accurately detected by satellite sensors [50].…”

Section: Evaluation Of Seasonal Precipitationmentioning

confidence: 99%

Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia

et al. 2015

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Satellite precipitation products (SPPs) potentially constitute an alternative to sparse rain gauge networks for assessing the spatial distribution of precipitation. However, applications of these products are still limited due to the lack of robust quality assessment. This study compares daily, monthly, seasonal, and annual rainfall amount at 342 rain gauges over Malaysia to estimations using five SPPs (3B42RT, 3B42V7, GPCP-1DD, PERSIANN-CDR, and CMORPH) and a ground-based precipitation product (APHRODITE). The performance of the precipitation products was evaluated from 2003 to 2007 using continuous (RMSE, R 2 , ME, MAE, and RB) and categorical (ACC, POD, FAR, CSI, and HSS) statistical approaches. Overall, 3B42V7 and APHRODITE performed the best, while the worst performance was shown by GPCP-1DD. 3B42RT, 3B42V7, and PERSIANN-CDR slightly overestimated observed precipitation by 2%, 4.7%, and 2.1%, respectively. By contrast, APHRODITE and CMORPH significantly underestimated precipitations by 19.7% and 13.2%, respectively, whereas GPCP-1DD only slightly underestimated by 2.8%. All six precipitation products performed better in the northeast monsoon than in the southwest OPEN ACCESSRemote Sens. 2015, 7 1505 monsoon. The better performances occurred in eastern and southern Peninsular Malaysia and in the north of East Malaysia, which receives higher rainfall during the northeast monsoon, whereas poor performances occurred in the western and dryer Peninsular Malaysia. All precipitation products underestimated the no/tiny (<1 mm/day) and extreme (≥20 mm/day) rainfall events, while they overestimated low (1-20 mm/day) rainfall events. 3B42RT and 3B42V7 showed the best ability to detect precipitation amounts with the highest HSS value (0.36). Precipitations during flood events such as those which occurred in late 2006 and early 2007 were estimated the best by 3B42RT and 3B42V7, as shown by an R 2 value ranging from 0.49 to 0.88 and 0.52 to 0.86, respectively. These results on SPPs' uncertainties and their potential controls might allow sensor and algorithm developers to deliver better products for improved rainfall estimation and thus improved water management.

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Section: Evaluation Of Seasonal Precipitationmentioning

confidence: 99%

Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia

et al. 2015

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“…A large number of studies have evaluated the performance of 3V42V6 in many parts of world [11][12][13][14][15]. Although some studies have also documented the performance of 3B42V7 in different regions of the world [14][15][16][17][18], limited studies have reported on its performance relative to its predecessor (3B42V6) and near real time product (3B42RT) in complex terrain regions having cryospheric environment.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Comparison of TRMM Multi-Satellite Precipitation Products With Reference to Rain Gauge Observations in Hunza River Basin, Karakoram Range, Northern Pakistan

et al. 2017

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Abstract:The performance evaluation of satellite-based precipitation products at local and regional scales is crucial for modification in satellite-based precipitation retrieval algorithms, as well as for the provision of guidance during the selection of substitute precipitation data. This study evaluated the performances of three Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products (3B42V6, 3B42RT and 3B42V7) with a reference to rain gauge observations in the Hunza River basin, northern Pakistan. Multi-spatial (pixel and basin) and temporal (daily, monthly, seasonal and annual) resolutions were considered for performance evaluation of TMPA products. Results revealed that the spatial pattern of observed precipitation over the basin was adequately captured by 3B42V7 but misplaced by 3B42V6 and 3B42RT. All TMPA products were unable to capture the intense precipitation events. On the daily time scale, the performance of TMPA products was very poor over both spatial scales. 3B42V6 underestimated the precipitation (31.25% and 44.27% on pixel and basin scales, respectively). By contrast, 3B42RT significantly overestimated the precipitation (47.91% and 38.62% on pixel and basin scales, respectively), while 3B42V7 showed overestimation (17.30%) on pixel scale and slight underestimation (6.24%) on the basin scale. On the seasonal scale, TMPA products showed significant biases with observed precipitation data. We found that the TMPA products performed relatively better on monthly and annual time scales and overall performance of 3B42V7 product was better than that of 3B42V6 and 3B42RT. The bias in 3B42V7 was improved by 85.90% compared with 3B42V6 and by 116.16% compared with 3B42RT. Thus, it is concluded that the TMPA products were unreliable to capture the intense precipitation events and retain high errors on daily and seasonal scales. Therefore, caution should be considered while using these precipitation estimates as a substitute data in hydrology, meteorology and climatology studies in Hunza River basin. However, due to the reasonable performance of monthly and annual 3B42V7 estimates, these can be used as an acceptable substitute for applications in the region.

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“…Further, a new algorithm, the Indian National Satellite System (INSAT) Multispectral Rainfall Algorithm (IMSRA), was developed which benefits from the high spatial and temporal resolutions of Kalpana-1 and more accurate rainfall data from TRMM-PR (Prakash et al, 2009;Gairola et al, 2010b). This algorithm also uses proper cloud classification schemes from TIR and water vapor channels of Kalpana-1 and the procedure is the same as the one used by Mishra et al (2010) for rainfall estimation by the Meteosat-First Generation satellite and TRMM-PR datasets. IMSRA is capable of estimating rainfall from Kalpana-1 satellite data at high spatial (0.25º latitude × 0.25º longitude) and temporal (3-hourly) resolutions.…”

Section: Introductionmentioning

confidence: 99%

Improved rainfall estimation over the Indian monsoon region by synergistic use of Kalpana-1 and rain gauge data

Gairola¹,

Prakash²,

Pal³

2015

Atmósfera

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RESUMENEn este trabajo se busca calcular la precipitación en la región del monzón de la India mediante el uso sinérgico de datos pluviométricos y del algoritmo multiespectral de precipitación del Sistema Nacional de Satélites de la India (IMSRA, por sus siglas en inglés), obtenido mediante el satélite geostacionario -temporales del satélite Kalpana-1, así como de estimaciones pluviométricas precisas. Se realiza un análisis progresiva se aplica a una resolución de 1º × 1º determinada por el análisis de autocorrelación espacial. Los resultados se comparan con cuatro productos multisatelitales independientes de precipitación global: el mapeo satelital global de precipitación; productos de monitoreo de precipitación en tiempo casi real y para investigación del análisis multisatelital de precipitación de la Misión de Medición de Lluvias Tropicales, y el producto del Proyecto Global sobre la Climatología de las Precipitaciones. El análisis objetivo de las estimaciones de precipitación muestra una notable mejoría respecto de las estimaciones basadas sólo en mediciones satelitales en el sur de la India. La comparación con observaciones pluviométricas independientes también muestra una considerable mejoría en términos de correlación, sesgo y error cuadrático medio, especialmente en regiones con buena densidad de mediciones de precipitación. Los resultados globales demuestran que el uso sinérgico de mediciones satelitales e in situ tiene el potencial de facilitar estimaciones de precipitación más exactas en la región del monzón de la India. ABSTRACTIn this paper, an attempt has been made to estimate rainfall over the Indian monsoon region by the synergistic use of the geostationary Kalpana-1 satellite-derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall -lutions of the Kalpana-1 satellite and accurate rainfall estimates from rain gauges. A preliminary analysis is done for the southwest monsoon season of 2010 at a daily scale. The successive correction method is applied Atmósfera 28(1), 51-61 (2015) doi: 10.20937/ATM.2015.28.01.05 © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias de la Atmósfera. This is an open access article under the CC BY-NC-ND License (http://creativecommons.org/licenses/by-nc-nd/4.0/). 52 R. M. Gairola et al.at a 1º latitude × 1º longitude resolution determined by the spatial autocorrelation analysis. Results are compared with four independent global multisatellite rainfall products, namely the Global Satellite Mapping of Precipitation, the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis near-real time and research version rainfall monitoring products, and the Global Precipitation Climatology Project rainfall product. The objectively analyzed rainfall estimates show noticeable improvement over the satellite-based rainfall estimates alone over southern India. Comparison with independent rain gauge observations shows a considerable improvement in terms of correlation, bias and root-mean-square error after objective analysis, especially over t...

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Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multisatellite sensors

Cited by 47 publications

References 28 publications

Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia

Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia

Evaluation and Comparison of TRMM Multi-Satellite Precipitation Products With Reference to Rain Gauge Observations in Hunza River Basin, Karakoram Range, Northern Pakistan

Improved rainfall estimation over the Indian monsoon region by synergistic use of Kalpana-1 and rain gauge data

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