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

Abstract

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...

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

“…7 Meteorological sub-divisional scale seasonal JJAS accumulated rainfall (mm) and its departure from the normal (given in parentheses) using a Kalpana-1 IMSRA, b GSMaP-NRT, c TMPA-3B42 and d IMD-SRG data sets for the southwest monsoon of 2013 IMSRA and TMPA data sets with respect to IMD-SRG based estimates clearly shows the potential of Kalpana-1 derived rainfall product for monsoon monitoring at meteorological sub-divisional scale. Furthermore, it is recently shown that the combined use of IMSRA and rain gauge observations provides better rainfall estimates over the Indian monsoon region (Gairola et al 2015). Thus, the merged satellite-gauge rainfall product would provide better rainfall estimates at meteorological sub-divisional scale.…”

Hybrid Assimilation of Satellite Rainfall Product with High Density Gauge Network to Improve Daily Estimation: A Case of Karnataka, India

“…The sparse distribution of rain gauges and weather radars mainly in mountainous and deeper oceanic regions limits various applications on global and regional scale. On the other hand, space-borne sensors provide homogeneous spatial and temporal distribution of rainfall (Gairola et al 2015).…”

“…To merge rain gauge observations with INSAT (Indian National SATellite) satellite retrieved rainfall at 1° × 1° spatial resolution, Roy Bhowmik and Das (2007) used an objective analysis method over the Indian landmass for ISM rainfall. Gairola et al (2015) developed a merged rainfall method by blending rain gauge observations with geostationary Kalpana-1 satellitederived IMSRA (INSAT retrieved Multi-Spectral Rainfall Algorithm) rainfall estimates using an objective criterion of successive correction method. Authors found considerable improvements in terms of correlation, bias and root-mean-square error after objective analysis, especially over the regions where density of rain gauge was better.…”

An assessment of Kalpana-1 rainfall product for drought monitoring over India at meteorological sub-division scale

“…However, a suitable bias correction in IMSRA rainfall data is needed before its use for drought monitoring and hydrological applications. Recently, Gairola, Prakash, and Pal (2015) demonstrated that the synergistic use of IMSRA and rain-gauge observations would provide better rainfall estimates over the Indian monsoon region as it benefits from the relative merits of both data sources. Hence, combined Kalpana-1 and rain-gauge rainfall estimates would be better for meteorological drought monitoring over India.…”