Uncertainty Analysis of TRMM Orbital Data Products over India - Indu, J.
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Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting and climate diagnostic applications. This information is crucial in refining of physical assumptions within existing algorithms. Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. The error components also tend to interact non linearly with…mehr

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Produktbeschreibung
Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting and climate diagnostic applications. This information is crucial in refining of physical assumptions within existing algorithms. Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. The error components also tend to interact non linearly with hydrologic modeling uncertainty. This work summarizes uncertainty analysis using 11 years of instantaneous satellite orbital data products (version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard the TRMM satellite namely TRMM microwave imager (TMI) and precipitation radar (PR). Results involving sampling and retrieval errors are presented over the Indian subcontinent. This study serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) mission.
Autorenporträt
Dr. J. Indu is working as Assistant Professor in the Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, India since 2015. Her research interests include data assimilation, microwave remote sensing, uncertainty modeling, nowcasting of precipitation, application of remote sensing and GIS in water resources engineering.