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Assimilation of INSAT‐3D hydro‐estimator method retrieved rainfall for short‐range weather prediction
Author(s) -
Kumar Prashant,
Varma Atul K.
Publication year - 2016
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2929
Subject(s) - environmental science , geostationary orbit , weather research and forecasting model , data assimilation , meteorology , climatology , satellite , numerical weather prediction , geology , geography , aerospace engineering , engineering
India launched a geostationary satellite INSAT‐3D on 26 July 2013 with an objective to monitor the Earth's surface using various spectral channels of meteorological importance. INSAT‐3D retrieved Hydro‐Estimator (HE) rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 merged rainfall and in situ observations from the India Meteorological Department (IMD). Results suggest that INSAT‐3D HE rainfall is of reasonably good quality and hence can be used for various meteorological applications. The Weather Research and Forecasting (WRF) model and its four‐dimensional variational (4D‐Var) data assimilation system are used to assimilate the INSAT‐3D retrieved high‐resolution HE rainfall product. Two parallel experiments are performed daily with and without assimilation of rainfall observations during the entire month of July 2014. Results show that assimilation of INSAT‐3D rainfall makes a good improvement in temperature and wind speed forecasts, and a marginal improvement in water vapour mixing ratio forecasts. Prediction of rainfall is also found to be improved with the use of INSAT‐3D rainfall over control experiments. Additionally, one case‐study is performed to assess the impact of INSAT‐3D rainfall on an unprecedented high rainfall event over Ahmedabad, India on 15 November 2014. Results show that the WRF model experiment with assimilation of INSAT‐3D rainfall is able to capture the heavy rainfall episode, which otherwise was unpredictable.