Impact of four dimensional assimilation of satellite data on long-range simulations over the Indian region during monsoon 2006

Abstract

A number of experiments were conducted to study the impact of updating model basic fields by satellite data (Quick Scatterometer (QSCAT) surface winds and Atmospheric Infrared Sounder (AIRS) temperature and humidity profiles) on long-range simulation during the Indian summer monsoon 2006. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model version5 (MM5) and its four dimensional data assimilation (FDDA) technique was used for the numerical simulations. The spatial distribution and temporal variation in model simulated basic meteorological parameters and rainfall were verified against the observed fields from National Center for Environmental Prediction (NCEP) analysis and Tropical Rainfall Measuring Mission (TRMM), respectively. The overall analysis of the results from QSCAT surface wind assimilation as compared to control simulation (CNT; without the satellite data assimilation) suggest that a better representation of a single level wind field during model integration fail to make significant improvement in the model simulation both in the basic meteorological parameters and rainfall. The assimilation of temperature and humidity profiles from the AIRS during model integration significantly improved the rainfall prediction during monsoon period. It is found that the improvement in rainfall prediction is attributed to improved thermodynamics structure due to AIRS profile assimilation and the degree of improvement is more in temperature prediction as compared to humidity prediction. It is also found that the prediction over the regions, such as south west part of India and foothills of Himalaya, where a complex orography exists, is not significantly benefited from satellite data assimilation which highlights the need of improvement in the model in addition to a better representation of atmospheric state.