Abstract
Monsoon depressions form over the sea, which is a typical data-sparse region for conventional observations. The Moderate Resolution Imaging Spectroradiometer (MODIS) provides for very high-horizontal resolution temperature and humidity soundings. Such high-resolution satellite data can improve the poorly analyzed depressions. The objective of this study is to investigate the impact of ingesting and assimilating the MODIS temperature and humidity profiles on the prediction of a monsoon depression, which formed over the Bay of Bengal during September 2006 using three- dimensional variational data assimilation (3DVAR). The NCAR Weather Research and Forecast model (WRF) has been utilized in this study. The results of the study indicate that the simulated sea level pressure fields from the 3DVAR run is in better agreement with the sea level pressure field from the NCEP-FNL analysis as compared to the control run. Higher spatial correlation and the lower rms errors of the sea level pressure field are associated with the 3DVAR run. The simu- lated structure of the spatial precipitation pattern for the assimilation experiments (3DVAR) are closer to the TRMM ob- servations with more rainfall simulated over the east coast regions in the assimilation experiments. The 3DVAR runs clearly shows lower number of false alarms, higher probability of detection and larger value of equitable threat score for the 48 hours accumulated precipitation as compared to the control run. The results also indicate that the 3DVAR has larger positive bias values for precipitation as compared to the control run. For the 3DVAR run, the results reveal lower rms errors for temperatures at all levels and dew point temperatures, except for the upper troposphere. However, the rms errors for the wind speeds are not lower for the 3DVAR run. Overall, the results of this study indicate a very positive im- pact of the 3DVAR assimilation of MODIS observations on the simulation of a monsoon depression over India.
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