The impact of assimilation of satellite derived wind observations for the prediction of a monsoon depression over India using a mesoscale model
Vazhappilly Francis, Xavier
The Penn State/NCAR mesoscale model (MM5) has been used in this study to ingest and assimilate the INSAT-CMV (Indian National Satellite System-Cloud Motion Vector) wind observations using analysis nudging (four-dimensional data assimilation, FDDA) to improve the prediction of a monsoon depression which occurred over the Bay of Bengal, India during 28 July 2005 to 31 July 2005. To determine the impact of assimilation of INSAT-CMV winds on the prediction of a monsoon depression, three sets of numerical experiments (NOFDDA, FDDA and FDDA CMV) were designed. While the FDDA CMV run assimilated satellite derived winds only, the FDDA run assimilated both satellite and conventional observations. The NOFDDA run used neither satellite nor conventional observations. The results of the study indicate that the simulated sea level pressure field from the FDDA run is more consistent with the sea level pressure field from NCEP-FNL compared to the FDDA CMV and NOFDDA runs. The highest correlation and lowest rms error of the sea level pressure field are associated with the FDDA run, and this provides a quantitative verification of the improvement due to the assimilation of satellite derived winds and the conventional upper air observations for the prediction of monsoon depression. All the three model simulated winds are in good agreement with the analysis winds at 850 hPa, 500 hPa and 200 hPa levels. The simulated structure of the spatial precipitation pattern for the assimilation experiments (FDDA and FDDA CMV) are closer to the TRMM observations with more rainfall simulated over the east coast regions in the assimilation experiments. The rms errors of the wind speed for the FDDA run show lower values at 500 hPa for all the three model runs, with a reduction in all three levels of up to 0.8-1.4 m s for the FDDA run and 0.5-1.9 m s for the FDDA CMV run with respect to the NOFDDA run. The statistical significance of the sea level pressure and the precipitation differences between the FDDA and the NOFDDA as well as the differences between the FDDA CMV and the NOFDDA have been calculated using the two-tailed Student's t-test and were found to be statistically significant. The influence of varying the nudging coefficients in the FDDA experiment has been studied.
Published inInternational Journal of Remote Sensing