Studies on the Impacts of 3D-VAR Assimilation of Satellite Observations on the Simulation of Monsoon Depressions over India - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 631

Chapter 26
Studies on the Impacts of 3D-VAR Assimilation
of Satellite Observations on the Simulation
of Monsoon Depressions over India
A. Chandrasekar and M. Govindan Kutty
Abstract Variational data assimilation provides a convenient means of optimally
combining the “first-guess” or “background” meteorological fields with the obser-
vations. The background fields are typically obtained from the numerical weather
prediction output of a model while the observations can be either the meteorological
model variables or even non-model variables. In the three-dimensional variational
(3D-VAR) method the analysis state is obtained by optimally combining the “first-
guess” and the “observations” at the same analysis time.
The present article begins with a brief overview of the characteristics of the
monsoon disturbances that form over the Indian region during the summer monsoon
season. Subsequently, the 3D-VAR method is briefly introduced together with
details of the mesoscale model employed in this study. The next section outlines
the results of the impact of the 3D-VAR assimilation of satellite observations
in the simulation of a few monsoon disturbances over India using the Weather
Research and Forecast (WRF) model. The satellite observations utilized in the
3D-VAR assimilation study presented in this article include (1) temperature and
humidity profiles from Moderate Resolution Imaging Spectroradiometer (MODIS),
(2) temperature and humidity profiles from Advanced TIROS Vertical Sounder
(ATOVS), and (3) total precipitable water from Special Sensor microwave imager
(SSMI), respectively. In order to discern the impact of 3D-VAR assimilation of
satellite observations a (base or control) numerical experiment called “control run”
is performed, which is identical to the assimilated run (called “3D-VAR run”) except