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 639

to monitor atmospheric temperature and moisture, respectively. The MODIS data
is extremely valuable as it can be utilized to improve our understanding of the
processes in the Earth system encompassing the land, oceans, and in the lower
atmosphere of the Planet Earth. The MODIS observations are indeed performing
a vital role in the development of globally, interactive Earth system models which
are utilized to predict the global changes accurately. The predicted global changes
can assist policy framers in arriving at sound and pragmatic decisions concerning
the protection of the Earth's environment. A brief technical description of the
MODIS instrument is as follows. The MODIS instrument provides high radiometric
sensitivity in 36 spectral bands ranging in wavelength from 0.4 to

m. Two
bands are imaged at a nominal resolution of 250 m at nadir, with five bands at
500 m, and the remaining 29 bands are at 1 km resolution. The MODIS temperature
and water vapour profiles consists of 30 gridded variables related to atmospheric
stability, atmospheric temperature, moisture profiles, total atmospheric water vapour
and total ozone. All the above-mentioned variables are available during both day
time and night time conditions at 5 km pixel resolution whenever nine field of
view (FOV) pixels or more are cloud free. The atmospheric temperature and
humidity profiles available at high spatial resolution from MODIS provides an
extensive source of information on the atmospheric structure in clear skies and
hence can be utilized most fruitfully to improve the initial state of the atmosphere.
A validation of temperature and humidity profiles with concurrent Arabian Sea
Monsoon experiment (ARMEX) Global Positioning System (GPS) radiosonde
data was performed during July 2002. The root mean square error (RMSE) of
temperature below 500 hPa was

14:4

2:5 ı K while the RMSE for the specific humidity

1 -

gkg 1 ( Simon and Rahman 2003 ). The temperature and
humidity profiles available on 14 vertical levels were utilized in this study.

profile was less than 2

26.2.3.5 Tropical Rainfall Measuring Mission (TRMM)

The Tropical Rainfall Measuring Mission (TRMM) sensor provides for a broad
sampling footprint between

35 ı S, and is responsible for the detailed and
comprehensive dataset on the space and time distribution of rainfall and latent heat-
ing over the oceanic and tropical continental regions. The TRMM algorithm 3B42
provides adjusted 24 h cumulative estimates of the rain using merged microwave
and infrared precipitation information ( Adler et al. 2000 ). The TRMM adjusted
Geostationary Observational Environmental satellite precipitation index (AGPI)
is produced by using cases of coincident TRMM combined instruments with
TRMM Microwave Imager (TMI) and Precipitation Radar (PR) algorithm ( Haddad
et al. 1997 ). The 3B42 algorithm provides for a three hourly rain rate at

35 ı Nand

0:25 ı 0:25 ı
horizontal resolution. In this study the TRMM data obtained by using the 3B42V6
algorithm was utilized for the validation of model predicted rainfall.