Retrieval of Atmospheric Temperature Profiles from AMSU-A Measurement Using Artificial Neural Network and Its Applications for Estimating Tropical Cyclone Intensity for ‘Gonu’ and ‘Nargis’ - Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change - page 389

Retrieval of Atmospheric
Temperature Profiles from AMSU-A
Measurement Using Artificial Neural
Network and Its Applications for
Estimating Tropical Cyclone Intensity
for 'Gonu' and 'Nargis'
A.K. Mitra*, A.K. Sharma and P.K. Kundu 1
India Meteorological Department, New Delhi
1 Department of Mathematics, Jadavpur University, Kolkata - 700032
*e-mail: [e-mail]
1. Introduction
The vertical structure of temperature and water vapour plays an important role
in the meteorological processes of the atmosphere. For years the radiosonde
network has been the primary observing system for monitoring tropospheric
temperature and water vapour. Routine observations are very difficult over the
oceanic region due to logistic problems and high cost factors. The radiosonde
networks are limited only over land regions. The interpretation of satellite
radiances requires the inversion of the radiative transfer equation (RTE), where
measurements of radiation performed at different frequencies are related to the
energy from different atmospheric regions. The solution, thus obtained, is highly
indeterminate for a set of observed radiances. The degree of indetermination is
associated with the spectral resolution and the number of spectral channels.
These radiances are basically a function of the vertical distribution of water
vapour and temperature in the atmosphere and not simply of their average
values. The retrieval of these vertical profiles from the radiances is an ill-
posed problem that cannot be solved directly (Isaacs et al., 1986). Due to the
difficulty of obtaining correct RTE solutions, several approaches and methods
were developed to extract information from the satellite data by retrieving
geophysical parameters from satellite radiances.