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 392

(brightness temperatures, albedo) and perform data mapping between the
measurement grids of the different instruments [AVHRR/HIRS (High-resolution
Infrared Radiation Sounder), AMSU-MHS (Microwave Humidity Sounder)/
HIRS mapping]. Finally AAPP supplies calibrated data for the same time periods
of brightness temperature for all ATOVS channels (AMSU-A, AMSU-B/MHS
and HIRS) located in the terrestrial coordinates (latitude and longitude) and
mapped in a common grid resolution i.e., level 1d . Angular variations including
limb correction has been accounted for each channel in AAPP before using as
input to IAPP scheme.
The IAPP developed by the SSEC (Space Science and Engineering Center)/
University of Wisconsin (Li et al., 2000) has been used to retrieve atmospheric
parameters in both clear and cloudy atmospheres from ATOVS radiances from
HIRS/3 (High Resolution Infrared Radiation Sounder), AMSU-A, and AMSU-
B, which were preprocessed by AAPP into the level 1d data format for retrievals.
The retrieval algorithm for ATOVS data processing is composed of four steps:
(1) HIRS/3 cloud detection and removal of cloud effects; (2) bias correction
for the HIRS/3 radiative transfer calculations; (3) regression solution for
parameters to be retrieved; and (4) nonlinear iterative physical retrieval of the
atmospheric temperature profile, moisture profile, atmospheric total ozone,
surface skin temperature and microwave surface emissivity through solving
the radiative transfer equation (RTE). In the IAPP, a fast and accurate
transmittance model is generated for the RTE calculation; it is called Pressure
Layer Optical Depth (PLOD) (Hannon et al., 1996) and uses 42 pressure level
vertical coordinates from 0.1 to 1050 hPa. In the physical retrieval process, the
following quantity is computed to check the convergence
a i =| A i - A i-1 | (1)
where the vector A contains levels of atmospheric temperatures. If a i+1 > a i
within two iterations, it means the iteration diverges and the iteration stops. In
this case the retrieval is set to the first guess; otherwise it keeps iterating until
a i < 0.10, or a maximum of 14 iterations is reached. Usually, more than 95% of
solutions obtain convergence.
In this study, the ancillary data inputs that are used for retrievals are: high-
resolution topography (supplied), and numerical model data from NCEP
analysis. The IAPP performs temperature and moisture retrieval calculation
on a 3 × 3 HIRS field of view (FOV) matrix. HIRS and AMSU-A radiances are
used for cloud/clear fov determination. Depending on clouds, a HIRS+AMSU-
A retrieval, a AMSU-A only retrieval, or no retrieval is made for each 3 × 3
fov matrix. The use of numerical model data as a first guess explains the need
to reorder the satellite data. This is done in a way that they match the NCEP
data with a time window of 1.5 hours around the model forecast time. Similarly,
if surface observations are not available, numerical model data is used to define
surface conditions, or, with neither available, window channel radiances are
used to best approximate surface conditions. Unlike infrared observations, the