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 402

this small sample of storms, the technique is simple to implement and could be
very useful to operational forecasters.
In order to avert the 'gap' in sounding retrievals and to keep continuity,
the number of polar orbiting satellites are needed to have a close watch for the
analysis of tropical disturbances. With the launch of INSAT-3D geostationary
satellite, which will be having sounder payload, the observed frequencies will
increase and it is hoped that the temporal resolution as well the 'data gap'
problem would be solved.
5. Concluding Remarks
In this study, two different schemes, the neural network approach and the
physical retrieval method used in IAPP, have been briefly described. The RMS
errors of the retrieved profiles of the neural network are compared with IAPP.
The results indicate that the trained network can obtain much better accuracy
in the training region and a comparable accuracy in other regions. Although
the iterative physical retrieval procedure in IAPP can give sometime substantial
improvement but most of the time it is unable to yield all the low-level
atmospheric information contained in the low-level and window channel
measurements. However, the neural network can easily and effectively include
these measurements specifically at the lower levels and about the 200 hPa.
The RMS errors of ANN are found to be less than 2.9°C at the surface, 0.9° to
2.8°C between 700 and 300 hPa and less than 2 °C between 300 and 200 hPa.
Finally, case studies of upper tropospheric warm core anomalies of two cyclones
have been done. The warm core anomaly at 200 hPa , near the centre of the
cyclone has been computed. It has been found that the temperature anomaly at
200 hPa can be an indicator of the intensity of tropical cyclones and as a cyclone
intensifies, the warm core temperature anomaly increases with different
intensities of the cyclone, which is the indication of the positive relationship of
cyclone intensities with a warm core anomaly.
Based on the performance of the above studies, we conclude that the neural
network retrieval scheme could show great promise for use in operational
retrievals of temperature profiles from the NOAA (K, L, M, N) satellites
measurement over Indian Ocean. The assimilation of these satellite data in a
numerical weather prediction model could be very useful to operational
forecasting.
Acknowledgments
Authors are grateful to Director General of Meteorology, Dr L.S. Rathore for
his constant encouragement during the course of study. Authors are very much
thankful to Dr. Nigel Atkinson, UK MET office, for the installations of the
AAPP6.1. Thanks are also due to Dr. Hal Woolf for providing the valuable
software support for IAPP software package installation. Level 1b format files