Abstract
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 illposed 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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bhatia, R.C., Khanna, P.N., Prasad, Kanti and Rama Rao, Y.V. (1999). A preliminary study of the impact of NOAA soundings retrievals on a limited area model (LAM) forecasts. Proceedings of INTROMET-97. Vayumandal, 29(1-4): 147-149.
Butler, C.T., Meredith, R.V.Z. and Stogryn, A.P. (1996). Retrieving atmospheric temperature parameters from DMSP SSM/T-1 data with a neural network. J. Geophys. Res., 101: 7075-7083.
Brueske, Kurt F. and Velden, Christopher S. (2003). Satellite-Based Tropical Cyclone Intensity Estimation Using the NOAA-KLM Series Advanced Microwave Sounding Unit (AMSU). Monthly Weather Review. 131: 687-697.
Hsieh, W.W. and Tang, B. (1998). Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Amer. Meteor. Soc., 79: 1855- 1870.
Kidder, S.Q., Goldberg, M.D., Zehr, R.M., DeMaria, M., Purdom, J.F.W., Velden, C.S., Grody, N.C. and Kusselson, S.J. (2000). Satellite analysis of tropical cyclones using the Advanced Microwave Sounding Unit (AMSU). Bulletin of the American Meteorological Sociey, 81: 1241-1259.
Isaacs, R.G., Hoffman, R.N. and Kaplan, L.D. (1986). Satellite remote sensing of meteorological parameters for global numerical weather prediction. Rev. Geophys, 24: 701-743.
Khanna, P.N. and Kelker, R.R. (1993). Temperature Sounding of the atmosphere over Indian region using satellite data. Mausam, 44(2): 167-174.
Klaes, D. and Schraidt, R. (1999). The European ATOVS and AVHRR processing package (AAPP). In: Techn. Proc.10th Intern ATOVS Study Conf. Boulder USA, 27.01-02.02.
Li, J., Wolf, W.W., Menzel, W.P., Zhang, W.J., Huang, H.L. and Achtor, T.H. (2000). Global sounding of the atmosphere from ATOVS measurement: The algorithm and validation. J Appl. Meteor., 39: 1248-1268.
Motteler, H.E., Larrabee, S.L., McMillin, L. et al. (1995). Comparison of neural networks and regression-based methods for temperature retrievals. Applied Optics, 34: 5390- 5397.
Mo, T. (1999). AMSU-A antenna pattern corrections. IEEE Trans. Geosci. Remote Sens., 37: 103-112.
Nath, S., Mitra, A.K. and Roy Bhomwik, S.K. (2008). Improving the quality of INSAT derived quantitative precipitation estimates using a neural network method. Geofizika, 25: 41-51.
Rigone, J.L. and Stogryn, A.P. (1977). Data processing for the DMSP microwave radiometer system. In: Proc. Eleventh International Symposium on Remote Sensing of the Environment. University of Michigan Press.
Shi, L. (2001). Retrieval of atmospheric temperature profiles from AMSU-A measurement using a neural network approach. J. Atmos. Oceanic Technol., 18: 340-347.
Stogryn, A.P., Butler, C.T. and Bartolac, T.J. (1994). Ocean surface wind retrievals from special sensor microwave imager data with neural networks. J. Geophys. Res., 90: 981-984.
Interpreting UW-CIMSS Advanced Microwave Sounding Unit (AMSU) and Imagery/ Products. In: UW-CIMSS Advanced Microwave Sounding Unit (AMSU) Homepage http://amsu.ssec.wisc.edu/explanation.html
Velden, C.S., Goodman, B.M. and Merrill, R.T. (1991). Western North Pacific tropical cyclone intensity estimation from NOAA polar-orbiting satellite microwave data. Mon. Wea. Rev., 119: 159-168.
Yang, C.C., Prasher, S.O. and Mehuys, G.R. (1997). An artificial neural network to estimate soil temperature. Can. J. Soil Sci., 77: 421-429.
Yao, Zhigang, Chen., Hongbin and Lin, Longfu (2005). Retrieving Atmospheric Temperature Profiles from AMSU-A Data with Neural Networks. Advances in Atmospheric Sciences, 22: 606-616.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Capital Publishing Company
About this chapter
Cite this chapter
Mitra, A.K., Sharma, A.K., Kundu, P.K. (2014). 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’. In: Mohanty, U.C., Mohapatra, M., Singh, O.P., Bandyopadhyay, B.K., Rathore, L.S. (eds) Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7720-0_34
Download citation
DOI: https://doi.org/10.1007/978-94-007-7720-0_34
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7719-4
Online ISBN: 978-94-007-7720-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)