Retrieval of atmospheric thermal profiles from meteorological satellite soundings using neural networks

Donald D. Bustamante; Arthur W. Dudenhoeffer; James L. Cogan

doi:10.1117/12.170005

2 March 1994 Retrieval of atmospheric thermal profiles from meteorological satellite soundings using neural networks

Donald D. Bustamante, Arthur W. Dudenhoeffer, James L. Cogan

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.170005
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

In this study, neural networks have been used to retrieve thermal profiles from near polar, sun-synchronous meteorological satellite data obtained from the TIROS Operational Vertical Sounder (TOVS). Data were collected using the SeaSpace TeraScan satellite tracking system for thirteen sites ranging from the Southwestern United States to Canada. Earth-centered radiances, latitude, longitude, elevation, and angular information (satellite zenith angle, solar zenith angle, scatter phase angle, and sun reflection angle) were used as inputs to a backpropagation neural network. The network architecture consisted of one hidden layer of 30 neurons. The output layer provided temperature at the meteorological `mandatory' levels as well as the surface. Truth consisted of the thermal profiles obtained from a conventional algorithm, the TOVS Export Package. The results demonstrate that thermal profiles with Root Mean Square Errors of less than 4 C (typically < 3 C) can be obtained from the trained neural network. As expected, the accuracy of the thermal profiles is greatest at higher altitudes. These results are obtained without the computational overhead and complexity of conventional approaches.

Citation Download Citation

Donald D. Bustamante, Arthur W. Dudenhoeffer, and James L. Cogan "Retrieval of atmospheric thermal profiles from meteorological satellite soundings using neural networks", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.170005

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