Using NNs to retrieve multiple geophysical parameters from satellite data

A new approach in satellite retrievals, multi-parameter empirical retrievals, is introduced. It is shown that single-parameter retrievals, compared with multi-parameter retrievals, contain significant additional "artificial" systematic and random errors. These errors may be avoided using multi-parameter retrieval algorithms. Neural networks (NNs) are well suited for developing such multi-parameter retrieval algorithms. The NN approach for developing empirical multi-parameter algorithms is discussed. A new NN empirical algorithm (OMBNN3) which simultaneously retrieves four geophysical parameters: surface wind speed, columnar water vapor, columnar liquid water, and sea surface temperature from five special sensor microwave imager brightness temperatures (T19V, T19H, T22V, T37V, and T37H) is presented and compared with several single-parameter algorithms to illustrate advantages of the multi-parameter approach.