Passive microwave relative humidity retrievals using feedforward neural networks

A technique for retrieving atmospheric humidity profiles using passive microwave spectral observations from satellite and Multilaq er Feedforward Neural Networks (MFNN) is introduced in this paper. Relative humidity retrievals on a global scale from simulated radiances at fifteen frequencies between 23.8 and 183.3 GHz yielded rms errors in relative humidity of 6-14% over ocean and 6-15% over land at pressure levels ranging from 131 mbar to 1013 mbar. Comparison with a combined statistical and physical iterative retrieval scheme shows that superior retrievals can be obtained at a lower computational cost using MFNN.