Empirical algorithms to retrieve surface rain-rate from Special Sensor Microwave Imager over a mid-latitude basin

The capability of some empirical algorithms to estimate surface rain-rate at mid-latitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. We propose three retrieval techniques based on a multivariate regression, a Bayesian maximum a posteriori inversion and on an artificial feed-forward Neural Network. Three algorithms available in literature are also included as benchmarks. The training data set is derived from coincident SSM/I images and half hourly rain-rate data obtained from a rain-gauge network, placed along the river Tiber basin in Central Italy, during 9 years (from 1992 to 2000). The work points out that an algorithm based on regression or Neural Network is a good estimator of low precipitation, while it tends to underestimate high rain rates. The best results have been achieved with the Bayesian method