Satellite-based retrieval of precipitable water vapor over land by using a neural network approach

A method based on neural networks is proposed to retrieve integrated precipitable water vapor (IPWV) over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Water vapor values provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to train the network. The performance of the network was demonstrated by using a separate data set of AMSR-E observations and the corresponding IPWV values from ECMWF. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data during clear-sky conditions. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas. In addition, results were compared with the IPWV values obtained from in situ instruments, a ground-based radiometer, and a global positioning system (GPS) receiver located in Rome, and a local network of GPS receivers in Como. An rms agreement of 0.34 cm was found between the ground-based radiometer and the neural network retrievals, and of 0.35 cm and 0.40 cm with the GPS located in Rome and Como, respectively.