A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations

The analysis of microwave observations over land to determine atmospheric a nd surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the b asis of efficient retrieval methods for nonlinear cases; however, first gue ss estimates, which are used in variational assimilation methods to avoid p roblems of solution nonuniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first guess. Conceptual br idges are established between the neural network and variational assimilati on methods. The new neural method retrieves the surface skin temperature, t he integrated water vapor content, the cloud liquid water path and the micr owave surface emissivities between 19 and 85 GHz over land from Special Sen sor Microwave Imager observations. The retrieval, in parallel, of all these quantities improves the results for consistancy reasons. A database to tra in the neural network is calculated with a radiative transfer model and a g lobal collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data, and from microwave emissivity atlases previously calculated. The results of the neural networ k inversion axe very encouraging. The theoretical RMS error of the surface temperature retrieval over the globe is 1.3 K in clear-sky conditions and 1 .6 K in cloudy scenes. Water vapor is retrieved with a theoretical RMS erro r of 3.8 kg m(-2) in clear conditions and 4.9 kg m(-2) in cloudy situations . The theoretical RMS error in cloud liquid water path is 0.08 kg m(-2). Th e surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.