Advanced Infra-Red Water Vapour Estimator (AIRWAVE) algorithm updates

The AIRWAVE (Advanced Infra-Red WAter Vapour Estimator) algorithm has been developed for the retrieval of TCWV from the measurements of the Along Track Scanning Radiometer (ATSR) missions. The first version of the algorithm makes use of the TIR channels of ATSR-like instruments, exploiting the dual viewing geometries to infer the TCWV over the sea. When applied to the whole ATSR missions (ATSR-1, ATSR-2 and AATSR) it produced TCVW in good agreement with the results obtained by the Special Sensor Microwave/Imager (SSM/I) and radiosondes. The algorithm makes use of a set of tabulated parameters. In the first version of the algorithm, these parameters were fixed along the whole globe, and were calculated as a weighted average of the ones for Tropical and Mid-Latitude scenarios. We have updated the algorithm with a new set of parameters that improve the performances at all latitudes. The new sets of parameters were calculated accounting for the four seasons and atmospheric state (e.g. Tropical, Mid-Latitude and Polar conditions in January, April, July and October). We computed the new set of parameters with a Radiative Transfer forward Model (RTM) that was specifically developed to simulate ATSR radiances. We have also exploited the RTM to evaluate the impact of atmospheric and surface conditions (e.g. atmospheric temperature and water vapour profiles, HNO3, CO2, CFCs amount, Sea Surface Temperature (SST)) and instrument viewing angles on the calculated parameters. Since each of the simulated conditions produce a different set of retrieval parameters, we had to solve how automatically chose the most suitable ones to be used in each case. The choice is now performed through the use of a Neural Network (NN) that was trained using the above mentioned parameters over a set of radiances simulated with the RTM code and ECMWF temperature, pressure and water vapour profiles. Here we present the results of the application of the updated AIRWAVE algorithm to a subset of ATSR data and evaluate the performances with respect to the ones of the original version.