An innovative calibration method for the inversion of satellite observations
Résumé
Retrieval schemes often use two important components: (1) a Radiative Transfer Model (RTM) inside the retrieval procedure or to construct the learning dataset for the training of the statistical retrieval algorithms, and (2) a Numerical Weather Prediction (NWP) model to provide a first guess or, again, to construct a learning dataset. This is particularly true in operational centers. As a consequence, any physical retrieval or similar method is limited by inaccuracies in the RTM and NWP models on which it is based. In this paper, a method for partially compensating for these errors as part of the sensor calibration is presented and evaluated. In general RTM/NWP errors are minimized as best as possible prior to the training of the retrieval method, and then tolerated. The proposed method reduces these unknown and generally non-linear residual errors by training a separate preprocessing Neural Network (NN) to produce calibrated radiances from real satellite data that approximate those radiances produced by the “flawed” NWP and RTM models. The final “compensated/flawed” retrieval assures better internal consistency of the retrieval procedure and then produces more accurate results. To our knowledge, this type of NN model has not been used yet for this purpose. The calibration approach is illustrated here on one particular application: the retrieval of atmospheric water vapour from the Advanced Microwave Scanning Radiometer - Earth observing system (AMSR-E) and the Humidity Sounder for Brazil (HSB) measurements for non-precipitating scenes, over land and ocean. Before being inverted, the real observations are “projected” into the space of the RTM simulation space from which the retrieval is designed. Validation of results is performed with radiosounde measurements and NWP analysis departures. This study shows that the NN calibration of the AMSR-E/HSB observations improves water vapour inversion, over ocean and land, for both clear and cloudy situations. The NN calibration is efficient and very general, being applicable to a large variety of problems. The nonlinearity of the NN allows for the calibration procedure to be state-dependent and adaptable to specific cases (e. g., the same correction will not be applied to medium range measurement and to extreme conditions). Its multivariate nature allows for a full exploitation of the complex correlation structure among the instrument channels, making the calibration of each single channel more robust. The procedure would make it possible to “project” the satellite observations in a reference observational space defined by radiosounde measurements, RTM simulations, or other instrument observational space.
Origine : Fichiers éditeurs autorisés sur une archive ouverte