Improving an optimal estimation algorithm for surface and atmospheric parameter retrieval using passive microwave data in the Arctic

In this study we present improvements on an integrated retrieval method for atmospheric and surface parameters in the Arctic. The instrument used is the Advanced Microwave Scanning Radiometer - Earth Observing System (EOS) (AMSR-E) radiometer on board NASAa s Aqua satellite. The core of the method is a forward model which can ingest bulk data for seven geophysical parameters to reproduce the brightness temperatures observed by a passive microwave radiometer. The method inverts the forward model and produces ensembles of the seven parameters: wind speed, integrated water vapor, liquid water path, sea and ice temperature, sea ice concentration and multi-year ice fraction. The method is constrained using numerical weather prediction data in order to retrieve a set of geophysical parameters that best fit the measurements. An iterative method minimizes the cost function and finds the optimal ensemble of the seven parameters that best match the observed brightness temperatures