Experiences With an Optimal Estimation Algorithm for Surface and Atmospheric Parameter Retrieval From Passive Microwave Data in the Arctic

We present experiences in using an integrated retrieval method for atmospheric and surface parameters in the Arctic using passive microwave data from the AMSR-E radiometer. 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 retrieval 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 multiyear 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. A sensitivity study demonstrates the method is robust and that the solution it provides is not dependent on initialization conditions. The retrieval parameters have been compared with the Arctic Systems Reanalysis model data as well as columnar water vapor retrieved from satellite microwave sounders and the Remote Sensing Systems AMSR-E ocean retrieval product in order to determine the feasibility of using the same setup over pure surface with 100% and 0% sea ice cover, respectively. Sea ice concentration retrieval shows good skill for pure surface cases. Ice types retrieval is in good agreement with scatterometer backscatter data. Deficiencies have been identified in using the forward model over sea ice for retrieving atmospheric parameters, that are connected to the treatment of surface emissivity and surface temperature. The retrieval agrees well with legacy atmospheric retrieval products in open ocean areas.