Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms

The accuracy of the sea ice concentration estimates in polar regions is reduced by the effects of atmospheric emission and absorption. A method is presented where a fast atmospheric radiative transfer model and Numerical Weather Prediction (NWP) model fields are used to correct brightness temperatures before they enter the sea ice concentration algorithm. The skill of the method is a function of the errors in the NWP model fields modulated by the sensitivities of the sea ice concentration algorithm used. The NWP model fields representing the most significant atmospheric parameters, i.e. water vapour, cloud liquid water, surface temperature and wind speed over open water are evaluated using remote sensing data. For wind speed and total water vapour, it is found that the standard deviation of the difference is less than the RMS error quoted for the remote sensing algorithms. The best consistency is found for water vapour followed by wind speed. The NWP model cloud liquid water displays standard deviations much higher than the RMS error of the remote sensing algorithm and close to the total average content. Nine sea ice concentration algorithms are further evaluated in a sensitivity study to the above-mentioned atmospheric constituents using a detailed atmospheric radiative transfer model. The result shows that the class of algorithms based solely on the 19 and 37 GHz vertically polarised channels display the smallest sensitivity to all three atmospheric parameters: total water vapour, wind speed and cloud liquid water. Finally, it is demonstrated that this method overcomes many problems associated with conventional weather filtering over mixed ice-water and new-ice pixels and allows the retrieval of sea ice concentrations below 10%.