Mapping of snow depth by blending satellite and in-situ data using two-dimensional optimal interpolation-application to AMSR2

Abstract

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January-February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA's Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.