Correlation-Based Interpolation of NSCAT Wind Data

Abstract

Daily NASA Scatterometer (NSCAT) wind estimates cover about 75% of the globe. The remaining data gaps, which require interpolation, are regularly distributed in space and time. The development of this interpolation algorithm was guided by a balance between the smoothness of the end product and its fidelity to the original data. Three-dimensional matrices of autocorrelation coefficients incorporate information about the dominant propagation pattern into the interpolation program. These coefficients are continuously updated in space and time and are used as weights to interpolate each point in a regular space?time grid. For the first step, European Centre for Medium-Range Weather Forecasts (ECMWF) wind data are used to simulate the NSCAT data distribution and interpolated using two different methods: one uses a single set of coefficients from a prescribed function based on the average decorrelation scales, and the other uses the locally estimated autocorrelation coefficients. The comparison of these results with the original ECMWF maps favors those based on the autocorrelation. For the second step, daily maps of bin-averaged NSCAT wind data are compared to those interpolated by the correlation-based method and to those interpolated by successive corrections. Average differences between the original and interpolated fields are presented for the areas covered by the swath and for the gaps. The two-dimensional wavenumber spectra are also compared. The correlation-based interpolation method retains relatively more small-scale signal while significantly reducing the swath signature.