Open AccessValidation of Two-Dimensional Variational Ambiguity Removal on SeaWinds Scatterometer Data
Author(s) -
J. Vogelzang,
Ad Stoffelen,
Anton Verhoef,
J. de Vries,
Hans Bonekamp
Publication year - 2009
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/2008jtecha1232.1
Subject(s) - scatterometer , buoy , meteorology , variational analysis , tropical cyclone , environmental science , remote sensing , wind speed , computer science , geology , mathematics , geography , oceanography
A two-dimensional variational ambiguity removal technique (2DVAR) is presented. It first makes an analysis based on the ambiguous scatterometer wind vector solutions and a model forecast, and next selects the ambiguity closest to the analysis as solution. 2DVAR is applied on SeaWinds scatterometer data and its merits for nowcasting applications are shown in a general statistical comparison with model forecasts and buoy observations, and in a number of case studies. The sensitivity of 2DVAR to changes in the parameters of its underlying error model is studied. It is shown that observational noise in the nadir swath of SeaWinds is effectively suppressed by application of 2DVAR in combination with the multisolution scheme (MSS). MSS retains the local wind vector probability density function after inversion, rather than only a limited number of ambiguous solutions. As a consequence, the influence of the background increases, but this can be mitigated by switching off variational quality control. A case study on an extratropical cyclone of hurricane force intensity observed with SeaWinds at 25-km resolution shows that reliable wind estimates can be obtained for wind speeds up to 40 m s−1 and more. At 25 km, the results of 2DVAR with MSS compare better with buoy measurements than with the ECMWF model. At 100-km resolution this is reversed, proving that 2DVAR retrieves small-scale features absent in the ECMWF model.