Title :
NWP Model Error Structure Functions Obtained From Scatterometer Winds
Author :
Vogelzang, Jur ; Stoffelen, Ad
Author_Institution :
R. Netherlands Meteorol. Inst. (KNMI), De Bilt, Netherlands
fDate :
7/1/2012 12:00:00 AM
Abstract :
Wind vectors derived from scatterometer measurements are spatially detailed as compared to global numerical weather prediction (NWP) model fields. Since the Advanced Scatterometer (ASCAT)´s wind vector ambiguities are, in general, well defined, ambiguity removal results in accurate wind fields. The dense and regular spatial sampling of ASCAT winds represents a unique resource to study the NWP model field spatial error structure. The current level 2 ASCAT data processor employs 2-D variational ambiguity removal (2DVAR), in which an analysis is made from the ambiguous wind solutions and a prior NWP wind field using a variational technique, and, subsequently, the ambiguity closest to the analysis is selected as best wind. 2DVAR will yield an optimal analysis when the structure functions (background error correlations in the potential domain) are well specified. In this paper, a new method is presented to calculate structure functions from autocorrelations of observed scatterometer wind components minus NWP model predictions (O-B). It is based on direct integration of the differential equations relating structure functions and observed autocorrelations. Reprocessing ASCAT data at 12.5-km grid size with structure functions obtained this way shows a considerable increase in the spectral density of the analysis for scales from about 800 to about 100 km, with the largest effect at scales of around 250 km. In line with this finding, it is shown in a case study that a more detailed analysis leads to fewer ambiguity removal errors for ASCAT data recorded over a frontal zone with rapidly varying wind direction.
Keywords :
atmospheric techniques; data analysis; differential equations; geophysical signal processing; integration; meteorological radar; remote sensing by radar; variational techniques; wind; 2D variational ambiguity removal; 2DVAR; ASCAT regular spatial sampling; Advanced Scatterometer; NWP model error structure functions; NWP model field spatial error structure; NWP model predictions; autocorrelations; differential equations; direct integration; global NWP model fields; level 2 ASCAT data processor; numerical weather prediction; potential domain background error correlations; prior NWP wind field; scatterometer measurements; scatterometer wind components; scatterometer winds; variational technique; wind vector ambiguities; wind vectors; Correlation; Data assimilation; Numerical models; Predictive models; Radar measurements; Weather forecasting; Wind; Advanced Scatterometer (ASCAT); ocean vector winds; radar remote sensing; scatterometry;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2011.2168407