Presenting Author: Dr. Ad Stoffelen

In many applications winds near the coast and at high resolution are required. Therefore, KNMI develops ASCAT scatterometer wind products at higher resolution and nearer to the coast. EUMETSAT currently applies spatial averaging kernels to suppress noise in the measurements, which, on the other hand, prevent wind retrieval in coastal regions due to their spatial extent. By replacing the Hamming filter kernels (i.e., cosine weighting function) with a simple box (i.e., constant weighting function over a limited radial distance), we can produce 25-km sampled winds which do provide sea-surface wind information up to just 25 km off the coastline. Moreover, these boxes may be optimally chosen such that they are located close to the coastline, but still not contaminated by land, and maximize the number of wind observations at shorter distance off the coast. Later on, these different swath gridding and spatial averaging strategies will be applied to the 12.5-km product and processing may be sustained at even shorter distances to the coast down to 15 km. Different spatial averaging strategies may allow more noise in the L1 data and thus in the L2 retrieved winds. To suppress this random noise KNMI has developed spatial filtering techniques, which maintain small-scale meteorologically-relevant spatially-coherent structures in the resulting scatterometer wind fields. This filter, by the so-called Multiple Solution Scheme (MSS) and 2-Dimensional Variational Ambiguity Removal (2D-VAR), will be illustrated. The MSS collects additional information from the scatterometer wind inversion step, i.e., information on the probability of all possible winds, as retrieved from the input local backscatter measurements. This wind vector probability distribution at the swath grid is subsequently used as input to the 2DVAR, which provides a meteorologically balanced and spatially coherent wind field. The method may also be applied on single-look SAR data.

2025 International OVWST Meeting May 5th - 8th Darmstadt, Germany

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