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The CMOD7 Geophysical Model Function for ASCAT and ERS Wind Retrievals

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A new geophysical model function (GMF), called CMOD7, has been developed for intercalibrated ERS (ESCAT) and ASCAT C-band scatterometers. It is valid for their combined incidence angle range and being… Click to show full abstract

A new geophysical model function (GMF), called CMOD7, has been developed for intercalibrated ERS (ESCAT) and ASCAT C-band scatterometers. It is valid for their combined incidence angle range and being used to generate wind climate data records. CMOD7 has been developed in several steps as a successor of CMOD5.n. First, CMOD5.n has been adapted to the ASCAT transponder calibration, which is considered more accurate than any ESCAT gain calibration. This results in a linear scaling of the backscatter values. Second, for low winds, there is a clear mismatch between CMOD5.n and the measurements. An independently developed ASCAT C-band GMF, C2013, which performs particularly well for low winds was adopted to improve low winds for the ASCAT incidence angle range. Third, retrievals with CMOD5.n show wind speed probability distribution functions (pdf) that undesirably depend on wind vector cell (WVC) position across the swath. To overcome this effect, a higher order calibration is applied, which matches the wind speed pdfs for all WVCs of ASCAT and ESCAT. The resulting CMOD7 GMF indeed shows overall improved performance on all relevant quality parameters compared to CMOD5.n. It is found that the standard deviations of error for wind speed and wind direction of ASCAT are improved. The same holds for the maximum-likelihood estimates, showing an 8% improved consistency with the local triplet of backscatter measurements. As a consequence, triple collocation with moored buoy and numerical weather prediction winds results in smaller wind vector components and wind direction retrieval errorsim.

Keywords: wind speed; geophysical model; model function; low winds

Journal Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year Published: 2017

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