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Quadruple Collocation Analysis of In‐Situ, Scatterometer, and NWP Winds

J. Vogelzang, Ad Stoffelen

2021Journal of Geophysical Research Oceans56 citationsDOI

Abstract

Abstract Triple collocation is an established technique for retrieving linear calibration coefficients and observation error variances of a physical quantity observed simultaneously by three different observation systems. The formalism is extended to an arbitrary number of systems, and representativeness errors and associated cross‐covariances are included in a natural way. It is applied to quadruple collocations of ocean surface vector winds from two scatterometers (ASCAT‐A, ASCAT‐B, or ScatSat), buoy measurements, and NWP model forecasts. There are 15 possible sets of quadruple collocation equations, 12 of which are solvable for the essential variables (calibration coefficients, observation error variances, and common variance) as well as two additional error covariances, each set leading to a different solution. The quadruple collocation equations by themselves give little information on the representativeness errors involved; these have to be estimated using other methods. The spreading in the solutions is a measure of the accuracy of the underlying error model. Variation of the scale at which the spatial variances are evaluated yields an optimal scale of 100–200 km. From triple collocation subsets the error in the scatterometer error standard deviations is found to be 0.03–0.05 m s −1 , more than expected on statistical grounds. A more precise determination requires an error model that better describes the data error properties.

Topics & Concepts

Collocation (remote sensing)BuoyScatterometerRepresentativeness heuristicCalibrationObservational errorMathematicsOrthogonal collocationScale (ratio)Collocation methodApplied mathematicsComputer scienceMeteorologyStatisticsMathematical analysisWind speedGeologyGeographyOrdinary differential equationMachine learningDifferential equationOceanographyCartographyOcean Waves and Remote SensingOceanographic and Atmospheric ProcessesSoil Moisture and Remote Sensing
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