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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. Plain Language Summary: When a quantity like wind speed over the ocean is measured at (almost) the same time and place by three different measuring systems, it is possible to calculate the calibration of two systems with respect to the third as well as the measurement errors in each of the three systems. This is not possible if the measurements are made by only two systems. In case of quadruple collocations there are four measurement systems, and besides the calibrations and the measurement errors also two additional error correlations can be obtained. The advent of new satellite systems makes it possible to perform quadruple collocation analyses of the wind speed at the ocean surface. Triple and quadruple collocation analyses give no clue on how to improve the error model; such improvements must be found using other techniques. However, they do show the weaknesses of the error model. In particular, they show that the scatterometer error estimates are less precise than previously thought, but it should be remembered that this is "the error in the error": the scatterometer errors in the wind components are around 0.5 m s−1, and the accuracy of this estimate is 0.05 m s−1—an order of magnitude smaller, so quite useful. Key Points: Triple and quadruple collocation analyses show consistent results to a high degreeScatterometer error estimates from triple collocation are well within 0.05 m s−1When using prior information on error covariances, quadruple collocation analyses can provide limited information on representativeness errors [ABSTRACT FROM AUTHOR]

In summer, the prevailing northerly flow off west Iberia occasionally intensifies and weakens with significant consequences for weather conditions and marine ecology. High‐resolution Advanced SCATterometer (ASCAT) wind products on a 6.25 km grid (ASCAT‐6.25) were used to characterize wind maxima downwind to the major Iberian capes. Wind intensifications downwind Cape Finisterre are large and detached from the coast. They are considered as the response to a large feature in the coastal morphology. Intensifications downwind Cape Roca and Cape São Vicente are smaller and closer to the coast. These are triggered by interaction of supercritical or transcritical flow with topography. ASCAT‐6.25 evening and morning passes show two distinct regimes during intensification events: a southern regime with wind maximum moving toward the coast during the day and a northern regime in which this diurnal shift is not so clear. Another aspect that distinguishes these regimes is the later occurrence of the wind maximum in the southern portion of the coast. Also, wind relaxation events can be identified in ASCAT data. These are followed by a poleward flow of warm water that interrupts the upwelling conditions over west Iberia south of 41°N. Synoptic conditions favorable for the occurrence of intensifications and relaxations were obtained from NWP models. This study was supplemented by RapidScat observations during the summers of 2015 and 2016 at different phases of the diurnal cycle. RapidScat and ASCAT wind fields in combination with NWP data confirm that wind intensifications are multiday events, clearly distinct from the sea breeze circulation. Key Points: Details of expansion fans downwind of the Iberian major capes are well visible in ASCAT‐6.25 wind dataASCAT‐6.25 and RapidSCAT wind data show that a northern and southern regime exist during intensification events over west IberiaASCAT and RapidSCAT wind data show evidence of relaxation events off west Iberia [ABSTRACT FROM AUTHOR]

Wind scatterometry is an established technique for accurately measuring wind vectors over the world's oceans. Inversion of the geophysical model function (GMF) in general leads to a number of ambiguous solutions, and thus an ambiguity removal procedure for selecting the best solution is required. In this study, Two‐Dimensional Variational Ambiguity Removal (2DVAR) is considered. 2DVAR produces an analysis of the ambiguous scatterometer wind solutions (observations, o) and collocated ECMWF forecasts (background, b), and then selects the ambiguity closest to the analysis. The properties of 2DVAR are determined by the observation and background error variances and by the background error correlations (BECs). Empirical BECs (EBECs) can be obtained from o − b correlations, and their application in 2DVAR is known to have a beneficial effect on ASCAT wind quality, notably in situations where there is a mismatch between the measured and forecasted position of mesoscale structures. In this study EBECs are applied to wind retrieval from Ku‐band scatterometer systems, in particular RapidScat on the International Space Station and OSCAT on OceanSat‐3. These systems have poor direction skill in the nadir part of the swath, which can be improved by letting 2DVAR take the full wind vector probability density function into account. It will be shown that for Ku‐band systems 2DVAR with EBECs yields a wind product that correlates better with buoys and has less quality control flagging than the default product. This is due to greater detail in the 2DVAR analysis. BECs are defined in the wind potential and stream function domain. While EBECs are much broader than the default Gaussian BECs used in 2DVAR, their effect on the analysis in the spatial wind domain is determined by their much narrower second derivatives, as demonstrated by a single‐observation analysis. Background error correlations (BECs) and single‐observation analysis wind fields for default Gaussian BECs in the tropics, the extratropics and for empirical BECs [ABSTRACT FROM AUTHOR]

The two-dimensional variational ambiguity removal (2DVAR) method provides a spatial analysis of the sampled ocean vector winds to resolve the local Advanced Scatterometer (ASCAT) dual wind vector ambiguity. Like other variational meteorological data assimilation systems in numerical weather prediction (NWP), 2DVAR combines ASCAT observations with prior NWP background information, in this case from the European Centre for Medium-range Weather Forecasts (ECMWF). Although 2DVAR is generally effective, it may select the wrong ambiguity under certain conditions, e.g. when the background mislocates frontal areas or low-pressure centres, or when it misses convective systems. The relative influence of the ASCAT and ECMWF wind fields in the resulting 2DVAR analysis field can be controlled by adjusting the background error spatial correlation structure, and the background and/or observation error variances. In this article an adaptive 2DVAR approach is proposed to improve ASCAT ambiguity removal, using background error spatial correlations estimated from the autocorrelation of observed scatterometer wind components minus ECMWF forecasts, and using observation and background errors estimated from triple collocation analysis on collocated buoy, ASCAT and ECMWF data. The triple collocations are segregated into several categories according to the ASCAT-derived parameters that have proven to be effective in detecting the correct position of frontal lines and low-pressure centres. Verification using a typical cyclone case and collocated ASCAT and buoy winds shows that the 2DVAR analysis as well as the ASCAT ambiguity removal is improved significantly by putting more weight on the ASCAT observations using empirically determined spatial background error structure functions and situation-dependent observation/background error variances. [ABSTRACT FROM AUTHOR]