Strengths and weakness of remotely sensed winds are discussed, along with the current capabilities for remotely sensing winds and stress. Future missions are briefly mentioned. The observational needs for a wide range of wind and stress applications are provided. These needs strongly support a short list of desired capabilities of future missions and constellations.

In a quest for a generic unbiased scatterometer wind inversion method, the different inversion procedures currently in use are revisited in this paper. A careful examination of both the errors in the wind and in the measurement domain, combined with the nonlinear shape of the geophysical model function (GMF), leads to a generic and novel Bayesian wind retrieval approach in the measurement domain. In this approach the shape of the GMF solution manifold in measurement space is more important than the specified noise. This shape is related to the system wind direction sensitivity, and when this sensitivity is uniform, realistic and precise wind direction distributions are retrieved, even when measurements lie far from the GMF manifold. A simplified measurement space transformation that produces such uniform sensitivity for the European Remote Sensing Satellite (ERS) scatterometer is presented and shown to have reduced wind direction bias compared to the more traditional (measurement-noise normalized) inversion for ERS. Moreover, the simplified wind inversion reveals a similar performance to the current operational ERS wind inversion, but is potentially more generally applicable. The simplified method is then applied to SeaWinds but is ineffective. In this case the instrument geometry results in a low sensitivity to wind direction at a few specific directions. As a consequence, certain wind direction solutions remain favored in the SeaWinds inversion.

A good assessment of the information content of scatterometer winds is particularly important in order to assimilate them in weather analysis. Besides retrieval problems in cases of a confused sea state, a particularly acute problem of Ku-band scatterometry is the sensitivity to rain. Elimination of poor quality data is therefore a prerequisite for the successful use of the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) or QuikSCAT winds. Following the quality control for the European Remote-Sensing Satellite and NSCAT scatterometers performed at the Royal Netherlands Meteorological Institute, the authors further develop this methodology for QuikSCAT and define a quality indicator called the normalized residual (Rn). In order to characterize and validate the normalized residual, the authors use collocated Special Sensor Microwave Imager rain and European Centre for Medium-Range Weather Forecasts wind data. The results show indeed correlation between Rn and data quality... Abstract A good assessment of the information content of scatterometer winds is particularly important in order to assimilate them in weather analysis. Besides retrieval problems in cases of a confused sea state, a particularly acute problem of Ku-band scatterometry is the sensitivity to rain. Elimination of poor quality data is therefore a prerequisite for the successful use of the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) or QuikSCAT winds. Following the quality control for the European Remote-Sensing Satellite and NSCAT scatterometers performed at the Royal Netherlands Meteorological Institute, the authors further develop this methodology for QuikSCAT and define a quality indicator called the normalized residual (Rn). In order to characterize and validate the normalized residual, the authors use collocated Special Sensor Microwave Imager rain and European Centre for Medium-Range Weather Forecasts wind data. The results show indeed correlation between Rn and data quality...

The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility delivers operational wind products from the Advanced Scatterometer (ASCAT) at 25 km and 12.5 km Wind Vector Cell (WVC) spacing. In these products, based on the backscatter processing performed at EUMETSAT, data closer than ~ 70 km (25 km products) or ~ 35 km (12.5 km products) to the coast are flagged because of land contamination. An alternative wind product is presented here which uses a different way of averaging the full resolution (FR) backscatter measurements from ASCAT. The FR backscatter measurements are screened for land contamination in the coastal zone, thus allowing the construction of WVCs that follow the coast line. The implied alternative spatial averaging allows good quality winds over sea as close as 15-20 km to the shore. The alternative (coastal) and nominal products are compared, and the resulting winds are validated with buoy winds, both in coastal and open sea regions. In regions far away from the coast, the ASCAT coastal and nominal products appear to be of identical quality, but fewer WVCs pass the quality control steps for the nominal product, indicating that the coastal product better resolves sub-WVC wind variability. In the coastal region, we anticipate enhanced wind variability due to katabatic and sea breeze effects, among others. However, the quality of the coastal winds in terms of buoy wind component difference standard deviation is almost as good as for the open sea winds.

Scatterometers estimate the relative atmosphere–ocean motion at spatially high resolution and provide accurate inertial-scale ocean wind forcing information, which is crucial for many ocean, atmosphere, and climate applications. An empirical scatterometer ocean stress (SOS) product is estimated and validated using available statistical information. A triple collocation dataset of scatterometer, and moored buoy and numerical weather prediction (NWP) observations together with two commonly used surface layer (SL) models are used to characterize the SOS. First, a comparison between the two SL models is performed. Although their roughness length and the stability parameterizations differ somewhat, the two models show little differences in terms of stress estimation. Second, a triple collocation exercise is conducted to assess the true and error variances explained by the observations and the SL models. The results show that the uncertainty in the NWP dataset is generally larger than in the buoy and scatterometer wind/stress datasets, but it depends on the spatial scales of interest. The triple collocation analysis also shows that scatterometer winds are as close to real winds as to equivalent neutral winds, provided that the appropriate scaling is used. An explanation for this duality is that the small stability effects found in the analysis are masked by the uncertainty in SL models and their inputs. The triple collocation analysis shows that scatterometer winds can be straightforwardly and reliably transformed to wind stress. This opens the door for the development of wind stress swath (level 2) and gridded (level 3) products for the Advanced Scatterometer (ASCAT) on board Meterological Operation (MetOp) and for further geophysical development.