[1] The quality of marine wind vector retrieved from variational data assimilation (VAR) of Synthetic Aperture Radar (SAR) backscatter observation is assessed. It is found that the observation is most sensitive to wind speed. The retrieved wind direction from VAR is largely influenced by background wind direction and most of the SAR observation variability is assigned to wind speed. Non-linearity of the Geophysical Model Function (GMF) introduces wind speed bias, modulated by wind direction anisotropy (updownwind/crosswind difference). The examination of the background wind vector departure from observation reveals two regimes: a quasi-linear response to wind direction for high background wind speed; and a rather monotonic response with two sharp transitions located at crosswind directions for low background wind speed. Information content of SAR observation is estimated using the entropy reduction approach, both analytically and from Monte-Carlo simulations. Crosswind directions have the lowest information content and correspond to those where non-linearity introduces largest discrepancies between analytic and Monte-Carlo estimations. The linear approximation of the GMF needed in the incremental VAR formulation is examined. The retrieved winds using the incremental formulation are in good agreement with those using the non-linear GMF. Monte-Carlo simulations reveal specific situations, around sharp transitions at crosswind directions, where both linear and non-linear VAR formulations may produce more noise than extract information form observations. Citation: Choisnard, J., and S. Laroche (2008), Properties of variational data assimilation for synthetic aperture radar wind retrieval,

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Environment Canada (EC) and Hydro-Québec (HQ) have been collaborating in a Research & Development and Demonstration project on a high resolution wind energy dedicated forecasting system (SPÉO: Système de Prévision ÉOlien under its French acronym). This project emphasizes the operational tests and the forecast of high impact events, e.g. wind ramps. It was found that SPÉO improves the Canadian Regional Deterministic Prediction System (RDPS), by about 18% in terms of the RMSE (Root Mean Square Error) of the predicted wind speed when compared with mast observations from three wind power plants. The improvement is most significant in the cold season. When the average wind speed measured at all wind turbines (nacelle anemometer) is used as a reference, SPÉO improves the RMSE of the average wind speed at a wind power plant in complex terrain (24%) compared with that of RDPS. However, there is almost no improvement for two other wind power plants located in less complex terrain. The average wind speed is corrected with the average wind speed measured at all turbines, and is then fed into a wind-to-power conversion module for power production forecasts. The power production forecast is improved by 6% on average in complex terrain when SPÉO winds are used as input compared to the RDPS. The most important finding of this project is SPÉO's ability to predict ramps due to mountain waves/downslope winds. The proposed forecast index for ramps based on the Froude number is useful for predicting the onset of this kind of ramp when a high resolution NWP model is unavailable.