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Remote sensing observations used in offshore wind energy are described in three parts: ground-based techniques and applications, airborne techniques and applications, and satellite-based techniques and applications. Ground-based remote sensing of winds is relevant, in particular, for new large wind turbines where meteorological masts do not enable observations across the rotor-plane, i.e., at 100 to 200 m above ground level. Light detection and ranging (LiDAR) and sound detection and ranging (SoDAR) offer capabilities to observe winds at high heights. Airborne synthetic aperture radar (SAR) used for ocean wind mapping provides the basis for detailed offshore wind farm wake studies and is highly useful for development of new wind retrieval algorithms from C-, L-, and X-band data. Satellite observations from SAR and scatterometer are used in offshore wind resource estimation. SAR has the advantage of covering the coastal zone where most offshore wind farms are located. The number of samples from scatterometer is relatively high and the scatterometer-based estimate on wind resources appears to agree well with coastal offshore meteorological observations in the North Sea. Finally, passive microwave ocean winds have been used to index the potential offshore wind power production, and the results compare well with observed power production (mainly land-based) covering nearly two decades for the Danish area. [ABSTRACT FROM PUBLISHER]

Summary: A physically-based passive microwave technique is proposed to estimate precipitation intensity and extinction from ground. Multi-frequency radiometric measurements are inverted to retrieve surface rain rate, columnar precipitation contents and rainfall microwave extinction. A new inversion methodology, based on an artificial neural-network feed-forward algorithm, is evaluated and compared against a previously developed regression technique. Both retrieval techniques are trained by numerical simulations of a radiative transfer model applied to microphysically-consistent precipitating cloud structures. Cloud microphysics is characterized by using parameterized hydrometeor drop size distribution, spherical particle shape and dielectric composition. The radiative transfer equation is solved for plane-parallel seven-layer structures, including liquid, melted, and ice spherical hydrometeors. The proposed neural-network inversion technique is tested and compared with the regression algorithm on synthetic data in order to understand their potential and to select the best frequency set for rainfall rate, columnar contents and extinction estimation. Available ground-based radiometric measurements at 13.0, 23.8, and 31.6GHz are used for experimentally testing and comparing the neural-network retrieval algorithm. Comparison with rain gauge data and rain extinction measurements, derived from three satellite beacon channels at 18.7, 39.6, and 49.5GHz acquired at Pomezia (Rome, Italy), are performed and discussed for a selected case of light-to-moderate rainfall. [Copyright &y& Elsevier]