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This paper describes a state-of-the-art model of the DTU 10MW Reference Wind Turbine mounted on the LIFES50+ OO-Star Wind Floater Semi 10MW floating substructure, implemented in FAST v8.16. The purpose of this implementation is to serve as a reference for different activities carried out within the LIFES50+ project. Attention is given to the changes necessary to adapt the numerical model of the onshore DTU 10MW Reference Wind Turbine to a floating foundation. These changes entail controller, tower structural properties, floating substructure hydrodynamics and mooring system. The basic DTU Wind Energy controller was tuned in order to avoid the “negative damping” problem. The flexible tower was extended down to the still water level to capture some of the floater flexibility. The mooring lines were implemented in MoorDyn, which includes dynamic effects and allows the user to define multi-segmented mooring lines. Hydrodynamics were precomputed in the radiation-diffraction solver WAMIT, while viscous drag effects are captured by the Morison drag term. The floating substructure was defined in HydroDyn to approximate the main drag loads on the structure, keeping in mind that only circular members can be modelled. A first set of simulations for system identification purposes was carried out to assess system properties such as natural frequencies and response to regular waves. The controller was tested in a simulation with uniform wind ranging from cut-in to cut-out wind speed. A set of simulations in stochastic wind and waves was carried out to characterize the global response of the floating wind turbine. The results are presented and the main physical phenomena are discussed. The model will form the basis for further studies in the LIFES50+ project and is available for free use.

The Earth Explorer Atmospheric Dynamics Mission (ADM-Aeolus) of ESA will be the first-ever satellite to provide global observations of wind profiles from space. Its single payload, namely the Atmospheric Laser Doppler Instrument (ALADIN) is a directdetection high spectral resolution Doppler Wind Lidar (DWL), operating at 355 nm, with a fringe-imaging receiver (analysing aerosol and cloud backscatter) and a double-edge receiver (analysing molecular backscatter). In order to meet the stringent mission requirements on wind retrieval, ESA is conducting various science support activities for the consolidation of the on-ground data processing, calibration and sampling strategies. Results from a recent laboratory experiment to study Rayleigh-Brillouin scattering and improve the characterisation of the molecular lidar backscatter signal detected by the ALADIN double-edge Fabry- Perot receiver will be presented in this paper. The experiment produced the most accurate ever-measured Rayleigh-Brillouin scattering profiles for a range of temperature, pressure and gases, representative of Earth’s atmosphere. The measurements were used to validate the Tenti S6 model, which is implemented in the ADM-Aeolus ground processor. First results from the on-going Vertical Aeolus Measurement Positioning (VAMP) study will be also reported. This second study aims at the optimisation of the ADM-Aeolus vertical sampling in order to maximise the information content of the retrieved winds, taking into account the atmospheric dynamical and optical heterogeneity. The impact of the Aeolus wind profiles on Numerical Weather Prediction (NWP) and stratospheric circulation modelling for the different vertical sampling strategies is also being estimated.

Preparations for the European Space Agency’s (ESA) Atmospheric Dynamics Mission (ADM-Aeolus), which is scheduled for launch in 2011, are in full progress. The direct detection high spectral resolution Doppler Wind Lidar (DWL) satellite instrument will be the first to measure wind profiles from space, from the surface up to 30 km altitude [1] and [2]. To achieve this, a laser at 355 nm pulsed at 100 Hz is pointed towards the atmosphere. Backscattered light, both from molecules and aerosols, is detected by two independent spectrometers. A dual channel Fabry-Perot spectrometer to measure both sides of the Rayleigh (molecule) spectral peak, and a high resolution Fizeau spectrometer to measure the location of the Mie (aerosol) peak. In preparation of this mission, besides the actual construction of the instrument and satellite, a number of supporting activities have been initiated by ESA, including instrument simulation and ground processing software development, ground and air-borne measurement campaigns, and studies dedicated to specific issues such as calibration and sampling strategies. An important part of the preparations is to study the possibilities of calibrating the wind results by means of surface reflections. To estimate the effect of water motion due to waves, a simple wave model has been combined with a reflectivity model of the water surface. This includes specular reflection on smooth water surfaces and Lambertian reflection on foam caused by wind streaks and breaking waves. The effect of subsurface reflection is still being investigated. Using this model, the average net water movement that will be observed by the DWL instrument is estimated. Another part of the preparations includes the development of Level 2B (i.e., wind) processing software (L2Bp). This software will accept as input measurement data files (Level 1B) and uses an estimate of the atmospheric temperature and pressure profiles (from a numerical weather prediction model) to retrieve the wind profile from the spectrometer data. This software will be made available by ESA as source code, free of charge, to all interested users in the meteorological/research community, and may be used as standalone software, or integrated as subroutine in a larger system for use in scientific or operational applications. The working of the L2Bp will be illustrated at the conference.

International audience; Total column amounts of CO, CH4, CO2 and N2O retrieved from SCIAMACHY nadir observations in its near-infrared channels have been compared to data from a ground-based quasi-global network of Fourier-transform infrared (FTIR) spectrometers. The SCIAMACHY data considered here have been produced by three different retrieval algorithms, WFM-DOAS (version 0.5 for CO and CH4 and version 0.4 for CO2 and N2O), IMAP-DOAS (version 1.1 and 0.9 (for CO)) and IMLM (version 6.3) and cover the January to December 2003 time period. Comparisons have been made for individual data, as well as for monthly averages. To maximize the number of reliable coincidences that satisfy the temporal and spatial collocation criteria, the SCIAMACHY data have been compared with a temporal 3rd order polynomial interpolation of the ground-based data. Particular attention has been given to the question whether SCIAMACHY observes correctly the seasonal and latitudinal variability of the target species. The present results indicate that the individual SCIAMACHY data obtained with the actual versions of the algorithms have been significantly improved, but that the quality requirements, for estimating emissions on regional scales, are not yet met. Nevertheless, possible directions for further algorithm upgrades have been identified which should result in more reliable data products in a near future.

International audience; Total column amounts of CO, CH4, CO2 and N2O retrieved from SCIAMACHY nadir observations in its near-infrared channels have been compared to data from a ground-based quasi-global network of Fourier-transform infrared (FTIR) spectrometers. The SCIAMACHY data considered here have been produced by three different retrieval algorithms, WFM-DOAS (version 0.4, 0.41 for CH4), IMAP-DOAS (version 0.9) and IMLM (version 5.5) and cover the January to December 2003 time period. Comparisons have been made for individual data, as well as for monthly averages. To maximize the number of reliable coincidences that satisfy the temporal and spatial collocation criteria, the SCIAMACHY data have been compared with a temporal 3rd order polynomial interpolation of the ground-based data. Particular attention has been given to the question whether SCIAMACHY observes correctly the seasonal and latitudinal variability of the target species. The ensemble of comparisons, discussed in this paper, demonstrate the capability of SCIAMACHY, using any of the three algorithms, to deliver products for the target species under consideration, which are already useful for qualitative geophysical studies on a global scale. It is expected that the remaining uncertainties in the data products will decrease in future versions of the algorithm to also allow more quantitative investigations on a regional scale.

International audience; The near-infrared spectra measured with the SCIAMACHY instrument on board the ENVISAT satellite suffer from several instrument calibration problems. The effects of three important instrument calibration issues on the retrieved methane (CH4) and carbon monoxide (CO) total columns have been investigated: the effects of the growing ice layer on the near-infrared detectors, the effects of the orbital variation of the instrument dark signal, and the effects of the dead/bad detector pixels. Corrections for each of these instrument calibration issues have been defined. The retrieved CH4 and CO total columns including these corrections show good agreement with CO measurements from the MOPITT satellite instrument and with CH4 model calculations by the chemistry transport model TM3. Using a systematic approach, it is shown that all three instrument calibration issues have a significant effect on the retrieved CH4 and CO total columns. However, the impact on the CH4 total columns is more pronounced than for CO, because of its smaller variability. Results for three different wavelength ranges are compared and show good agreement. The growing ice layer and the orbital variation of the dark signal show a systematic, but time-dependent effect on the retrieved CH4 and CO total columns, whereas the effect of the dead/bad pixels is rather unpredictable: some dead pixels show a random effect, some more systematic, and others no effect at all. The importance of accurate corrections for each of these instrument calibration issues is illustrated using examples where inaccurate corrections lead to a wrong interpretation of the results.