Assessment of the impact of assimilation of a network of coastal wind profiling radars on simulating offshore winds in and above the wind turbine layer

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Monday, 3 February 2014: 4:15 PM
Room C114 (The Georgia World Congress Center )
Irina V. Djalalova, NOAA/ESRL/PSD and CIRES/Univ. of Colorado, Boulder, CO; and L. Bianco, J. M. Wilczak, J. B. Olson, J. Carley, M. Marquis, R. M. Banta, Y. Pichugina, and J. W. Cline

During the summer of 2004 a network of 11 wind profiling radars was deployed in New England as part of the New England Air Quality Study (NEAQS). We utilize observations from this data set to determine the impact that assimilation of these data into NWP models would have on simulating coastal and offshore winds. Data denial assimilation experiments use the NOAA/ESRL Rapid Refresh (RAP, 13km horizontal resolution) and High-Resolution Rapid Refresh (HRRR, 3km horizontal resolution) models, and an experimental version of the NOAA/NWS/NCEP North American Mesoscale forecast system (NAM), which includes hourly analysis/forecast cycles with its 12 km North American and 4 km Contiguous United States (CONUS) nest domains. These models are used for two one-week periods to assess the impact of assimilation of the 11 radar wind profilers. Verification is done using the on-shore wind profiler network, as well as an additional wind profiler located on the ship RV Ron Brown that cruised the Gulf of Maine. Model simulations with and without assimilation of the profiler data are compared at the locations of the profilers. Ron Brown profiler data is compared to the model both at/near the height of hypothetical offshore turbines as well as through the lower troposphere. The goals of this study are: 1. Focus future research aimed at informing requirements for an observation network to support U.S. offshore wind energy and for improving our understanding of the offshore wind resource. 2. Determine if additional lower-tropospheric profilers can improve near-coastal and offshore wind resource assessments. 3. Evaluate skill of several numerical weather prediction (NWP) models in the offshore area. 4. Evaluate spatial and temporal variability of offshore turbine-height winds.