scholarly journals Quantifying sensitivity in numerical weather prediction‐modeled offshore wind speeds through an ensemble modeling approach

Wind Energy ◽  
2021 ◽  
Author(s):  
Mike Optis ◽  
Andrew Kumler ◽  
Joseph Brodie ◽  
Travis Miles
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Offshore Wind ◽  
Ensemble Modeling ◽  
Numerical Weather ◽  
Wind Speeds ◽  
Modeling Approach

Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

2005 ◽  
Vol 133 (2) ◽  
pp. 409-429 ◽  
Author(s):  
Dudley B. Chelton ◽  
Michael H. Freilich
Keyword(s):  
Wind Stress ◽  
Numerical Weather Prediction ◽  
Measurement Errors ◽  
Prediction Models ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Random Component ◽  
Numerical Weather ◽  
Wind Speeds ◽  
Time Periods

Abstract Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s−1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s−1. While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s−1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

scholarly journals An Assessment of Marine Surface Winds from the Australian Bureau of Meteorology Numerical Weather Prediction Systems

2007 ◽  
Vol 22 (3) ◽  
pp. 613-636 ◽  
Author(s):  
Eric W. Schulz ◽  
Jeffrey D. Kepert ◽  
Diana J. M. Greenslade
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Australian Bureau ◽  
Limited Area ◽  
Surface Winds ◽  
Numerical Weather ◽  
Wind Speeds ◽  
Marine Surface Layer ◽  
Marine Surface ◽  
Prediction Systems

Abstract A method for routinely verifying numerical weather prediction surface marine winds with satellite scatterometer winds is introduced. The marine surface winds from the Australian Bureau of Meteorology’s operational global and regional numerical weather prediction systems are evaluated. The model marine surface layer is described. Marine surface winds from the global and limited-area models are compared with observations, both in situ (anemometer) and remote (scatterometer). A 2-yr verification shows that wind speeds from the regional model are typically underestimated by approximately 5%, with a greater bias in the meridional direction than the zonal direction. The global model also underestimates the surface winds by around 5%–10%. A case study of a significant marine storm shows that where larger errors occur, they are due to an underestimation of the storm intensity, rather than to biases in the boundary layer parameterizations.

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