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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-80
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-80
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 23 May 2019

Model evaluation paper | 23 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Impact of model improvements on 80-m wind speeds during the second Wind Forecast Improvement Project (WFIP2)

Laura Bianco1,2, Irina V. Djalalova1,2, James M. Wilczak2, Joseph B. Olson1,2, Jaymes S. Kenyon1,2, Aditya Choukulkar1,2, Larry K. Berg3, Harindra J. S. Fernando4, Eric P. Grimit5, Raghavendra Krishnamurthy4, Julie K. Lundquist6,7, Paytsar Muradyan8, Mikhail Pekour3, Yelena Pichugina1,2, Mark T. Stoelinga5, and David D. Turner2 Laura Bianco et al.
  • 1University of Colorado/Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA
  • 2National Oceanic and Atmospheric Administration/Earth Systems Research Laboratory, Boulder, CO, USA
  • 3Pacific Northwest National Laboratory, Richland, WA, USA
  • 4Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA
  • 5Vaisala Inc., Seattle, WA, USA
  • 6Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA
  • 7National Renewable Energy Laboratory, Golden, CO, USA
  • 8Argonne National Laboratory, Lemont, IL, USA

Abstract. During the second Wind Forecast Improvement Project (WFIP2; Oct 2015–Mar 2017, Columbia River Gorge and Basin area) several improvements to the parameterizations applied in the High Resolution Rapid Refresh (HRRR – 3 km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST – 750 m horizontal grid spacing) Numerical Weather Prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80-m wind speeds (hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and 3 profiling lidars for verification. Improvements due to the experimental physics (EXP vs CNT runs) versus those due to finer horizontal grid spacing (HRRRNEST vs HRRR), and the combination of the two are compared, using standard bulk statistics such as Mean Absolute Error (MAE) and Mean Bias Error (bias). On average, the HRRR 80-m wind speed MAE is reduced by 3–4 % due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5 % on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80-m wind speed MAE, up to 7–8 %. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena, also looking for the causes of model weaknesses. Finally, bias correction methods are applied to the 80-m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors.

Laura Bianco et al.
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Status: open (until 18 Jul 2019)
Status: open (until 18 Jul 2019)
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Short summary
During the second Wind Forecast Improvement Project improvements to the parameterizations were applied to the High Resolution Rapid Refresh and the High Resolution Rapid Refresh Nest Numerical Weather Prediction models. The impacts of the new parameterizations on the forecast of 80-m wind speeds are assessed, using observations from sodars and profiling lidars for verification. Improvements are evaluated as a function of the model's initialization time, time of the day, season, site elevation.
During the second Wind Forecast Improvement Project improvements to the parameterizations were...
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