Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2)
Item
Title (Dublin Core)
Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2)
Description (Dublin Core)
<p>During the second Wind Forecast Improvement Project
(WFIP2; October 2015–March 2017, held in the Columbia River Gorge and Basin
area of eastern Washington and Oregon states), several improvements to the
parameterizations used 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 (wind turbine hub
height) from the HRRR and HRRRNEST models are assessed, using observations
collected by 19 sodars and three profiling lidars for comparison. Improvements
due to the experimental physics (EXP vs. CNT runs) and 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. Causes of model weaknesses are identified.
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.</p>
(WFIP2; October 2015–March 2017, held in the Columbia River Gorge and Basin
area of eastern Washington and Oregon states), several improvements to the
parameterizations used 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 (wind turbine hub
height) from the HRRR and HRRRNEST models are assessed, using observations
collected by 19 sodars and three profiling lidars for comparison. Improvements
due to the experimental physics (EXP vs. CNT runs) and 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. Causes of model weaknesses are identified.
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.</p>
Creator (Dublin Core)
L. Bianco
I. V. Djalalova
J. M. Wilczak
J. B. Olson
J. S. Kenyon
A. Choukulkar
L. K. Berg
H. J. S. Fernando
E. P. Grimit
R. Krishnamurthy
J. K. Lundquist
P. Muradyan
M. Pekour
Y. Pichugina
M. T. Stoelinga
D. D. Turner
Subject (Dublin Core)
Geology
QE1-996.5
Publisher (Dublin Core)
Copernicus Publications
Date (Dublin Core)
2019-11-01T00:00:00Z
Type (Dublin Core)
article
Identifier (Dublin Core)
10.5194/gmd-12-4803-2019
1991-959X
1991-9603
https://doaj.org/article/8cceb9fe0bd74780a1c12da7907c2ddf
Source (Dublin Core)
Geoscientific Model Development, Vol 12, Pp 4803-4821 (2019)
Language (Dublin Core)
EN
Relation (Dublin Core)
https://www.geosci-model-dev.net/12/4803/2019/gmd-12-4803-2019.pdf
https://doaj.org/toc/1991-959X
https://doaj.org/toc/1991-9603
Provenance (Dublin Core)
Journal Licence: CC BY