scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Author(s):  
N. S. Wagenbrenner ◽  
J. M. Forthofer ◽  
B. K. Lamb ◽  
K. S. Shannon ◽  
B. W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

scholarly journals Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

2016 ◽  
Vol 16 (8) ◽  
pp. 5229-5241 ◽  
Author(s):  
Natalie S. Wagenbrenner ◽  
Jason M. Forthofer ◽  
Brian K. Lamb ◽  
Kyle S. Shannon ◽  
Bret W. Butler
Keyword(s):  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Prediction Models ◽  
Wildland Fire ◽  
Weather Prediction ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Wind Model ◽  
Near Surface ◽  
Numerical Weather

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

Temporal Changes in Wind as Objects for Evaluating Mesoscale Numerical Weather Prediction

2009 ◽  
Vol 24 (5) ◽  
pp. 1374-1389 ◽  
Author(s):  
Daran L. Rife ◽  
Christopher A. Davis ◽  
Jason C. Knievel
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Grid Point ◽  
Surface Wind ◽  
Temporal Changes ◽  
High Temporal Resolution ◽  
Numerical Weather ◽  
Wind Events

Abstract The study describes a method of evaluating numerical weather prediction models by comparing the characteristics of temporal changes in simulated and observed 10-m (AGL) winds. The method is demonstrated on a 1-yr collection of 1-day simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) over southern New Mexico. Temporal objects, or wind events, are defined at the observation locations and at each grid point in the model domain as vector wind changes over 2 h. Changes above the uppermost quartile of the distributions in the observations and simulations are empirically classified as significant; their attributes are analyzed and interpreted. It is demonstrated that the model can discriminate between large and modest wind changes on a pointwise basis, suggesting that many forecast events have an observational counterpart. Spatial clusters of significant wind events are highly continuous in space and time. Such continuity suggests that displaying maps of surface wind changes with high temporal resolution can alert forecasters to the occurrence of important phenomena. Documented systematic errors in the amplitude, direction, and timing of wind events will allow forecasters to mentally adjust for biases in features forecast by the model.

scholarly journals Can Reanalysis Products Outperform Mesoscale Numerical Weather Prediction Models in Modeling the Wind Resource in Simple Terrain?

2021 ◽  
Author(s):  
Vincent Pronk ◽  
Nicola Bodini ◽  
Mike Optis ◽  
Julie K. Lundquist ◽  
Patrick Moriarty ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Stability ◽  
Negative Bias ◽  
Numerical Weather ◽  
Wind Resource ◽  
Mesoscale Numerical Weather Prediction

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at site in Oklahoma, United States, and in a U.S. Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~ −1 m s−1 ) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a slight negative bias (~ −0.5 m s−1 ) offshore and a slight positive bias (~ +0.5 m s−1) at the land-based site. Surprisingly, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED’s higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle both onshore and offshore.

scholarly journals Improved Gridded Wind Speed Forecasts by Statistical Postprocessing of Numerical Models with Block Regression

2016 ◽  
Vol 31 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Michaël Zamo ◽  
Liliane Bel ◽  
Olivier Mestre ◽  
Joël Stein
Keyword(s):  
Wind Speed ◽  
Random Forests ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Near Surface ◽  
Numerical Weather ◽  
Spatial Coverage

Abstract Numerical weather forecast errors are routinely corrected through statistical postprocessing by several national weather services. These statistical postprocessing methods build a regression function called model output statistics (MOS) between observations and forecasts that is based on an archive of past forecasts and associated observations. Because of limited spatial coverage of most near-surface parameter measurements, MOS have been historically produced only at meteorological station locations. Nevertheless, forecasters and forecast users increasingly ask for improved gridded forecasts. The present work aims at building improved hourly wind speed forecasts over the grid of a numerical weather prediction model. First, a new observational analysis, which performs better in terms of statistical scores than those operationally used at Météo-France, is described as gridded pseudo-observations. This analysis, which is obtained by using an interpolation strategy that was selected among other alternative strategies after an intercomparison study conducted internally at Météo-France, is very parsimonious since it requires only two additive components, and it requires little computational resources. Then, several scalar regression methods are built and compared, using the new analysis as the observation. The most efficient MOS is based on random forests trained on blocks of nearby grid points. This method greatly improves forecasts compared with raw output of numerical weather prediction models. Furthermore, building each random forest on blocks and limiting those forests to shallow trees does not impair performance compared with unpruned and pointwise random forests. This alleviates the storage burden of the objects and speeds up operations.

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