From Numerical Weather Prediction Outputs to Accurate Local Surface Wind Speed: Statistical Modeling and Forecasts

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.

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An Investigation of the Stability Dependence of SST-Induced Vertical Mixing over the Ocean in the Operational Met Office Model

Journal of Climate ◽

10.1175/jcli-d-16-0086.1 ◽

2017 ◽

Vol 30 (1) ◽

pp. 91-107 ◽

Cited By ~ 2

Author(s):

Qingtao Song ◽

Dudley B. Chelton ◽

Steven K. Esbensen ◽

Andrew R. Brown

Keyword(s):

Wind Speed ◽

Weather Prediction ◽

Surface Wind ◽

Vertical Mixing ◽

Surface Wind Speed ◽

Satellite Observations ◽

Sea Surface ◽

Stable Conditions ◽

Wide Range ◽

The Impact

This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability [Formula: see text] between 0 and −2) typically found over the ocean is required in order for the model surface wind response to SST to match the satellite observations. Likewise, the reduced mixing in stable conditions in the new parameterization is also relatively small; for the range of the gradient Richardson number typically found over the ocean, the mixing was reduced by a maximum of only 10%, which is too small by more than an order of magnitude to be consistent with the satellite observations.

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Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

Atmosphere ◽

10.3390/atmos11070738 ◽

2020 ◽

Vol 11 (7) ◽

pp. 738 ◽

Cited By ~ 1

Author(s):

Wenqing Xu ◽

Like Ning ◽

Yong Luo

Keyword(s):

Wind Speed ◽

Decision Tree ◽

Wind Power ◽

Surface Wind ◽

Surface Wind Speed ◽

Gradient Boosting ◽

Post Processing ◽

Near Surface ◽

Wind Power Forecasting ◽

Numerical Weather

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

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Geophysical validation of WINDSAT surface wind data and its impact on numerical weather prediction

10.1117/12.680923 ◽

2006 ◽

Cited By ~ 1

Author(s):

Robert Atlas ◽

O. Reale ◽

J. Ardizzone ◽

J. Terry ◽

J.-C. Jusem ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Surface Wind ◽

Wind Data ◽

Numerical Weather

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Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system

Applied Energy ◽

10.1016/j.apenergy.2018.12.076 ◽

2019 ◽

Vol 237 ◽

pp. 1-10 ◽

Cited By ~ 29

Author(s):

Han Wang ◽

Shuang Han ◽

Yongqian Liu ◽

Jie Yan ◽

Li Li

Keyword(s):

Wind Speed ◽

Wind Power ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Correction Algorithm ◽

Wind Power Forecasting ◽

Numerical Weather ◽

Forecasting System ◽

Power Forecasting

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Parametrizing Horizontally-Averaged Wind and Temperature Profiles in the Urban Roughness Sublayer

Boundary-Layer Meteorology ◽

10.1007/s10546-019-00472-1 ◽

2019 ◽

Vol 173 (3) ◽

pp. 321-348 ◽

Cited By ~ 2

Author(s):

Natalie E. Theeuwes ◽

Reinder J. Ronda ◽

Ian N. Harman ◽

Andreas Christen ◽

C. Sue B. Grimmond

Keyword(s):

Wind Speed ◽

Numerical Weather Prediction ◽

Prediction Models ◽

Weather Prediction ◽

Roughness Sublayer ◽

Vertical Profiles ◽

Flux Boundary Condition ◽

Numerical Weather ◽

Urban Roughness Sublayer ◽

Urban Roughness

Abstract Tower-based measurements from within and above the urban canopy in two cities are used to evaluate several existing approaches that parametrize the vertical profiles of wind speed and temperature within the urban roughness sublayer (RSL). It is shown that current use of Monin–Obukhov similarity theory (MOST) in numerical weather prediction models can be improved upon by using RSL corrections when modelling the vertical profiles of wind speed and friction velocity in the urban RSL using MOST. Using anisotropic building morphological information improves the agreement between observed and parametrized profiles of wind speed and momentum fluxes for selected methods. The largest improvement is found when using dynamically-varying aerodynamic roughness length and displacement height. Adding a RSL correction to MOST, however, does not improve the parametrization of the vertical profiles of temperature and heat fluxes. This is expected since sources and sinks of heat are assumed uniformly distributed through a simple flux boundary condition in all RSL formulations, yet are highly patchy and anisotropic in a real urban context. Our results can be used to inform the choice of surface-layer representations for air quality, dispersion, and numerical weather prediction applications in the urban environment.

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

10.5194/acp-2015-761 ◽

2016 ◽

Cited By ~ 1

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.

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