scholarly journals Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hao Chen ◽  
Yngve Birkelund ◽  
Stian Normann Anfinsen ◽  
Reidar Staupe-Delgado ◽  
Fuqing Yuan
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Arctic Region ◽  
The Arctic ◽  
Renewable Energy Resources ◽  
Energy Potential ◽  
Numerical Weather

AbstractMapping Arctic renewable energy resources, particularly wind, is important to ensure the transition into renewable energy in this environmentally vulnerable region. The statistical characterisation of wind is critical for effectively assessing energy potential and planning wind park sites and is, therefore, an important input for wind power policymaking. In this article, different probability density functions are used to model wind speed for five wind parks in the Norwegian Arctic region. A comparison between wind speed data from numerical weather prediction models and measurements is made, and a probability analysis for the wind speed interval corresponding to the rated power, which is largely absent in the existing literature, is presented. The results of the present study suggest that no single probability function outperforms across all scenarios. However, some differences emerged from the models when applied to different wind parks. The Nakagami and Generalised extreme value distributions were chosen for the numerical weather predicted prediction and the observed wind speed modelling, respectively, due to their superiority and stability compared with other methods. This paper, therefore, provides a novel direction for understanding the numerical weather prediction wind model and shows that its speed statistical features are better captured than those of real wind.

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

2019 ◽  
Vol 173 (3) ◽  
pp. 321-348 ◽  
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.

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.

Impact of assimilation of radiosonde and UAV observations on numerical weather prediction analyses and forecasts in the Arctic and Antarctic

2020 ◽  
Author(s):  
Timo Vihma ◽  
Tuomas Naakka ◽  
Qizhen Sun ◽  
Tiina Nygård ◽  
Michael Tjernström ◽  
...  
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The Arctic ◽  
Observation Site ◽  
Near Surface ◽  
Numerical Weather ◽  
Background Fields ◽  
The Impact

<p>Weather forecasting in the Arctic and Antarctic is a challenge above all due to rarity of observations to be assimilated in numerical weather prediction (NWP) models. As observations are expensive and logistically challenging, it is important to evaluate the benefit that additional observations could bring to NWP.</p><p>Considering the Arctic, in this study the effects of the spatial coverage of the network on numerical weather prediction were evaluated by comparing radiosonde observations from land station taken from Integrated Global Radiosonde Archive (IGRA) and radiosonde observations from expeditions in the Arctic Ocean with operational analyses and background fields (12‐hr forecasts) of the European Centre for Medium Range Weather Forecasts (ECMWF). The focus was on 850 hPa level temperature for the period January 2016 – September 2018. Comparison of the analyses and background fields showed that radiosoundings had a remarkable impact on improving operational analyses but the impact had a large geographical variation. In particular, radiosonde observations from islands (Jan Mayen and Bear Island) in the northern North Atlantic and from Arctic expeditions substantially improved analyses suggesting that those observations were critical for the quality of analyses and forecasts. Comparison of two cases with and without assimilation of radiosonde sounding data from expeditions of Icebreaker Oden in 2016 and 2018 in the central Artic Ocean showed that satellite observations were not able to compensate for the large spatial gap in the radiosounding network. In the areas where the network is reasonably dense, the density of the sounding network was not the most critical factor for the quality of background fields. Instead, the quality of background field was more related to how radiosonde observations were utilized in the assimilation and to the quality of those observations.</p><p>Considering the Antarctic, we applied radiosonde sounding and Unmanned Aerial Vehicles (UAV) observations from an RV Polarstern cruise in the ice-covered Weddell Sea in austral winter 2013 to evaluate the impact of their assimilation in the Polar version of the Weather Research and Forecasting (Polar WRF) model. Our experiments revealed small or moderate impacts of radiosonde and UAV data assimilation. In any case, the assimilation of sounding data from both radiosondes and UAVs improved the analyses of air temperature, wind speed, and humidity at the observation site for most of the time. Further, the impact on the results of 5-day long Polar WRF experiments was often felt over distances of at least 300 km from the observation site. All experiments succeeded in capturing the main features of the evolution of near-surface variables, but the effects of data assimilation varied between different cases. Due to the limited vertical extent of the UAV observations, the impact of their assimilation was limited to the lowermost 1-2 km layer, and assimilation of radiosonde data was more beneficial for modelled sea level pressure and near-surface wind speed. Considering the perspectives for technological advance, atmospheric soundings applying UAV have a large potential to supplement conventional radiosonde sounding observations.</p><p>The differences in the results obtained for the Arctic and Antarctic are discussed.</p>

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.

scholarly journals Manual and Semiautomated Wind Direction Editing for Use in the Generation of Synthetic Aperture Radar Wind Speed Imagery

2007 ◽  
Vol 46 (6) ◽  
pp. 776-790 ◽  
Author(s):  
George S. Young ◽  
Todd D. Sikora ◽  
Nathaniel S. Winstead
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Numerical Weather Prediction ◽  
Wind Direction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Synthetic Aperture ◽  
Numerical Weather ◽  
Sar Imagery ◽  
Aperture Radar

Abstract Previous studies have demonstrated that satellite synthetic aperture radar (SAR) can be used as an accurate scatterometer, yielding wind speed fields with subkilometer resolution. This wind speed generation is only possible, however, if a corresponding accurate wind direction field is available. The potential sources of this wind direction information include satellite scatterometers, numerical weather prediction models, and SAR itself through analysis of the spatial patterns caused by boundary layer wind structures. Each of these wind direction sources has shortcomings that can lead to wind speed errors in the SAR-derived field. Manual and semiautomated methods are presented for identifying and correcting numerical weather prediction model wind direction errors. The utility of this approach is demonstrated for a set of cases in which the first-guess wind direction data did not adequately portray the features seen in the SAR imagery. These situations include poorly resolved mesoscale phenomena and misplaced synoptic-scale fronts and cyclones.

scholarly journals Computational fluid dynamic (CFD) simulation of snowdrift in alpine environments, including a local weather model, for operational avalanche warning

2008 ◽  
Vol 48 ◽  
pp. 150-158 ◽  
Author(s):  
Simon Schneiderbauer ◽  
Thomas Tschachler ◽  
Johann Fischbacher ◽  
Walter Hinterberger ◽  
Peter Fischer
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Wind Field ◽  
Snow Depth ◽  
Prediction Models ◽  
Fluid Dynamic ◽  
Weather Prediction ◽  
Time Varying ◽  
Numerical Weather ◽  
Avalanche Warning

AbstractA new continuum approach to snowdrift modelling is introduced. In addition, numerical studies are carried out to identify the influence of time-varying wind conditions on snowdrift simulations. We compare the snowdrift patterns at Grimming mountain, Austria, derived using a time-averaged wind field and a time-varying wind field obtained from the numerical weather prediction model INCA. The results show significant differences in the deposition patterns and snow depth even after a 6 hour drift period. Using time-averaged boundary conditions leads to an underprediction of the resulting snow depth caused by averaging the wind speed, which lets gusts of wind disappear while snow transport is a non-linear function of the wind speed. Using numerical weather prediction models for snowdrift simulation therefore provides enhanced knowledge of the snow depth for local avalanche warning services.

scholarly journals FOCUSED–Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3405
Author(s):  
Zdravko Kunić ◽  
Bernard Ženko ◽  
Biljana Mileva Boshkoska
Keyword(s):  
Decision Support ◽  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Support Vector ◽  
Correction Algorithm ◽  
Short Term ◽  
Data Set ◽  
Numerical Weather

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm’s performance on a data set comprising five years of real data measurements at the Croatian bridge “Krk” and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model.

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

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