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 Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast

2017 ◽  
Vol 21 (1) ◽  
pp. 37 ◽  
Author(s):  
Hua Deng ◽  
Yan Li ◽  
Yingchao Zhang ◽  
Hou Zhou ◽  
Peipei Cheng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Least Square ◽  
Support Vector ◽  
Surface Model ◽  
Short Term ◽  
Numerical Weather

The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction. Predicción de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la predicción estadísticaResumenLa estimación de la energía eólica está relacionada con la predicción en la variación de los vientos en pequeños intervalos de tiempo. Se seleccionaron cuatro torres eólicas ubicadas al interior de Mongolia para estudiar los recursos eólicos en la complejidad de un altiplano. Se utilizó la investigación climática a mesoscala y la combinación del esquema de la Universidad Yonsei con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah), con resolución de 1km horizontal y 10 minutos, como el modelo numérico de predicción meteorológica (NWP, del inglés Numerical Weather Prediction). Se utilizaron tres técnicas estadísticas, persistencia, propagación hacia atrás en redes neuronales artificiales y máquina de vectores de soporte-mínimos cuadrados (LS-SVM, del inglés Least Square Support Vector Machine), para mejorar la predicción de la velocidad del viento en una turbina con la altura del eje a 70 metros y se complementó con los resultados del WRF/YSU/Noah. Las técnicas de predicción físico-estadísticas actuales tienen un buen desempeo en tres escalas de tiempo: (1) corto plazo, un día en adelante; (2) mediano plazo, de seis días en adelante; (3) cercano, una hora en adelante. Este método de predicción, que combina los resultados WRF/YSU/Noah con los métodos de persistencia y LS-SVM incrementa la precisión de predicción entre 26,3 y 49,4 por ciento, comparado con los resultados directos del modelo numérico WRF/YSU/Noah. Además, este método diferencia la variabilidad de las estaciones y el ciclo diurno en la velocidad y la dirección del viento.

scholarly journals Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3587-3603 ◽  
Author(s):  
D. E. Robertson ◽  
D. L. Shrestha ◽  
Q. J. Wang
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

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.

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.

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