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

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.

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Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2021-4-50-68 ◽

2021 ◽

Vol 4 ◽

pp. 50-68

Author(s):

S.А. Lysenko ◽

◽

P.О. Zaiko ◽

Keyword(s):

Land Use ◽

Air Temperature ◽

Numerical Weather Prediction ◽

Land Surface ◽

Lead Time ◽

Weather Prediction ◽

Wrf Model ◽

Surface Model ◽

Numerical Weather ◽

Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

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A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

Sustainability ◽

10.3390/su14010296 ◽

2021 ◽

Vol 14 (1) ◽

pp. 296

Author(s):

Mohanad A. Deif ◽

Ahmed A. A. Solyman ◽

Mohammed H. Alsharif ◽

Seungwon Jung ◽

Eenjun Hwang

Keyword(s):

Support Vector Machine ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Support Vector ◽

Gray Wolf ◽

Particle Swarm Optimizer ◽

Numerical Weather ◽

Air Temperatures ◽

Gray Wolf Optimizer ◽

Accuracy And Stability

Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.

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Evaluation of a NOAH-MP Land Surface Model Ensemble over the Mississippi Basin

10.5194/egusphere-egu2020-21890 ◽

2020 ◽

Author(s):

Jonas Rothermel ◽

Maike Schumacher

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Land Surface ◽

Hydrological Cycle ◽

Weather Prediction ◽

Surface Model ◽

Empirical Parameter ◽

Numerical Weather ◽

Parameter Values

Physical-based Land Surface Models (LSMs) have deepened the understanding of the hydrological cycle and serve as the lower boundary layer in atmospheric models for numerical weather prediction. As any numerical model, they are subject to various sources of uncertainty, including simplified model physics, unknown empirical parameter values and forcing errors, particularly precipitation. Quantifying these uncertainties is important for assessing the predictive power of the model, especially in applications for environmental hazard warning. Data assimilation systems also benefit from realistic model error estimates.In this study, the LSM NOAH-MP is evaluated over the Mississippi basin by running a large ensemble of model configurations with suitably perturbed forcing data and parameter values. For this, sensible parameter distributions are obtained by performing a thorough sensitivity analysis, identifying the most informative parameters beforehand by a screening approach. The ensemble of model outputs is compared against various hydrologic and atmospheric feedback observations, including SCAN soil moisture data, GRACE TWS anomaly data and AmeriFlux evapotranspiration measurements. The long-term aim of this study is to improve land-surface states via data assimilation and to investigate their influence on short- to midterm numerical weather prediction. Thus, the uncertainty of the simulated model states, such as snow, soil moisture in various layers, and groundwater are thoroughly studied to estimate the relative impact of possible hydrologic data sets in the assimilation.

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Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements

Remote Sensing ◽

10.3390/rs12030583 ◽

2020 ◽

Vol 12 (3) ◽

pp. 583 ◽

Cited By ~ 2

Author(s):

Xiaoni Wang ◽

Catherine Prigent

Keyword(s):

Numerical Weather Prediction ◽

Land Surface ◽

Weather Prediction ◽

In Situ Measurements ◽

The Other ◽

Surface Model ◽

Night Time ◽

Barren Land ◽

Numerical Weather

This study evaluates the diurnal cycle of Land Surface Temperature (LST) from Numerical Weather Prediction (NWP) reanalyses (ECMWF ERA5 and ERA Interim), as well as from infrared satellite estimates (ISCCP and SEVIRI/METEOSAT), with in situ measurements. Data covering a full seasonal cycle in 2010 are studied. Careful collocations and cloud filtering are applied. We first compare the reanalysis and satellite products at continental and regional scales, and then we concentrate on comparisons with the in situ observations, under a large variety of environments. SEVIRI shows better agreement with the in situ measurements than the other products, with bias often less than ±2K and correlation of 0.99. Over snow or arid surface, ISCCP tends to have more systematic errors than the other products. ERA5 agrees better to the in situ over barren land than ERA Interim, particularly at night time, thanks to the new surface model. However, over vegetated surfaces, both reanalyses tend to have higher/lower temperature at night/day time than the in situ measurements, probably related to the surface processes and its interactions with atmosphere in the NWP model.

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

Sensors ◽

10.3390/s21103405 ◽

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.

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Exploration of Synthetic Terrestrial Snow Mass Estimation via Assimilation of AMSR‐E Brightness Temperature Spectral Differences Using the Catchment Land Surface Model and Support Vector Machine Regression

Water Resources Research ◽

10.1029/2020wr027490 ◽

2021 ◽

Vol 57 (2) ◽

Author(s):

Jing Wang ◽

Barton A. Forman ◽

Yuan Xue

Keyword(s):

Support Vector Machine ◽

Brightness Temperature ◽

Land Surface ◽

Land Surface Model ◽

Support Vector ◽

Surface Model ◽

Support Vector Machine Regression ◽

Mass Estimation ◽

Snow Mass

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Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

Advances in Meteorology ◽

10.1155/2016/8152413 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Tien Du Duc ◽

Lars Robert Hole ◽

Duc Tran Anh ◽

Cuong Hoang Duc ◽

Thuy Nguyen Ba

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Model Performance ◽

Wrf Model ◽

Horizontal Resolution ◽

Prediction System ◽

Regional Models ◽

Global Spectral Model ◽

Numerical Weather ◽

The Us

The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

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Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.511-512.927 ◽

2014 ◽

Vol 511-512 ◽

pp. 927-930

Author(s):

Shuai Zhang ◽

Hai Rui Wang ◽

Jin Huang ◽

He Liu

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Wind Speed ◽

Particle Swarm ◽

Least Square ◽

Support Vector ◽

Short Term ◽

Swarm Optimization ◽

Wind Speed Prediction ◽

Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

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