scholarly journals The efficiency of the WRF model for simulating typhoons

2014 ◽  
Vol 2 (1) ◽  
pp. 287-313 ◽  
Author(s):  
T. Haghroosta ◽  
W. R. Ismail ◽  
P. Ghafarian ◽  
S. M. Barekati
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Model Simulation ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Heat Fluxes ◽  
Data Set ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
And Control

Abstract. The Weather Research Forecast (WRF) model includes various configuration options related to physics parameters, which can affect the performance of the model. In this study, different numerical experiments were conducted to determine the best combination of physics parameterization schemes for the simulation of sea surface temperatures, latent heat flux, sensible heat flux, precipitation rate, and wind speed that characterized typhoons. Through these experiments, several physics parameterization options within the WRF model were exhaustively tested for typhoon Noul, which had originated in the South China Sea in November 2008. The model domain consisted of one coarse domain and one nested domain. The resolution of the coarse domain was 30 km, and that of the nested domain was 10 km. In this study, model simulation results were compared with the Climate Forecast System Reanalysis (CFSR) data set. Comparisons between predicted and control data were made through the use of standard statistical measurements. The results facilitated the determination of the best combination of options suitable for predicting each physics parameter. Then, the suggested best combinations were examined for seven other typhoons and the solutions were confirmed. Finally, the best combination was compared with other introduced combinations for wind speed prediction for typhoon Washi (2011). The contribution of this study is to have attention to the heat fluxes besides the other parameters. The outcomes showed that the suggested combinations are comparable with the ones in the literature.

scholarly journals The efficiency of the Weather Research and Forecasting (WRF) model for simulating typhoons

2014 ◽  
Vol 14 (8) ◽  
pp. 2179-2187 ◽  
Author(s):  
T. Haghroosta ◽  
W. R. Ismail ◽  
P. Ghafarian ◽  
S. M. Barekati
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Model Simulation ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Heat Fluxes ◽  
Weather Research And Forecasting ◽  
Data Set ◽  
Wind Speed Prediction ◽  
Weather Research

Abstract. The Weather Research and Forecasting (WRF) model includes various configuration options related to physics parameters, which can affect the performance of the model. In this study, numerical experiments were conducted to determine the best combination of physics parameterization schemes for the simulation of sea surface temperatures, latent heat flux, sensible heat flux, precipitation rate, and wind speed that characterized typhoons. Through these experiments, several physics parameterization options within the Weather Research and Forecasting (WRF) model were exhaustively tested for typhoon Noul, which originated in the South China Sea in November 2008. The model domain consisted of one coarse domain and one nested domain. The resolution of the coarse domain was 30 km, and that of the nested domain was 10 km. In this study, model simulation results were compared with the Climate Forecast System Reanalysis (CFSR) data set. Comparisons between predicted and control data were made through the use of standard statistical measurements. The results facilitated the determination of the best combination of options suitable for predicting each physics parameter. Then, the suggested best combinations were examined for seven other typhoons and the solutions were confirmed. Finally, the best combination was compared with other introduced combinations for wind-speed prediction for typhoon Washi in 2011. The contribution of this study is to have attention to the heat fluxes besides the other parameters. The outcomes showed that the suggested combinations are comparable with the ones in the literature.

scholarly journals An Analog Technique to Improve Storm Wind Speed Prediction Using a Dual NWP Model Approach

2018 ◽  
Vol 146 (12) ◽  
pp. 4057-4077 ◽  
Author(s):  
Jaemo Yang ◽  
Marina Astitha ◽  
Luca Delle Monache ◽  
Stefano Alessandrini
Keyword(s):  
United States ◽  
Wind Speed ◽  
Weather Prediction ◽  
Wrf Model ◽  
Atmospheric Modeling ◽  
Training Dataset ◽  
Limited Area ◽  
Wind Speed Prediction ◽  
Northeast United States ◽  
Speed Prediction

Abstract This study presents a new implementation of the analog ensemble method (AnEn) to improve the prediction of wind speed for 146 storms that have impacted the northeast United States in the period 2005–16. The AnEn approach builds an ensemble by using a set of past observations that correspond to the best analogs of numerical weather prediction (NWP). Unlike previous studies, dual-predictor combinations are used to generate AnEn members, which include wind speed, wind direction, and 2-m temperature, simulated by two state-of-the-science atmospheric models [the Weather Research and Forecasting (WRF) Model and the Regional Atmospheric Modeling System–Integrated Community Limited Area Modeling System (RAMS–ICLAMS)]. Bias correction is also applied to each analog to gain additional benefits in predicting wind speed. Both AnEn and the bias-corrected analog ensemble (BCAnEn) are tested with a weighting strategy, which optimizes the predictor combination with root-mean-square error (RMSE) minimization. A leave-one-out cross validation is implemented, that is, each storm is predicted using the remaining 145 as the training dataset, with modeled and observed values over 80 stations in the northeast United States. The results show improvements of 9%–42% and 1%–29% with respect to original WRF and ICLAMS simulations, as measured by the RMSE of individual storms. Moreover, for two high-impact tropical storms (Irene and Sandy), BCAnEn significantly reduces the error of raw prediction (average RMSE reduction of 22% for Irene and 26% for Sandy). The AnEn and BCAnEn techniques demonstrate their potential to combine different NWP models to improve storm wind speed prediction, compared to the use of a single NWP.

scholarly journals Impacts of High-Resolution Urban Canopy Parameters within the WRF Model on Dynamical and Thermal Fields over Guangzhou, China

2019 ◽  
Vol 58 (5) ◽  
pp. 1155-1176
Author(s):  
Chong Shen ◽  
Xiaoyang Chen ◽  
Wei Dai ◽  
Xiaohui Li ◽  
Jie Wu ◽  
...  
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Urban Canopy ◽  
Urban Morphology ◽  
Meteorological Model ◽  
Thermal Fields ◽  
Ground Heat Flux ◽  
Surface Skin Temperature

AbstractOn urban scales, the detailed characteristics of land-use information and building properties are vital to improving the meteorological model. The WRF Model with high-spatial-resolution urban fraction (UF) and urban morphology (UM) is used to study the impacts of these urban canopy parameters (UCPs) on dynamical and thermal meteorological fields in two representative seasons in Guangzhou. The results of two seasons are similar and as follows. 1) The impacts of updated UF and UM are obvious on wind speed but minor on temperature and humidity. In the urban environment, the results with updated UF and UM are more consistent with observations compared with the default UCPs, which means the performance of the model has been improved. 2) The dynamical factors associated with wind speed are analyzed. Turbulent kinetic energy (TKE) is significantly affected by UM but little by UF. And both UF and UM are found to influence friction velocity U*. The UM and greater UF attained larger U*. 3) In addition, the thermal fields are analyzed. The UM and increased UF induce higher surface skin temperature (TSK) and ground heat flux in the daytime, indicating that more heat is transported from the surface to the soil. At night, more heat is transported from the soil to the surface, producing higher TSK. For sensible heat flux (HFX), greater UF induces larger HFX during the daytime. But the effects of UM are complex, which makes HFX decrease during the daytime and increase at night. Finally, larger UF attains lower latent heat in the daytime.

scholarly journals Response of water temperatures and stratification to changing climate in three lakes with different morphometry

2016 ◽  
Author(s):  
Madeline R. Magee ◽  
Chin H. Wu
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Heat Fluxes ◽  
Surface Heat Fluxes ◽  
Lake Mendota ◽  
Abrupt Changes ◽  
Bottom Waters ◽  
Schmidt Stability

Abstract. Water temperatures in three morphometrically different lakes are simulated using a one-dimensional hydrodynamic lake model over the century (1911–2014) to elucidate the effects of increasing air temperature and decreasing wind speed on lake thermal variables (water temperature, stratification dates, strength of stratification, and surface heat fluxes). During the study period, epilimnetic temperatures increased, hypolimnetic temperatures decreased, and the length of the stratified season increased for the study lakes due to earlier stratification onset and later fall overturn. Additionally, there was an abrupt change in epilimnion temperature after 1930 in both Lake Mendota and Lake Wingra, and three changes, after 1934, 1995, and 2008 for Fish Lake. There was a significant change in the slope of trend of stratification duration after 1940 in Lake Mendota and a significant change in trend after 1981 for Fish Lake. Schmidt stability showed a statistically significant increasing trend for both deep lakes, with the larger trend and greater variability in the larger surface area lake. Sensible heat flux in all three lakes increases over the simulation period while longwave heat flux decreases. The shallow study lake had a greater change in latent heat flux and net heat flux, illustrating the role of lake depth to surface heat fluxes. Sensible heat flux in all three lakes had similar timing of abrupt changes, but the magnitude of the change increased with increasing depth. Abrupt changes in latent heat flux appear to be independent of lake morphometry, indicating that the timing of change may be primarily driven by climate. Perturbing drivers showed that increasing air temperature and decreasing wind speed caused earlier stratification onset and later fall overturn. For hypolimnetic water temperature, however, increasing air temperature warmed bottom waters while decreasing wind speed cooled bottom waters, indicating that the change of hypolimnetic temperatures globally may be influenced by local changes in wind speed. Overall, lake depth impacts the presence of stratification and magnitude of Schmidt stability, while lake surface area drives differences in hypolimnion temperature, hypolimnetic heating, variability of Schmidt stability, and stratification onset and fall overturn dates.

scholarly journals Evapotranspiration monitoring based on thermal infrared data over agricultural landscapes: comparison of a simple energy budget model and a SVAT model

2018 ◽  
Author(s):  
Guillaume Bigeard ◽  
Benoit Coudert ◽  
Jonas Chirouze ◽  
Salah Er-Raki ◽  
Gilles Boulet ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Sensible Heat Flux ◽  
Agricultural Landscapes ◽  
Heat Fluxes ◽  
Model Parameters ◽  
Scaling Effects ◽  
Data Set ◽  
Svat Model

Abstract. The overall purpose of our work is to take advantage of Thermal Infra-Red (TIR) imagery to estimate landscape evapotranspiration fluxes over agricultural areas, relying on two approaches of increasing complexity and input data needs: a Surface Energy Balance (SEB) model, TSEB, used directly at the landscape scale with TIR forcing, and the aggregation of a Soil-Vegetation-Atmosphere Transfer (SVAT) model, SEtHyS, run at high resolution (≃100 m) and constrained by assimilation of TIR data. Within this preliminary study, models skills are compared thanks to large in situ database covering different crops, stress and climate conditions. Domains of validity are assessed and the possible loss of performance resulting from inaccurate but realistic inputs (forcing and model parameters) due to scaling effects are quantified. The in situ data set came from 3 experiments carried out in southern France and in Morocco. On average, models provide half-hourly averaged estimations of latent heat flux (LE) with a RMSE of around 55 W m−2 for TSEB and 47 W m−2 for SEtHyS, and estimations of sensible heat flux (H) with a RMSE of around 29 W m−2 for TSEB and 38 W m−2 for SEtHyS. TSEB has been shown to be more flexible and requires one single set of parameters but lead to low performances on rising vegetation and stressed conditions. An in-depth study on the Priestley-Taylor key parameter highlights its marked diurnal cycle and the need to adjust its value to improve flux partition between sensible and latent heat fluxes (1.5 and 1.25 for south-western France and Morocco, respectively). Optimal values of 1.8 to 2 were hilighted under cloudy conditions, which is of particular interest with the emergence of low altitude drone acquisition. SEtHyS is valid in more cases while it required a finer parameters tuning and a better knowledge of surface and vegetation. This study participates to lay the ground for exploring the complementarities between instantaneous and continuous dynamic evapotranspiration mapping monitored with TIR data.

THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR

2020 ◽  
Vol 2020 (57) ◽  
pp. 35-41
Author(s):  
K.S. Klen ◽  
◽  
M.K. Yaremenko ◽  
V.Ya. Zhuykov ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Error ◽  
Average Energy ◽  
Interpolation Formula ◽  
Wind Speed Prediction ◽  
Wind Generator ◽  
Prediction Horizon ◽  
Speed Prediction ◽  
Using Data ◽  
Predictor Corrector

The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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