scholarly journals Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain

2018 ◽  
Vol 57 (3) ◽  
pp. 733-753 ◽  
Author(s):  
Sergio Fernández-González ◽  
María Luisa Martín ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
Jesús Lorenzana ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Boundary Conditions ◽  
Complex Terrain ◽  
Wrf Model ◽  
Wind Farms ◽  
Ensemble Prediction ◽  
Mountain Range ◽  
Ensemble Prediction System ◽  
Initial And Boundary Conditions

AbstractWind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) as initial and boundary conditions was similar, although each performed better under certain meteorological conditions. With regard to physical schemes, there is no single combination of parameterizations that performs best during all weather conditions. Nevertheless, some combinations have been identified as inefficient, and therefore their use is discouraged. As a result, the validation of an ensemble prediction system composed of the best 12 deterministic simulations shows the most accurate results, obtaining relative errors in wind speed forecasts that are <15%.

scholarly journals New Developments in Ensemble-based Probabilistic Forecasting of Solar Radiation: The WRF-Solar Ensemble Prediction System

2021 ◽  
Author(s):  
Manajit Sengupta ◽  
Pedro Jimenez ◽  
Jaemo Yang ◽  
Ju-Hye Kim ◽  
Yu Xie
Keyword(s):  
Sensitivity Analysis ◽  
Solar Radiation ◽  
Solar Irradiance ◽  
Solar Power ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Radiation Data ◽  
Solar Generation

&lt;p&gt;The demand for increased accuracy in predicting solar power has grown considerably over recent years due to a rapid growth in grid-tied solar generation both utility scale and distributed. To increase confidence in forecasting solar power there is a need to provide reliable probabilistic solar radiation information that also minimizes error and uncertainty. Funded by the U.S. Department of Energy, the Weather Research and Forecasting (WRF)-Solar ensemble prediction system (WRF-Solar EPS) has been recently developed by a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research. The WRF-Solar EPS is now ready to be disseminated to support the integration of solar generation resources and improve accuracy of day-ahead and intraday probabilistic solar forecasts. The first stage of our framework in developing WRF-Solar EPS required a specially designed method using a tangent linear (TL) sensitivity analysis to efficiently investigate uncertainties of WRF-Solar variables in forecasting clouds and solar irradiance. For the second stage, we applied a methodology to introduce stochastic perturbations in 14 key variables ascertained through the TL sensitivity analysis in generating ensemble members. A user-friendly interface is provided in WRF-Solar EPS, in which the parameters of stochastic perturbations can be controlled by configuration files. Lastly, we implemented an analog technique as an ensemble post-processing method to improve the performance of ensemble solar irradiance forecasts. This presentation will summarize the work performed in the past 3 years to understand the interactions between cloud physics, land surface, boundary layer and radiative transfer models through the development of a probabilistic cloud optimized day-ahead forecasting system based on WRF-Solar. For evaluation of forecasts, we adapt and use satellite-derived solar radiation data, e.g., the National Solar Radiation Data Base (NSRDB) as well as ground-measured observations. A comprehensive analysis to assess gridded model outputs over the Contiguous U.S is performed. The importance of evaluation of the WRF-Solar model with the NSRDB lies in the fact that the knowledge of the cloud-caused uncertainties in predicting solar irradiance over a wide range of regions provides model developers a detailed understanding of model strength and weaknesses in predicting clouds. Overall, we will present the detailed research steps that resulted in the development of the WRF-Solar EPS. We will also present a detailed validation demonstrating the improvements provided by this model. Moreover, we will also introduce the user&amp;#8217;s guide for WRF-Solar EPS and future extension of this research.&lt;/p&gt;

Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model

2020 ◽  
Vol 148 (12) ◽  
pp. 4823-4835
Author(s):  
Cristina L. Archer ◽  
Sicheng Wu ◽  
Yulong Ma ◽  
Pedro A. Jiménez
Keyword(s):  
Wind Speed ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Wind Farms ◽  
Near Surface ◽  
Large Eddy ◽  
The Way

AbstractAs wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) Model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF Model with the Fitch parameterization. The first issue is a simple “bug” in the WRF code, and the second issue is the excessive value of a coefficient, called CTKE, that relates TKE to the turbine electromechanical losses. These two issues directly affect the way that a wind farm wake evolves, and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of CTKE that is one-quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependence on parameters such as atmospheric stability, grid resolution, and wind farm layout. We present the results obtained with the Fitch parameterization in the WRF Model for a single turbine with and without the bug fix and the corrected CTKE and compare them with high-fidelity large-eddy simulations. These two issues have not been discovered before because they interact with one another in such a way that their combined effect is a somewhat realistic vertical TKE profile at the wind farm and a realistic wind speed deficit in the wake. All WRF simulations that used the Fitch wind farm parameterization are affected, and their conclusions may need to be revisited.

scholarly journals Ensemble Forecast Uncertainty of the 1 December 2011 Wasatch Windstorm

2015 ◽  
Vol 30 (6) ◽  
pp. 1749-1761 ◽  
Author(s):  
John Lawson ◽  
John Horel
Keyword(s):  
Boundary Conditions ◽  
Salt Lake ◽  
Flow Analysis ◽  
Wrf Model ◽  
Ensemble Forecast ◽  
Sudden Increase ◽  
Error Growth ◽  
Forecast Lead Time ◽  
Northern Utah ◽  
Initial And Boundary Conditions

Abstract A downslope windstorm on 1 December 2011 led to considerable damage along a narrow 50-km swath at the western base of the Wasatch Mountains in northern Utah. Operational forecasts issued by the Salt Lake City National Weather Service Forecast Office provided accurate guidance for the event at 1–2-day lead times, partially based on locally generated high-resolution numerical forecasts. Forecasters highlighted the possibility of the windstorm 4 days in advance. To address the apparent reduced uncertainty for this windstorm, three 11-member three-domain ensemble forecasts were initialized at 0000 UTC 25 November, 0000 UTC 27 November, and 0000 UTC 29 November 2011 using the Weather Research and Forecasting (WRF) Model with initial and boundary conditions supplied by Global Ensemble Forecast System Reforecast, version 2 (GEFS/R2). Eight of the 11 ensemble members from the 29 November 2011 forecast (60 h before the windstorm) generated a strong, localized windstorm with outliers arising from reduced cross-barrier flow. Analysis of kinetic energy error growth suggests that the reduced uncertainty of 60-h forecasts was not primarily a result of the underdispersion of GEFS/R2 initial and boundary conditions but was related to a regional reduction in error growth in midtropospheric flow upstream of northern Utah. The ensemble initialized 2 days earlier (27 November, 108 h before the windstorm) contains fewer members that generate strong windstorms, while no members generate a windstorm in the ensemble initialized on 25 November (156 h prior). This sudden increase in uncertainty with forecast lead time results from the sensitivity of the ensemble solutions to the lateral boundary conditions imposed by the GEFS/R2 between 0000 UTC 29 November and 0000 UTC 30 November.

scholarly journals On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex Terrain

2013 ◽  
Vol 52 (7) ◽  
pp. 1610-1617 ◽  
Author(s):  
Pedro A. Jiménez ◽  
Jimy Dudhia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Complex Terrain ◽  
Grid Point ◽  
Surface Wind ◽  
Winter Season ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Surface Flow ◽  
High Horizontal Resolution

AbstractThe ability of the Weather Research and Forecasting (WRF) model to reproduce the surface wind direction over complex terrain is examined. A simulation spanning a winter season at a high horizontal resolution of 2 km is compared with wind direction records from a surface observational network located in the northeastern Iberian Peninsula. A previous evaluation has shown the ability of WRF to reproduce the wind speed over the region once the effects of the subgrid-scale topography are parameterized. Hence, the current investigation complements the previous findings, providing information about the model's ability to reproduce the direction of the surface flow. The differences between the observations and the model are quantified in terms of scores explicitly designed to handle the circular nature of the wind direction. Results show that the differences depend on the wind speed. The larger the wind speed is, the smaller are the wind direction differences. Areas with more complex terrain show larger systematic differences between model and observations; in these areas, a statistical correction is shown to help. The importance of the grid point selected for the comparison with observations is also analyzed. A careful selection is relevant to reducing comparative problems over complex terrain.

scholarly journals Application of Kalman Filter and Breeding Ensemble Technique to Forecast the Tropical Cyclone Activity

2021 ◽  
Author(s):  
Cong Thanh ◽  
Dao Nguyen Quynh Hoa ◽  
Tran Tan Tien
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclone ◽  
Wrf Model ◽  
Weather Forecast ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
Ensemble Technique ◽  
Ensemble Transform Kalman Filter ◽  
Numerical Weather Forecast ◽  
Regional Atmospheric Modeling

Tropical cyclone (TC) is one of the major meteorology disasters, as they lead to deaths, destroy the infrastructure and the environment. Therefore, how to improve the predictability of TC’s activities, such as formation, track, and intensity, is very important and is considered an important task for current operational predicting TC centers in many countries. However, predicting TC’s activities has remained a big challenge for meteorologists due to our incomplete understanding of the multiscale interaction of TCs with the ambient environment and the limitation of numerical weather forecast tools. Hence, this chapter will exhibit some techniques to improve the ability to predict the formation and track of TCs using an ensemble prediction system. Particularly, the Local Ensemble Transform Kalman Filter (LETKF) scheme and its implementation in the WRF Model, as well as the Vortex tracking method that has been applied for the forecast of TCs formation, will be presented in subSection 1. Application of Breeding Ensemble to Tropical Cyclone Track Forecasts using the Regional Atmospheric Modeling System (RAMS) model will be introduced in subSection 2.

scholarly journals A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

2015 ◽  
Vol 30 (5) ◽  
pp. 1158-1181 ◽  
Author(s):  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Morris L. Weisman ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Wrf Model ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

scholarly journals Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

2020 ◽  
Vol 12 (6) ◽  
pp. 973
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Satellite Data ◽  
Surface Wind ◽  
Wrf Model ◽  
Wind Farms ◽  
Resource Assessment ◽  
Near Surface ◽  
Wind Resource Assessment ◽  
Wind Resource

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.

Simulation of Bay of Bengal Tropical Cyclones with WRF Model: Impact of Initial and Boundary Conditions

2010 ◽  
Vol 33 (4) ◽  
pp. 294-314 ◽  
Author(s):  
U. C. Mohanty ◽  
Krishna K. Osuri ◽  
A. Routray ◽  
M. Mohapatra ◽  
Sujata Pattanayak
Keyword(s):  
Boundary Conditions ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Wrf Model ◽  
Initial And Boundary Conditions

scholarly journals Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System

2013 ◽  
Vol 141 (5) ◽  
pp. 1506-1526 ◽  
Author(s):  
Christophe Lavaysse ◽  
Marco Carrera ◽  
Stéphane Bélair ◽  
Normand Gagnon ◽  
Ronald Frenette ◽  
...  
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Initial Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Surface Parameter ◽  
Ensemble Prediction System ◽  
Surface Parameters ◽  
Screen Level ◽  
The Impact

Abstract The aim of this study is to assess the impact of uncertainties in surface parameter and initial conditions on numerical prediction with the Canadian Regional Ensemble Prediction System (REPS). As part of this study, the Canadian version of the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme has been coupled to Environment Canada’s numerical weather prediction model within the REPS. For 20 summer periods in 2009, stochastic perturbations of surface parameters have been generated in several experiments. Each experiment corresponds to 20 simulations differing by the perturbations at the initial time of one or several surface parameters or prognostic variables. The sensitivity to these perturbations is quantified especially for 2-m temperature, 10-m wind speed, cloud fraction, and precipitation up to 48-h lead time. Spatial variability of these sensitivities over the North American continent shows that soil moisture, albedo, leaf area index, and SST have the largest impacts on the screen-level variables. The temporal evolution of these sensitivities appears to be closely linked to the diurnal cycle of the boundary layer. The surface perturbations are shown to increase the ensemble spread of the REPS for all screen-level variables especially for 2-m temperature and 10-m wind speed during daytime. A preliminary study of the impact on the ensemble forecast has shown that the inclusion of the surface perturbations tends to significantly increase the 2-m temperature and 10-m wind speed skill.

scholarly journals Typhoon-Position-Oriented Sensitivity Analysis. Part I: Theory and Verification

2013 ◽  
Vol 70 (8) ◽  
pp. 2525-2546 ◽  
Author(s):  
Kosuke Ito ◽  
Chun-Chieh Wu
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Initial Perturbation ◽  
Ensemble Prediction ◽  
Track Forecast ◽  
Position Change ◽  
Ensemble Prediction System ◽  
Sensitivity Analysis Method ◽  
Verification Time ◽  
Divergence Field

Abstract A new sensitivity analysis method is proposed for the ensemble prediction system in which a tropical cyclone (TC) position is taken as a metric. Sensitivity is defined as a slope of linear regression (or its approximation) between state variable and a scalar representing the TC position based on ensemble simulation. The experiment results illustrate important regions for ensemble TC track forecast. The typhoon-position-oriented sensitivity analysis (TyPOS) is applied to Typhoon Shanshan (2006) for the verification time of up to 48 h. The sensitivity field of the TC central latitude with respect to the vorticity field obtained from large-scale random initial perturbation is characterized by a horizontally tilted pattern centered at the initial TC position. These sensitivity signals are generally maximized in the middle troposphere and are far more significant than those with respect to the divergence field. The results are consistent with the sensitivity signals obtained from existing methods. The verification experiments indicate that the signals from TyPOS quantitatively reflect an ensemble-mean position change as a response to the initial perturbation. Another experiment with Typhoon Dolphin (2008) demonstrates the long-term analysis of forecast sensitivity up to 96 h. Several additional tests have also been carried out to investigate the dependency among ensemble members, the impacts of using different horizontal grid spacing, and the effectiveness of ensemble-Kalman-filter-based perturbations.

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