scholarly journals Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain

2021 ◽  
Author(s):  
Julian Quimbayo-Duarte ◽  
Johannes Wagner ◽  
Norman Wildmann ◽  
Thomas Gerz ◽  
Juerg Schmidli
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Wind Speed ◽  
Complex Terrain ◽  
Grid Spacing ◽  
Short Term ◽  
Valley Wind ◽  
Layer Flow ◽  
Horizontal Grid

Abstract. We evaluate the influence of a forest parametrization on the simulation of the boundary layer flow over moderate complex terrain in the context of the Perdigão 2017 field campaign. The numerical simulations are performed using the Weather research and forecasting model using its large eddy simulation mode (WRF-LES). The short-term high resolution (40 m horizontal grid spacing) and long-term (200 m horizontal grid spacing) WRF-LES are evaluated for an integration time of 12 hours and 1.5 months, respectively, with and without forest parameterization. The short-term simulations focus on low-level jet events over the valley, while the long-term simulations cover the whole intensive observation period (IOP) of the field campaign. The results are validated using lidar and meteorological tower observations. The mean diurnal cycle during the IOP shows a significant improvement of the along-valley wind speed and the wind direction when using the forest parametrization. However, the drag imposed by the parametrization results in an underestimation of the cross-valley wind speed, which can be attributed to a poor representation of the land surface characteristics. The evaluation of the high-resolution WRF-LES shows a positive influence of the forest parametrization on the simulated winds in the first 500 m above the surface.

scholarly journals Improving boundary layer flow simulations over complex terrain by applying a forest parameterization in WRF

2019 ◽  
Author(s):  
Johannes Wagner ◽  
Norman Wildmann ◽  
Thomas Gerz
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Boundary Layer Flow ◽  
Complex Terrain ◽  
Potential Temperature ◽  
Short And Long Term ◽  
Layer Flow ◽  
The Impact ◽  
Double Ridge

Abstract. The impact of a forest parameterization on the simulation of boundary layer flows over complex terrain is investigated. Short- and long-term simulations are run for 12 hours and 1.5 months, respectively, with and without forest parameterization and the results are compared to lidar and meteorological tower observations. The test cases are based on the Perdigao 2017 campaign. Short-term simulations focus on low-level jet events over the double-ridge, while long-term simulations cover the whole intensive observation period of the campaign. Simulations without forest parameterization do not reproduce the interaction of the boundary layer flow with the double ridge satisfactorily. Surface winds are overestimated and flow separation and recirculation zones are not or only partly simulated. The additional drag of the forest parameterization considerably improves the agreement of simulated and observed wind speed and potential temperature by reducing the positive wind speed bias and increasing the correlation. The positive effect of the forest parameterization on the boundary layer flow is visible both in the short- and long-term simulations.

scholarly journals Dynamical Downscaling of Wind Speed in Complex Terrain Prone To Bora-Type Flows

2011 ◽  
Vol 50 (8) ◽  
pp. 1676-1691 ◽  
Author(s):  
Kristian Horvath ◽  
Alica Bajić ◽  
Stjepan Ivatek-Šahdan
Keyword(s):  
Wind Speed ◽  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Mesoscale Model ◽  
Spectral Power ◽  
Grid Spacing ◽  
Model Data ◽  
Mean Wind Speed ◽  
The Mean ◽  
Horizontal Grid

AbstractThe results of numerically modeled wind speed climate, a primary component of wind energy resource assessment in the complex terrain of Croatia, are given. For that purpose, dynamical downscaling of 10 yr (1992–2001) of the 40-yr ECMWF Re-Analysis (ERA-40) was performed to 8-km horizontal grid spacing with the use of a spectral, prognostic full-physics model Aire Limitée Adaptation Dynamique Développement International (ALADIN; the “ALHR” version). Then modeled data with a 60-min frequency were refined to 2-km horizontal grid spacing with a simplified and cost-effective model version, the so-called dynamical adaptation (DADA). The statistical verification of ERA-40-, ALHR-, and DADA-modeled wind speed on the basis of data from measurement stations representing different regions of Croatia suggests that downscaling was successful and that model accuracy generally improves as horizontal resolution is increased. The areas of the highest mean wind speeds correspond well to locations of frequent and strong bora flow as well as to the prominent mountain peaks. The best results are achieved with DADA and contain bias of 1% of the mean wind speed for eastern Croatia while reaching 10% for complex coastal terrain, mainly because of underestimation of the strongest winds. Root-mean-square errors for DADA are significantly smaller for flat terrain than for complex terrain, with relative values close to 12% of the mean wind speed regardless of the station location. Spectral analyses suggest that the shape of the kinetic energy spectra generally relaxes from k−3 at the upper troposphere to the shape of orographic spectra near the surface and shows no seasonal variability. Apart from the buildup of energy on smaller scales of motions, it is shown that mesoscale simulations contain a considerable amount of energy related to near-surface and mostly divergent meso-β-scale (20–200 km) motions. Spectral decomposition of measured and modeled data in temporal space indicates a reasonable performance of all model datasets in simulating the primary maximum of spectral power related to synoptic and larger-than-diurnal mesoscale motions, with somewhat increased accuracy of mesoscale model data. The primary improvement of dynamical adaptation was achieved for cross-mountain winds, whereas mixed results were found for along-mountain wind directions. Secondary diurnal and tertiary semidiurnal maxima are significantly better simulated with the mesoscale model for coastal stations but are somewhat more erroneous for the continental station. The mesoscale model data underestimate the spectral power of motions with less-than-semidiurnal periods.

scholarly journals Using Simulated Dropsondes to Understand Extreme Updrafts and Wind Speeds in Tropical Cyclones

2018 ◽  
Vol 146 (11) ◽  
pp. 3901-3925 ◽  
Author(s):  
Daniel P. Stern ◽  
George H. Bryan
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Tropical Cyclones ◽  
Grid Spacing ◽  
Wind Speeds ◽  
Eddy Simulation ◽  
Wind Gusts ◽  
Large Eddy ◽  
Horizontal Grid ◽  
Insight Into

Abstract Extreme updrafts (≥10 m s−1) and wind gusts (≥90 m s−1) are ubiquitous within the low-level eyewall of intense tropical cyclones (TCs). Previous studies suggest that both of these features are associated with coherent subkilometer-scale vortices. Here, over 100 000 “virtual” dropsonde trajectories are examined within a large-eddy simulation (31.25-m horizontal grid spacing) of a category 5 hurricane in order to gain insight into the nature of these features and to better understand and interpret dropsonde observations. At such a high resolution, profiles of wind speed and vertical velocity from the virtual sondes are difficult to distinguish from those of real dropsondes. PDFs of the strength of updrafts and wind gusts compare well between the simulated and observed dropsondes, as do the respective range of heights over which these features are found. Individual simulated updrafts can be tracked for periods of up to several minutes, revealing structures that are both coherent and rapidly evolving. It appears that the updrafts are closely associated with vortices and wind speed maxima, consistent with previous studies. The peak instantaneous wind gusts in the simulations (up to 150 m s−1) are substantially stronger than have ever been observed. Using the virtual sondes, it is demonstrated that the probability of sampling such extremes is vanishingly small, and it is argued that actual intense TCs might also be characterized by gusts of these magnitudes.

scholarly journals Impact of model improvements on 80-m wind speeds during the second Wind Forecast Improvement Project (WFIP2)

2019 ◽  
Author(s):  
Laura Bianco ◽  
Irina V. Djalalova ◽  
James M. Wilczak ◽  
Joseph B. Olson ◽  
Jaymes S. Kenyon ◽  
...  
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Weather Prediction ◽  
Mean Bias Error ◽  
Grid Spacing ◽  
Improvement Project ◽  
Experimental Physics ◽  
Wind Speeds ◽  
The Impact ◽  
Horizontal Grid

Abstract. During the second Wind Forecast Improvement Project (WFIP2; Oct 2015–Mar 2017, Columbia River Gorge and Basin area) several improvements to the parameterizations applied in the High Resolution Rapid Refresh (HRRR – 3 km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST – 750 m horizontal grid spacing) Numerical Weather Prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80-m wind speeds (hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and 3 profiling lidars for verification. Improvements due to the experimental physics (EXP vs CNT runs) versus those due to finer horizontal grid spacing (HRRRNEST vs HRRR), and the combination of the two are compared, using standard bulk statistics such as Mean Absolute Error (MAE) and Mean Bias Error (bias). On average, the HRRR 80-m wind speed MAE is reduced by 3–4 % due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5 % on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80-m wind speed MAE, up to 7–8 %. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena, also looking for the causes of model weaknesses. Finally, bias correction methods are applied to the 80-m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors.

scholarly journals Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2)

2019 ◽  
Vol 12 (11) ◽  
pp. 4803-4821 ◽  
Author(s):  
Laura Bianco ◽  
Irina V. Djalalova ◽  
James M. Wilczak ◽  
Joseph B. Olson ◽  
Jaymes S. Kenyon ◽  
...  
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Weather Prediction ◽  
Mean Bias Error ◽  
Grid Spacing ◽  
Improvement Project ◽  
Experimental Physics ◽  
Wind Speeds ◽  
The Impact ◽  
Horizontal Grid

Abstract. During the second Wind Forecast Improvement Project (WFIP2; October 2015–March 2017, held in the Columbia River Gorge and Basin area of eastern Washington and Oregon states), several improvements to the parameterizations used in the High Resolution Rapid Refresh (HRRR – 3 km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST – 750 m horizontal grid spacing) numerical weather prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80 m wind speeds (wind turbine hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and three profiling lidars for comparison. Improvements due to the experimental physics (EXP vs. CNT runs) and those due to finer horizontal grid spacing (HRRRNEST vs. HRRR) and the combination of the two are compared, using standard bulk statistics such as mean absolute error (MAE) and mean bias error (bias). On average, the HRRR 80 m wind speed MAE is reduced by 3 %–4 % due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5 % on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80 m wind speed MAE, up to 7 %–8 %. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena. Causes of model weaknesses are identified. Finally, bias correction methods are applied to the 80 m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors.

Toward unifying short-term and next-day convection-allowing ensemble forecast systems with a continuously-cycling 3-km ensemble Kalman filter over the entire conterminous United States

2020 ◽  
pp. 1-83
Author(s):  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
David C. Dowell
Keyword(s):  
United States ◽  
Kalman Filter ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Ensemble Forecast ◽  
Grid Spacing ◽  
Short Term ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Horizontal Grid

Abstract Using the Weather Research and Forecasting model, 80-member ensemble Kalman filter (EnKF) analyses with 3-km horizontal grid spacing were produced over the entire conterminous United States (CONUS) for 4 weeks using 1-h continuous cycling. For comparison, similarly configured EnKF analyses with 15-km horizontal grid spacing were also produced. At 0000 UTC, 15- and 3-km EnKF analyses initialized 36-h, 3-km, 10-member ensemble forecasts that were verified with a focus on precipitation. Additionally, forecasts were initialized from operational Global Ensemble Forecast System (GEFS) initial conditions (ICs) and experimental “blended” ICs produced by combining large scales from GEFS ICs with small scales from EnKF analyses using a low-pass filter. The EnKFs had stable climates with generally small biases, and precipitation forecasts initialized from 3-km EnKF analyses were more skillful and reliable than those initialized from downscaled GEFS and 15-km EnKF ICs through 12–18 and 6–12 h, respectively. Conversely, after 18 h, GEFS-initialized precipitation forecasts were better than EnKF-initialized precipitation forecasts. Blended 3-km ICs reflected the respective strengths of both GEFS and high-resolution EnKF ICs and yielded the best performance considering all times: blended 3-km ICs led to short-term forecasts with similar or better skill and reliability than those initialized from unblended 3-km EnKF analyses and ~18–36-h forecasts possessing comparable quality as GEFS-initialized forecasts. This work likely represents the first time a convection-allowing EnKF has been continuously cycled over a region as large as the entire CONUS, and results suggest blending high-resolution EnKF analyses with low-resolution global fields can potentially unify short-term and next-day convection-allowing ensemble forecast systems under a common framework.

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

2011 ◽  
Vol 139 (6) ◽  
pp. 2008-2024 ◽  
Author(s):  
Brian C. Ancell ◽  
Clifford F. Mass ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Complex Terrain ◽  
Error Variance ◽  
Small Scale ◽  
Observation Error ◽  
Grid Spacing ◽  
Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

scholarly journals Artificial Intelligence Techniques for Estimation of Optimum Weibull Parameters for Wind Speed Distribution

2019 ◽  
Vol 8 (4S2) ◽  
pp. 150-156
Keyword(s):  
Wind Speed ◽  
Tamil Nadu ◽  
Optimization Algorithms ◽  
Coefficient Of Determination ◽  
Short Term ◽  
Weibull Parameters ◽  
Wind Speed Data ◽  
Speed Distribution ◽  
Wind Speed Distribution

The main objective of this study is to estimate the optimum Weibull scale and shape parameters for wind speed distribution at three stations of the state of Tamil Nadu, India using Nelder-Mead, Broyden–Fletcher–Goldfarb–Shanno, and Simulated annealing optimization algorithms. An attempt has been made for the first time to apply these optimization algorithms to determine the optimum parameters. The study was conducted for long term wind speed data (38 years), short term wind speed data (5 years) and also with single year’s wind speed data to assess the performance of the algorithm for different quantum of data. The efficiency of these algorithms are analyzed using various statistical indicators like Root mean square error (RMSE), Correlation coefficient (R), Mean absolute error (MAE) and coefficient of determination (R2). The results suggest that the performance of three algorithms is similar irrespective of the quantum of the dataset. The estimated Weibull parameters are almost similar for short term and long term dataset. There is a marginal variation in the obtained parameters when only single year’s wind data is considered for the analysis. The Weibull probability distribution curve fits very well on the wind speed histogram when only single year’s wind speed data is considered and fits marginally well when short term and long term wind speed data is considered

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