Effects of Trees on Pedestrian Wind Comfort in an Urban Area Using a CFD model

2020 ◽  
Author(s):  
Geon Kang ◽  
Jae-Jin Kim
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Successful Implementation ◽  
Cfd Model ◽  
Local Data ◽  
Wind Speeds ◽  
National University ◽  
Computational Fluid Dynamics Cfd ◽  
Wind Comfort ◽  
Comfort Criteria

<p>This study investigated the effects of trees on the pedestrian wind comfort in the Pukyong National University (PKNU) campus. For this, we implemented the tree’s drag parameterization scheme to a computational fluid dynamics (CFD) model and validated the simulated results against a field measurement. The CFD model well reproduced the measured wind speeds and TKEs in the downwind region of the trees, indicating successful implementation of the tree drag parameterization schemes. Besides, we compared the wind speeds, wind directions, and temperatures simulated by the CFD model coupled to the local data assimilation and prediction system (LDAPS), one of the numerical weather prediction models operated by the Korean Meteorological Administration (KMA) to those observed at the automated weather station (AWS). We performed the simulations for one week (00 UTC 2 – 23 UTC 9 August 2015). The LDAPS overestimated the observed wind speeds (RMSE = 1.81 m s<sup>–1</sup>), and the CFD model markedly improved the wind speed RMSE (1.16 m s<sup>–1</sup>). We applied the CFD model to the simulations of the trees' effects on pedestrian wind comfort in the PKNU campus in views of wind comfort criteria based on the Beaufort wind force scale (BWS). We will present the trees' effects on pedestrian wind comfort in the PKNU campus in detail.</p>

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

The Bow and Arrow Mesoscale Convective Structure

2013 ◽  
Vol 141 (5) ◽  
pp. 1648-1672 ◽  
Author(s):  
Kelly M. Keene ◽  
Russ S. Schumacher
Keyword(s):  
Heavy Rainfall ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Severe Weather ◽  
Cold Pool ◽  
Horizontal Shear ◽  
Convective Systems ◽  
Wind Speeds ◽  
Bow And Arrow

Abstract The accurate prediction of warm-season convective systems and the heavy rainfall and severe weather associated with them remains a challenge for numerical weather prediction models. This study looks at a circumstance in which quasi-stationary convection forms perpendicular to, and above the cold-pool behind strong bow echoes. The authors refer to this phenomenon as a “bow and arrow” because on radar imagery the two convective lines resemble an archer’s bow and arrow. The “arrow” can produce heavy rainfall and severe weather, extending over hundreds of kilometers. These events are challenging to forecast because they require an accurate forecast of earlier convection and the effects of that convection on the environment. In this study, basic characteristics of 14 events are documented, and observations of 4 events are presented to identify common environmental conditions prior to the development of the back-building convection. Simulations of three cases using the Weather Research and Forecasting Model (WRF) are analyzed in an attempt to understand the mechanisms responsible for initiating and maintaining the convective line. In each case, strong southwesterly flow (inducing warm air advection and gradual isentropic lifting), in addition to directional and speed convergence into the convective arrow appear to contribute to initiation of convection. The linear orientation of the arrow may be associated with a combination of increased wind speeds and horizontal shear in the arrow region. When these ingredients are combined with thermodynamic instability, there appears to be a greater possibility of formation and maintenance of a convective arrow behind a bow echo.

scholarly journals Fast Numerical Wind Turbine Candidate Site Evaluation

2021 ◽  
Vol 11 (7) ◽  
pp. 2953
Author(s):  
Matija Perne ◽  
Primož Mlakar ◽  
Boštjan Grašič ◽  
Marija Zlata Božnar ◽  
Juš Kocijan
Keyword(s):  
Wind Turbine ◽  
Prediction Models ◽  
Weather Prediction ◽  
Measurement Data ◽  
Experimental Modelling ◽  
Numerical Weather ◽  
Nonlinear Regression Models ◽  
Wind Speeds ◽  
Long Time

A long-term measured wind speed time series from the location is typically used when deciding on placing a small wind turbine at a particular location. These data take a long time to collect. The presented novel method of measuring for a shorter time, using the measurement data for training an experimental model, and predicting the wind in a longer time period enables one to avoid most of the wait for the data collection. As the model inputs, the available long-term signals that consist of measurements from the meteorological stations in the vicinity and numerical weather predictions are used. Various possible experimental modelling methods that are based on linear or nonlinear regression models are tested in the field sites. The study area is continental with complex terrain, hilly topography, diverse land use, and no prevailing wind. It is shown that the method gives good results, showing linear regression is most advantageous, and that it is easy enough to use to be practically applicable in small wind projects of limited budget. The method is better suited to small turbines than to big ones because the turbines sited at low heights and in areas with low average wind speeds, where numerical weather prediction models are less accurate, tend to be small.

Sensitivity of Tropical Cyclone Simulations to Parametric Uncertainties in Air–Sea Fluxes and Implications for Parameter Estimation

2014 ◽  
Vol 142 (6) ◽  
pp. 2290-2308 ◽  
Author(s):  
Benjamin W. Green ◽  
Fuqing Zhang
Keyword(s):  
Wind Speed ◽  
Hurricane Katrina ◽  
Prediction Models ◽  
Weather Prediction ◽  
The Other ◽  
High Wind ◽  
Surface Exchange ◽  
Near Surface ◽  
Wind Speeds ◽  
Two Parameters

Abstract Tropical cyclones (TCs) are strongly influenced by fluxes of momentum and moist enthalpy across the air–sea interface. These fluxes cannot be resolved explicitly by current-generation numerical weather prediction models, and therefore must be accounted for via empirical parameterizations of surface exchange coefficients (CD for momentum and Ck for moist enthalpy). The resultant model uncertainty is examined through hundreds of convection-permitting Weather Research and Forecasting Model (WRF) simulations of Hurricane Katrina (2005) by varying four key parameters found in commonly used parameterizations of the exchange coefficient formulas. Two of these parameters effectively act as multiplicative factors for the exchange coefficients over all wind speeds (one each for CD and Ck); the other two parameters control the behavior of CD at very high wind speeds (i.e., above 33 m s−1). It is found that both the intensity and the structure of TCs are highly dependent upon the two multiplicative parameters. The multiplicative parameter for CD has a considerably larger impact than the one for Ck on the relationship between maximum 10-m wind speed and minimum sea level pressure: CD alters TC structure, with higher values shifting the radius of maximum winds inward and strengthening the low-level inflow; Ck only affects structure by uniformly strengthening/weakening the primary and secondary circulations. The TC exhibits the greatest sensitivities to the two multiplicative parameters after a few hours of model integration, suggesting that these parameters could be estimated by assimilating near-surface observations. The other two parameters are likely more difficult to estimate because the TC is only marginally sensitive to them in small areas of high wind speed.

Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

2005 ◽  
Vol 133 (2) ◽  
pp. 409-429 ◽  
Author(s):  
Dudley B. Chelton ◽  
Michael H. Freilich
Keyword(s):  
Wind Stress ◽  
Numerical Weather Prediction ◽  
Measurement Errors ◽  
Prediction Models ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Random Component ◽  
Numerical Weather ◽  
Wind Speeds ◽  
Time Periods

Abstract Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s−1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s−1. While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s−1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

Regime-dependent statistical post-processing of wind speed forecasts

2020 ◽  
Author(s):  
Sam Allen ◽  
Chris Ferro ◽  
Frank Kwasniok
Keyword(s):  
Wind Speed ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lead Times ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Atmospheric Flow ◽  
Wind Speeds ◽  
Systematic Biases ◽  
Processing Techniques

<p>Raw output from deterministic numerical weather prediction models is typically subject to systematic biases. Although ensemble forecasts provide invaluable information regarding the uncertainty in a prediction, they themselves often misrepresent the weather that occurs. Given their widespread use, the need for high-quality wind speed forecasts is well-documented. Several statistical approaches have therefore been proposed to recalibrate ensembles of wind speed forecasts, including a heteroscedastic censored regression approach. An extension to this method that utilises the prevailing atmospheric flow is implemented here in a quasigeostrophic simulation study and on reforecast data. It is hoped that this regime-dependent framework can alleviate errors owing to changes in the synoptic-scale atmospheric state. When the wind speed strongly depends on the underlying weather regime, the resulting forecasts have the potential to provide substantial improvements in skill upon conventional post-processing techniques. This is particularly pertinent at longer lead times, where there is more improvement to be gained upon current methods, and in weather regimes associated with wind speeds that differ greatly from climatology. In order to realise this potential, however, an accurate prediction of the future atmospheric regime is required.</p>

Assessment of Observational Environments of the Automated Synoptic Observing Systems in Korea Using a CFD Model

2020 ◽  
Author(s):  
Jung-Eun Kang ◽  
Jae-Jin Kim
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Computational Domain ◽  
Cfd Model ◽  
Meteorological Observation ◽  
Wind Speeds ◽  
Air Temperatures ◽  
Computational Fluid Dynamics Cfd ◽  
Meteorological Observations ◽  
Observing Systems

<p>  In this study, we analyzed the observation environments of the automated synoptic observing systems (ASOSs) using a computational fluid dynamics (CFD) model, focusing on the observational environments of air temperatures, wind speeds, and wind directions. The computational domain sizes are 2000 m × 2000 m × 750 m, and the grid sizes are 10 m × 10 m × 5 m in the x-, y-, and z- directions, respectively. We conducted the simulations for eight inflow directions (northerly, northeasterly, easterly, southeasterly, southerly, southwesterly, westerly, northwesterly) using the ASOS-observation wind speeds and air temperatures averaged in August from 2010 to 2019. We analyzed the effects of the surrounding buildings and terrains on the meteorological observations of the ASOSs, by comparing the wind speeds, wind directions, and air temperatures simulated at the ASOSs with those of inflows. The results showed that the meteorological observation environments were quite dependent on whether there existed the obstacles and surface heating on their surfaces at the observation altitude of the ASOSs.</p>

Effects of Over Fire Air on the Combustion and NOx Emission Characteristics of a 600 MW Opposed Swirling Fired Boiler

2012 ◽  
Vol 512-515 ◽  
pp. 2135-2142 ◽  
Author(s):  
Yu Peng Wu ◽  
Zhi Yong Wen ◽  
Yue Liang Shen ◽  
Qing Yan Fang ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Empirical Method ◽  
Nox Emission ◽  
Emission Characteristics ◽  
Cfd Model ◽  
Nitrogen Release ◽  
Computational Fluid Dynamics Cfd ◽  
Low Nox Emission

A computational fluid dynamics (CFD) model of a 600 MW opposed swirling coal-fired utility boiler has been established. The chemical percolation devolatilization (CPD) model, instead of an empirical method, has been adapted to predict the nitrogen release during the devolatilization. The current CFD model has been validated by comparing the simulated results with the experimental data obtained from the boiler for case study. The validated CFD model is then applied to study the effects of ratio of over fire air (OFA) on the combustion and nitrogen oxides (NOx) emission characteristics. It is found that, with increasing the ratio of OFA, the carbon content in fly ash increases linearly, and the NOx emission reduces largely. The OFA ratio of 30% is optimal for both high burnout of pulverized coal and low NOx emission. The present study provides helpful information for understanding and optimizing the combustion of the studied boiler

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