scholarly journals An Evaluation of a Diagnostic Wind Model (CALMET)

2008 ◽  
Vol 47 (6) ◽  
pp. 1739-1756 ◽  
Author(s):  
Weiguo Wang ◽  
William J. Shaw ◽  
Timothy E. Seiple ◽  
Jeremy P. Rishel ◽  
Yulong Xie
Keyword(s):  
Wind Speed ◽  
Spatial Variability ◽  
Environmental Protection Agency ◽  
Mesoscale Model ◽  
Weather Conditions ◽  
Lake Breeze ◽  
Wind Model ◽  
Meteorological Model ◽  
Front Position ◽  
Relative Differences

Abstract A U.S. Environmental Protection Agency (EPA)-approved diagnostic wind model [California Meteorological Model (CALMET)] was evaluated during a typical lake-breeze event under fair weather conditions in the Chicago region. The authors focused on the performance of CALMET in terms of simulating winds that were highly variable in space and time. The reference winds were generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) assimilating system, with which CALMET results were compared. Statistical evaluations were conducted to quantify overall model differences in wind speed and direction over the domain. Below 850 m above the surface, relative differences in (layer averaged) wind speed were about 25%–40% during the simulation period; wind direction differences generally ranged from 6° to 20°. Above 850 m, the differences became larger because of the limited number of upper-air stations near the studied domain. Analyses implied that model differences were dependent on time because of time-dependent spatial variability in winds. Trajectory analyses were made to examine the likely spatial dependence of CALMET deviations from the reference winds within the domain. These analyses suggest that the quality of CALMET winds in local areas depended on their proximity to the lake-breeze front position. Large deviations usually occurred near the front area, where observations cannot resolve the spatial variability of wind, or in the fringe of the domain, where observations are lacking. Results simulated using different datasets and model options were also compared. Differences between CALMET and the reference winds tended to be reduced with data sampled from more stations or from more uniformly distributed stations. Suggestions are offered for further improving or interpreting CALMET results under complex wind conditions in the Chicago region, which may also apply to other regions.

scholarly journals Modeling NOx, CO, SO2 and PM emissions from Sabzevar cement plant using SCREEN3 software

2022 ◽  
Author(s):  
SS Keykhosravi ◽  
F Nejadkoorki ◽  
Amin Toosi
Keyword(s):  
Wind Speed ◽  
Environmental Protection Agency ◽  
Sampling Period ◽  
Weather Conditions ◽  
Cement Industry ◽  
Fuel Oil ◽  
Physical Factors ◽  
Factors Associated ◽  
Cement Factory ◽  
Clean Air

Introduction: Nowadays, the cement industry is regarded as one of the most important air pollution industries globally. This study aimed to simulate the emission of NOx, CO, SO2, and PM pollutants caused by the Sabzevar Cement Factory chimney by SCREEN3 software.  Materials and Methods: In this study, the SCREEN3 software was employed for the distribution of NOx, CO, SO2, and PM pollutants. The inputs of the model include the concentration and emission of pollutant gases, physical factors associated with the cement factory chimney, wind speed and direction, ambient temperature, and stability classes.  Results: The results of this study indicated that the maximum concentrations of NOx, CO, SO2, and PM by the SCREEN3 software occurred in unstable weather conditions (B) and wind speed of 5 m.s. The highest concentrations of NOx, CO, and PM (use of gas) were at a distance of 1400 meters from the factory chimney with the rates of 0.9, 0.32, 6.2 μg.m³, respectively. Moreover, the highest concentrations of NOx, CO, SO2, and PM (using fuel oil) were predicted at a distance of 1100 m from the factory chimney with 19.5, 360, 9, and 7.9 μg.m³, respectively. A comparison of the obtained results with the standard of the Environmental Protection Agency of Iran (EPA) revealed that the concentrations of NOx, CO, SO2, and PM were not higher than the standards.  Conclusion: The comparison of results with EPA standard and Iranian clean air standard showed that NOX, CO, SO2, and PM concentrations were not higher than standards during the sampling period.

scholarly journals Using MM5 to Hindcast the Ocean Surface Forcing Fields over the Gulf of Maine and Georges Bank Region*

2005 ◽  
Vol 22 (2) ◽  
pp. 131-145 ◽  
Author(s):  
Changsheng Chen ◽  
R. C. Beardsley ◽  
Song Hu ◽  
Qichun Xu ◽  
Huichan Lin
Keyword(s):  
Wind Speed ◽  
Gulf Of Maine ◽  
Mesoscale Model ◽  
Longwave Radiation ◽  
Weather Conditions ◽  
Heat Fluxes ◽  
Accurate Estimation ◽  
Georges Bank ◽  
In Situ Data

Abstract The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is applied to the Gulf of Maine/Georges Bank (GoM/GB) region. This model is configured with two numerical domains with horizontal resolutions of 30 and 10 km, respectively, and driven by the NCAR-Eta weather model through a nested grid approach. Comparison of model-computed winds, wind stress, and heat flux with in situ data collected on moored meteorological buoys in the western GoM and over GB in 1995 shows that during the passage of atmospheric fronts over this region, MM5 provides a reasonable prediction of wind speed but not wind direction, and provides a relatively accurate estimation of longwave radiation but overestimates sensible and latent fluxes. The nudging data assimilation approach with inclusion of in situ wind data significantly improves the accuracy of the predicted wind speed and direction. Incorporation of the Fairall et al. air–sea flux algorithms with inclusion of Advanced Very High Resolution Radiometer (AVHRR)-derived SST improves the accuracy of the predicted latent and sensible heat fluxes in the GoM/GB region for both stable and unstable weather conditions.

Statistical Analysis of Weather Effects on PM2.5

2012 ◽  
Vol 610-613 ◽  
pp. 1033-1040
Author(s):  
Wei Dai ◽  
Jia Qi Gao ◽  
Bo Wang ◽  
Feng Ouyang
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Relative Humidity ◽  
Statistical Methods ◽  
Predictive Analytics ◽  
Hypothesis Test ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Statistical Distribution ◽  
Weather Effects

Effects of weather conditions including temperature, relative humidity, wind speed, wind and direction on PM2.5 were studied using statistical methods. PM2.5 samples were collected during the summer and the winter in a suburb of Shenzhen. Then, correlations, hypothesis test and statistical distribution of PM2.5 and meteorological data were analyzed with IBM SPSS predictive analytics software. Seasonal and daily variations of PM2.5 have been found and these mainly resulted from the weather effects.

scholarly journals SARS-CoV-2 and the Weather: Correlation between COVID-19 and Meteorological Variables in 3 Cities in Mexico

2021 ◽  
pp. 74-91
Author(s):  
Hermes Ulises Ramirez-Sanchez ◽  
Alma Delia Ortiz-Bañuelos ◽  
Aida Lucia Fajardo-Montiel
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Ecological Study ◽  
Metropolitan Areas ◽  
Meteorological Factors ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Dew Point ◽  
Meteorological Variables ◽  
Infected Person

Meteorological factors such as temperature, humidity, atmospheric pressure, wind speed and direction are associated with the dispersion of the SARS-CoV-2 virus through aerosols, particles <5μm are suspended in the air being infective at least three hours and dispersing from eight to ten meters. It has been shown that a 10-minute conversation, an infected person produces up to 6000 aerosol particles, which remain in the air from minutes to hours, depending on the prevailing weather conditions. Objective: To establish the correlation between meteorological variables, confirmed cases and deaths from COVID-19 in the 3 most important cities of Mexico. Methodology: A retrospective ecological study was conducted to evaluate the correlation of meteorological factors with COVID-19 cases and deaths in three Mexican cities. Results: The correlations between health and meteorological variables show that in the CDMX the meteorological variables that best correlate with the health variables are Temperature (T), Dew Point (DP), Wind speed (WS), Atmospheric Pressure (AP) and Relative Humidity (RH) in that order. In the ZMG are T, WS, RH, DP and AP; and in the ZMM are RH, WS, DP, T and AP. Conclusions In the 3 Metropolitan Areas showed that the meteorological factors that best correlate with the confirmed cases and deaths from COVID-19 are the T, RH; however, the correlation coefficients are low, so their association with health variables is less than other factors such as social distancing, hand washing, use of antibacterial gel and use of masks.

scholarly journals Spatial variability and its scale dependency of observed and modeled soil moisture under different climate conditions

2012 ◽  
Vol 9 (9) ◽  
pp. 10245-10276 ◽  
Author(s):  
B. Li ◽  
M. Rodell
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Warm Season ◽  
Weather Conditions ◽  
Data Sets ◽  
Scale Dependency ◽  
Convex Shape ◽  
Climate Conditions ◽  
Spatial Moments

Abstract. Past studies on soil moisture spatial variability have been mainly conducted in catchment scales where soil moisture is often sampled over a short time period. Because of limited climate and weather conditions, the observed soil moisture often exhibited smaller dynamic ranges which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture measurements (from a continuously monitored network across the US), modeled and satellite retrieved soil moisture obtained in a warm season (198 days) were examined at large extent scales (>100 km) over three different climate regions. The investigation on in situ measurements revealed that their spatial moments strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean across dry, intermediate, and wet climates. These climate specific features were vaguely or partially observable in modeled and satellite retrieved soil moisture estimates, which is attributed to the fact that these two data sets do not have climate specific and seasonal sensitive mean soil moisture values, in addition to lack of dynamic ranges. From the point measurements to satellite retrievals, soil moisture spatial variability decreased in each climate region. The three data sources all followed the power law in the scale dependency of spatial variability, with coarser resolution data showing stronger scale dependency than finer ones. The main findings from this study are: (1) the statistical distribution of soil moisture depends on spatial mean soil moisture values and thus need to be derived locally within any given area; (2) the boundedness of soil moisture plays a pivoting role in the dependency of soil moisture spatial variability/skewness on its mean (and thus climate conditions); (3) the scale dependency of soil moisture spatial variability changes with climate conditions.

scholarly journals WRF Model Experiments on the Antarctic Atmosphere in Winter

2011 ◽  
Vol 139 (4) ◽  
pp. 1279-1291 ◽  
Author(s):  
Esa-Matti Tastula ◽  
Timo Vihma
Keyword(s):  
Boundary Layer ◽  
Surface Layer ◽  
Wind Speed ◽  
Surface Pressure ◽  
Mesoscale Model ◽  
Wrf Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Temperature Bias ◽  
Radiation Parameterizations

Abstract The standard and polar versions 3.1.1 of the Weather Research and Forecasting (WRF) model, both initialized by the 40-yr ECMWF Re-Analysis (ERA-40), were run in Antarctica for July 1998. Four different boundary layer–surface layer–radiation scheme combinations were used in the standard WRF. The model results were validated against observations of the 2-m temperature, surface pressure, and 10-m wind speed at 9 coastal and 2 inland stations. The best choice for boundary layer and radiation parameterizations of the standard WRF turned out to be the Yonsei University boundary layer scheme in conjunction with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) surface layer scheme and the Rapid Radiative Transfer Model for longwave radiation. The respective temperature bias was on the order of 3°C less than the biases obtained with the other combinations. Increasing the minimum value for eddy diffusivity did, however, improve the performance of the asymmetric convective scheme by 0.8°C. Averaged over the 11 stations, the error growths in 24-h forecasts were almost identical for the standard and Polar WRF, but in 9-day forecasts Polar WRF gave a smaller 2-m temperature bias. For the Vostok station, however, the standard WRF gave a less positively biased 24-h temperature forecast. On average, the polar version gave the least biased surface pressure simulation. The wind speed simulation was characterized by low correlation values, especially under weak winds and for stations surrounded by complex topography.

scholarly journals A Deterministic Sensor Deployment Method for Target Coverage

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ye Jiang ◽  
Shuyan Xiao ◽  
Jian Liu ◽  
Bo Chen ◽  
Bangbang Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Gas Sensors ◽  
Estimation Method ◽  
Weather Conditions ◽  
Pso Algorithm ◽  
Gas Diffusion ◽  
Optimization Criterion ◽  
Sensor Deployment ◽  
Entropy Estimation ◽  
Gas Leakage

In order to monitor the gas leakage, the gas sensors are deployed conventionally in chemical industry park, with little considerations given to the gas characteristics and weather conditions, which give rise to the problems of coverage hole and coverage repetition. To solve the problems, this paper proposes a deterministic sensor deployment method with the gas diffusion models which takes into account wind speed and direction and then studies the influence of wind speed and direction on the monitoring error of gas sensors. Then, we research the deterministic deployment method of gas sensors in condition of the main wind speed and direction somewhere. Firstly, we use the CFD theory to simulate the gas diffusion situation so as to obtain the concentration value of the relevant points. Secondly, we put forward a new optimization criterion, namely, the more alarm concentration points covered by gas sensors, the coverage performance is better, and the deployment method is better. Accordingly, a new objection function is built. Thirdly, we obtain the weight values of the function using entropy estimation method. Finally, we deploy the gas sensors determinately using particle swarm optimization (PSO) algorithm. The simulation results show that the proposed method can improve the monitoring efficiency and the coverage performance of gas sensor network.

scholarly journals Assessment of a Coastal Offshore Wind Climate by Means of Mesoscale Model Simulations Considering High-Resolution Land Use and Sea Surface Temperature Data Sets

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 379 ◽  
Author(s):  
Yuka Kikuchi ◽  
Masato Fukushima ◽  
Takeshi Ishihara
Keyword(s):  
Land Use ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mesoscale Model ◽  
Horizontal Resolution ◽  
Sea Surface ◽  
Offshore Wind ◽  
Observation Data ◽  
Wind Climate

In this study, offshore wind climate assessments are carried out by using mesoscale model Weather Research and Forecasting (WRF) and validated by measurement at a demonstration site located 3.1 km offshore of Choshi. An optimal nudging method is investigated by using offshore and meteorological observations. The land-use datasets are then created from a higher-resolution land-use data by using a maximum area sampling scheme according to the horizontal resolution of the mesoscale model. Finally, the sea surface temperature datasets are corrected by observation data. It is found that the relative error of annual wind speed is reduced from 7.3% to 2.2% and the correlation coefficient between predicted and measured wind speed is improved from 0.80 to 0.84 by considering the effects of land-use and sea surface temperature.

scholarly journals Analysis of Wind Speed Shear and Turbulence LiDAR Measurements to Support Offshore Wind in the Northeast United States

2018 ◽  
Author(s):  
Anthony Viselli ◽  
Nathan Faessler ◽  
Matthew Filippelli
Keyword(s):  
United States ◽  
Wind Speed ◽  
Wind Farm ◽  
Mesoscale Model ◽  
American Petroleum Institute ◽  
Offshore Wind ◽  
Wind Speed Profile ◽  
Northeast United States ◽  
Lidar Measurements ◽  
Wind Shears

This paper presents wind speed measurements collected at 40m to 200m above sea-level to support the New England Aqua Ventus I 12 MW Floating Offshore Wind Farm to be located 17km offshore the Northeast United States. The high-altitude wind speed data are unique and represent some of the first measurements made offshore in this part of the country which is actively being developed for offshore wind. Multiple LiDAR measurements were made using a DeepCLiDAR floating buoy and LiDARs located on land on a nearby island. The LiDARs compared favorably thereby confirming the LiDAR buoy measurements. Wind speed shear profiles are presented. The measurements are compared against industry standard mesoscale model outputs and offshore design codes including the American Bureau of Shipping, American Petroleum Institute, and DNV-GL guides. Significant variation in the vertical wind speed profile occurs throughout the year. This variation is not currently addressed in offshore wind design standards which typically recommend the use of only a few values for wind shear in operational and extreme conditions. The mean wind shears recorded were also higher than industry recommended values. Additionally, turbulence measurements made from the LiDAR, although not widely accepted in the scientific community, are presented and compared against industry guidelines.

scholarly journals Simulation of Atmospheric Circulation over Tahiti and of Local Effects on the Transport of 210Pb

2009 ◽  
Vol 137 (6) ◽  
pp. 1863-1880 ◽  
Author(s):  
P. Heinrich ◽  
X. Blanchard
Keyword(s):  
Atmospheric Circulation ◽  
General Circulation ◽  
Transport Model ◽  
Mesoscale Model ◽  
Natural Radionuclide ◽  
Circulation Model ◽  
Spatial And Temporal Variability ◽  
Local Circulation ◽  
Meteorological Model ◽  
The Impact

Abstract Atmospheric transport of the natural radionuclide 210Pb is simulated by a general circulation model (GCM) and calculated surface concentrations are compared with those recorded at the Tahiti station on a daily scale. Numerical results for 2006 show the underestimation of concentrations for most recorded peaks. The purpose of this paper is to explain the observed discrepancies, to evaluate the GCM physical parameterizations, and to determine by numerical means the concentrations at Tahiti for a pollutant circulating across the South Pacific Ocean. Three meteorological situations in 2006 are further analyzed. Circulation over Tahiti for these periods is simulated by a mesoscale meteorological model using four nested grids with resolutions ranging from 27 to 1 km. The calculated wind fields are validated by those observed at two stations on the northwest coast of Tahiti, which is exposed both to topography-induced vortices and to thermally driven local breezes. Atmospheric dispersion of an offshore plume is then calculated by a particle Lagrangian transport model, driven by the mesoscale model at 1- and 81-km resolutions, representing local and global circulations, respectively. Simulations at 1-km resolution show the complex atmospheric circulation over Tahiti, which results in a large spatial and temporal variability of 210Pb surface concentrations on an hourly scale. The impact of local circulation is, however, limited when daily averaged concentrations at the station are considered. Under the studied regimes, transport simulations at the two resolutions lead to similar daily averaged concentrations. The deficiencies of the GCM in simulating daily averaged 210Pb concentrations could be attributable to the deep convection parameterization.

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