Train Your Brain With Dr. Kawashima 2.0.3.7 For Mac

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. National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting ( WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at. National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting ( WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at.

National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting ( WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern. Hu, Jianlin; Li, Xun; Huang, Lin; Ying, Qi; Zhang, Qiang; Zhao, Bin; Wang, Shuxiao; Zhang, Hongliang 2017-11-01 Accurate exposure estimates are required for health effect analyses of severe air pollution in China.

Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting ( WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2).

Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are -0.11 and 0.24, respectively, which are better than the MFB (-0.25 to -0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and.

J. Hu 2017-11-01 Full Text Available Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ model with meteorological inputs from the Weather Research and Forecasting ( WRF model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC, the Emission Inventory for China by School of Environment at Tsinghua University (SOE, the Emissions Database for Global Atmospheric Research (EDGAR, and the Regional Emission inventory in Asia version 2 (REAS2.

Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities.

The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB and mean fractional errors (MFEs of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16 and MFE (0.26–0.31 of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h concentrations are also improved, with mean normalized bias (MNB of 0.03 and mean normalized errors (MNE of 0.14, compared to MNB.

Vincent, Claire Louise; Liu, Yubao 2010-05-01 The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al.

Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction.

It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the. Giannaros, Christos; Nenes, Athanasios; Giannaros, Theodore M.; Kourtidis, Konstantinos; Melas, Dimitrios 2018-03-01 This study presents a comprehensive modeling approach for simulating the spatiotemporal distribution of urban air temperatures with a modeling system that includes the Weather Research and Forecasting ( WRF) model and the Single-Layer Urban Canopy Model (SLUCM) with a modified treatment of the impervious surface temperature. The model was applied to simulate a 3-day summer heat wave event over the city of Athens, Greece.

The simulation, using default SLUCM parameters, is capable of capturing the observed diurnal variation of urban temperatures and the Urban Heat Island (UHI) in the greater Athens Area (GAA), albeit with systematic biases that are prominent during nighttime hours. These biases are particularly evident over low-intensity residential areas, and they are associated with the surface and urban canopy properties representing the urban environment. A series of sensitivity simulations unravels the importance of the sub-grid urban fraction parameter, surface albedo, and street canyon geometry in the overall causation and development of the UHI effect. The sensitivities are then used to determine optimal values of the street canyon geometry, which reproduces the observed temperatures throughout the simulation domain. The optimal parameters, apart from considerably improving model performance (reductions in mean temperature bias from 0.30 °C to 1.58 °C), are also consistent with actual city building characteristics - which gives confidence that the model set-up is robust, and can be used to study the UHI in the GAA in the anticipated warmer conditions in the future. J.; Zavodsky, B.; Molthan, A. 2017-12-01 The operational National Water Model (NWM), implemented in August 2016, is an instantiation of the Weather Research and Forecasting hydrological extension package ( WRF-Hydro).

Currently, the NWM only covers the contiguous United States, but will be expanded to include an Alaska domain in the future. It is well known that Alaska presents several hydrological modeling challenges, including unique arctic/sub-arctic hydrological processes not observed elsewhere in the United States and a severe lack of in-situ observations for model initialization.

This project sets up an experimental version of WRF-Hydro in Alaska mimicking the NWM to gauge the ability of WRF-Hydro to represent hydrological processes in Alaska and identify model calibration challenges. Recent and upcoming launches of hydrology-focused NASA satellite missions such as the Soil Moisture Active Passive (SMAP) and Surface Water Ocean Topography (SWOT) expand the spatial and temporal coverage of observations in Alaska, so this study also lays the groundwork for assimilating these NASA datasets into WRF-Hydro in the future. Coen; Marques Cameron; John Michalakes; Edward G.

Patton; Philip J. Riggan; Kara M. Yedinak 2012-01-01 A wildland fire behavior module ( WRF-Fire) was integrated into the Weather Research and Forecasting ( WRF) public domain numerical weather prediction model.

The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties. Kumar, Sujay V.; Peters-Lidard, Christa D.; Eastman, Joseph L.; Tao, Wei-Kuo 2007-01-01 Scientists have made great strides in modeling physical processes that represent various weather and climate phenomena. Many modeling systems that represent the major earth system components (the atmosphere, land surface, and ocean) have been developed over the years. However, developing advanced Earth system applications that integrates these independently developed modeling systems have remained a daunting task due to limitations in computer hardware and software.

Recently, efforts such as the Earth System Modeling Ramework (ESMF) and Assistance for Land Modeling Activities (ALMA) have focused on developing standards, guidelines, and computational support for coupling earth system model components. In this article, the development of a coupled land-atmosphere hydrometeorological modeling system that adopts these community interoperability standards, is described. The land component is represented by the Land Information System (LIS), developed by scientists at the NASA Goddard Space Flight Center. The Weather Research and Forecasting ( WRF) model, a mesoscale numerical weather prediction system, is used as the atmospheric component. LIS includes several community land surface models that can be executed at spatial scales as fine as 1km.

The data management capabilities in LIS enable the direct use of high resolution satellite and observation data for modeling. Similarly, WRF includes several parameterizations and schemes for modeling radiation, microphysics, PBL and other processes. Thus the integrated LIS- WRF system facilitates several multi- model studies of land-atmosphere coupling that can be used to advance earth system studies.

Cameron-Smith; R. Weiss 2013-01-01 This paper presents a step in the development of a top-down method to complement the bottom-up inventories of halocarbon emissions in California using high frequency observations, forward simulations and inverse methods.

The Scripps Institution of Oceanography high-frequency atmospheric halocarbons measurement sites are located along the California coast and therefore the evaluation of transport in the chosen Weather Research Forecast ( WRF) model at these sites is crucial fo. Cameron-Smith; R. Weiss 2012-01-01 This paper presents a step in the development of a top-down method to complement the bottom-up inventories of halocarbon emissions in California using high frequency observations, forward simulations and inverse methods. The Scripps Institution of Oceanography high-frequency atmospheric halocarbon measurement sites are located along the California coast and therefore the evaluation of transport in the chosen Weather Research Forecast ( WRF) model at these sites is crucial for inverse mo. Zheng 2018-05-01 Full Text Available Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources. In this paper, an adjoint-based four-dimensional variational (4D-Var assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach.

This system is developed based on the Weather Research and Forecasting ( WRF modeling system, including the system coupled to chemistry ( WRF-Chem, with tangent linear and adjoint codes (WRFPLUS, and with data assimilation (WRFDA, all in version 3.6. In WRF-CO2 4D-Var, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4D-Var solves for the optimized CO2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden–Fletcher–Goldfarb–Shanno (BFGS minimization algorithm (L-BFGS-B and the second uses the Lanczos conjugate gradient (CG in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO2.

The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models is assessed by comparing against finite difference sensitivity. The system's effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO2 inversions. Zheng, Tao; French, Nancy H. F.; Baxter, Martin 2018-05-01 Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources.

In this paper, an adjoint-based four-dimensional variational (4D-Var) assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach. This system is developed based on the Weather Research and Forecasting ( WRF) modeling system, including the system coupled to chemistry ( WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with data assimilation (WRFDA), all in version 3.6.

In WRF-CO2 4D-Var, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4D-Var solves for the optimized CO2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO2. The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models is assessed by comparing against finite difference sensitivity.

The system's effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO2 inversions. Advances in the land surface model (LSM) and planetary boundary layer (PBL) components of the WRF-CMAQ coupled meteorology and air quality modeling system are described.

The aim of these modifications was primarily to improve the modeling of ground level concentrations of trace c. Otte, T.; Bowden, J. H.; Nolte, C. J.; Herwehe, J. A.; Faluvegi, G.; Shindell, D.

2010-12-01 The WRF Model is being used at the U.S. EPA for dynamical downscaling of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The WRF model has been successfully linked to the ModelE fields in their raw hybrid vertical coordinate, and continuous, multi-year WRF downscaling simulations have been performed. WRF will be used to downscale decadal time slices of ModelE for recent past, current, and future climate as the simulations being conducted for the IPCC Fifth Assessment Report become available.

This presentation will focus on the sensitivity to interior nudging within the RCM. The use of interior nudging for downscaled regional climate simulations has been somewhat controversial over the past several years but has been recently attracting attention.

Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that interior nudging can be beneficial toward achieving the desired downscaled fields. In this study, the value of nudging will be shown using fields from ModelE that are downscaled using WRF. Several different methods of nudging are explored, and it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields. Litta 2012-01-01 Full Text Available The thunderstorms are typical mesoscale systems dominated by intense convection. Mesoscale models are essential for the accurate prediction of such high-impact weather events. In the present study, an attempt has been made to compare the simulated results of three thunderstorm events using NMM and ARW model core of WRF system and validated the model results with observations. Both models performed well in capturing stability indices which are indicators of severe convective activity.

Comparison of model-simulated radar reflectivity imageries with observations revealed that NMM model has simulated well the propagation of the squall line, while the squall line movement was slow in ARW. From the model-simulated spatial plots of cloud top temperature, we can see that NMM model has better captured the genesis, intensification, and propagation of thunder squall than ARW model. The statistical analysis of rainfall indicates the better performance of NMM than ARW.

Comparison of model-simulated thunderstorm affected parameters with that of the observed showed that NMM has performed better than ARW in capturing the sharp rise in humidity and drop in temperature. This suggests that NMM model has the potential to provide unique and valuable information for severe thunderstorm forecasters over east Indian region. Sarmiento 2017-05-01 Full Text Available As part of the Indianapolis Flux (INFLUX experiment, the accuracy and biases of simulated meteorological fields were assessed for the city of Indianapolis, IN. The INFLUX project allows for a unique opportunity to conduct an extensive observation-to- model comparison in order to assess model errors for the following meteorological variables: latent heat and sensible heat fluxes, air temperature near the surface and in the planetary boundary layer (PBL, wind speed and direction, and PBL height. In order to test the sensitivity of meteorological simulations to different model packages, a set of simulations was performed by implementing different PBL schemes, urban canopy models (UCMs, and a model subroutine that was created in order to reduce an inherent model overestimation of urban land cover.

It was found that accurately representing the amount of urban cover in the simulations reduced the biases in most cases during the summertime (SUMMER simulations. The simulations that used the BEP urban canopy model and the Bougeault & Lacarrere (BouLac PBL scheme had the smallest biases in the wintertime (WINTER simulations for most meteorological variables, with the exception being wind direction. The model configuration chosen had a larger impact on model errors during the WINTER simulations, whereas the differences between most of the model configurations during the SUMMER simulations were not statistically significant. By learning the behaviors of different PBL schemes and urban canopy models, researchers can start to understand the expected biases in certain model configurations for their own simulations and have a hypothesis as to the potential errors and biases that might occur when using a multi-physics ensemble based modeling approach. 2015-02-01 Computational and Information Sciences Directorate Battlefield Environment Division (ATTN: RDRL- CIE -M) White Sands Missile Range, NM 8. PERFORMING.meteorological parameters, which became our focus. We found that elevation accounts for a significant portion of the variance in the model error.

Train Your Brain With Dr. Kawashima 2.0.3.7 For Mac Os X

The.found that elevation accounts for a significant portion of the variance in the model error of surface temperature and relative humidity predictions. Mohebbi, A.; Chang, H. I.; Hondula, D. 2017-12-01 The online Weather Research and Forecasting model with coupled chemistry module ( WRF-Chem) is applied to simulate the transport, deposition and emission of the dust aerosols in an intense dust outbreak event that took place on July 5th, 2011 over Arizona.

Train Your Brain With Dr. Kawashima 2.0.3.7 For Mac

Goddard Chemistry Aerosol Radiation and Transport (GOCART), Air Force Weather Agency (AFWA), and University of Cologne (UoC) parameterization schemes for dust emission were evaluated. The model was found to simulate well the synoptic meteorological conditions also widely documented in previous studies. The chemistry module performance in reproducing the atmospheric desert dust load was evaluated using the horizontal field of the Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectro (MODIS) radiometer Terra/Aqua and Aerosol Robotic Network (AERONET) satellites employing standard Dark Target (DT) and Deep Blue (DB) algorithms. To assess the temporal variability of the dust storm, Particulate Matter mass concentration data (PM10 and PM2.5) from Arizona Department of Environmental Quality (AZDEQ) ground-based air quality stations were used. The promising performance of WRF-Chem indicate that the model is capable of simulating the right timing and loading of a dust event in the planetary-boundary-layer (PBL) which can be used to forecast approaching severe dust events and to communicate an effective early warning.

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