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1
Mandel, J., J. D. Beezley, and A. K. Kochanski. "Coupled atmosphere-wildland fire modeling with WRF-Fire version 3.3." Geoscientific Model Development Discussions 4, no. 1 (March 9, 2011): 497–545. http://dx.doi.org/10.5194/gmdd-4-497-2011.
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Abstract. We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various kinds of ignition. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.
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Wang, Jian, Weimin Bao, Qianyu Gao, Wei Si, and Yiqun Sun. "Coupling the Xinanjiang model and wavelet-based random forests method for improved daily streamflow simulation." Journal of Hydroinformatics 23, no. 3 (March 22, 2021): 589–604. http://dx.doi.org/10.2166/hydro.2021.111.
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Abstract Daily streamflow modeling is an important tool for water resources management and flood mitigation. This study compared the performance of the Xinanjiang (XAJ) model and random forests (RF) method in a daily streamflow simulation, and proposed several hybrid models based on the XAJ model, wavelet analysis, and RF method (including XAJ-RF model, WRF model, and XAJ-WRF model). The proposed methods were applied to Shiquan station, located in the Upper Han River basin in China. Five performance measures (NSE, RMSE, PBIAS, MAE, and R) were adopted to evaluate the modeling accuracy. Results showed that XAJ-RF model had a relatively higher level of accuracy than that of the XAJ model and the RF model. Compared to the RF and XAJ-RF models, the performance statistics of WRF and XAJ-WRF were better. The results indicated that the coupled XAJ-RF model can be effectively applied and provide a useful alternative for daily streamflow modeling and the application of wavelet analysis contributed to the increasing accuracy of streamflow modeling. Moreover, 14 wavelet functions from various families were tested to analyze the impact of various mother wavelets on the XAJ-WRF model.
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Pleim, Jonathan E. "Comment on “Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics”." Advances in Meteorology 2011 (2011): 1–3. http://dx.doi.org/10.1155/2011/464753.
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A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most serious of the study's deficiencies is reporting WRF/Chem results for both meteorological and chemical quantities using the asymmetric convective model version 2 (ACM2). While the ACM2 is a valid model option for WRF, it has not yet been implemented for the chemical portion of the WRF/Chem model. Hence, the reported air quality modeling results using ACM2 are invalid. Furthermore, publication of these results gives the erroneous impression that the ACM2 model is not well suited for air quality applications when, in fact, it is the default boundary layer model in the community multiscale air quality (CMAQ) model.
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Hammerberg, Kristopher, Milena Vuckovic, and Ardeshir Mahdavi. "Approaches to Urban Weather Modeling: A Vienna Case Study." Applied Mechanics and Materials 887 (January 2019): 344–52. http://dx.doi.org/10.4028/www.scientific.net/amm.887.344.
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Given the adverse implications of both urbanization and global climate change for cities, specifically regarding issues such as human health and comfort, local air quality, and increased summertime energy use in buildings, it is becoming imperative to develop models that can accurately predict the complex and nonlinear interactions between the surrounding urban fabric and local climatic context. Over the past years, a number of comprehensive tools have been widely applied for the generation of near-surface urban climatic information. In this paper, we report on the potential of two alternative approaches to urban climate modeling. Specifically, we compare the climatic output generated with Urban Weather Generator (UWG) and the Weather Research and Forecasting (WRF) model. The WRF model has been widely applied due to its capability of downscaling global weather data to finer resolutions, thus representing the location-specific microclimatic information, while considering the interactions with the surrounding urban and regional context. However, this approach is computationally intensive. The UWG was recently introduced as a simpler alternative to such complex models. The tool morphs rural weather data to represent urban conditions given a set of location-specific morphological parameters. In the present paper, WRF and UWG methods were compared based on empirical data pertaining to air temperature, wind speed, and humidity, collected from 12 locations in the city of Vienna, Austria, over 5 distinct time periods. In general, our results suggest that, as compared to the WRF model, the UWG model results are closer to monitored data. However, during the extreme conditions in summer, the WRF model was found to perform better. It was further noted that the discrepancy between the two models increases with decreasing temperatures, thus revealing a higher offset between UWG and WRF output during the winter period.
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Kochanski, A. K., E. R. Pardyjak, R. Stoll, A. Gowardhan, M. J. Brown, and W. J. Steenburgh. "One-Way Coupling of the WRF–QUIC Urban Dispersion Modeling System." Journal of Applied Meteorology and Climatology 54, no. 10 (October 2015): 2119–39. http://dx.doi.org/10.1175/jamc-d-15-0020.1.
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AbstractSimulations of local weather and air quality in urban areas must account for processes spanning from meso- to microscales, including turbulence and transport within the urban canopy layer. Here, the authors investigate the performance of the building-resolving Quick Urban Industrial Complex (QUIC) Dispersion Modeling System driven with mean wind profiles from the mesoscale Weather Research and Forecasting (WRF) Model. Dispersion simulations are performed for intensive observation periods 2 and 8 of the Joint Urban 2003 field experiment conducted in Oklahoma City, Oklahoma, using an ensemble of expert-derived wind profiles from observational data as well as profiles derived from WRF runs. The results suggest that WRF can be used successfully as a source of inflow boundary conditions for urban simulations, without the collection and processing of intensive field observations needed to produce expert-derived wind profiles. Detailed statistical analysis of tracer concentration fields suggests that, for the purpose of the urban dispersion, WRF simulations provide wind forcing as good as individual or ensemble expert-derived profiles. Despite problems capturing the strength and the elevation of the Great Plains low-level jet, the WRF-simulated near-surface wind speed and direction were close to observations, thus assuring realistic forcing for urban dispersion estimates. Tests performed with multilayer and bulk urban parameterizations embedded in WRF did not provide any conclusive evidence of the superiority of one scheme over the other, although the dispersion simulations driven by the latter showed slightly better results.
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Mandel, J., J. D. Beezley, and A. K. Kochanski. "Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011." Geoscientific Model Development 4, no. 3 (July 7, 2011): 591–610. http://dx.doi.org/10.5194/gmd-4-591-2011.
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Abstract. We describe the physical model, numerical algorithms, and software structure of a model consisting of the Weather Research and Forecasting (WRF) model, coupled with the fire-spread model (SFIRE) module. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. SFIRE is implemented by the level set method, which allows a submesh representation of the burning region and a flexible implementation of various kinds of ignition. The coupled model is capable of running on a cluster faster than real time even with fine resolution in dekameters. It is available as a part of the Open Wildland Fire Modeling (OpenWFM) environment at http://openwfm.org, which contains also utilities for visualization, diagnostics, and data processing, including an extended version of the WRF Preprocessing System (WPS). The SFIRE code with a subset of the features is distributed with WRF 3.3 as WRF-Fire.
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Wang, Wei, Jia Liu, Chuanzhe Li, Yuchen Liu, Fuliang Yu, and Entao Yu. "An Evaluation Study of the Fully Coupled WRF/WRF-Hydro Modeling System for Simulation of Storm Events with Different Rainfall Evenness in Space and Time." Water 12, no. 4 (April 24, 2020): 1209. http://dx.doi.org/10.3390/w12041209.
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With the aim of improving the understanding of water exchanges in medium-scale catchments of northern China, the spatiotemporal characteristics of rainfall and several key water cycle elements e.g., soil moisture, evapotranspiration and generated runoff, were investigated using a fully coupled atmospheric-hydrologic modeling system by integrating the Weather Research and Forecasting model (WRF) and its terrestrial hydrologic component WRF-Hydro (referred to as the fully coupled WRF/WRF-Hydro). The stand-alone WRF model (referred to as WRF-only) is also used as a comparison with the fully coupled system, which was expected to produce more realistic simulations, especially rainfall, by allowing the redistribution of surface and subsurface water across the land surface. Six storm events were sorted by different spatial and temporal distribution types, and categorical and continuous indices were used to distinguish the applicability in space and time between WRF-only and the fully coupled WRF/WRF-Hydro. The temporal indices showed that the coupled WRF-Hydro could improve the time homogeneous precipitation, but for the time inhomogeneous precipitation, it might produce a larger false alarm than WRF-only, especially for the flash storm that occurred in July, 2012. The spatial indices showed a lower mean bias error in the coupled system, and presented an enhanced simulation of both space homogeneous and inhomogeneous storm events than WRF-only. In comparison with WRF-only, the fully coupled WRF/WRF-Hydro had a closer to the observations particularly in and around the storm centers. The redistributions fluctuation of spatial precipitation in the fully coupled system was highly correlated with soil moisture, and a low initial soil moisture could lead to a large spatial fluctuated range. Generally, the fully coupled system produced slightly less runoff than WRF-only, but more frequent infiltration and larger soil moisture. While terrestrial hydrologic elements differed with relatively small amounts in the average of the two catchments between WRF-only and the fully coupled WRF/WRF-Hydro, the spatial distribution of elements in the water cycle before and after coupling with WRF-Hydro was not consistent. The soil moisture, runoff and precipitation in the fully coupled system had a similar spatial trend, but evapotranspiration did not always display the same.
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Eidhammer, Trude, Adam Booth, Sven Decker, Lu Li, Michael Barlage, David Gochis, Roy Rasmussen, Kjetil Melvold, Atle Nesje, and Stefan Sobolowski. "Mass balance and hydrological modeling of the Hardangerjøkulen ice cap in south-central Norway." Hydrology and Earth System Sciences 25, no. 8 (August 3, 2021): 4275–97. http://dx.doi.org/10.5194/hess-25-4275-2021.
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Abstract. A detailed, physically based, one dimensional column snowpack model (Crocus) has been incorporated into the hydrological model, Weather Research and Forecasting (WRF)-Hydro, to allow for direct surface mass balance simulation of glaciers and subsequent modeling of meltwater discharge from glaciers. The new system (WRF-Hydro/Glacier) is only activated over a priori designated glacier areas. This glacier area is initialized with observed glacier thickness and assumed to be pure ice (with corresponding ice density). This allows for melting of the glacier to continue after all accumulated snow has melted. Furthermore, the simulation of surface albedo over the glacier is more realistic, as surface albedo is represented by snow, where there is accumulated snow, and glacier ice, when all accumulated snow is melted. To evaluate the WRF-Hydro/Glacier system over a glacier in southern Norway, WRF atmospheric model simulations were downscaled to 1 km grid spacing. This provided meteorological forcing data to the WRF-Hydro/Glacier system at 100 m grid spacing for surface and streamflow simulation. Evaluation of the WRF downscaling showed a good comparison with in situ meteorological observations for most of the simulation period. The WRF-Hydro/Glacier system reproduced the glacier surface winter/summer and net mass balance, snow depth, surface albedo and glacier runoff well compared to observations. The improved estimation of albedo has an appreciable impact on the discharge from the glacier during frequent precipitation periods. We have shown that the integrated snowpack system allows for improved glacier surface mass balance studies and hydrological studies.
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Givati, Amir, Barry Lynn, Yubao Liu, and Alon Rimmer. "Using the WRF Model in an Operational Streamflow Forecast System for the Jordan River." Journal of Applied Meteorology and Climatology 51, no. 2 (February 2011): 285–99. http://dx.doi.org/10.1175/jamc-d-11-082.1.
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AbstractThe Weather Research and Forecasting (WRF) model was employed to provide precipitation forecasts during the 2008/09 and 2009/10 winters (wet season) for Israel and the surrounding region where complex terrain dominates. The WRF precipitation prediction has been coupled with the Hydrological Model for Karst Environment (HYMKE) to forecast the upper Jordan River streamflow. The daily WRF precipitation forecasts were verified against the measurements from a dense network of rain gauges in northern and central Israel, and the simulation results using the high-resolution WRF indicated good agreement with the actual measurements. The daily precipitation amount calculated by WRF at rain gauges located in the upper parts of the Jordan River basin showed good agreement with the actual measurements. Numerical experiments were carried out to test the impact of the WRF model resolution and WRF microphysical schemes, to determine an optimal model configuration for this application. Because of orographic forcing in the region, it is necessary to run WRF with a 4–1.3-km grid increment and with sophisticated microphysical schemes that consider liquid water, ice, snow, and graupel to produce quality precipitation predictions. The hydrological modeling system that ingests the high-resolution WRF forecast precipitation produced good results and improved upon the operational streamflow forecast method for the Jordan River that is now in use. The modeling tools presented in this study are used to support the water-resource-assessment process and studies of seasonal hydroclimatic forecasting in this region.
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Liu, Zheng, Axel Schweiger, and Ron Lindsay. "Observations and Modeling of Atmospheric Profiles in the Arctic Seasonal Ice Zone." Monthly Weather Review 143, no. 1 (January 1, 2015): 39–53. http://dx.doi.org/10.1175/mwr-d-14-00118.1.
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Abstract The authors use the Polar Weather Research and Forecasting (WRF) Model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) in the summer of 2013 over the Beaufort Sea. With the SIZRS dropsonde data, the performance of WRF simulations and two forcing datasets is evaluated: the Interim ECMWF Re-Analysis (ERA-Interim) and the Global Forecast System (GFS) analysis. General features of observed mean profiles, such as low-level temperature inversion, low-level jet (LLJ), and specific humidity inversion are reproduced by all three models. A near-surface warm bias and a low-level moist bias are found in ERA-Interim. WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing. The improvement in the mean LLJ is likely related to the lower values of the boundary layer diffusion in WRF than in ERA-Interim and GFS, which also explains the lower near-surface temperature in WRF than the forcing. The relative humidity profiles have large differences between the observations, the ERA-Interim, and the GFS. The WRF simulated relative humidity closely resembles the forcings, suggesting the need to obtain more and better-calibrated humidity data in this region. The authors find that the sea ice concentrations in the ECMWF model are sometimes significantly underestimated due to an inappropriate thresholding mechanism. This thresholding affects both ERA-Interim and the ECMWF operational model. The scale of impact of this issue on the atmospheric boundary layer in the marginal ice zone is still unknown.
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Lin, Haipeng, Xu Feng, Tzung-May Fu, Heng Tian, Yaping Ma, Lijuan Zhang, Daniel J. Jacob, et al. "WRF-GC (v1.0): online coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.2.1) for regional atmospheric chemistry modeling – Part 1: Description of the one-way model." Geoscientific Model Development 13, no. 7 (July 16, 2020): 3241–65. http://dx.doi.org/10.5194/gmd-13-3241-2020.
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Abstract. We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. WRF and GEOS-Chem are both open-source community models. WRF-GC offers regional modellers access to the latest GEOS-Chem chemical module, which is state of the science, well documented, traceable, benchmarked, actively developed by a large international user base, and centrally managed by a dedicated support team. At the same time, WRF-GC enables GEOS-Chem users to perform high-resolution forecasts and hindcasts for any region and time of interest. WRF-GC uses unmodified copies of WRF and GEOS-Chem from their respective sources; the coupling structure allows future versions of either one of the two parent models to be integrated into WRF-GC with relative ease. Within WRF-GC, the physical and chemical state variables are managed in distributed memory and translated between WRF and GEOS-Chem by the WRF-GC coupler at runtime. We used the WRF-GC model to simulate surface PM2.5 concentrations over China during 22 to 27 January 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem Classic nested-China simulation. Both models were able to reproduce the observed spatiotemporal variations of regional PM2.5, but the WRF-GC model (r=0.68, bias =29 %) reproduced the observed daily PM2.5 concentrations over eastern China better than the GEOS-Chem Classic model did (r=0.72, bias =55 %). This was because the WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the pollution meteorology during the study period. The WRF-GC model is parallelized across computational cores and scales well on massively parallel architectures. In our tests where the two models were similarly configured, the WRF-GC simulation was 3 times more efficient than the GEOS-Chem Classic nested-grid simulation due to the efficient transport algorithm and the Message Passing Interface (MPI)-based parallelization provided by the WRF software framework. WRF-GC v1.0 supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no chemical feedbacks. The development of two-way coupling capabilities, i.e., the ability to simulate radiative and microphysical feedbacks of chemistry to meteorology, is under way. The WRF-GC model is open source and freely available from http://wrf.geos-chem.org (last access: 10 July 2020).
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Sharma, Ashish, Harindra J. S. Fernando, Alan F. Hamlet, Jessica J. Hellmann, Michael Barlage, and Fei Chen. "Urban meteorological modeling using WRF: a sensitivity study." International Journal of Climatology 37, no. 4 (July 12, 2016): 1885–900. http://dx.doi.org/10.1002/joc.4819.
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Feng, Xu, Haipeng Lin, Tzung-May Fu, Melissa P. Sulprizio, Jiawei Zhuang, Daniel J. Jacob, Heng Tian, et al. "WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions." Geoscientific Model Development 14, no. 6 (June 23, 2021): 3741–68. http://dx.doi.org/10.5194/gmd-14-3741-2021.
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Abstract. We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. In WRF-GC, meteorological and chemical calculations are performed on the exact same 3-D grid system; grid-scale advection of meteorological variables and chemical species uses the same transport scheme and time steps to ensure mass conservation. Prescribed size distributions are applied to the aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. WRF-GC is computationally efficient and scalable to massively parallel architectures. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016. Our sensitivity simulations show that including ARI and ACI improves the model's performance in simulating regional meteorology and air quality. WRF-GC generally reproduces the magnitudes and spatial variability of observed aerosol and cloud properties and surface meteorological variables over East Asia during January 2015 and July 2016, although WRF-GC consistently shows a low bias against observed aerosol optical depths over China. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations of PM2.5 in January 2015 (normalized mean bias of −9.3 %, spatial correlation r of 0.77) and afternoon ozone in July 2016 (normalized mean bias of 25.6 %, spatial correlation r of 0.56) over eastern China. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org (last access: 20 June 2021).
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Noble, Stephen, Brian Viner, Robert Buckley, and Steven Chiswell. "Skill of Mesoscale Models in Forecasting Springtime Macrophysical Cloud Properties at the Savannah River Site in the Southeastern US." Atmosphere 11, no. 11 (November 6, 2020): 1202. http://dx.doi.org/10.3390/atmos11111202.
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Predicting boundary layer clouds is important for the accurate modeling of pollutant dispersion. Higher resolution mesoscale models would be expected to produce better forecasts of cloud properties that affect dispersion. Using ceilometer observations, we assess the skill of two operational mesoscale models (RAMS and WRF) to forecast cloud base altitude and cloud fraction at the Savannah River Site in the southeastern US during the springtime. Verifications were performed at small spatial and temporal scales necessary for dispersion modeling. Both models were unreliable with a 50% (RAMS) and a 46% (WRF) rate of predicting clouds observed by the ceilometer which led to low cloud fraction predictions. Results indicated that WRF better predicted daytime cloud bases from convection that occurred frequently later in the period and RAMS better predicted nighttime cloud bases. Using root mean squared error (RMSE) to score the forecast periods also highlighted this diurnal dichotomy, with WRF scores better during the day and RAMS scores better at night. Analysis of forecast errors revealed divergent model cloud base biases—WRF low and RAMS high. A hybrid solution which weighs more heavily the RAMS nighttime forecasts and WRF daytime forecasts will likely provide the best prediction of cloud properties for dispersion.
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Lin, Ning, James A. Smith, Gabriele Villarini, Timothy P. Marchok, and Mary Lynn Baeck. "Modeling Extreme Rainfall, Winds, and Surge from Hurricane Isabel (2003)." Weather and Forecasting 25, no. 5 (October 1, 2010): 1342–61. http://dx.doi.org/10.1175/2010waf2222349.1.
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Abstract Landfalling tropical cyclones present major hazards for the eastern United States. Hurricane Isabel (September 2003) produced more than $3.3 billion in damages from wind, inland riverine flooding, and storm surge flooding, and resulted in 17 fatalities. Case study analyses of Hurricane Isabel are carried out to investigate multiple hazards from landfalling tropical cyclones. The analyses focus on storm evolution following landfall and center on simulations using the Weather Research and Forecasting Model (WRF). WRF simulations are coupled with the 2D, depth-averaged hydrodynamic Advanced Circulation Model (ADCIRC), to examine storm surge in the Chesapeake Bay. Analyses of heavy rainfall and flooding include an examination of the structure and evolution of extreme rainfall over land. Intercomparisons of simulated rainfall from WRF with Hydro-NEXRAD rainfall fields and observations from rain gauge networks are presented. A particular focus of these analyses is the evolving distribution of rainfall, relative to the center of circulation, as the storm moves over land. Similar analyses are carried out for the wind field of Hurricane Isabel as it moves over the mid-Atlantic region. Outer rainbands, which are not well captured in WRF simulations, played a major role in urban flooding and wind damage, especially for the Baltimore metropolitan region. Wind maxima in outer rainbands may also have played a role in storm surge flooding in the upper Chesapeake Bay.
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Zheng, Tao, Nancy H. F. French, and Martin Baxter. "Development of the WRF-CO2 4D-Var assimilation system v1.0." Geoscientific Model Development 11, no. 5 (May 4, 2018): 1725–52. http://dx.doi.org/10.5194/gmd-11-1725-2018.
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Abstract. 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.
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Li, Dan, Elie Bou-Zeid, Mary Lynn Baeck, Stephen Jessup, and James A. Smith. "Modeling Land Surface Processes and Heavy Rainfall in Urban Environments: Sensitivity to Urban Surface Representations." Journal of Hydrometeorology 14, no. 4 (August 1, 2013): 1098–118. http://dx.doi.org/10.1175/jhm-d-12-0154.1.
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Abstract High-resolution simulations with the Weather Research and Forecasting Model (WRF) are used in conjunction with observational analyses to investigate land surface processes and heavy rainfall over the Baltimore–Washington metropolitan area. Analyses focus on a 6-day period, 21–26 July 2008, which includes a major convective rain event (23–24 July), a prestorm period (21–22 July), and a dry-down period (25–26 July). The performance of WRF in capturing land–atmosphere interactions, the bulk structure of the atmospheric boundary layer, and the rainfall pattern in urban environments is explored. Results indicate that WRF captures the incoming radiative fluxes and surface meteorological conditions. Mean profiles of potential temperature and humidity in the atmosphere are also relatively well reproduced, both preceding and following the heavy rainfall period. However, wind features in the lower atmosphere, including low-level jets, are not accurately reproduced by WRF. The biases in the wind fields play a central role in determining errors in WRF-simulated rainfall fields. The study also investigates the sensitivity of WRF simulations to different urban surface representations. It is found that urban surface representations have a significant impact on the surface energy balance and the rainfall distribution. As the impervious fraction increases, the sensible heat flux and the ground heat flux increase, while the latent heat flux decreases. The impact of urban surface representations on precipitation is as significant as that of microphysical parameterizations. The fact that changing urban surface representations can significantly alter the rainfall field suggests that urbanization plays an important role in modifying the regional precipitation pattern.
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Lahmers, Timothy M., Christopher L. Castro, and Pieter Hazenberg. "Effects of Lateral Flow on the Convective Environment in a Coupled Hydrometeorological Modeling System in a Semiarid Environment." Journal of Hydrometeorology 21, no. 4 (April 2020): 615–42. http://dx.doi.org/10.1175/jhm-d-19-0100.1.
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AbstractEvidence for surface and atmosphere coupling is corroborated in both modeling and observation-based field experiments. Recent advances in high-performance computing and development of convection-permitting regional-scale atmospheric models combined with high-resolution hydrologic models have made modeling of surface–atmosphere interactions feasible for the scientific community. These hydrological models can account for the impacts of the overland flow and subsurface flow components of the hydrologic cycle and account for the impact of lateral flow on moisture redistribution at the land surface. One such model is the Weather Research and Forecasting (WRF) regional atmospheric model that can be coupled to the WRF-Hydro hydrologic model. In the present study, both the uncoupled WRF (WRF-ARW) and otherwise identical WRF-Hydro model are executed for the 2017 and 2018 summertime North American monsoon (NAM) seasons in semiarid central Arizona. In this environment, diurnal convection is impacted by precipitation recycling from the land surface. The goal of this work is to evaluate the impacts that surface runoff and shallow subsurface flow, as depicted in WRF-Hydro, have on surface–atmosphere interactions and convection in a coupled atmospheric simulation. The current work assesses the impact of surface hydrologic processes on 1) local surface energy budgets during the NAM throughout Arizona and 2) the spectral behavior of diurnally driven NAM convection. Model results suggest that adding surface and subsurface flow from WRF-Hydro increases soil moisture and latent heat near the surface. This increases the amount of instability and moisture available for deep convection in the model simulations and enhances the organization of convection at the peak of the diurnal cycle.
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Li, Zhihui, Xiangzheng Deng, Qingling Shi, Xinli Ke, and Yingcheng Liu. "Modeling the Impacts of Boreal Deforestation on the Near-Surface Temperature in European Russia." Advances in Meteorology 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/486962.
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Boreal deforestation plays an important role in affecting regional and global climate. In this study, the regional temperature variation induced by future boreal deforestation in European Russia boreal forest region was simulated based on future land cover change and the Weather Research and Forecasting (WRF) model. This study firstly tested and validated the simulation results of the WRF model. Then the land cover datasets in different years (2000 as baseline year, 2010, and 2100) was used in the WRF model to explore the impacts of boreal deforestation on the near-surface temperature. The results indicated that the WRF model has good ability to simulate the temperature change in European Russia. The land cover change in European Russia boreal forest region, which will be characterized by the conversion from boreal forests to croplands (boreal deforestation) in the future 100 years, will lead to significant change of the near-surface temperature. The regional annual temperature will decrease by 0.58°C in the future 100 years, resulting in cooling effects to some extent and making the near-surface temperature decrease in most seasons except the spring.
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Gilliam, Robert C., and Jonathan E. Pleim. "Performance Assessment of New Land Surface and Planetary Boundary Layer Physics in the WRF-ARW." Journal of Applied Meteorology and Climatology 49, no. 4 (April 1, 2010): 760–74. http://dx.doi.org/10.1175/2009jamc2126.1.
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Abstract The Pleim–Xiu land surface model, Pleim surface layer scheme, and Asymmetric Convective Model (version 2) are now options in version 3.0 of the Weather Research and Forecasting model (WRF) Advanced Research WRF (ARW) core. These physics parameterizations were developed for the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and have been used extensively by the air quality modeling community, so there was a need based on several factors to extend these parameterizations to WRF. Simulations executed with the new WRF physics are compared with simulations produced with the MM5 and another WRF configuration with a focus on the replication of near-surface meteorological conditions and key planetary boundary layer features. The new physics in WRF is recommended for retrospective simulations, in particular, those used to drive air quality simulations. In the summer, the error of all variables analyzed was slightly lower across the domain in the WRF simulation that used the new physics than in the similar MM5 configuration. This simulation had an even lower error than the other more common WRF configuration. For the cold season case, the model simulation was not as accurate as the other simulations overall, but did well in terms of lower 2-m temperature error in the western part of the model domain (plains and Rocky Mountains) and most of the Northeast. Both MM5 and the other WRF configuration had lower errors across much of the southern and eastern United States in the winter. The 2-m water vapor mixing ratio and 10-m wind were generally well simulated by the new physics suite in WRF when contrasted with the other simulations and modeling studies. Simulated planetary boundary layer features were compared with both wind profiler and aircraft observations, and the new WRF physics results in a more precise wind and temperature structure not only in the stable boundary layer, but also within most of the convective boundary layer. These results suggest that the WRF performance is now at or above the level of MM5. It is thus recommended to drive future air quality applications.
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Ghizoni, Mariana Medeiros, Silvana Maldaner, Greice Scherer Ritter, and Alcimoni Nelci Comin. "Previsão do campo de vento empregando o modelo WRF para a análise do potencial eólico." Ciência e Natura 40 (March 12, 2019): 237. http://dx.doi.org/10.5902/2179460x35526.
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The WRF (Weather Research and Forecast) is a numerical modeling applied to the atmosphere. This model was developed for weather forecasting and investigation of atmospheric mesoscale phenomena. The WRF is a public domain and free distribution, with different applications ranging from the field of meteorology to engineering, and can be applied in situations of idealized atmosphere or real atmosphere. Presently, the wind field simulated by this model has been used as real data. Thus, the WRF model started to be employed in wind analysis for wind power generation. In this work, the wind field was simulated using the WRF model, in Cachoeira do Sul, Rio Grande do Sul, Brazil.
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He, J., R. He, and Y. Zhang. "Impacts of air–sea interactions on regional air quality predictions using WRF/Chem v3.6.1 coupled with ROMS v3.7: southeastern US example." Geoscientific Model Development Discussions 8, no. 11 (November 13, 2015): 9965–10009. http://dx.doi.org/10.5194/gmdd-8-9965-2015.
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Abstract. Air–sea interactions have significant impacts on coastal convection and surface fluxes exchange, which are important for the spatial and vertical distributions of air pollutants that affect public health, particularly in densely populated coastal areas. To understand the impacts of air–sea interactions on coastal air quality predictions, sensitivity simulations with different cumulus parameterization schemes and atmosphere–ocean coupling are conducted in this work over southeastern US in July 2010 using the Weather Research and Forecasting Model with Chemistry (WRF/Chem). The results show that different cumulus parameterization schemes can result in an 85 m difference in the domain averaged planetary boundary layer height (PBLH), and 4.8 mm difference in the domain averaged daily precipitation. Comparing to WRF/Chem without air–sea interactions, WRF/Chem with a 1-D ocean mixed layer model (WRF/Chem-OML) and WRF/Chem coupled with a 3-D Regional Ocean Modeling System (WRF/Chem-ROMS) predict the domain averaged changes in the sea surface temperature of 0.1 and 1.0 °C, respectively. The simulated differences in the surface concentrations of ozone (O3) and PM2.5 between WRF/Chem-ROMS and WRF/Chem can be as large as 17.3 ppb and 7.9 μg m−3, respectively. The largest changes simulated from WRF/Chem-ROMS in surface concentrations of O3 and particulate matter with diameter less than and equal to 2.5 μm (PM2.5) occur not only along coast and remote ocean, but also over some inland areas. Extensive validations against observations, show that WRF/Chem-ROMS improves the predictions of most cloud and radiative variables, and surface concentrations of some chemical species such as sulfur dioxide, nitric acid, maximum 1 h and 8 h O3, sulfate, ammonium, nitrate, and particulate matter with diameter less than and equal to 10 μm (PM10). This illustrates the benefits and needs of using coupled atmospheric–ocean model with advanced model representations of air–sea interactions for regional air quality modeling.
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Nuryanto, Danang Eko, Yuaning Fajariana, Radyan Putra Pradana, Rian Anggraeni, Imelda Ummiyatul Badri, and Ardhasena Sopaheluwakan. "Modeling of Heavy Rainfall Triggering Landslide Using WRF Model." Agromet 34, no. 1 (June 9, 2020): 55–65. http://dx.doi.org/10.29244/j.agromet.34.1.55-65.
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This study revealed the behavior of heavy rainfall before landslide event based on the Weather Research Forecasting (WRF) model. Simulations were carried out to capture the heavy rainfall patterns on 27 November 2018 in Kulonprogo, Yogyakarta. The modeling was performed with three different planetary boundary layer schemes, namely: Yonsei University (YSU), Sin-Hong (SH) and Bougeault and Lacarrere (BL). Our results indicated that the variation of rainfall distribution were small among schemes. The finding revealed that the model was able to capture the radar’s rainfall pattern. Based on statistical metric, WRF-YSU scheme was the best outperforming to predict a temporal pattern. Further, the study showed a pattern of rainfall development coming from the southern coastal of Java before 13:00 LT (Local Time=WIB=UTC+7) and continued to inland after 13:00 LT. During these periods, the new clouds were developed. Based on our analysis, the cloud formation that generated rainfall started at 10:00 LT, and hit a peak at 13:00 LT. A starting time of cloud generating rainfall may be an early indicator of landslide.
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Islam, Md Ashraful, Javed Meandad, Saurav Dey Shuvo, and Alamgir Kabir. "Modeling of Lightning Events using WRF-derived Microphysical Parameters." Dhaka University Journal of Earth and Environmental Sciences 8, no. 2 (January 30, 2021): 41–50. http://dx.doi.org/10.3329/dujees.v8i2.54838.
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Numerical simulation of lightning events in Bangladesh has been carried out by using Weather Research and Forecasting Model with Advanced Research Dynamic solver (WRF-ARW). Three major lightning events have been considered for the case study; Case_1, lightning occurrence in Netrokona district in March 24 2017, Case_2, lightning event in Barishal district in April 23 2017, and case_3, lightning event in Sherpur district in April 29, 2018. The model simulation was run in 9 km and 3 km of horizontal resolution using six hourly NCEP-FNL datasets. Yonsei University (YSU) PBL scheme, Rapid Radiative Transfer Model (RRTM) long-wave scheme for radiation, and Kain-Fritsch cumulus parameterization scheme is used for this study. The obtained results from the simulation could reasonably capture the lightning condition of the atmosphere for all the three cases. The WRF simulation give reasonable agreement with the available observational data with some spatial and temporal variations, for example the Convective Available Potential Energy (CAPE) values observed are 1299 J/Kg, 3150 J/kg, 1221 J/kg and CAPE values simulated are 1618 J/kg, 3275 J/kg and 1023 J/kg for case_1, case_2 and case_3 respectively. The regression analysis of the flash count with the microphysical parameters is also studied. It is found that there is strong correlation between the lightning flash counts with the microphysical parameters. This study will help to understand the lightning better and will help to design a better lightning forecasting system.
The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(2), 2019, P 41-50
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Floors, R., E. Batchvarova, S. E. Gryning, A. N. Hahmann, A. Peña, and T. Mikkelsen. "Atmospheric boundary layer wind profile at a flat coastal site – wind speed lidar measurements and mesoscale modeling results." Advances in Science and Research 6, no. 1 (May 31, 2011): 155–59. http://dx.doi.org/10.5194/asr-6-155-2011.
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Abstract. Wind profiles up to 600 m height are investigated. Measurements of mean wind speed profiles were obtained from a novel wind lidar and compared to model simulations from a mesoscale model (WRF-ARW v3.1). It is found that WRF is able to predict the mean wind profile rather well and typically within 1–2 m s−1 to the individual measured values. WRF underpredicts the normalized wind profile, especially for stable conditions. The effect of baroclinicity on the upper part of the wind profile is discussed.
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Jeong, Ju-Hee, Inbo Oh, Yoon-Hee Kang, Jin-Hee Bang, Hyeyeon An, Hyeon-Bae Seok, Yoo-Keun Kim, Jihyung Hong, and Jiyoung Kim. "WRF Modeling Approach for Improvement of Air Quality Modeling in the Seoul Metropolitan Region: Seasonal Sensitivity Analysis of the WRF Physics Options." Journal of Environmental Science International 25, no. 1 (January 29, 2016): 67–83. http://dx.doi.org/10.5322/jesi.2016.25.1.67.
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Fersch, Benjamin, Alfonso Senatore, Bianca Adler, Joël Arnault, Matthias Mauder, Katrin Schneider, Ingo Völksch, and Harald Kunstmann. "High-resolution fully coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation." Hydrology and Earth System Sciences 24, no. 5 (May 13, 2020): 2457–81. http://dx.doi.org/10.5194/hess-24-2457-2020.
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Abstract. The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases, and energy. Nonlinear feedback and scale-dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local-area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identically calibrated parameter settings for the land surface model (Noah-Multiparametrization; Noah-MP). The simulations are evaluated based on extensive observations at the Terrestrial Environmental Observatories (TERENO) Pre-Alpine Observatory for the Ammer (600 km2) and Rott (55 km2) river catchments in southern Germany, covering a 5-month period (June–October 2016). The sensitivity of seven land surface parameters is tested using the Latin-Hypercube–One-factor-At-a-Time (LH-OAT) method, and six sensitive parameters are subsequently optimized for six different subcatchments, using the model-independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash–Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of the classic WRF and fully coupled WRF-Hydro models, both using the calibrated parameters from the offline model, shows only tiny alterations for radiation and precipitation but considerable changes for moisture and heat fluxes. By comparison with TERENO Pre-Alpine Observatory measurements, the fully coupled model slightly outperforms the classic WRF model with respect to evapotranspiration, sensible and ground heat flux, the near-surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation, whereas soil moisture and precipitation change randomly.
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Matsangouras, I. T., P. T. Nastos, and I. Pytharoulis. "Synoptic-mesoscale analysis and numerical modeling of a tornado event on 12 February 2010 in northern Greece." Advances in Science and Research 6, no. 1 (July 20, 2011): 187–94. http://dx.doi.org/10.5194/asr-6-187-2011.
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Abstract. Tornadoes are furious convective weather phenomena, with the maximum frequency over Greece during the cold period (autumn, winter).This study analyzes the tornado event that occurred on 12 February 2010 near Vrastama village, at Chalkidiki's prefecture, a non urban area 45 km southeast of Thessaloniki in northern Greece. The tornado developed approximately between 17:10 and 17:35 UTC and was characterized as F2 (Fujita Scale). The tornado event caused several damages to an industrial building and at several olive-tree farms. A synoptic survey is presented along with satellite images, radar products and vertical profile of the atmosphere. Additionally, the nonhydrostatic WRF-ARW atmospheric numerical model (version 3.2.0) was utilized in analysis and forecast mode using very high horizontal resolution (1.333 km × 1.333 km) in order to represent the ambient atmospheric conditions. A comparison of statistical errors between WRF-ARW forecasts and ECMWF analysis is presented, accompanied with LGTS 12:00 UTC soundings (Thessaloniki Airport) and forecast soundings in order to verify the WRF-ARW model. Additionally, a comparison between WRF-ARW and ECMWF thermodynamic indices is also presented. The WRF-ARW high spatial resolution model appeared to simulate with significant accuracy a severe convective event with a lead period of 18 h.
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Nikfal, Amirhossein, Abbas Ranjbar Saadatabadi, Mehdi Rahnama, Sahar Tajbakhsh, and Mohammad Moradi. "Intercomparisons of some dust models over West Asia." E3S Web of Conferences 99 (2019): 01012. http://dx.doi.org/10.1051/e3sconf/20199901012.
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Evaluation and assessment of dust model results is of primary importance to get a better understanding of the models' performance, and therefore, enhancing the models' set up and structure. Besides some SDS-WAS dust models, two other high resolution WRF-Chem runs have been carried out for two dust episodes over the West Asia with alterations in the soil erodibility fields as one of the primary criteria of dust sources. The main aim of this article was to investigate the high resolution WRF-Chem modeling with the default and altered soil erosion, against the WMO SDS-WAS models. In this paper we investigated the application of WRF-Chem dust modeling for the region of interest (Iran), which cannot be seen entirely by the SDS-WAS models' domains. Comparisons of modelled dust surface concentrations with ground based measurements on 8 air quality stations show that the high resolution WRF-Chem could more or less lead to better predictions. For some cases, the results of the high resolution WRF-Chem unexpectedly presented a declined performance, which indicate that the improvements in the horizontal resolution and soil erodibility could not always lead to improved dust predictions, and more factors such as the model set-up and structure should be considered.
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Rogers, Raphael E., Aijun Deng, David R. Stauffer, Brian J. Gaudet, Yiqin Jia, Su-Tzai Soong, and Saffet Tanrikulu. "Application of the Weather Research and Forecasting Model for Air Quality Modeling in the San Francisco Bay Area." Journal of Applied Meteorology and Climatology 52, no. 9 (September 2013): 1953–73. http://dx.doi.org/10.1175/jamc-d-12-0280.1.
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AbstractThe Weather Research and Forecasting (WRF) model is evaluated by conducting various sensitivity experiments over central California including the San Francisco Bay Area (SFBA), with the goal of establishing a WRF model configuration to be used by the Bay Area Air Quality Management District (BAAQMD) for its air quality applications. For the two selected cases, a winter particulate matter case and a summer ozone case, WRF solutions are evaluated both quantitatively by comparing the error statistics and qualitatively by analyzing the model-simulated mesoscale features. Model evaluation is also performed for the SFBA, Sacramento Valley, and San Joaquin Valley subregions. The recommended WRF configuration includes use of the Rapid Radiative Transfer Model/Dudhia (or RRTMG) radiation schemes and the Pleim–Xiu land surface physics, combined with a multiscale four-dimensional data assimilation strategy throughout the simulation period to assimilate the available observations, including standard observations from the World Meteorological Organization and local special observations. With the recommended model configuration, WRF is able to simulate the meteorological variables with reasonable error, with the added value, although relatively small, of assimilating the additional BAAQMD local special observations. Mesoscale features, simulated reasonably well for both cases, include the upslope and downslope flows that occur along the mountains that surround the Central Valley of California, as well as the mesoscale eddies that develop within the valley.
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Bukovsky, Melissa S., and David J. Karoly. "A Regional Modeling Study of Climate Change Impacts on Warm-Season Precipitation in the Central United States*." Journal of Climate 24, no. 7 (April 1, 2011): 1985–2002. http://dx.doi.org/10.1175/2010jcli3447.1.
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Abstract In this study, the Weather Research and Forecasting (WRF) model is employed as a nested regional climate model to dynamically downscale output from the National Center for Atmospheric Research’s (NCAR’s) Community Climate System Model (CCSM) version 3 and the National Centers for Environmental Prediction (NCEP)–NCAR global reanalysis (NNRP). The latter is used for verification of late-twentieth-century climate simulations from the WRF. This analysis finds that the WRF is able to produce precipitation that is more realistic than that from its driving systems (the CCSM and NNRP). It also diagnoses potential issues with and differences between all of the simulations completed. Specifically, the magnitude of heavy 6-h average precipitation events, the frequency distribution, and the diurnal cycle of precipitation over the central United States are greatly improved. Projections from the WRF for late-twenty-first-century precipitation show decreases in average May–August (MJJA) precipitation, but increases in the intensity of both heavy precipitation events and rain in general when it does fall. A decrease in the number of 6-h periods with rainfall accounts for the overall decrease in average precipitation. The WRF also shows an increase in the frequency of very heavy to extreme 6-h average events, but a decrease in the frequency of all events lighter than those over the central United States. Overall, projections from this study suggest an increase in the frequency of both floods and droughts during the warm season in the central United States.
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Siewert, Jolanta, and Krzysztof Kroszczynski. "GIS Data as a Valuable Source of Information for Increasing Resolution of the WRF Model for Warsaw." Remote Sensing 12, no. 11 (June 10, 2020): 1881. http://dx.doi.org/10.3390/rs12111881.
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The Weather Research and Forecasting (WRF) model is commonly associated with meteorological data, but its algorithms may also use geographical data. The objective of this paper is to evaluate the impact of the high resolution CORINE Land Cover (CLC) data and the SRTM topography on the estimation accuracy of the weather model parameters in the WRF microscale simulations (200 × 200 m) for Warsaw. In the presented studies, the authors propose their own method of attaching the CLC data to the WRF microscale modeling for the CLC border areas, where first calculational domains reach beyond areas of CLC coverage. As a part of the research, the adaptation of the proposed method was examined by the assessment of the WRF microscale modeling simulations for Warsaw. The modified IGBP MODIS land use/land cover (LULC) and USGS GMTED2010 terrain elevation geographical data (30 arc seconds) was applied for the WRF simulations as default. As higher resolution geographical data (100 m), the LULC from CORINE Land Cover (CLC) 2018 data, and the SRTM topography were adopted. In this study the forecasts of air temperature and relative humidity at 2 m, and wind (speed and direction) at 10 m above ground level obtained using the WRF model for particular simulations were evaluated against measurements made at the Warsaw airports: Chopin (EPWA) and Babice (EPBC). The research has indicated that for microscale calculation fields there are noticeable changes in the meteorological parameter values when the CLC and the SRTM data are integrated into the WRF model, which in most cases yielded more accurate values of temperature and relative humidity at 2 m. This has also proved the correctness of the proposed methodology of the CLC data adoption. The improvement in the forecasted meteorological parameters is different for the particular locations and depends on the degree of the LULC and topography data change after higher resolution data adoption.
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Hegarty, Jennifer D., Jasper Lewis, Erica L. McGrath-Spangler, John Henderson, Amy Jo Scarino, Philip DeCola, Richard Ferrare, Micheal Hicks, Rebecca D. Adams-Selin, and Ellsworth J. Welton. "Analysis of the Planetary Boundary Layer Height during DISCOVER-AQ Baltimore–Washington, D.C., with Lidar and High-Resolution WRF Modeling." Journal of Applied Meteorology and Climatology 57, no. 11 (November 2018): 2679–96. http://dx.doi.org/10.1175/jamc-d-18-0014.1.
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AbstractThe daytime planetary boundary layer (PBL) was examined for the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Baltimore (Maryland)–Washington, D.C., campaign of July 2011 using PBL height (PBLH) retrievals from aerosol backscatter measurements from ground-based micropulse lidar (MPL), the NASA Langley Research Center airborne High Spectral Resolution Lidar-1 (HSRL-1), and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. High-resolution Weather Research and Forecasting (WRF) Model simulations with horizontal grid spacing of 1 km and different combinations of PBL schemes, urban parameterization, and sea surface temperature inputs were evaluated against PBLHs derived from lidars, ozonesondes, and radiosondes. MPL and WRF PBLHs depicted a growing PBL in the morning that reached a peak height by midafternoon. WRF PBLHs calculated from gridded output profiles generally showed more rapid growth and higher peak heights than did the MPLs, and all WRF–lidar differences were dependent on model configuration, PBLH calculation method, and synoptic conditions. At inland locations, WRF simulated an earlier descent of the PBL top in the afternoon relative to the MPL retrievals and radiosonde PBLHs. At Edgewood, Maryland, the influence of the Chesapeake Bay breeze on the PBLH was captured by both the ozonesonde and WRF data but generally not by the MPL PBLH retrievals because of generally weaker gradients in the aerosol backscatter profile and limited normalized relative backscatter data near the top height of the marine layer.
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Gao, Xiaoyu, and Shanhong Gao. "Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling." Advances in Meteorology 2020 (November 10, 2020): 1–19. http://dx.doi.org/10.1155/2020/8816185.
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Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.
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Guo, Zhenhai, and Xia Xiao. "Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods." Abstract and Applied Analysis 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/941648.
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The accurate assessment of wind power potential requires not only the detailed knowledge of the local wind resource but also an equivalent power curve with good effect for a local wind farm. Although the probability distribution functions (pdfs) of the wind speed are commonly used, their seemingly good performance for distribution may not always translate into an accurate assessment of power generation. This paper contributes to the development of wind power assessment based on the wind speed simulation of weather research and forecasting (WRF) and two improved power curve modeling methods. These approaches are improvements on the power curve modeling that is originally fitted by the single layer feed-forward neural network (SLFN) in this paper; in addition, a data quality check and outlier detection technique and the directional curve modeling method are adopted to effectively enhance the original model performance. The proposed two methods, named WRF-SLFN-OD and WRF-SLFN-WD, are able to avoid the interference from abnormal output and the directional effect of local wind speed during the power curve modeling process. The data examined are from three stations in northern China; the simulation indicates that the two developed methods have strong abilities to provide a more accurate assessment of the wind power potential compared with the original methods.
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Sun, Mingkun, Zhijia Li, Cheng Yao, Zhiyu Liu, Jingfeng Wang, Aizhong Hou, Ke Zhang, Wenbo Huo, and Moyang Liu. "Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios." Water 12, no. 3 (March 20, 2020): 874. http://dx.doi.org/10.3390/w12030874.
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The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).
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Yuan, Yongping, Ruoyu Wang, Ellen Cooter, Limei Ran, Prasad Daggupati, Dongmei Yang, Raghavan Srinivasan, and Anna Jalowska. "Integrating multimedia models to assess nitrogen losses from the Mississippi River basin to the Gulf of Mexico." Biogeosciences 15, no. 23 (November 29, 2018): 7059–76. http://dx.doi.org/10.5194/bg-15-7059-2018.
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Abstract. This study describes and implements an integrated, multimedia, process-based system-level approach to estimating nitrogen (N) fate and transport in large river basins. The modeling system includes the following components: (1) Community Multiscale Air Quality (CMAQ), (2) Weather Research and Forecasting Model (WRF), (3) Environmental Policy Integrated Climate (EPIC), and (4) Soil and Water Assessment Tool (SWAT). The previously developed Fertilizer Emission Scenario Tool for CMAQ (FEST-C), an advanced user interface, integrated EPIC with the WRF model and CMAQ. The FEST-C system, driven by process-based WRF weather simulations, includes atmospheric N additions to agricultural cropland and agricultural cropland contributions to ammonia emissions. This study focuses on integrating the watershed hydrology and water quality model with FEST-C system so that a full multimedia assessment on water quality in large river basins to address impacts of fertilization, meteorology, and atmospheric N deposition on water quality can be achieved. Objectives of this paper are to describe how to expand the previous effort by integrating the SWAT model with the FEST-C (CMAQ/WRF/EPIC) modeling system, as well as to demonstrate application of the Integrated Modeling System (IMS) to the Mississippi River basin (MRB) to simulate streamflow and dissolved N loadings to the Gulf of Mexico (GOM). IMS simulation results generally agree with US Geological Survey (USGS) observations/estimations; the annual simulated streamflow is 218.9 mm and USGS observation is 211.1 mm and the annual simulated dissolved N is 2.1 kg ha−1 and the USGS estimation is 2.8 kg ha−1. Integrating SWAT with the CMAQ/WRF/EPIC modeling system allows for its use within large river basins without losing EPIC's more detailed biogeochemistry processes, which will strengthen the assessment of impacts of future climate scenarios, regulatory and voluntary programs for N oxide air emissions, and land use and land management on N transport and transformation in large river basins.
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Arnault, Joel, Sven Wagner, Thomas Rummler, Benjamin Fersch, Jan Bliefernicht, Sabine Andresen, and Harald Kunstmann. "Role of Runoff–Infiltration Partitioning and Resolved Overland Flow on Land–Atmosphere Feedbacks: A Case Study with the WRF-Hydro Coupled Modeling System for West Africa." Journal of Hydrometeorology 17, no. 5 (May 1, 2016): 1489–516. http://dx.doi.org/10.1175/jhm-d-15-0089.1.
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Abstract The analysis of land–atmosphere feedbacks requires detailed representation of land processes in atmospheric models. The focus here is on runoff–infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff–infiltration partitioning is described as a purely vertical process. In WRF-Hydro, runoff is enhanced with lateral water flows. The study region is the Sissili catchment (12 800 km2) in West Africa, and the study period is from March 2003 to February 2004. The WRF setup here includes an outer and inner domain at 10- and 2-km resolution covering the West Africa and Sissili regions, respectively. In this WRF-Hydro setup, the inner domain is coupled with a subgrid at 500-m resolution to compute overland and river flow. Model results are compared with TRMM precipitation, model tree ensemble (MTE) evapotranspiration, Climate Change Initiative (CCI) soil moisture, CRU temperature, and streamflow observation. The role of runoff–infiltration partitioning and resolved overland flow on land–atmosphere feedbacks is addressed with a sensitivity analysis of WRF results to the runoff–infiltration partitioning parameter and a comparison between WRF and WRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff–infiltration partitioning at the scale of the Sissili area (~100 × 100 km2), but not of area A (500 × 2500 km2). In the inner domain, where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionally resolved overland flow here clearly increases infiltration and evapotranspiration at the beginning of the wet season when soils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial water balance studies and reproduces observed daily discharge with a Nash–Sutcliffe model efficiency coefficient of 0.43.
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Cao, Yanni, Guido Cervone, Zachary Barkley, Thomas Lauvaux, Aijun Deng, and Alan Taylor. "Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling." Geoscientific Model Development 10, no. 9 (September 19, 2017): 3425–40. http://dx.doi.org/10.5194/gmd-10-3425-2017.
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Abstract. Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studies have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.
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Yu, S., R. Mathur, J. Pleim, D. Wong, R. Gilliam, K. Alapaty, C. Zhao, and X. Liu. "Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF–CMAQ: model description, development, evaluation and regional analysis." Atmospheric Chemistry and Physics 14, no. 20 (October 24, 2014): 11247–85. http://dx.doi.org/10.5194/acp-14-11247-2014.
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Abstract. This study implemented first, second and glaciation aerosol indirect effects (AIE) on resolved clouds in the two-way coupled Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQ-predicted aerosol distributions and WRF meteorological conditions. The performance of the newly developed WRF–CMAQ model, with alternate Community Atmospheric Model (CAM) and Rapid Radiative Transfer Model for GCMs (RRTMG) radiation schemes, was evaluated with observations from the Clouds and the See http://ceres.larc.nasa.gov/. Earth's Radiant Energy System (CERES) satellite and surface monitoring networks (AQS, IMPROVE, CASTNET, STN, and PRISM) over the continental US (CONUS) (12 km resolution) and eastern Texas (4 km resolution) during August and September of 2006. The results at the Air Quality System (AQS) surface sites show that in August, the normalized mean bias (NMB) values for PM2.5 over the eastern US (EUS) and the western US (WUS) are 5.3% (−0.1%) and 0.4% (−5.2%) for WRF–CMAQ/CAM (WRF–CMAQ/RRTMG), respectively. The evaluation of PM2.5 chemical composition reveals that in August, WRF–CMAQ/CAM (WRF–CMAQ/RRTMG) consistently underestimated the observed SO42- by −23.0% (−27.7%), −12.5% (−18.9%) and −7.9% (−14.8%) over the EUS at the Clean Air Status Trends Network (CASTNET), Interagency Monitoring of Protected Visual Environments (IMPROVE) and Speciated Trends Network (STN) sites, respectively. Both configurations (WRF–CMAQ/CAM, WRF–CMAQ/RRTMG) overestimated the observed mean organic carbon (OC), elemental carbon (EC) and and total carbon (TC) concentrations over the EUS in August at the IMPROVE sites. Both configurations generally underestimated the cloud field (shortwave cloud forcing, SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both configurations captured SWCF and longwave cloud forcing (LWCF) very well for the 4 km simulation over eastern Texas, when all clouds were resolved by the finer resolution domain. The simulations of WRF–CMAQ/CAM and WRF–CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, cloud optical depth (COD), cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August, except for a greater overestimation of PM2.5 due to the overestimations of SO42-, NH4+, NO3-, and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to the lower SWCF values, and fewer convective clouds in September. This work shows that inclusion of indirect aerosol effect treatments in WRF–CMAQ represents a significant advancement and milestone in air quality modeling and the development of integrated emissions control strategies for air quality management and climate change mitigation.
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Mortezazadeh, Mohammad, Zahra Jandaghian, and Liangzhu Leon Wang. "Integrating CityFFD and WRF for modeling urban microclimate under heatwaves." Sustainable Cities and Society 66 (March 2021): 102670. http://dx.doi.org/10.1016/j.scs.2020.102670.
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Mohebbi, Amin, Gabriel T. Green, Simin Akbariyeh, Fan Yu, Brendan J. Russo, and Edward J. Smaglik. "Development of Dust Storm Modeling for Use in Freeway Safety and Operations Management: An Arizona Case Study." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 4, 2019): 175–87. http://dx.doi.org/10.1177/0361198119839978.
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Extreme weather conditions such as strong winds, hail, heavy rainfall, heavy snowfall, and high air temperature impact roads, traffic, and operational decisions. Strong winds in arid regions may pick up fine dust particles and create massive blowing plumes dramatically reducing the visibility. This reduced visibility severely impairs driving ability causing catastrophic crashes. The purpose of this research was to investigate the impacts of dust storms on freeway safety and operations. Interstates 8, 10, 15, 17, 19, and 40 running through Arizona were studied in relation to dust loading and crash risks. To achieve this, nine severe Arizona dust storms (2009–2016) were modeled using Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem). WRF is a mesoscale numerical weather prediction system with a software architecture allowing for parallel computation. When coupled with a chemistry module (WRF-Chem), it could be used to model the fate and transport of the particulate matter. Dust hot spots were calculated based on Getis-Ord Gi* statistical method and were correlated to dust storm caused crashes. It was shown that a positive Gi* accompanied by dust loading of at least 50 kgm–2 will result in a crash with a 90% confidence level. The outcome of this research could be used by local and federal transportation agencies to communicate warnings to drivers for improved safety.
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Weckwerth, Tammy M., Lindsay J. Bennett, L. Jay Miller, Joël Van Baelen, Paolo Di Girolamo, Alan M. Blyth, and Tracy J. Hertneky. "An Observational and Modeling Study of the Processes Leading to Deep, Moist Convection in Complex Terrain." Monthly Weather Review 142, no. 8 (August 1, 2014): 2687–708. http://dx.doi.org/10.1175/mwr-d-13-00216.1.
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Abstract A case study of orographic convection initiation (CI) that occurred along the eastern slopes of the Vosges Mountains in France on 6 August 2007 during the Convective and Orographically-Induced Precipitation Study (COPS) is presented. Global positioning system (GPS) receivers and two Doppler on Wheels (DOW) mobile radars sampled the preconvective and storm environments and were respectively used to retrieve three-dimensional tomographic water vapor and wind fields. These retrieved data were supplemented with temperature, moisture, and winds from radiosondes from a site in the eastern Rhine Valley. High-resolution numerical simulations with the Weather Research and Forecasting (WRF) Model were used to further investigate the physical processes leading to convective precipitation. This unique, time-varying combination of derived water vapor and winds from observations illustrated an increase in low-level moisture and convergence between upslope easterlies and downslope westerlies along the eastern slope of the Vosges Mountains. Uplift associated with these shallow, colliding boundary layer flows eventually led to the initiation of moist convection. WRF reproduced many features of the observed complicated flow, such as cyclonic (anticyclonic) flow around the southern (northern) end of the Vosges Mountains and the east-side convergent flow below the ridgeline. The WRF simulations also illustrated spatial and temporal variability in buoyancy and the removal of the lids prior to convective development. The timing and location of CI from the WRF simulations was surprisingly close to that observed.
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Zhao, C., Y. Wang, Y. Choi, and T. Zeng. "Summertime impact of convective transport and lightning NO<sub>x</sub> production over North America: modeling dependence on meteorological simulations." Atmospheric Chemistry and Physics 9, no. 13 (July 3, 2009): 4315–27. http://dx.doi.org/10.5194/acp-9-4315-2009.
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Abstract. Global-scale chemical transport model simulations indicate lightning NOx dominates upper tropospheric O3 production above Eastern North America during summertime but vary in their estimates. To improve our understanding, a regional-scale model (REAM) with higher resolution is applied. To examine the uncertainties in modeling the impact of convective transport and lightning NOx production on upper tropospheric chemical tracer distributions, REAM simulations of chemical tracers are driven by two meteorological models, WRF and MM5, with different cumulus convective parameterizations. The model simulations are evaluated using INTEX-A aircraft measurements and satellite measurements of NO2 columns and cloud top pressure, and we find that mid and upper tropospheric trace gas concentrations are affected strongly by convection and lightning NOx production. WRF with the KF-eta convection scheme simulates larger convective updraft mass fluxes below 150 hPa than MM5 with the Grell scheme. The inclusion of the entrainment and detrainment processes leads to more outflow in the mid troposphere in WRF than MM5. The ratio of C2H6/C3H8 is found to be a sensitive parameter to convective outflow; the simulation by WRF-REAM is in closer agreement with INTEX-A measurements than MM5-REAM, implying that convective mass fluxes by WRF are more realistic. WRF also simulates lower cloud top heights (10–12 km) than MM5 (up to 16 km), and hence smaller amounts of estimated (intra-cloud) lightning NOx and lower emission altitudes. WRF simulated cloud top heights are in better agreement with GOES satellite measurements than MM5. Simulated lightning NOx production difference (due primarily to cloud top height difference) is mostly above 12 km. At 8–12 km, the models simulate a contribution of 60–75% of NOx and up to 20 ppbv of O3 from lightning, although the decrease of lightning NOx effect from the Southeast to Northeast and eastern Canada is overestimated. The model differences and biases found in this study reflect some major uncertainties of upper tropospheric NOx and O3 simulations driven by those in meteorological simulations and lightning parameterizations.
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Nehrkorn, Thomas, John Henderson, Mark Leidner, Marikate Mountain, Janusz Eluszkiewicz, Kathryn McKain, and Steven Wofsy. "WRF Simulations of the Urban Circulation in the Salt Lake City Area for CO2 Modeling." Journal of Applied Meteorology and Climatology 52, no. 2 (February 2013): 323–40. http://dx.doi.org/10.1175/jamc-d-12-061.1.
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AbstractA recent National Research Council report highlighted the potential utility of atmospheric observations and models for detecting trends in concentrated emissions from localized regions, such as urban areas. The Salt Lake City (SLC), Utah, area was chosen for a pilot study to determine the feasibility of using ground-based sensors to identify trends in anthropogenic urban emissions over a range of time scales (from days to years). The Weather Research and Forecasting model (WRF) was combined with a Lagrangian particle dispersion model and an emission inventory to model carbon dioxide (CO2) concentrations that can be compared with in situ measurements. An accurate representation of atmospheric transport requires a faithful modeling of the meteorological conditions. This study examines in detail the ability of different configurations of WRF to reproduce the observed local and mesoscale circulations, and the diurnal evolution of the planetary boundary layer (PBL) in the SLC area. Observations from the Vertical Transport and Mixing field experiment in 2000 were used to examine the sensitivity of WRF results to changes in the PBL parameterization and to the inclusion of an urban canopy model (UCM). Results show that for urban locations there is a clear benefit from parameterizing the urban canopy for simulation of the PBL and near-surface conditions, particularly for temperature evolution at night. Simulation of near-surface CO2 concentrations for a 2-week period in October 2006 showed that running WRF at high resolution (1.33 km) and with a UCM also improves the simulation of observed increases in CO2 during the early evening.
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Hines, Keith M., and David H. Bromwich. "Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet Meteorology*." Monthly Weather Review 136, no. 6 (June 1, 2008): 1971–89. http://dx.doi.org/10.1175/2007mwr2112.1.
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Abstract A polar-optimized version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was developed to fill climate and synoptic needs of the polar science community and to achieve an improved regional performance. To continue the goal of enhanced polar mesoscale modeling, polar optimization should now be applied toward the state-of-the-art Weather Research and Forecasting (WRF) Model. Evaluations and optimizations are especially needed for the boundary layer parameterization, cloud physics, snow surface physics, and sea ice treatment. Testing and development work for Polar WRF begins with simulations for ice sheet surface conditions using a Greenland-area domain with 24-km resolution. The winter month December 2002 and the summer month June 2001 are simulated with WRF, version 2.1.1, in a series of 48-h integrations initialized daily at 0000 UTC. The results motivated several improvements to Polar WRF, especially to the Noah land surface model (LSM) and the snowpack treatment. Different physics packages for WRF are evaluated with December 2002 simulations that show variable forecast skill when verified with the automatic weather station observations. The WRF simulation with the combination of the modified Noah LSM, the Mellor–Yamada–Janjić boundary layer parameterization, and the WRF single-moment microphysics produced results that reach or exceed the success standards of a Polar MM5 simulation for December 2002. For summer simulations of June 2001, WRF simulates an improved surface energy balance, and shows forecast skill nearly equal to that of Polar MM5.
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Powers, Jordan G., Joseph B. Klemp, William C. Skamarock, Christopher A. Davis, Jimy Dudhia, David O. Gill, Janice L. Coen, et al. "The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions." Bulletin of the American Meteorological Society 98, no. 8 (August 1, 2017): 1717–37. http://dx.doi.org/10.1175/bams-d-15-00308.1.
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Abstract Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.
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Ma, P. L., P. J. Rasch, J. D. Fast, R. C. Easter, W. I. Gustafson Jr., X. Liu, S. J. Ghan, and B. Singh. "Assessing the CAM5 physics suite in the WRF-Chem model: implementation, resolution sensitivity, and a first evaluation for a regional case study." Geoscientific Model Development 7, no. 3 (May 6, 2014): 755–78. http://dx.doi.org/10.5194/gmd-7-755-2014.
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Abstract. A suite of physical parameterizations (deep and shallow convection, turbulent boundary layer, aerosols, cloud microphysics, and cloud fraction) from the global climate model Community Atmosphere Model version 5.1 (CAM5) has been implemented in the regional model Weather Research and Forecasting with chemistry (WRF-Chem). A downscaling modeling framework with consistent physics has also been established in which both global and regional simulations use the same emissions and surface fluxes. The WRF-Chem model with the CAM5 physics suite is run at multiple horizontal resolutions over a domain encompassing the northern Pacific Ocean, northeast Asia, and northwest North America for April 2008 when the ARCTAS, ARCPAC, and ISDAC field campaigns took place. These simulations are evaluated against field campaign measurements, satellite retrievals, and ground-based observations, and are compared with simulations that use a set of common WRF-Chem parameterizations. This manuscript describes the implementation of the CAM5 physics suite in WRF-Chem, provides an overview of the modeling framework and an initial evaluation of the simulated meteorology, clouds, and aerosols, and quantifies the resolution dependence of the cloud and aerosol parameterizations. We demonstrate that some of the CAM5 biases, such as high estimates of cloud susceptibility to aerosols and the underestimation of aerosol concentrations in the Arctic, can be reduced simply by increasing horizontal resolution. We also show that the CAM5 physics suite performs similarly to a set of parameterizations commonly used in WRF-Chem, but produces higher ice and liquid water condensate amounts and near-surface black carbon concentration. Further evaluations that use other mesoscale model parameterizations and perform other case studies are needed to infer whether one parameterization consistently produces results more consistent with observations.
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Ma, P. L., P. J. Rasch, J. D. Fast, R. C. Easter, W. I. Gustafson Jr., X. Liu, S. J. Ghan, and B. Singh. "Assessing the CAM5 physics suite in the WRF-Chem model: implementation, evaluation, and resolution sensitivity." Geoscientific Model Development Discussions 6, no. 4 (November 29, 2013): 6157–218. http://dx.doi.org/10.5194/gmdd-6-6157-2013.
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Abstract. A suite of physical parameterizations (deep and shallow convection, turbulent boundary layer, aerosols, cloud microphysics, and cloud fraction) from the global climate model Community Atmosphere Model version 5.1 (CAM5) has been implemented in the regional model Weather Research and Forecasting with Chemistry (WRF-Chem). A downscaling modeling framework with consistent physics has also been established in which both global and regional simulations use the same emissions and surface fluxes. The WRF-Chem model with the CAM5 physics suite is run at multiple horizontal resolutions over a domain encompassing the northern Pacific Ocean, northeast Asia, and northwest North America for April 2008 when the ARCTAS, ARCPAC, and ISDAC field campaigns took place. These simulations are evaluated against field campaign measurements, satellite retrievals, and ground-based observations, and are compared with simulations that use a set of common WRF-Chem parameterizations. This manuscript describes the implementation of the CAM5 physics suite in WRF-Chem, provides an overview of the modeling framework and an initial evaluation of the simulated meteorology, clouds, and aerosols, and quantifies the resolution dependence of the cloud and aerosol parameterizations. We demonstrate that some of the CAM5 biases, such as high estimates of cloud susceptibility to aerosols and the underestimation of aerosol concentrations in the Arctic, can be reduced simply by increasing horizontal resolution. We also show that the CAM5 physics suite performs similarly to a set of parameterizations commonly used in WRF-Chem, but produces higher ice and liquid water condensate amounts and near-surface black carbon concentration. Further evaluations that use other mesoscale model parameterizations and perform other case studies are needed to infer whether one parameterization consistently produces results more consistent with observations.
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LeGrand, Sandra L., Chris Polashenski, Theodore W. Letcher, Glenn A. Creighton, Steven E. Peckham, and Jeffrey D. Cetola. "The AFWA dust emission scheme for the GOCART aerosol model in WRF-Chem v3.8.1." Geoscientific Model Development 12, no. 1 (January 7, 2019): 131–66. http://dx.doi.org/10.5194/gmd-12-131-2019.
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Abstract. Airborne particles of mineral dust play a key role in Earth's climate system and affect human activities around the globe. The numerical weather modeling community has undertaken considerable efforts to accurately forecast these dust emissions. Here, for the first time in the literature, we thoroughly describe and document the Air Force Weather Agency (AFWA) dust emission scheme for the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol model within the Weather Research and Forecasting model with chemistry (WRF-Chem) and compare it to the other dust emission schemes available in WRF-Chem. The AFWA dust emission scheme addresses some shortcomings experienced by the earlier GOCART-WRF scheme. Improved model physics are designed to better handle emission of fine dust particles by representing saltation bombardment. WRF-Chem model performance with the AFWA scheme is evaluated against observations of dust emission in southwest Asia and compared to emissions predicted by the other schemes built into the WRF-Chem GOCART model. Results highlight the relative strengths of the available schemes, indicate the reasons for disagreement, and demonstrate the need for improved soil source data.