scholarly journals 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

2021 ◽  
Author(s):  
Xu Feng ◽  
Haipeng Lin ◽  
Tzung-May Fu ◽  
Melissa P. Sulprizio ◽  
Jiawei Zhuang ◽  
...  
Keyword(s):  
Open Source ◽  
Spatial Correlation ◽  
Atmospheric Chemistry ◽  
Eastern China ◽  
Wrf Model ◽  
Temporal Correlation ◽  
Cloud Microphysics ◽  
Shortwave Radiation ◽  
Spatial Distributions ◽  
Meteorological Model

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 chemical model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol-radiation interactions (ARI) and aerosol-cloud interactions (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. We apply prescribed size distributions to the 10 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. 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, with the goal of evaluating the simulated aerosol and cloud properties and the impacts of ARI and ACI on meteorology and air quality. WRF-GC reproduces the day-to-day variability of the aerosol optical depth (AOD) observed by the Aerosol Robotic Network (AERONET) project at four representative Chinese sites in January 2015, with temporal correlation coefficients of 0.56 to 0.85. The magnitudes and spatial distributions of the simulated liquid cloud effective radii, liquid cloud optical depths, surface downward shortwave radiation, and surface temperature over China for July 2016 are in good agreement with aircraft, satellite, and surface observations. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations and spatial distributions of PM2.5 in January 2015 (normalized mean bias = −6.6 %, spatial correlation r = 0.74) and afternoon ozone in July 2016 (normalized mean bias = 19 %, spatial correlation r = 0.56) over Eastern China, respectively. Our sensitivity simulations show that including the ARI and ACI improved the model's performance in simulating ozone concentrations over China in July, 2016. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org.

scholarly journals 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

2021 ◽  
Vol 14 (6) ◽  
pp. 3741-3768
Author(s):  
Xu Feng ◽  
Haipeng Lin ◽  
Tzung-May Fu ◽  
Melissa P. Sulprizio ◽  
Jiawei Zhuang ◽  
...  
Keyword(s):  
Open Source ◽  
Spatial Correlation ◽  
Atmospheric Chemistry ◽  
Eastern China ◽  
Wrf Model ◽  
Chemical Species ◽  
Cloud Droplet ◽  
Cloud Microphysics ◽  
Meteorological Variables ◽  
Meteorological Model

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).

WRF-GC: online two-way coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling

2020 ◽  
Author(s):  
Xu Feng ◽  
Haipeng Lin ◽  
Tzung-May Fu
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Cloud Microphysics ◽  
Chemical Transport Model ◽  
Meteorological Model ◽  
Ozone Concentrations ◽  
Chemistry Modeling ◽  
Nested Grid

<p>We developed the two-way version of the WRF-GC model, which is an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem chemical transport model, for regional air quality and atmospheric chemistry modeling. WRF-GC allows the two parent models to be updated independently, such that WRF-GC can stay state-of-the-science. The meteorological fields and chemical variables are transferred between the two models in the coupler to simulate the feedback of gases and aerosols to meteorological processes via interactions with radiation and cloud microphysics. We used the WRF-GC model to simulate surface PM<sub>2.5</sub> concentrations over China during January 22 to 27, 2015 and compared the results to the outcomes from classic GEOS-Chem nested-grid simulations as well as the surface observations. For PM<sub>2.5</sub> simulations, both models were able to reproduce the spatiotemporal variations, but the WRF-GC (r = 0.68, bias = 29%) performing better than GEOS-Chem (r = 0.72, bias = 55%) especially over Eastern China. For ozone simulations, we found that including aerosol-chemistry-cloud-radiation interactions reduced the mean bias of simulated surface ozone concentrations from 34% to 29% compared to observed afternoon ozone concentrations. WRF-GC is computationally efficient, with the physical and chemical variables managed in distributed memory. At similar resolutions, WRF-GC simulations were three times faster than the classic GEOS-Chem nested-grid simulations, due to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. We envision WRF-GC to become a powerful tool for advancing science, serving the public, and informing policy-making.</p>

scholarly journals 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

2020 ◽  
Vol 13 (7) ◽  
pp. 3241-3265 ◽  
Author(s):  
Haipeng Lin ◽  
Xu Feng ◽  
Tzung-May Fu ◽  
Heng Tian ◽  
Yaping Ma ◽  
...  
Keyword(s):  
Open Source ◽  
Atmospheric Chemistry ◽  
Message Passing ◽  
Message Passing Interface ◽  
Eastern China ◽  
State Variables ◽  
Meteorological Model ◽  
Chemistry Modeling ◽  
Meteorological Observations ◽  
Upper Level

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).

scholarly journals WRF-GC: online coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling, Part 1: description of the one-way model (v1.0)

2020 ◽  
Author(s):  
Haipeng Lin ◽  
Xu Feng ◽  
Tzung-May Fu ◽  
Heng Tian ◽  
Yaping Ma ◽  
...  
Keyword(s):  
Open Source ◽  
Atmospheric Chemistry ◽  
Eastern China ◽  
Spatiotemporal Variability ◽  
Massively Parallel ◽  
Easy Access ◽  
State Variables ◽  
Grid Simulation ◽  
Nested Grid ◽  
State Of The Science

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. Both WRF and GEOS-Chem are open-source and community-supported. WRF-GC provides regional chemistry modellers easy access to the GEOS-Chem chemical module, which is stably-configured, 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 gives GEOS-Chem users the ability to perform high-resolution forecasts and hindcasts for any location and time of interest. WRF-GC is designed to be easy to use, massively parallel, extendable, and easy to update. The WRF-GC coupling structure allows future versions of either one of the two parent models to be immediately integrated into WRF-GC. This enables WRF-GC to stay state-of-the-science with traceability to parent model versions. Physical and chemical state variables in WRF and in GEOS-Chem are managed in distributed memory and translated between the two models by the WRF-GC Coupler at runtime. We used the WRF-GC model to simulate surface PM2.5 concentrations over China during January 22 to 27, 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem nested-grid 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 model did (r = 0.72, bias = 55 %). This was mainly because our WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the spatiotemporal variability of the planetary boundary layer heights over China during the simulation period. Both parent models and the WRF-GC Coupler are parallelized across computational cores and can scale to massively parallel architectures. The WRF-GC simulation was three times more efficient than the GEOS-Chem nested-grid simulation at similar resolutions and for the same number of computational cores, owing to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. WRF-GC scales nearly perfectly up to a few hundred cores on a variety of computational platforms. Version 1.0 of the WRF-GC model supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no feedbacks from GEOS-Chem. 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.

scholarly journals Kinetic mass-transfer calculation of water isotope fractionation due to cloud microphysics in a regional meteorological model

2019 ◽  
Vol 19 (3) ◽  
pp. 1753-1766
Author(s):  
I-Chun Tsai ◽  
Wan-Yu Chen ◽  
Jen-Ping Chen ◽  
Mao-Chang Liang
Keyword(s):  
Mass Transfer ◽  
Water Vapor ◽  
Isotope Fractionation ◽  
Atmospheric Transport ◽  
Isotope Composition ◽  
Lower Boundary ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Meteorological Model ◽  
Sensitivity Tests

Abstract. In conventional atmospheric models, isotope exchange between liquid, gas, and solid phases is usually assumed to be in equilibrium, and the highly kinetic phase transformation processes inferred in clouds are yet to be fully investigated. In this study, a two-moment microphysical scheme in the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model was modified to allow kinetic calculation of isotope fractionation due to various cloud microphysical phase-change processes. A case of a moving cold front is selected for quantifying the effect of different factors controlling isotopic composition, including water vapor sources, atmospheric transport, phase transition pathways of water in clouds, and kinetic-versus-equilibrium mass transfer. A base-run simulation was able to reproduce the ∼ 50 ‰ decrease in δD that was observed during the frontal passage. Sensitivity tests suggest that all the above factors contributed significantly to the variations in isotope composition. The thermal equilibrium assumption commonly used in earlier studies may cause an overestimate of mean vapor-phase δD by 11 ‰, and the maximum difference can be more than 20 ‰. Using initial vertical distribution and lower boundary conditions of water stable isotopes from satellite data is critical to obtain successful isotope simulations, without which the δD in water vapor can be off by about 34 ‰ and 28 ‰, respectively. Without microphysical fractionation, the δD in water vapor can be off by about 25 ‰.

On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

2020 ◽  
Vol 35 (3) ◽  
pp. 891-919 ◽  
Author(s):  
Ricardo Fonseca ◽  
Marouane Temimi ◽  
Mohan Satyanarayana Thota ◽  
Narendra Reddy Nelli ◽  
Michael John Weston ◽  
...  
Keyword(s):  
Wind Speed ◽  
Eddy Covariance ◽  
United Arab Emirates ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Shortwave Radiation ◽  
Surface Drag ◽  
Near Surface

Abstract The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models’ performance is evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s−1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. While the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data.

scholarly journals Kinetic mass-transfer calculation of water isotope fractionation due to cloud microphysics in a regional meteorological model

2018 ◽  
Author(s):  
I-Chun Tsai ◽  
Wan-Yu Chen ◽  
Jen-Ping Chen ◽  
Mao-Chang Liang
Keyword(s):  
Mass Transfer ◽  
Water Vapor ◽  
Isotope Fractionation ◽  
Atmospheric Transport ◽  
Isotope Composition ◽  
Lower Boundary ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Meteorological Model ◽  
Gas Phases

Abstract. In conventional atmospheric models, isotope exchange between liquid and gas phases is usually assumed to be in equilibrium, and the highly kinetic phase transformation processes inferred in clouds are yet to be fully investigated. In this study, a two-moment microphysical scheme in the NCAR Weather Research and Forecasting (WRF) model was modified to allow kinetic calculation of isotope fractionation due to various cloud microphysical phase-change processes. A case of moving cold front is selected for quantifying the effect of different factors controlling isotopic composition, including water vapor sources, atmospheric transport, phase transition pathways of water in clouds, and kinetic versus equilibrium mass transfer. A base-run simulation was able to reproduce the ~ 50 ‰ decrease in δD that observed during the frontal passage. Sensitivity tests suggest that all the above factors contributed significantly to the variations in isotope composition. The thermal equilibrium assumption commonly used in earlier studies may cause an overestimate of mean vapor-phase δD by 11 ‰, and the maximum difference can be more than 20 ‰. Without microphysical fractionation, the δD in water vapor can be off by about 25 ‰. Also, using initial vertical distribution and lower boundary conditions of water isotopes from satellite data are critical to successful isotope simulations, without which the δD in water vapor can be off by about 34 and 28 ‰, respectively.

Simulation of karst floods with a hydrological model improved by meteorological model coupling

2021 ◽  
Keyword(s):  
Flood Control ◽  
Underground Structure ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Karst Area ◽  
Peak Time ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Flood Peak ◽  
Flood Warning

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

scholarly journals An advanced scheme for wet scavenging and liquid-phase chemistry in a regional online-coupled chemistry transport model

2013 ◽  
Vol 13 (3) ◽  
pp. 1177-1192 ◽  
Author(s):  
C. Knote ◽  
D. Brunner
Keyword(s):  
Aqueous Phase ◽  
Climate Model ◽  
Transport Model ◽  
Measurement Data ◽  
Coupled System ◽  
Cloud Microphysics ◽  
Microphysics Scheme ◽  
Meteorological Model ◽  
Wet Scavenging ◽  
Phase Chemistry

Abstract. Clouds are reaction chambers for atmospheric trace gases and aerosols, and the associated precipitation is a major sink for atmospheric constituents. The regional chemistry-climate model COSMO-ART has been lacking a description of wet scavenging of gases and aqueous-phase chemistry. In this work we present a coupling of COSMO-ART with a wet scavenging and aqueous-phase chemistry scheme. The coupling is made consistent with the cloud microphysics scheme of the underlying meteorological model COSMO. While the choice of the aqueous-chemistry mechanism is flexible, the effects of a simple sulfur oxidation scheme are shown in the application of the coupled system in this work. We give details explaining the coupling and extensions made, then present results from idealized flow-over-hill experiments in a 2-D model setup and finally results from a full 3-D simulation. Comparison against measurement data shows that the scheme efficiently reduces SO2 trace gas concentrations by 0.3 ppbv (−30%) on average, while leaving O3 and NOx unchanged. PM10 aerosol mass was increased by 10% on average. While total PM2.5 changes only little, chemical composition is improved notably. Overestimations of nitrate aerosols are reduced by typically 0.5–1 μg m−3 (up to −2 μg m−3 in the Po Valley) while sulfate mass is increased by 1–1.5 μg m−3 on average (up to 2.5 μg m−3 in Eastern Europe). The effect of cloud processing of aerosols on its size distribution, i.e. a shift towards larger diameters, is observed. Compared against wet deposition measurements the system tends to underestimate the total wet deposited mass for the simulated case study.

Pathways to the Production of Precipitating Hydrometeors and Tropical Cyclone Development

2016 ◽  
Vol 144 (6) ◽  
pp. 2395-2420 ◽  
Author(s):  
J.-W. Bao ◽  
S. A. Michelson ◽  
E. D. Grell
Keyword(s):  
Tropical Cyclone ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Weather Research And Forecasting ◽  
Rapid Intensification ◽  
Latent Heating ◽  
Vertical Distributions ◽  
Double Moment ◽  
Microphysical Processes ◽  
Definition Of

Abstract Pathways to the production of precipitation in two cloud microphysics schemes available in the Weather Research and Forecasting (WRF) Model are investigated in a scenario of tropical cyclone intensification. Comparisons of the results from the WRF Model simulations indicate that the variation in the simulated initial rapid intensification of an idealized tropical cyclone is due to the differences between the two cloud microphysics schemes in their representations of pathways to the formation and growth of precipitating hydrometeors. Diagnoses of the source and sink terms of the hydrometeor budget equations indicate that the major differences in the production of hydrometeors between the schemes are in the spectral definition of individual hydrometeor categories and spectrum-dependent microphysical processes, such as accretion growth and sedimentation. These differences lead to different horizontally averaged vertical profiles of net latent heating rate associated with significantly different horizontally averaged vertical distributions and production rates of hydrometeors in the simulated clouds. Results from this study also highlight the possibility that the advantage of double-moment formulations can be overshadowed by the uncertainties in the spectral definition of individual hydrometeor categories and spectrum-dependent microphysical processes.

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