The Weather Research and Forecast Model: Software Architecture and Performance

Michalakes, John; Dudhia, Jimy; Gill, David; Henderson, Tom; Klemp, Joseph B.; Skamarock, William C.; Wang, W.

doi:10.1142/9789812701831_0012

Cited by 325 publications

(214 citation statements)

References 8 publications

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“…The selected period for the study covers November, 24e27, 2008. The Advanced Research Weather Research and Forecasting (WRF-ARW) Model v3.0.1.1 (Michalakes et al, 2004;Skamarock and Klemp, 2008) provides the necessary meteorological inputs for the air quality simulations. The High Elective Resolution Modeling System (HERMES; Baldasano et al, 2008b) is used to estimate emissions of gas-phase and aerosol pollutants.…”

Section: Methodsmentioning

confidence: 99%

Impact of HONO sources on the performance of mesoscale air quality models

Gonçalves

Dabdub

Chang

et al. 2012

Atmospheric Environment

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a b s t r a c tNitrous acid (HONO) photolysis constitutes a primary source of OH in the early morning, which leads to changes in model gas-phase and particulate matter concentrations. However, state-of-the-art models of chemical mechanisms share a common representation of gas-phase chemistry leading to HONO that fails in reproducing the observed profiles. Hence, there is a growing interest in improving the definition of additional HONO sources within air quality models, i.e. direct emissions or heterogeneous reactions. In order to test their feasibility under atmospheric conditions, the WRF-ARW/HERMES/CMAQ modeling system is applied with high horizontal resolution (4 Â 4 km 2 ) to Spain for November 24e27, 2008. HONO modeled sources include: (1) direct emissions from on-road transport; NO 2 hydrolysis on aerosol and ground surfaces, the latter with (2) kinetics depending exclusively on available surfaces for reaction and (3) refined kinetics considering also relative humidity dependence; and (4) photoenhanced NO 2 reduction on ground surfaces. The DOMINO measurement campaign performed in El Arenosillo (Southern Spain) provides valuable HONO observations. Modeled HONO results are consistently below observations, even when the most effective scenario is assessed, corresponding to contributions of direct emissions and NO 2 hydrolysis with the simplest kinetics parameterization. With the additional sources of HONO, PM 2.5 predictions can be up to 14% larger in urban areas. Quantified impacts on secondary pollutants have to be taken as a low threshold, due to the proven underestimation of HONO levels. It is fundamental to improve HONO sources definition within air quality models, both for the scientific community and decision makers.

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Section: Methodsmentioning

confidence: 99%

Impact of HONO sources on the performance of mesoscale air quality models

Gonçalves

Dabdub

Chang

et al. 2012

Atmospheric Environment

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“…The Advanced Research Weather Research and Forecasting (WRF-ARW) model v3.0.1.1 (Michalakes et al, 2004;Skamarock and Klemp, 2008) provides the meteorology to the chemical transport model. For the Spanish domain WRF-ARW is configured with a grid of 397 x 397 points corresponding to a 4 km x 4 km horizontal resolution and 38 1 vertical levels with 11 characterizing the planetary boundary layer (PBL).…”

Section: System Descriptionmentioning

confidence: 99%

Spatio-temporal variability of concentrations and speciation of particulate matter across Spain in the CALIOPE modeling system

Pay

Jiménez‐Guerrero

Jorba

et al. 2012

Atmospheric Environment

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The CALIOPE project, funded by the Spanish Ministry of the Environment, aims at establishing an air quality forecasting system for Spain. With this goal, CALIOPE modeling system was developed and applied with high resolution (4 km x 4 km, 1hr) using the HERMES emission model (including emissions of resuspended particles from paved roads) specifically built up for Spain. The present study provides an evaluation and an assessment of that air quality modeling system, namely WRF-ARW/HERMES/CMAQ/BSC-DREAM8b for a full year simulation in 2004 over Spain. The evaluation focused on the capability of the model to reproduce the temporal and spatial distribution of gas phase species (NO2, O3, and SO2) and particulate matter (PM10) against ground-based measurements from the Spanish air quality monitoring network. The evaluation of the modeling results on an hourly basis showed a strong dependency of the performance of the model on the type of zone (urban, suburban and rural) and the dominant emission sources (traffic, industrial, and background). The O3 chemistry is best represented in summer, when mean hourly variability and high peaks are generally well reproduced. The mean normalized error and bias meet the recommendations proposed by the United States Environmental Protection Agency (US-EPA) and the European regulations. Modeled O3 shows higher performance for urban than for rural stations, especially at traffic stations in large cities, since stations influenced by traffic emissions (i.e. high NOx emissions) are better characterized with a more pronounced daily variability. NOx/O3 chemistry is better represented under non-limited-NO2 regimes. SO 2 is mainly produced from isolated point sources (power generation and transformation industries) which generate large plumes of high SO 2 concentration affecting the air quality on a local to national scale where the meteorological pattern is crucial. The contribution of mineral dust from the Sahara desert through the BSC-DREAM8b model helps to satisfactorily reproduce episodic high PM10 concentration peaks at background stations. The model assessment indicates that one of the main air quality-related problems in Spain is the high level of O3. A quarter of the Iberian Peninsula presents more than 30 days exceeding the value 120 µg m -3 for the maximum 8-hr O3 concentration as a consequence of the transport of O3 precursors downwind the Madrid and Barcelona metropolitan areas, and industrial areas and cities in the Mediterranean coast.

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“…In order to compare the effect of using different resolutions, I simulate two wind events with the WRF model (Version 3.5) [Michalakes et al, 2004, Skamarock et al, 2005, one of which is weak and the other one strong compared to the composite of DWE described in the previous chapter. The wind events, also seen by local weather stations, were mainly identified with a condition on wind speed.…”

Section: Methodsmentioning

confidence: 99%

Strong wind events across Greenland�s coast and their influence on the ice sheet, sea ice and ocean

Oltmanns¹

2015

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In winter, Greenland's coastline adjacent to the subpolar North Atlantic and Nordic Seas is characterized by a large land-sea temperature contrast. Therefore, winds across the coast advect air across a horizontal temperature gradient and can result in significant surface heat fluxes both over the ice sheet (during onshore winds) and over the ocean (during offshore winds). Despite their importance, these winds have not been investigated in detail, and this thesis includes the first comprehensive study of their characteristics, dynamics and impacts. Using an atmospheric reanalysis, observations from local weather stations, and remote sensing data, it is suggested that high-speed wind events across the coast are triggered by the superposition of an upper level potential vorticity anomaly on a stationary topographic Rossby wave over Greenland, and that they intensify through baroclinic instability. Onshore winds across Greenland's coast can result in increased melting, and offshore winds drive large heat losses over major ocean convection sites.Strong offshore winds across the southeast coast are unique over Greenland, because the flow is funneled from the vast ice sheet inland into the narrow valley of Ammassalik at the coast, where it can reach hurricane intensity. In this region, the cold air, which formed over the northern ice sheet, is suddenly released during intense downslope wind events and spills over the Irminger Sea where the cold and strong winds can drive heat fluxes of up to 1000 W m −2 , with potential implications for deep water formation. Moreover, the winds advect sea ice away from the coast and out of a major glacial fjord.Simulations of these wind events in Ammassalik with the atmospheric Weather Research and Forecast Model show that mountain wave dynamics contribute to the acceleration of the downslope flow. In order to capture these dynamics, a high model 3 resolution with a detailed topography is needed. The effects of using a different resolution locally in the valley extend far downstream over the Irminger Sea, which has implications for the evolution and distribution of the heat fluxes.

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The Weather Research and Forecast Model: Software Architecture and Performance

Cited by 325 publications

References 8 publications

Impact of HONO sources on the performance of mesoscale air quality models

Impact of HONO sources on the performance of mesoscale air quality models

Spatio-temporal variability of concentrations and speciation of particulate matter across Spain in the CALIOPE modeling system

Strong wind events across Greenland�s coast and their influence on the ice sheet, sea ice and ocean

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