Meteorological Process Overview
Light can excite airborne molecules and turn them into other molecules.
When pollutants are emitted into the atmosphere from smoke stacks, tailpipes, fires, trees, dust storms, etc., they are transported by the wind (advection) and diffused by turbulence.
- Turbulent Dispersion
The atmosphere is a generally turbulent environment, especially close to the Earth’s surface, which means it contains several eddies of different length and time scales affecting pollutant transport. Mixing and random movement of eddies result in a diffusion-like transport that is often orders of magnitude stronger than molecular diffusion.
- Deep Convection
Clouds, especially thunderstorms, transport surface-based emissions to higher altitudes where these chemicals can live longer and be transported around the globe by the stronger winds aloft.
- Wet Deposition
Clouds produce rainfall, returning chemical compounds back to Earth's surface through wet deposition.
Meteorological Processes Overview
WeatherWeatherThe condition of the atmosphere at a particular place and time. Some familiar characteristics of the weather include wind, temperature, humidity, atmospheric pressure, cloudiness, and precipitation. Weather can change from hour to hour, day to day, and season to season. conditions strongly influence air quality. Wind, rain, temperature, clouds, sunshine, and humidity all impact chemistry in the air and near the ground. In this section you can read about the following:
Meteorology modeling is based on equations for the dynamics and physics of the atmosphere. These equations are translated into computer code and applied on 3-D gridsover any domaindomainsThe area in space or time period over which a prediction is made. and time duration of interest from global to local scales. Because meteorology is so critical for accurately predicting the build-up, transport, and removal of pollution, substantial effort is put into reproducing weather metrics reliably using state-of-science model evaluation methods.
The approach used to inform CMAQ meteorological processes includes both explicit partial differential equations of mass and energy conservation, as well as parameterizations that reproduce the general impacts of extremely complex processes. The equations are integrated forward in time using initial and boundary conditions, which are usually provided by a model at the global or regional scale. The equations simulate weather conditions, such as temperature, relative humidity and wind speed/direction. In retrospective applications (looking at past events), which is how CMAQ is primarily used, weather observations and analyses can be used to “nudge” the model-predicted values toward reality. This process, called four-dimensional data assimilation (see below), improves the accuracy of the weather model.
Air pollutants do not generally remain where they are emitted because of transport by the wind. Pollutants can be transported across cities, states, regions or even across the globe CMAQ simulates how winds move pollutants both vertically and horizontally in the atmosphere from local to global scales.
When pollutants are emitted into the atmosphere from sources like smoke stacks, tailpipes, fires, trees, and dust storms, they are transported by the wind, or advectionadvectionThe transfer of heat or matter by the flow of a fluid, especially horizontally in the atmosphere or the sea., and diffused by turbulence. Neither advection nor dispersion change the total pollutant mass concentration, just the spatial distribution. The atmosphere is a generally turbulent environment, especially close to the Earth’s surface, which means it contains several eddies of different length and time scales affecting pollutant transport. In this turbulent environment, mixing and random movement of eddies result in a diffusion-like transport that is orders of magnitude larger than molecular diffusion.
Planetary Boundary Layer (PBL) and Land-Surface Module (LSM)
The ACM2 (Pleim, 2007) is a combination of the ACM, which is a simple transilient model that was originally a modification of the Blackadar convective model (Blackadar, 1978), and an eddy diffusion model. Therefore, in convective conditions, the ACM2 can simulate rapid upward transport in buoyant plumes and local shear induced turbulent diffusion. The partitioning between the local and non-local transport components is derived from the fraction of non-local heat flux according to the model of Holtslag and Boville (1993). The algorithm transitions smoothly from eddy diffusion in stable conditions to the combined local and non-local transport in unstable conditions. The ACM2 is particularly well suited for consistent PBLPBLThe troposphere can be divided into two parts: a planetary boundary layer, PBL, extending upward from the surface to a height that ranges anywhere from 100 to 3000 m, and above it, the free atmosphere. The boundary layer is directly influenced by the presence of the Earth's surface, responding to such factors as frictional surface drag, solar heating, and evapotranspiration. Each of these factors generates turbulence of various-sized eddies. transport of atmospheric quantities, including both meteorological and chemical trace species.
Pleim Surface Layer
The Pleim surface layer scheme (Pleim, 2006) was developed as part of the Pleim-Xiu (PX) LSM, but it can be used with any LSM or PBL model. This scheme is based on similarity theory and includes parameterizations of a viscous sub-layer in the form of a quasi-laminar boundary layer resistance accounting for differences in the diffusivity of heat, water vapor, and trace chemical species. The surface layer similarity functions are estimated by analytical approximations from state variables.
Pleim-Xiu Land Surface Model
The PX LSM (Pleim and Xiu, 2003; Xiu and Pleim, 2001), originally based on the ISBA model (Noilhan and Planton, 1989), includes a 2-layer force-restore soil temperature and moisture model. The top layer is taken to be one centimeter (cm) thick, and the lower layer is 99 cm. The PX LSM features three pathways for moisture fluxes:
- Soil evaporation
- Evaporation from wet canopies
Evapotranspiration is controlled by bulk stomatal resistance that is dependent on:
- Root zone soil moisture
- Photosynthetically active radiation
- Air temperature
- Relative humidity at the leaf surface
Grid aggregate vegetation and soil parameters are derived from fractional coverages of land use categories and soil texture types. There are two indirect nudging schemes that correct biases in two-meter air temperature and relative humidity by dynamic adjustment of soil moisture (Pleim and Xiu, 2003) and deep soil temperature (Pleim and Gilliam, 2009). These physics options were put into WRF (Gilliam and Pleim, 2010) when it became the primary meteorological model platform for EPA air quality modeling. Note that a small utility program (IPXWRF) can be used to propagate soil moisture and temperature between consecutive WRF runs to create a continuous simulation of these quantities. Obsgrid pre-processor is used to generate soil nudging inputs for the PX LSM.
- Download the IPXWRF utility - IPXWRF is a .tar.gz file (16KB) containing Fortran code and a README documentation file.
- Dowload the Obsgrid utility - Obsgrid is a tar.gz file (442KB) containing Fortran code and a README documentation file.
- Section from WRF3.7 Technical Documentation related to air quality modeling EXIT- Description and procedures for using the Pleim-Xiu LSM, ACM2 PBL and Pleim Surface Layer Scheme in WRF including best practices and namelist options.
Clouds are a major component of the Earth system. Clouds produce rainfall, a key process in the hydrological cycle. They also block incoming sunlight during daytime, which cools the surface and changes chemical reactions occurring where we live and breathe. They also retain heat at night near. Clouds also impact air quality through transport, wet deposition (“dirty” rain), and chemical reactions occurring within the clouds. Lightning associated with thunderstorms is also important to air quality because of emissions (NOx) that can be transformed into ozone by sunlight. For these reasons, clouds play an integral role in the ability to precisely model air quality.
Clouds influence almost every aspect of weather, air quality, and climate. What’s more, these influences are different depending on the type of cloud considered. For example, thunderstorms can produce heavy rainfall and lightning, making them an immediate threat to life and property. Shallow clouds might seem more benign, but play a critical role in Earth’s energy balance and cover much of the Earth’s oceans. Here are a few important aspects of clouds in our Earth system:
- Clouds modify the radiative balance of Earth by reflecting and scattering incoming solar radiation (cooling effect) and by trapping outgoing terrestrial radiation (warming effect).
- Clouds produce rainfall, a key process in hydrological cycle. They also have large agricultural impacts, cause flooding (if excessive) or drought (if insufficient), and return chemical compounds back to Earth’s surface through wet deposition.
- Clouds, especially thunderstorms, transport surface-based emissions to higher altitudes where these chemicals can live longer and be transported around the globe by the stronger winds aloft.
Solar and terrestrial radiation, which are the largest contributors of energy to the atmosphere, play a major role in determining the weather and atmospheric chemical composition. The efficiency with which radiation is transported through the atmosphere influences the temperature and pressure as one moves from the ground to the upper atmosphere. Of course, solar radiation also evaporates water, causing it to cycle through the atmosphere and form clouds. The energy absorbed by airborne molecules via radiation can cause them to break apart and react with other compounds and initiate much of the photochemistry in the atmosphere. The cascade of reactions that follow this process quite commonly results in the formation of air quality pollutants like ozone and particulates. At ground level, radiation controls biological processes like photosynthesis, which releases compounds to the air like oxygen, carbon dioxide and hydrocarbons. The above processes all act simultaneously with complicated interdependencies.
Conceptually, atmospheric radiation is divided into two broad categories:
- Incoming solar radiation consists of shorter wavelengths (ultra-violet and visible).
- Outgoing terrestrial radiation, from the Earth’s surface, is made of longer wavelengths (infrared) and less energy.
The total radiation at any time and place must account for upward and downward radiative fluxes as well as any absorption that occurs. The earth’s surface, clouds, atmospheric gases and aerosols affect both short- and long-wave radiation because they reflect scatter and absorb radiation. Determining the fluxes requires solving equations representing the transfer of the radiation within the atmosphere.
Solving these above equations can differ between the long- and short-wavelength types, but each requires describing how the atmosphere and surface scatter and absorb radiation. Most meteorological models solve for the fluxes for one vertical column at a time, an approach that assumes that horizontal radiation transfer is not as important as vertical transfer. The mathematical methods used make approximations to reduce the computational time needed to make predictions. Popular methods include the Two- or Four-stream solutions and the Discreet Ordinate Solution.
Blackadar, A.K. (1978). Modeling pollutant transfer during daytime convection. Preprints, Fourth Symp. On Atmospheric Turbulence, Diffusion and Air Quality, 443–447.
Gilliam, R.C., J. E. Pleim, 2010: Performance assessment of new land-surface and planetary boundary layer physics in the WRF-ARW. Journal of Applied Meteorology and Climatology, 49, 760-774.
Holtslag, A.A.M., & Boville, B.A. (1993). Local versus nonlocal boundary-layer diffusion in a global climate model. J. Clim., 6, 1825–1842.
Noilhan, J., & Planton, S. (1989). A simple parameterization of land surface processes for meteorological models, Mon. Wea. Rev., 117, 536–549.
Pleim, J.E. (2006). A simple, efficient solution of flux-profile relationships in the atmospheric surface layer. J. Appl. Meteor. Climatol, 45, 341–347.
Pleim, J.E. & Gilliam, (2009). An indirect data assimilation scheme for deep soil temperature in the Pleim-Xiu land surface model. J. Appl. Meteor. Climatol., 48, 1362–1376, doi: 10.1175/2009JAMC2053.1Exit
Pleim, J.E., & Xiu, A. (2003). Development of a land surface model. Part II: Data assimilation. J. Appl. Meteor., 42, 1811–1822.
Wong, D.C., J. Pleim, R. Mathur, F. Binkowski, T. Otte, R. Gilliam, G. Pouliot, A. Xiu, J.O. Young, and D. Kang, WRF-CMAQ two-way coupled system with aerosol feedback: software development and preliminary results, doi:10.5194/gmd-5-299-2012, Geosci. Model Dev., 5, 299-312, 2012.
Xiu, A., & Pleim, J.E. (2001). Development of a land surface model. Part I: Application in a mesoscale meteorological model. J. Appl. Meteor., 40, 192 – 209.