Directorate

• Contents
• EXECUTIVE SUMMARY
• 1. SCOPE OF THE PROGRAM
• 2. IMPROVING OUR UNDERSTANDING OF EARTH-SYSTEM PROCESSES
• 2.1 Scale Interactions Associated with Precipitating Convective Systems
• 2.2 Understanding the Dynamics and Predictability of Weather Systems on Time Scales of 0-48 h
• 2.3 Fundamental Research on Precipitation Processes, with an Emphasis on their Improved Representation in Numerical Models
• 2.4 Understanding the Dynamics, Physics and Chemistry of Multi-Scale Atmospheric Chemical Constituent and Particulate Transport, Dispersion and Transformations
• 2.5 Fundamental Research in Boundary Layers and their Interfacial Interactions
• 3. ADVANCING THE SCIENCE OF ATMOSPHERIC PREDICTION
• 3.1 Advancing Short-Range Weather Prediction
• 3.2Advancing Data Assimilation Research
• 3.3 Improving Forecasting of Pollutants and Hazardous Materials
• 4. ADVANCED COMMUNITY RESEARCH TOOLS
• 4.1 WRF Community Model
• 4.2 Community Data Assimilation Techniques
• 4.3 Nested Community Climate Model
• 5. SUMMARY
• Acronym List
• Print Version
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Mesoscale & Microscale Meteorology Division Science Plan:

3.3 Improving Forecasting of Pollutants and Hazardous Materials

(contents)

Goal: To develop an advanced capacity for predicting pollutant and hazardous material distributions, transport and dispersion at microscale and mesoscale, and on 0-36 h time scales.

Accurate forecasting of extreme pollution and hazardous substance concentrations, both natural and anthropogenic, on a time scale of a few hours or less would have tremendous benefits to human welfare. This has implications for the work of our colleagues in RAL, for identifying the impacts of pollution on society, and for homeland security. For most airborne pollutants and hazardous materials, the most detrimental effects are caused by the peak, rather than the mean, concentration. Yet the bulk of air quality models predict only mean concentrations over a grid volume and averaging time. Current statistical/empirical dispersion models are typically tuned to specific environments.

 

To forecast chemical species concentrations from urban regions or in regions associated with cloud systems, mesoscale motions must be accurately represented in the modeling system. The Weather Research and Forecast (WRF) model is becoming our primary tool for examining tracer transport, cloud chemistry, and pollutant dispersion on the mesoscale and microscale.

For the mesoscale, simulations of WRF with tracers are being performed for regions, for example, central Mexico, in which air quality often deteriorates because of weak dynamical forcing and/or in regions of complex terrain. These simulations allow us to evaluate the performance of WRF in complex topography and assist us in preparations to use the WRF model in forecasting for chemistry field campaigns.

Predicting chemical species redistribution by deep convection is being pursued with the WRF model coupled with chemistry (WRF-Chem). WRF-Chem is being developed by NOAA scientists in collaboration with ACD and the WRF community. We are contributing to WRF-Chem via implementation of transport schemes and cloud chemistry modules. Our goal is to be able to accurately simulate in WRF-Chem all of the processes in deep convection that affect the distribution of chemical species. These processes include aqueous chemistry, effects of cloud microphysics, production of NOx by lightning, and modification of actinic flux by cloud scattering of UV radiation.

Wildland fire emissions are represented particularly poorly in air quality models, in part because the atmospheric transport models do not treat the plume produced by the fire and because the emissions fluxes do not incorporate atmospheric impacts on fire behavior, which controls the buoyancy of the fire plume, the chemical species that are produced, the rate at which gaseous and particulate emissions are produced, particle sizes, chemical composition, and size distribution, and ultimately the transport height to which the smoke plume penetrates. To improve the simulation of the impacts of fires on air quality, a wildland fire module (WRF-Fire) is being implemented as a plug-in module for WRF. In collaborative work with the Wildland Fire Program and ACD, we will use WRF-Fire in conjunction with WRF and WRF-Chem as a linked emissions-chemistry-dynamical transport system to study air quality episodes related to wildfires and release the system as an integrated community tool for next-generation air quality modeling.

Dispersion in the planetary boundary layer is a process driven by the stochastic nature of turbulence and random mesoscale events. Therefore, contaminant concentrations should be found on a probabilistic basis, which requires, at a minimum, estimates of both the mean concentration and its variance. The probability of occurrence of rare or infrequent peak concentrations can be determined based on the concentration mean and variance with a known, assumed, or calculated statistical frequency distribution. These issues can be addressed by developing a forecast model that includes the small-scale turbulence and stochastic forcing so that the probability distribution of concentrations -- including peak values -- can be obtained. Specific ensemble techniques may also provide valuable forecast guidance, especially in regard to the potential error in the forecasts.

To study pollutant transport and dispersion on the microscale, we are adapting the WRF model by implementing an explicit turbulence resolving model in a nested manner so that it depicts coupled micro- and mesoscale motions in complex environments. This multiscale geophysics model will be used to determine mean scalar concentrations, their variances, and their peak values. Additional development of the multiscale model will include implementation of chemistry modules, Lagrangian particle dispersion modules, and land use models. Our goal is to improve forecasts of the transport and dispersion of hazardous material, pollutants from urban regions, and mineral dust distributions from arid regions.

Further progress in predicting pollutant and hazardous material concentrations requires the assimilation of meteorological and chemical measurements into micro- and mesoscale models. Our strategic plans are to explore the assimilation of pollutants from, for example, urban areas and specific sources (smoke stacks) to further improve air quality forecasting. In addition, the sensitivity of scalar concentrations to a variety of initial states will be investigated via ensemble forecasting.

Next section: ADVANCED COMMUNITY RESEARCH TOOLS

 

 

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