Development of Techniques for Assimilating GOES Satellite Data into Regional-Scale Photochemical Models

EPA Grant Number: R826770
Title: Development of Techniques for Assimilating GOES Satellite Data into Regional-Scale Photochemical Models
Investigators: McNider, R. T. , Christopher, S. A. , Norris, W. B.
Current Investigators: McNider, R. T. , Biazar, Arastoo , Norris, W. B.
Institution: University of Alabama in Huntsville
EPA Project Officer: Shapiro, Paul
Project Period: July 1, 1998 through June 30, 2001 (Extended to January 17, 2004)
Project Amount: $404,127
RFA: Air Pollution Chemistry and Physics (1998) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air , Engineering and Environmental Chemistry


The purpose of this proposal is to improve the fidelity of the physical atmosphere in photochemical modeling systems, specifically Models-3. This is accomplished by enlisting methods of satellite remote sensing to reduce the uncertainty in the cloud and soil-moisture information which meteorological models pass to their photochemical counterparts. The factors forming the basis for the proposal include the following. Among the largest sources of uncertainty in regional photochemical modeling is the specification of clouds and soil moisture. Clouds dominate the availability of actinic flux, control the distribution of surface insolation, and govern the variations in surface temperatures. Soil-moisture availability feeds back to significantly affect the partitioning of sensible and latent heat flux, which also influences the magnitude of surface temperatures. Surface temperatures, in turn, affect the variations in biogenic emission rates, soil-moisture availability, and regional mixing heights. Meteorological models predict clouds but only in a highly parameterized manner, causing model estimates of spatial distributions and radiative characteristics to be subject to considerable error. In recent years progress has been made in controlling model error through assimilation of wind and temperature observations, but corresponding progress has been lacking in assimilating cloud and soil-moisture data. The primary reason is the sparseness of National Weather Service cloud observations and the almost total absence of soil-moisture observations. Thus, the variables so closely associated with variations in biogenic emission rates, photolysis rates, and boundary-layer depths are among the most poorly prescribed.


The approach will begin with a quantification of the magnitude of the error rendered by a widely used meteorological model, MM5, for a specified period. No satellite data will be assimilated into these baseline runs. The resultant cloud fields will be objectively evaluated against the cloud fields available in satellite images and against surface point measurements when available. Also, the air quality associated with the baseline meteorology will be estimated using the Models-3 framework.

Work will then proceed toward demonstrating that assimilating satellite information pertaining to clouds and soil moisture into MM5 can reduce the error significantly. Four satellite-data assimilation methods will be used. Each of these methods, singly and in combination, will be applied in MM5. Each will be objectively compared to the cloud fields in satellite images.

The final task will consist of using the meteorological fields produced from these assimilation methods to estimate the resultant air-quality. Because of the availability of extensive, research quality data and the occurrence of a number of periods of high ozone concentrations, model performance will be evaluated for the periods covered by the 1995 summer field campaigns of the Southern Oxidants Study and the North American Research Strategy for Tropospheric Ozone. Also, research quality PM2.5 data is expected to be available from the 1999 Nashville field campaign of the Southern Oxidants Study. The approach will be tested in photochemical simulations using the Models-3 regional-scale modeling system.

Expected Results:

An immediate result will be a demonstration, supported by statistical data, that a model such as MM5, when initialized only with National Weather Service cloud observations and informed guesses about soil-moisture availability, can be appreciably improved through the assimilation of satellite information. A longer-term result is expected to be the production of an upgraded version of the MM5 system having the capability to preprocess and assimilate satellite data so that clouds and soil moisture can be incorporated into Models-3 or other such photochemical systems. This proposal is consistent with the RFP in the area of air pollution chemistry and physics. It specifically relates to the development and diagnostic evaluation of emission-based models and describing the interaction of cloud processes with atmospheric chemistry.

Improvement in Risk Assessment or Risk Management: Although a quantitative estimate is not possible until the study is completed, the anticipated improvements in MM5 performance are expected to carry over into an improvement in the performance of the Models-3 photochemical modeling system. As a result better estimates will be possible of both short- and long-term exposure of human populations and forest ecosystems to ozone and PM2.5 concentrations.

Publications and Presentations:

Publications have been submitted on this project: View all 4 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 3 journal articles for this project

Supplemental Keywords:

atmospheric chemistry, geostationary satellite, mesoscale meteorology, RFA, Scientific Discipline, Air, Ecology, particulate matter, Environmental Chemistry, Environmental Monitoring, Environmental Engineering, Engineering, Chemistry, & Physics, air quality standards, GOES satellite, remote sensing, cloud condensation, PM 2.5, air modeling, latent heat flux, boundary layer, soil, regional scale, photolysis wavelength, PM2.5, actinic flux, biogenic emissions, meterology

Progress and Final Reports:

  • 1999 Progress Report
  • 2000 Progress Report
  • 2001 Progress Report
  • 2002 Progress Report
  • 2003
  • Final Report