An official website of the United States government.

We've made some changes to EPA.gov. If the information you are looking for is not here, you may be able to find it on the EPA Web Archive or the January 19, 2017 Web Snapshot.

Community Multiscale Air Quality Modeling System (CMAQ)

Continuous, Near Real-Time Evaluation of WRF-CMAQ

An Approach for the Rapid Scientific Evolution of the Modeling System

Background

Historically, the EPA’s Computational Exposure Division has evaluated retrospective, often annual length, simulations of WRF-CMAQ, summarizing the performance using monthly or seasonal statistical summaries.

Although informative, such an approach often masks finer scale temporal (i.e., diurnal to weekly) and spatial (mesoscale to synoptic) variability that greatly impacts the simulation of the atmosphere and hence air quality. 

In order to maintain WRF-CMAQ’s state-of-the-science status, as well as its ability to address emerging Agency needs, it is crucial that innovative evaluation approaches are developed and utilized that will allow for more rapid testing and hence more efficient evolution of the modeling system’s science.

Accordingly, the Division began running WRF-CMAQ continuously and in near real-time (CMAQ-NRT) in 2014, following the:

►        protocol established when EPA was directly involved with NOAA’s National   Air Forecast Capability (NAQFC) (Eder et al, 2019), and 

►        recommendations published in the Bulletin of the American Meteorological  Society entitled: “The Emergence of Weather-Related Test Beds Linking  Research and Forecasting Operations”( Ralph et al, 2013), and illustrated  in the adjacent  schematic.

Diagram of CMAQ Near Real Time frameworkFigure 1.    Flowchart of procedures recommended by the American Meteorological Society linking weather-related
research and forecasting operations.

CMAQ-NRT Protocol

CMAQ-NRT provides continuous and in near real-time evaluation at finer spatial and temporal scales which has allowed for immediate and ongoing analysis, thereby facilitating model evaluation (both performance and diagnostic) of PM2.5 (mass only) and O3 concentration.

         Observations obtained from EPA’s Air Quality System (AQS) are used in the evaluation incorporating roughly 450 PM2.5 mass and 900 O3 monitors.  Results are examined using a variety of statistical and visualization and are discussed by Division scientists in biweekly meetings while antecedent meteorological and air quality conditions remain familiar.

A compilation of typical statistical and graphical analyses used in the CMAQ-NRT evaluations for O3 and PM2.5 (Figure 2) is show below.

Figure 2Figure  2 .  Typical daily analysis for PM2.5 and O3 for June 3, 2019.

Advantages of running CMAQ-NRT are numerous, and, as documented in Environmental Manager (Eder et al, 2017) led to:

The identification, and when possible, the resolution of numerous issues that conventional evaluation techniques would likely miss, including:

            ●   excessive wind-blow dust events,

            ●   inaccurate planetary boundary conditions over the Great Lakes,

            ●   under-representation of Saharan dust transport, and:

Improvements in characterizing:

            ●   lateral meteorological and chemical boundary conditions,

            ●   wind blown dust episodes,

            ●   organic aerosol concentrations and partitioning.

Figure 3Figure  3.  Title page of Environmental Manager article documenting the CMAQ-NRT evaluation approach.

More recently, issues involving ozone deposition, unaccounted for pyro-technique emissions and unresolved cold pools in mountain valleys have been documented and addressed.

References

  Eder, B.; Kang, D.; Rao, S.; Mathur, R.; Yu, S.; Otte, T.; Schere, K.; Wayland, R.; Jackson, S.; Davidson, P.; McQueen, J.; Bridgers, G. Using National   Air Quality Forecast Guidance to Develop Local Air Quality Index Forecasts; Bull. Amer. Meteor. Soc. 2010, 91, 313-323.

Ralph, M.; Intrieri, J.; Andra, Jr., D.; Atlas, R.; Boukabara, S.; Bright, D.; Davidson, P.; Entwistle, J.; Gaynor, J.; Goodman, S.; Jiing, J.; Harless, A.; Huang, J.; Jedlovec, G.; Kain, J.; Koch, S.; Kuo, B.; Levit, J.; Murillo, S.; Riishojgaard, L.; Schneider, T.; Schneider, R.; Smith, T.; Weiss, S. The Emergence of Weather-Related Test Beds Linking Research and Forecasting Operations; Bull. Amer. Meteor. Soc. 2013, 94, 1187-1211.

Eder, B.; Gilliam, R.; Pouliot, G.; Mathur, R; and Pleim, J.  Continuous, Near Real-Time Evaluation of Air Quality Models; Environmental Managers, A&WMA, April, 2017.