When conducting an air dispersion modeling analysis, it is very important that the meteorological data quality is as high as possible. AERMET – the meteorological pre-processor for the AERMOD dispersion modeling system – contains internal options which can boost data quality by filling short gaps of missing cloud cover and temperature data.
With each successive update to the AERMOD modeling system published by the U.S. EPA to the Support Center for Regulatory Atmospheric Modeling (SCRAM) website, Lakes Software updates our AERMOD View and AERMET View applications to support the new models. In previous Modeling Tips, we’ve discussed how to choose earlier versions of the AERMOD executable in AERMOD View, but it is also possible to revert to earlier versions of AERMET when using AERMET View.
For a very long time, using AERMOD to calculate the maximum annual average over a multi-year period required multiple model runs with each year being processed separately. That changed with a U.S. EPA update which allowed the model to produce individual year annual average results via the Post-Processing Output File (POSTFILE).
When conducting an air dispersion modeling analysis with the AERMOD modeling system, modelers may encounter situations where they want to validate or verify the model’s calculations. To support this, the model has the ability to generate Debugging Output Files of intermediate calculation results. This is controlled by the Control Pathway using the DEBUGOPT model keyword.
Lakes Environmental recently released the new AERMOD View Version 11.0 which includes support for the latest U.S. EPA model releases dated 22112 for the AERMOD & AERMET dispersion modeling components. The AERMET 22112 update debuts an entirely new code-base for the regulatory meteorological preprocessor. As illustrated below, the new AERMET has been changed from a three-stage to a two-stage process.
When conducting an air dispersion modeling analysis, the ability to present high-quality graphical depictions of the output is extremely important. Such images provide context to modeled results, and they provide a valuable tool to modelers in understanding and explaining the model’s calculations.