Arizona Air Quality Seminar

The increasing availability of measurements of chemical and aerosol constituents offers an opportunity to improve our capability to predict and assess chemical weather and air quality through the integration of these measurements with predictions from regional to global chemical transport models. Central to this integration is a chemical data assimilation (DA) and/or inverse modeling system that is reasonably efficient, effective, and flexible in assimilating measurements spanning multiple spatiotemporal scales and multiple chemical/aerosol species. In this talk, I will introduce the main idea behind chemical weather prediction, as well as the advances and challenges in the development and application of DA and inverse modeling approaches in atmospheric chemistry and physics. I will also introduce a data assimilation system for the Earth sciences based on community models and software packages being developed at the National Center for Atmospheric Research. This system includes a global chemistry-climate model (Community Atmosphere Model with Chemistry or CAM-Chem), a regional weather-air quality model (Weather Research and Forecasting with Chemistry or WRF-Chem), and an ensemble Kalman filter DA software (Data Assimilation Research Testbed or DART). I will present three key scientific/technical problems that this community is attempting to address with these DA approaches. These are: 1) estimating sources and sinks of trace gases and aerosols, 2) assimilating multi-species and/or multi-platform chemical data research and/or operational chemical weather forecasting; and 3) conducting observing system simulation experiments (or OSSEs) to support future satellite observations of global atmospheric composition. I will end this talk by posing a question regarding the complementary roles of machine learning and data assimilation in chemical weather prediction, especially as we move towards improving Earth system predictability across local to global scales.

Zoom Link:  https://zoom.us/j/91295185866?pwd=cWppb3YrYXkrNEFLRXpIam5GbEwvQT09

7:00 am Mountain Time = 9:00 am Eastern Time = 3:00 pm Central European Time = 9:00 pm Novosibirsk