Data-constrained Air Quality Modeling

Description

Chemistry transport (CTM) and air quality (AQ) models play a crucial role in understanding the chemical composition of the troposphere, air quality, and human health. This research opportunity (RO) invites projects that will utilize CTMs and AQ models, along with in situ and remote-sensing data, to enhance the understanding and prediction of tropospheric chemistry and air quality at both regional and global levels.

An important component of the proposed projects is to show how in situ and remote-sensing data can improve the accuracy of CTMs and AQ models in replicating and forecasting tropospheric composition. Projects utilizing data assimilation techniques in these models are highly encouraged.

A range of CTMs (e.g., WRF-Chem, GEOS-Chem) and AQ models (e.g., CMAQ) are available to explore the processes influencing the atmospheric concentrations of trace gases and aerosols. In addition, several regional and global networks for in situ measurements (e.g., EPA’s AQS) and field campaigns (e.g., AGES+ (AEROMMA+CUPiDS, GOTHAAM, EPCAPE, STAQS), DISCOVER-AQ) can be utilized to improve and assess AQ model outputs.

Projects that focus on using satellite data (e.g., OMI, TROPOMI, TEMPO) and ground-based remote sensing (e.g., TOLNet, Pandora) will be prioritized. The use of data from NASA’s new TEMPO geostationary sensor is also encouraged.

• PhD in an Earth Science-related field.
• Experience in running and developing 3D atmospheric CTMs and applying data assimilation techniques.
• Strong analytical skills and proficiency in computer programming.
• Excellent written and verbal communication skills.