Welcome to our research group website!
We study modeling of air quality and atmospheric composition from local to regional scales. The topics of our research include:
- Research in air quality forecasting to improve the predictive capability of pollution episodes (e.g., haze conditions, forest fires, dust outbreaks)
- Data assimilation and inverse modeling: This means using atmospheric composition observations (e.g., satellite, ground-based, airborne) to improve air quality forecasts or better constrain emission sources
- Investigation on modeling of aerosols (particles in the atmosphere) and it's interactions with clouds and radiation, which are in part responsible for the uncertainties in climate change projections
If you want to know more about these topics, go to our Research page.
The image shows two MODIS-Aqua products for October 17th 2008 over the persistent Southeast Pacific stratocumulus deck, off the coasts of Chile and Peru. The background is a true color image of the clouds at 250 m resolution, with an iridescent overlay showing a 1km resolution cloud droplet number (Nd) retrieval for the same overpass. Red, green and blue colors show high (~1000 #/cm3), medium (~100 #/cm3) and low (~10 #/cm3) Nd values. The east to west Nd gradient is produced mainly by anthropogenic aerosols, which influence cloud properties. We have shown that applying these retrievals in a data assimilation framework can help improve model estimates of below-cloud aerosol, allowing the observing system to “see aerosols” even under cloudy conditions. For details refer to our PNAS paper.
Recent Publications
- Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning
- Forecasting Daily Fire Radiative Energy Using Data Driven Methods and Machine Learning Techniques
- Evaluation of Wildfire Plume Injection Heights Estimated from Operational Weather Radar Observations Using Airborne Lidar Retrievals