Publication Date

Summer 2022

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Meteorology and Climate Science

Advisor

Sen Chiao

Subject Areas

Meteorology

Abstract

As wildfires become more frequent and severe under climate change, there is a growing need to simulate the impact of fires on air quality and human health using numerical models. The National Air Quality Forecast Capability is an operational air quality forecasting system that provides nationwide ozone and particulate matter (PM2.5) prediction. It is made up of a chemical transport model – the Community Multiscale Air Quality (CMAQ) model – driven by a meteorological model. Recently, the Global Forecast System (GFS) replaced the North American Mesoscale Forecast System (NAM) to drive CMAQ. In addition to these two modeling systems, the High Resolution Rapid Refresh (HRRR) model also provides smoke prediction in a higher resolution. Evaluation of model performance tells us whether there is significant improvement with the upgrade or a higher resolution. In this study, GFS-CMAQ, NAM-CMAQ and HRRR forecast of near-surface temperature, moisture, wind, planetary boundary layer height (PBLH), PM2.5, and ozone are evaluated against observational data in the San Francisco Bay Area and the Sacramento Valley, California. GFS-CMAQ shows a warm and dry bias in the simulated meteorological fields. And it tends to overestimate daytime surface ozone and underestimate nighttime surface ozone, which might be related to the uncertainties in the ocean-atmospheric interaction simulation. Underestimation of surface PM2.5 by GFS-CMAQ can be potentially caused by missing wildfire sources, insufficient long-term fuel loading, and errors in smoke dispersion in the model. These results help to understand localized model biases and facilitate future model development.

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