Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD

Abstract

Satellite aerosol optical depth (AOD) data assimilation (DA) using numerical air quality forecast models has shown a limited improvement due to large uncertainties in the AOD observation operator. This study employed a machine learning (ML) algorithm to estimate the ground-level particulate matter (PM) from the Geostationary Ocean Color Imager (GOCI) AOD through the random forest with high accuracy. Analysis fields were subsequently produced by applying PM estimations to the Weather Research and Forecasting-Chemistry/three-dimensional variational DA system. Initialization of the model with the new analysis remarkably reduced the analysis error and increased the forecast skill. The PM10 prediction showed significant benefits for up to 24 forecast hours, whereas PM2.5 prediction was improved for up to six forecast hours. Considering a broad spatial coverage by satellites, the synergistic use of DA and ML can maximize the effectiveness of satellite DA for air quality forecasts at the ground.