GOES-R PM2.5 Evaluation and Bias Correction: A Deep Learning Approach

Abstract

Estimating surface-level fine particulate matter from satellite remote sensing can expand the spatial coverage of ground-based monitors. This approach is particularly effective in assessing rapidly changing air pollution events such as wildland fires that often start far away from centralized ground monitors. We developed Deep Neural Network (DNN) algorithm to improve hourly PM2.5 estimates informed by GOES-R; meteorology forecasts, and PM2.5 observations from AirNow. The surface-satellite-model collocated data sets for the period of 2020–2021 were used to assess the biases in GOES-GWR PM2.5 (only operationally available data set) against AirNow measurements at hourly and daily scales. Then a DNN based bias correction algorithm is used to improve the accuracies of GOES-GWR PM2.5. The DNN uses GOES-GWR PM2.5, GOES-R aerosol parameters, and HRRR meteorological fields as input and AirNow PM2.5 is used as target variable. The application of DNN reduced the PM2.5 biases as compared to GOES-GWR estimates. RMSE was also reduced to 6.55 μg/m3 from 8.72 μg/m3 in GOES-GWR estimates. The DNN model was also evaluated on two sets of independent data sets for its robustness. In the first independent data set for the first half of 2020, ∼89% of stations show an increase in correlation (r) and, ∼76% and ∼62% of stations show a reduction in bias. The IOA and r for the independent data were 0.77 and 0.61 (GWR: 0.68 and 0.53) and RMSE was 4.48 μg/m3 (GWR = 6.13 μg/m3) for the same period. The algorithm will be operationally deployed by NOAA and US-EPA to estimate surface level PM2.5 from satellite derived Aerosol optical depth.