Reply on RC2
Year: 2023
Type: peer-review
Abstract: . Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are trained using the historical measurement datasets independently collected at the environmental monitoring stations, and their operational forecasts onward by the inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions. In contrast, deterministic chemical transport models (CTM), which simulate the full life cycles of air pollutants, provide forecasts that are continuous in 3D field. However, owing to the complex error sources due to the emission, transport, and removal of pollutants, CTM forecasts are typically biased particularly in fine scale. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our recent regional-feature-selection machine learning prediction (RFSML v1.0) and a CTM forecast. The prediction fusion was conducted using the Bayesian theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented significant improvements than the pure CTM. Moreover, covariance inflation further enhanced the fused prediction particularly in the cases of severe underestimation.
Author: Jianbing Jin
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
OpenAlex ID: W4367051772