Comment on gmd-2022-301

Abstract

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.

Authors 8

1. Jianbing Jin corresponding

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

2. Arjo J. Segers

   Netherlands Organisation for Applied Scientific Research

   Affiliation as printed

   TNO, Department of Climate, Air and Sustainability, Utrecht, The Netherlands

3. Ke Li

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

4. Bufan Xu

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

5. Wei Han

   China Meteorological Administration

   Affiliation as printed

   Numerical Weather Prediction Center, Chinese Meteorological Administration, Beijing, China

6. Mijie Pang

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

7. Hai Xiang Lin Aachen Institute of Environmental Sciences (CML)

   Leiden University · Delft University of Technology

   Affiliation as printed

   Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands

   Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands

8. Hong Liao corresponding

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China