Comment on gmd-2023-219

Abstract

Abstract. Dust storms pose significant threats to human health and property. Accurate forecasting is crucial for taking precautionary measures. Dust models have suffered from uncertainties from emission and transport factors. Data assimilation can help refine biased models by incorporating available observations, leading to improved analyses and forecasts. The Ensemble Kalman Filter (EnKF) is a widely-used assimilation algorithm that effectively tunes models, particularly in terms of intensity adjustment. However, when the position of the simulation does not align consistently with the observations which is referred to as position error, the EnKF algorithm struggles. This is because its background covariance normally represents intensity uncertainty, while the positional errors in the long distance transport are difficult to be quantified and were usually neglected. In this paper, we propose a novel Neighboring Time Ensemble Kalman Filter (NTEnKF). In addition to the original ensembles quantifying dust loading variation, this methodology introduces extra ensembles from neighboring time for describing the potential spread of dust position. The enlarged ensemble captures both intensity and positional errors, allowing observations to be thoroughly resolved into the assimilation calculations. We tested this method on three major dust storm events that occurred in spring 2021. The results show that position error significantly degraded dust forecasting in terms of RMSE and NMB, and hindered the EnKF from assimilating valid observations. In contrast, the NTEnKF yielded substantial improvements in both dust analysis fields and forecasts compared to the EnKF.

Authors 10

1. Mijie Pang

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Joint International Research Laboratory of Climate and Environment Change, 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. Jianbing Jin corresponding

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Joint International Research Laboratory of Climate and Environment Change, 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

3. Arjo J. Segers

   Netherlands Organisation for Applied Scientific Research

   Affiliation as printed

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

4. Huiya Jiang

   Nanjing Agricultural University

   Affiliation as printed

   College of Environment and Resources, Nanjing Agricultural University

5. Wei Han

   China Meteorological Administration

   Affiliation as printed

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

6. Ji Xia

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Joint International Research Laboratory of Climate and Environment Change, 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. Fang Li

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Joint International Research Laboratory of Climate and Environment Change, 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

8. Jiandong Li

   Nanjing University of Information Science and Technology

   Affiliation as printed

   Joint International Research Laboratory of Climate and Environment Change, 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

9. Hai Xiang Lin Aachen Institute of Environment Sciences

   Leiden University · Delft University of Technology

   Affiliation as printed

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

   Institute of Environment Sciences, Leiden University, The Netherlands

10. Hong Liao

    Nanjing University of Information Science and Technology

    Affiliation as printed

    Joint International Research Laboratory of Climate and Environment Change, 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