Reply on AC1

Abstract

Abstract. Modeling and observational techniques are pivotal in aerosol research, yet each approach exhibits inherent limitations. Aerosol observation is constrained by its limited spatial and temporal coverage compared to models. On the other hand, models tend to possess higher uncertainties and biases compared to observations. Aerosol data assimilation has gained popularity as it combines the advantages of both methods. Despite numerous studies in this domain, few have addressed the challenges faced in assimilating aerosol data with significant differences in magnitude and degree of freedom between the model state and observations, especially in the vertical direction. These challenges can lead to the preservation or even exacerbation of structural inaccuracies within the assimilation process. This study investigates the sensitivity of dust aerosol data assimilation to the vertical structure of the aerosol profile. We assimilate a variety of dust observations, encompassing ground-based particulate matter (PM10) measurements and satellite-derived dust optical depth (DOD) data, using the Ensemble Kalman Filter (EnKF). The assimilation process is elucidated, detailing the assimilation of raw ground-based and satellite-based observations for an optimized three-dimensional (3D) posterior state. To demonstrate the impact of accurate versus erroneous prior aerosol vertical profiles on the assimilation result, we select three cases of super dust storms for analysis. Our findings reveal that the assimilation of ground observations would optimize the dust field at the ground in general. However, the vertical structure presents a more complex challenge. When the prior profile accurately reflects the true vertical structure, the assimilation process can successfully preserve this structure. Conversely, if the prior profile introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile. This is also found in the assimilation of DOD, which exhibits a comparable pattern in its sensitivity to the initial aerosol profile's accuracy.

Authors 8

1. 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

2. 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

3. Ting Yang

   Chinese Academy of Sciences · Institute of Atmospheric Physics · State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry

   Affiliation as printed

   State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

4. Xi Chen

   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. Arjo J. Segers

   Netherlands Organisation for Applied Scientific Research

   Affiliation as printed

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

6. 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

7. Hong Liao

   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

8. Wei Han corresponding

   China Meteorological Administration

   Affiliation as printed

   CMA Earth System Modeling and Prediction Centre, Chinese Meteorological Administration, Beijing, China