Preprints
https://doi.org/10.5194/gmd-2024-113
https://doi.org/10.5194/gmd-2024-113
Submitted as: methods for assessment of models
 | 
19 Sep 2024
Submitted as: methods for assessment of models |  | 19 Sep 2024
Status: this preprint is currently under review for the journal GMD.

The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2

Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Hai Xiang Lin, Hong Liao, and Wei Han

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.

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Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Hai Xiang Lin, Hong Liao, and Wei Han

Status: open (until 14 Nov 2024)

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Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Hai Xiang Lin, Hong Liao, and Wei Han
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Hai Xiang Lin, Hong Liao, and Wei Han

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Short summary
Aerosol data assimilation has gained popularity as it combines the advantages of model and observation. However, few have addressed the challenges in the prior vertical structure. A variety of observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.