Preprints
https://doi.org/10.5194/gmd-2023-219
https://doi.org/10.5194/gmd-2023-219
Submitted as: development and technical paper
 | 
20 Dec 2023
Submitted as: development and technical paper |  | 20 Dec 2023
Status: this preprint is currently under review for the journal GMD.

Neighbouring time ensemble Kalman filter (NTEnKF) data assimilation for dust storm forecasting

Mijie Pang, Jianbing Jin, Segers Arjo, Huiya Jiang, Wei Han, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Mijie Pang, Jianbing Jin, Segers Arjo, Huiya Jiang, Wei Han, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-219', Anonymous Referee #1, 28 Jan 2024
    • AC1: 'Reply on RC1', Jianbing Jin, 22 Mar 2024
  • RC2: 'Comment on gmd-2023-219', Anonymous Referee #2, 08 Feb 2024
    • AC2: 'Reply on RC2', Jianbing Jin, 22 Mar 2024
Mijie Pang, Jianbing Jin, Segers Arjo, Huiya Jiang, Wei Han, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Mijie Pang, Jianbing Jin, Segers Arjo, Huiya Jiang, Wei Han, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao

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Short summary
Dust storms can cause harm to health and infrastructure. Forecasting their intensity and position is important but challenging. We propose a new algorithm, NTEnKF, that considers both intensity and positional errors to improve dust storm forecasting. Evaluations on three major dust events in 2021 showed significant improvements compared to traditional EnKF methods. This research has implications for accurate dust forecasting.