1. Anomaly Based Synoptic Analysis and Model Prediction of Six Dust Storms Moving From Mongolia to Northern China in Spring 2021.
- Author
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Qian, Weihong, Leung, Jeremy Cheuk‐Hin, Ren, Jingjing, Du, Jun, Feng, Yerong, and Zhang, Banglin
- Subjects
DUST storms ,NUMERICAL weather forecasting ,PREDICTION models ,GEOPOTENTIAL height ,REMOTE-sensing images - Abstract
Six dust storms that originated from Mongolia and moved to northern China in Spring 2021 are investigated by the anomaly variable‐based analysis method, which is compared to the traditional total‐variable based analysis method. Strong surface wind is a force to form dust storms as commonly known in current practice. However, dust area depicted by total‐wind model product analysis and operational forecast has a large bias to observation even for leading 24 hr. The analysis method of anomaly based variables showed that the domain of an airborne dust storm observed from satellite images is usually located between two neighboring convergence lines of 850 hPa anomalous winds. This connection of dust storm associated with the anomalous wind convergence extends southwestward from an anomalous cyclone and moves from Mongolia to northern China or Northeast China. It is thermodynamically associated with an anomalous thermal contrast, which has a spatial structure of wind, temperature, and geopotential height anomalies in the troposphere. We further found that numerical weather prediction products of the ECMWF model are able to indicate the anomalous wind pattern with lead times of 4–8 days by applying the anomaly based analysis approach. Therefore, this approach can not only depict dust area better but also reveal predictors much easier compared to the traditional total‐variable or full‐field‐based analysis method. Key Points: Dust storm can be well detected by the anomaly based analysis of atmospheric variablesA dust domain is located between two neighboring convergence lines of 850 hPa anomalous windsThe ECMWF model can predict the anomalous wind pattern for leading 4–8 days [ABSTRACT FROM AUTHOR]
- Published
- 2022
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