Study on Daytime Atmospheric Mixing Layer Height Based on 2-Year Coherent Doppler Wind Lidar Observations at the Southern Edge of the Taklimakan Desert

The long-term atmospheric mixing layer height (MLH) information plays an important role in air quality and weather forecasting. However, it is not sufficient to study the characteristics of MLH using long-term high spatial and temporal resolution data in the desert. In this paper, over the southern edge of the Taklimakan Desert, the diurnal, monthly, and seasonal variations in the daytime MLH (retrieved by coherent Doppler wind lidar) and surface meteorological elements (provided by the local meteorological station) in a two-year period (from July 2021 to July 2023) were statistically analyzed, and the relationship between the two kinds of data was summarized. It was found that the diurnal average MLH exhibits a unimodal distribution, and the decrease rate in the MLH in the afternoon is much higher than the increase rate before noon. From the seasonal and monthly perspective, the most frequent deep mixing layer (>4 km) was formed in June, and the MLH is the highest in spring and summer. Finally, in terms of their mutual relationship, it was observed that the east-pathway wind has a greater impact on the formation of the deep mixing layer than the west-pathway wind; the dust weather with visibility of 1–10 km contributes significantly to the formation of the mixing layer; the temperature and relative humidity also exhibit a clear trend of a concentrated distribution at about the height of 3 km. The statistical analysis of the MLH deepens the understanding of the characteristics of dust pollution in this area, which is of great significance for the treatment of local dust pollution.

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Publication Details

Journal
Remote Sensing
Published
2024-08-16
DOI
https://doi.org/10.3390/rs16163005
Citations
2
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
0.57

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article

Study on Daytime Atmospheric Mixing Layer Height Based on 2-Year Coherent Doppler Wind Lidar Observations at the Southern Edge of the Taklimakan Desert

Haiyun Xia, Jinlong Yuan, Qing He, Amina Maituerdi et al.
2 citations
Remote Sensing
Atmospheric aerosols and clouds
0.57
article

Study on Daytime Atmospheric Mixing Layer Height Based on 2-Year Coherent Doppler Wind Lidar Observations at the Southern Edge of the Taklimakan Desert

Haiyun Xia, Jinlong Yuan, Qing He, Amina Maituerdi, Lian Su, Yue Wang
article en
2 citations

Abstract

The long-term atmospheric mixing layer height (MLH) information plays an important role in air quality and weather forecasting. However, it is not sufficient to study the characteristics of MLH using long-term high spatial and temporal resolution data in the desert. In this paper, over the southern edge of the Taklimakan Desert, the diurnal, monthly, and seasonal variations in the daytime MLH (retrieved by coherent Doppler wind lidar) and surface meteorological elements (provided by the local meteorological station) in a two-year period (from July 2021 to July 2023) were statistically analyzed, and the relationship between the two kinds of data was summarized. It was found that the diurnal average MLH exhibits a unimodal distribution, and the decrease rate in the MLH in the afternoon is much higher than the increase rate before noon. From the seasonal and monthly perspective, the most frequent deep mixing layer (>4 km) was formed in June, and the MLH is the highest in spring and summer. Finally, in terms of their mutual relationship, it was observed that the east-pathway wind has a greater impact on the formation of the deep mixing layer than the west-pathway wind; the dust weather with visibility of 1–10 km contributes significantly to the formation of the mixing layer; the temperature and relative humidity also exhibit a clear trend of a concentrated distribution at about the height of 3 km. The statistical analysis of the MLH deepens the understanding of the characteristics of dust pollution in this area, which is of great significance for the treatment of local dust pollution.

Remote SensingVol. 16(16)
University of Science and Technology of China (CN), China Meteorological Administration (CN), Nanjing University of Information Science and Technology (CN)
China Meteorological Administration
Openalex Percentile: Top 34%
Atmospheric aerosols and clouds
0.57
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