Classification of the Circulation Patterns Related to Strong Dust Weather in China Using a Combination of the Lamb–Jenkinson and k-Means Clustering Methods

Abstract

Sand and dust storms (SDSs) cause major disasters in northern China. They have serious impacts on human health, daily life, and industrial and agricultural production, in addition to threatening the regional ecological environment and social economy. Based on meteorological observational data and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset for spring 2000–2021, we used the Lamb–Jenkinson circulation classification method to classify the three major areas influencing SDSs in northern China. We also used the k-means clustering method to classify the overall circulation pattern in northern China. Our results show that the circulation types favoring SDSs in the southern basin of Xinjiang are southwesterly winds (SW), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in western Inner Mongolia and southern Mongolia are northwesterly winds (NW), northerly winds (N), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in central Inner Mongolia are northwesterly winds (NW), northerly winds (N), southwesterly winds (SW), and anticyclones (A). The 500 hPa and surface circulation patterns in China can be divided into nine types. Among them, five dominant circulation patterns favor strong SDSs: a cold high-pressure region and cold front (T1), a Mongolian cyclone (T2), a mixed type of Mongolian cyclone and cold front (T3), a thermal depression and cold front (T5), and a cold front (T8). During 2000–2004, the T8 circulation pattern occurred most frequently as the main influencing circulation. From 2005 to 2010, the T3 and T8 circulation patterns dominated. Circulation patterns T1 and T3 dominated during 2011–2015 and 2016–2020, respectively. We analyzed the main circulation patterns for four SDS events occurring in 2021 by combining the Lamb–Jenkinson and k-means methods. The SDS events in 2021 were closest to the T3 circulation pattern and were mainly influenced by Mongolian cyclones and surface cold fronts. The main propagation paths were westerly and northwesterly.

Synoptic Weather Patterns and Atmospheric Circulation Types of PM2.5 Pollution Periods in the Beijing-Tianjin-Hebei Region

“…BTH region is located at 113 • -120 • E and 36 • -43 • N. In order to fully include the study area in the inner nested grid, the calculation area is set as 28 • -48 • N and 102 • -132 • E, and the coordinates of the center point are set at 117 • E and 38 • N. The method of different grid point selection is to select points within the research area with an interval of ten longitudes and five latitudes respectively, with a total of 16 grid points. Geostrophic wind (u, v) and geostrophic vorticity (ξ u , ξ v ) at the center point O are calculated using the average daily sea level pressure values at 16 grid points within the region [34]. According to the calculated relation between geostrophic wind and geostrophic vorticity (also known as atmospheric circulation index relation), atmospheric circulation characteristics are divided (Figure 2).…”

Classification of Synoptic Weather Patterns associated with Extreme Wave Events in Different Regions of Western South Atlantic

“…K-Means divides data into 'k' groups, with 'k' being a value defined by the user. This approach has been successfully applied in various studies to classify meteorological and circulation patterns related to phenomena such as intense precipitation in Northeastern Iberian Peninsula Jiménez et al (2009), in Argentina (Bettolli et al, 2010), in Japan (Nguyen-Le et al, 2017), in Thailand (Nguyen-Le and Yamada, 2019), as well as dust storms in China (Yi et al, 2021), and precipitation variability in India (Neal et al, 2019). Other approaches (Boé et al, 2006;Camus et al, 2014;Antolínez et al, 2018;de Souza et al, 2022) involved the use of Weather Patterns for downscaling numerical models, aiming to improve regional detail with reduced computational costs.…”