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Winter Weather Regimes in Southeastern China and its Intraseasonal Variations

1
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
3
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Jiangsu Key Laboratory of Meteorological Observation and Information Processing/Jiangsu Technology & Engineering Center of Meteorological Sensor Network/School of Electronic & Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
4
Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing University of Aeronautics and Astronautics), Ministry of Education, Nanjing 210016, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(5), 271; https://doi.org/10.3390/atmos10050271
Received: 6 April 2019 / Revised: 29 April 2019 / Accepted: 10 May 2019 / Published: 14 May 2019
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Abstract

Extreme precipitation has often occurred in Southeastern China, while the possible mechanism is not clear. In order to bridge the scale gap between large-scale circulation and extreme precipitation, in this paper, the k-means clustering technique—a common method of weather-type (WT) analysis—was applied to regional 850-hPa wind fields. The reasonable determination of k values can make the later WT analyses more reliable. Thus, the Davies–Bouldin (BD) criterion index is used in the clustering process, and the optimal value of the k was determined. Then, we obtain and analyze the frequency, persistence, and progression of WTs. The rule of transitions from one WT to another may help explain some of the physical processes in winter. We found a special evolutionary chain (WT3→WT1→WT2→WT5→WT3) that can be used to explain the cold wave weather process in winter. Different WTs in the evolutionary chain correspond well to different stages of the cold wave weather process (gestation (WT3), outbreak (WT1), eastward withdrawal (WT2), and extinction (WT5)). In addition, we found that there are obvious differences in precipitation between December and February. After reassembling five kinds of WTs, two modes are formed: dry WTs and wet WTs. Our research shows that the intraseasonal variation of precipitation can be attributed to the fluctuation between the wet and dry WTs, and the different phases of teleconnection can correspond well with it. For example, the relative frequencies of wet WTs are higher in February. These WTs correspond to the positive phase of the WP and ENSO, the negative phase of the EA and EU, and the strong MJO state of the second, third, and eighth phase. Our work has well established the relationship between synoptic scale and large-scale circulation, which provides a reference for climate model simulation and future climate prediction. View Full-Text
Keywords: weather type; k-means cluster analysis; southeastern China; climate teleconnection weather type; k-means cluster analysis; southeastern China; climate teleconnection
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Wang, Y.; Jin, S.; Sun, X.; Wang, F. Winter Weather Regimes in Southeastern China and its Intraseasonal Variations. Atmosphere 2019, 10, 271.

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