A Survey on Clustering based Meteorological Data Mining

Tian, Weisheng; Zheng, Yinqiang; Yang, Runzhi; Ji, Sai; Wang, Jin

doi:10.14257/ijgdc.2014.7.6.19

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…In addition to the appropriate choice of the clustering indices, the preprocessing of the clustering indices plays an important role in the clustering of the subannual periods (Tian et al, ). Previous studies have rarely attempted to analyze the complex relationships among the clustering indices before using them in the clustering algorithm.…”

Section: Methodsmentioning

confidence: 99%

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

Lan

Lin

Liu

et al. 2018

Water Resources Research

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One model structural deficiency is that some dynamic characteristics (such as seasonal dynamics) in catchment conditions are not explicitly represented by hydrological models. This study integrates data mining techniques to develop a clustering preprocessing framework for the subannual calibration of hydrological models to simulate seasonal dynamic behaviors. The proposed framework aims to solve the problems caused by missing processes and deficiencies of hydrological models, providing guidance for future model development. A set of climatic‐land surface indices is provided and preprocessed using the maximal information coefficient and the principal component analysis. Two clustering operations are performed based on the preprocessed climatic index and land‐surface index systems. Hydrological data are clustered into subannual periods for calibration. The parameters are independently optimized for each subperiod using a modified parallel calibration scheme and are then combined to generate a continuous simulation. The framework is applied in calibrating the TOPMODEL. The results show that the performance of the model with a clustering preprocessing framework in the middle‐ and low‐flow conditions is significantly improved without reducing the simulation accuracy for high flows. The transposability of the model parameters from the calibration to validation period has been improved significantly as well. The anomalous parameter values may be attributed in part to the convergence problem when using an optimization algorithm. Though well applied in the TOPMODEL, the framework has the potential to be used in other hydrological models.

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Section: Methodsmentioning

confidence: 99%

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

Lan

Lin

Liu

et al. 2018

Water Resources Research

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“…Ward's method was employed with Euclidean distance used as similarity measurement. The hierarchical clustering technique classifies data into a hierarchical structure based on the Euclidean distance between two groups (Murtagh and Legendre 2014;Tian et al, 2014). The Euclidean distance (D) is the length of the line segment between i number of two points (x, y) in the euclidean space, as indicated in Eq.…”

Section: Ward's Methodsmentioning

confidence: 99%

Precipitation Moisture Sources of Ethiopian River Basins and Their Role During Drought Conditions

et al. 2022

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In this study, we identified and investigated the annual climatological moisture sources for the Ethiopian river basins during 1980–2018. First, according to cluster analysis, the 12 river basins of this country were grouped into four regions: northeast (NE), southeast (SE), southwest (SW), and west (W), which were characterised by similar annual precipitation features. Global outputs from the Lagrangian FLEXPART model were used to investigate the air mass humidity gain before reaching each region. This revealed five main oceanic moisture sources located in the Mediterranean Sea, Red Sea, Indian Ocean, Persian Gulf, and the Arabian Sea, in addition to three main terrestrial moisture sources located in the African continent, Arabian Peninsula, and the regions themselves. Once the main climatological sources of moisture for each region were identified, a forward-in-time analysis of air masses over each source was performed to calculate the moisture contribution to precipitation (E – P) < 0 over the defined regions. The most important source at the annual scale for the NE, SW, and W regions is the African continent, while for the SE, it is the Indian Ocean. Indeed, terrestrial moisture sources are the major contributors (>50%) to the precipitation over the NE, SW, and W, whereas oceanic sources are the major contributors to the SE. Another analysis identified the years affected by drought conditions in the regions. The role of the sources was evaluated for those years affected by severe and extreme drought, revealing the heterogeneous and also direct influences on the regions. Finally, according to the normalised difference vegetation index, the impacts of annual severe and extreme droughts were more prominent in areas of the NE and SE, but also in the SW during 1984.

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“…The algorithm takes k as the parameter and divides n samples into k categories so that the sum of squares within the group is less than the sum of squares between groups. The intra-class similarity is therefore high, and the inter-class similarity is low [31,32]. The k-means clustering method is adopted to classify the principal components obtained in the first step and the specific number of classifications is determined by the standard function.…”

Section: K-meansmentioning

confidence: 99%

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

Wang

Chen

et al. 2021

Atmosphere

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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.

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A Survey on Clustering based Meteorological Data Mining

Cited by 14 publications

References 33 publications

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate‐Land Surface Variations

Precipitation Moisture Sources of Ethiopian River Basins and Their Role During Drought Conditions

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

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