Meteorological data analysis using self-organizing maps

Abstract

A data analysis task is described, which is focused on the clustering of high-dimensional meteorological data collected long term (more than 43 years) at 128 weather stations in Greece. The proposed hybrid method combines (a) the assignment of the stations to two-dimensional grids of nodes via self-organizing maps (SOMs) of various sizes and (b) statistical clustering of the SOM nodes. The areas resulting from clustering have well-defined meteorological profiles; they are also described by distinct combinations of morphological and geographical characteristics, indicating that morphology and geographical location largely affect the meteorological measurements. The most salient data parameters per area as well as over the entire map are determined, whereby the parameters and parameter ranges that shape the various meteorological profiles are exposed. The classification of stations with missing and noise-contaminated meteorological measurements into their expected areas demonstrates the prediction capability and robustness of the proposed hybrid method. C 2008 Wiley Periodicals, Inc.

Mapping of oceanographic properties along a middle Adriatic transect using Self-Organising Maps

“…The SOM has been successfully applied in classifying different patterns and problems (Kohonen, 2001), including a wide range of geoscience and oceanography issues (Liu et al, 2006;Liu and Weisberg, 2011), particularly related to meteorological data (Tambouratzis and Tambouratzis, 2008), climate simulations and projections (Yip and Yau, 2012), satellite field mapping, such as satellite ocean colour and chlorophyll, SST and altimetry data (Iskandar, 2010) and in situ physical, biological and geochemical data mapping (Kropp and Klenke, 1997).…”

A spatially explicit and temporally highly resolved analysis of variations in fog occurrence over Europe

“…SOMs are widely applied in various scientific disciplines, including synoptic climatology and meteorology, where they are employed to derive patterns in atmospheric observations (e.g. Hewitson and Crane, ; Liu and Weisberg, ; ; Tambouratzis and Tambouratzis, ; Sá et al . ).…”

“…SOMs are widely applied in various scientific disciplines, including synoptic climatology and meteorology, where they are employed to derive patterns in atmospheric observations (e.g. Hewitson and Crane, 2002;Liu and Weisberg, 2005;Tambouratzis and Tambouratzis, 2008;Sá et al, 2012). A SOM consists of a grid of neurons, each neuron representing its own class (here each class represents one fog pattern).…”

Identifying prototype states within hydrodynamic model outputs using a self-organizing feature map

“…Applications have included synoptic climatology [18], extreme weather analysis [19] and climate change studies [20]. Types of meteorological data analysed include air pressure [21], [22], air temperature and humidity [19] [23] [24], as well as evaporation and precipitation [25]- [27].…”