Hierarchical clustering of self-organizing maps for cloud classification

Ambroise, Christophe; Sèze, Geniève; Badran, Fouad; Thiria, Sylvie

doi:10.1016/s0925-2312(99)00141-1

Cited by 76 publications

(41 citation statements)

References 8 publications

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“…Cavazos (1999Cavazos ( , 2000 used SOMs to identify and classify patterns representative of extreme wintertime precipitation in Central America and the Balkans respectively, identifying large-scale circulation anomalies associated with local extreme precipitation events. Ambroise et al (2000) used SOMs for cloud classification. Malmgren and Winter (1999) used SOMs to classify climate zones in Puerto Rico.…”

Section: Description Of the Self-organizing Map Algorithmmentioning

confidence: 99%

Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic

2006

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An analysis of late twentieth and twenty-first century predictions of Arctic circulation patterns in a ten-model ensemble of global climate system models, using the method of self-organizing maps (SOMs), is presented. The model simulations were conducted in support of the fourth assessment report of the intergovernmental panel on climate change (IPCC). The analysis demonstrates the utility of SOMs for climate analysis, both as a tool to evaluate the accuracy of climate model predictions, and to provide a useful alternative view of future climate change.It is found that not all models accurately simulate the frequency of occurrence of Arctic circulation patterns. Some of the models tend to overpredict strong high-pressure patterns while other models overpredict the intensity of cyclonic circulation regimes. In general, the ensemble of models predicts an increase in cyclonically dominated circulation patterns during both the winter and summer seasons, with the largest changes occurring during the first half of the twenty-first century. Analysis of temperature and precipitation anomalies associated with the different circulation patterns reveals coherent patterns that are consistent with the different circulation regimes and highlight the dependence of local changes in these quantities to changes in the synoptic scale circulation patterns.

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Section: Description Of the Self-organizing Map Algorithmmentioning

confidence: 99%

Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic

2006

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“…This has been pointed out by many authors (Saunders 1986;Desbois et al 1982;Coakley 1983;Inoue 1985;Baum et al 1997;Jin and Rossow 1997). Different approaches have used the information from these spectral bands, for example, physically based threshold techniques Gléau 2005, 2010), clustering techniques without a priori knowledge (Sèze and Pawlowska 2001; Ambroise et al 2000), and neuronal and fuzzy logic approaches (Baum et al 1997;Miller and Emery 1997). Recently, the multispectral capabilities of the new generation of VIS and IR imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua platforms or the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat geostationary satellite have been investigated.…”

Section: Introductionmentioning

confidence: 99%

A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

Aires¹,

Marquisseau²,

Prigent³

et al. 2011

Monthly Weather Review

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A statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations. Clear sky and low, medium, and opaque-high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

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“…In order to establish if there exist any such set of temporal stable patterns related to observed weather or climate phenomena we select weather station data described in [Flores et al, 1996]. The records of the observed weather temporal series [Ambroise et al¸2000;Malmgren and Winter, 1999 ;Tian et al, 1999] are clustered with SOM [Kohonen, 2001;Kasi, et al, 2000;Tirri, 1991;Duller, 1998] and rules describing each obtained cluster were built applying TDIDT [Quinlan, 1993] to each cluster records. The described process is shown in figure 1.…”

Section: Proposed Solutionmentioning

confidence: 99%

Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT

Cogliati

Britos

García-Martínez

IFIP International Federation for Information Processing

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Abstract. The purpose of the present article is to investigate if there exist any such set of temporal stable patterns in temporal series of meteorological variables studying series of air temperature, wind speed and direction an atmospheric pressure in a period with meteorological conditions involving nocturnal inversion of air temperature in Allen, Río Negro, Argentina. Our conjecture is that there exist independent stable temporal activities, the mixture of which give rise to the weather variables; and these stable activities could be extracted by Self Organized Maps plus Top Down Induction Decision Trees analysis of the data arising from the weather patterns, viewing them as temporal signals.

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Hierarchical clustering of self-organizing maps for cloud classification

Cited by 76 publications

References 8 publications

Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic

Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic

A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT

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