Data-driven clustering of rain events: microphysics information derived from macro-scale observations
Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of <q>rain events</q>. The aim of the first part of this paper is to establish a parsi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2017-04-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | http://www.atmos-meas-tech.net/10/1557/2017/amt-10-1557-2017.pdf |
| _version_ | 1818152021780332544 |
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| author | M. D. Dilmi C. Mallet L. Barthes A. Chazottes |
| author_facet | M. D. Dilmi C. Mallet L. Barthes A. Chazottes |
| author_sort | M. D. Dilmi |
| collection | DOAJ |
| description | Rain time series records are generally studied using rainfall rate
or accumulation parameters, which are estimated for a fixed duration
(typically 1 min, 1 h or 1 day). In this study we use the concept of
<q>rain events</q>. The aim of the first part of this paper is to establish a
parsimonious characterization of rain events, using a minimal set of
variables selected among those normally used for the characterization of
these events. A methodology is proposed, based on the combined use of a
genetic algorithm (GA) and self-organizing maps (SOMs). It can be
advantageous to use an SOM, since it allows a high-dimensional data space to
be mapped onto a two-dimensional space while preserving, in an unsupervised
manner, most of the information contained in the initial space topology. The
2-D maps obtained in this way allow the relationships between variables to be
determined and redundant variables to be removed, thus leading to a minimal
subset of variables. We verify that such 2-D maps make it possible to
determine the characteristics of all events, on the basis of only five
features (the event duration, the peak rain rate, the rain event depth, the
standard deviation of the rain rate event and the absolute rain rate
variation of the order of 0.5). From this minimal subset of variables,
hierarchical cluster analyses were carried out. We show that clustering into
two classes allows the conventional convective and stratiform classes to be
determined, whereas classification into five classes allows this convective–stratiform
classification to be further refined. Finally, our study made
it possible to reveal the presence of some specific relationships between
these five classes and the microphysics of their associated rain events. |
| first_indexed | 2024-12-11T13:48:06Z |
| format | Article |
| id | doaj.art-220a2517c9064c258a092fa17d6b79c2 |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-11T13:48:06Z |
| publishDate | 2017-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-220a2517c9064c258a092fa17d6b79c22022-12-22T01:04:24ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482017-04-011041557157410.5194/amt-10-1557-2017Data-driven clustering of rain events: microphysics information derived from macro-scale observationsM. D. Dilmi0C. Mallet1L. Barthes2A. Chazottes3LATMOS-CNRS/UVSQ/UPSay, 11 boulevard d'Alembert, 78280 Guyancourt, FranceLATMOS-CNRS/UVSQ/UPSay, 11 boulevard d'Alembert, 78280 Guyancourt, FranceLATMOS-CNRS/UVSQ/UPSay, 11 boulevard d'Alembert, 78280 Guyancourt, FranceLATMOS-CNRS/UVSQ/UPSay, 11 boulevard d'Alembert, 78280 Guyancourt, FranceRain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of <q>rain events</q>. The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective–stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.http://www.atmos-meas-tech.net/10/1557/2017/amt-10-1557-2017.pdf |
| spellingShingle | M. D. Dilmi C. Mallet L. Barthes A. Chazottes Data-driven clustering of rain events: microphysics information derived from macro-scale observations Atmospheric Measurement Techniques |
| title | Data-driven clustering of rain events: microphysics information derived from macro-scale observations |
| title_full | Data-driven clustering of rain events: microphysics information derived from macro-scale observations |
| title_fullStr | Data-driven clustering of rain events: microphysics information derived from macro-scale observations |
| title_full_unstemmed | Data-driven clustering of rain events: microphysics information derived from macro-scale observations |
| title_short | Data-driven clustering of rain events: microphysics information derived from macro-scale observations |
| title_sort | data driven clustering of rain events microphysics information derived from macro scale observations |
| url | http://www.atmos-meas-tech.net/10/1557/2017/amt-10-1557-2017.pdf |
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