CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation

A weather pattern clustering method is applied and calibrated to Argentinean daily weather stations in order to predict daily precipitation data. The clustering technique is based on k-means and is applied to a set of 17 atmospheric variables from the ERA-40 reanalysis covering the period 1979-1999....

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Detalles Bibliográficos
Autores principales: D'Onofrio, Alejandro, Segura, Enrique Carlos
Publicado: 2010
Materias:
Atmospheric variables
Clustering techniques
Different domains
Domain size
Down-scaling
Dynamical variables
K-means
Local scale
Precipitation data
Predictive systems
Reanalysis
Regional climate
Relative operating characteristics
ROC curves
Sensitivity tests
Skill Score
Spatial domains
Weather patterns
Weather stations
Moisture
Pattern recognition systems
Time domain analysis
calibration
diurnal variation
downscaling
precipitation (climatology)
regional climate
statistical analysis
weather forecasting
weather station
Argentina
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650009_v98_n3_p405_Donofrio
http://hdl.handle.net/20.500.12110/paper_01650009_v98_n3_p405_Donofrio
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_01650009_v98_n3_p405_Donofrio
record_format dspace
spelling paper:paper_01650009_v98_n3_p405_Donofrio2023-06-08T15:14:28Z CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation D'Onofrio, Alejandro Segura, Enrique Carlos Atmospheric variables Clustering techniques Different domains Domain size Down-scaling Dynamical variables K-means Local scale Precipitation data Predictive systems Reanalysis Regional climate Relative operating characteristics ROC curves Sensitivity tests Skill Score Spatial domains Weather patterns Weather stations Moisture Pattern recognition systems Time domain analysis calibration diurnal variation downscaling precipitation (climatology) regional climate statistical analysis weather forecasting weather station Argentina A weather pattern clustering method is applied and calibrated to Argentinean daily weather stations in order to predict daily precipitation data. The clustering technique is based on k-means and is applied to a set of 17 atmospheric variables from the ERA-40 reanalysis covering the period 1979-1999. The set of atmospheric variables represent the different components of the atmosphere (dynamical, thermal and moisture). Different sensitivity tests are applied to optimize (1) the number of observations (weather patterns) per cluster, (2) the spatial domain size of the weather pattern around the station and (3) the number of members of the ensembles. All the sensitivity tests are compared using the ROC (Relative Operating Characteristic) Skill Score (RSS) derived from the ROC curve used to assess the performance of a predictive system. First, we found the number of observations per cluster to be optimum for values larger than 39. Second, the spatial domain size (̃4° × 4°) was found to be closer to a local scale than to a synoptic scale, certainly due to a dominant role of the moisture components in the optimization of the transfer function. Indeed, when reducing the set of variables to the subset of dynamical variables, the predictive skill of the method is significantly reduced, but at the same time the domain size must be increased. A potential improvement of the method may therefore be to consider different domains for dynamical and non-dynamical variables. Third, the number of members per ensembles of simulations was estimated to be always two to three times larger than the mean number of observations per cluster (meaning that at least all the observed weather patterns are selected by one member). The skill of the statistical method to predict daily precipitation is found to be relatively homogeneous all over the country for different thresholds of precipitation. © Springer Science + Business Media B.V. 2009. Fil:D'onofrio, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Segura, E.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650009_v98_n3_p405_Donofrio http://hdl.handle.net/20.500.12110/paper_01650009_v98_n3_p405_Donofrio
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Atmospheric variables
Clustering techniques
Different domains
Domain size
Down-scaling
Dynamical variables
K-means
Local scale
Precipitation data
Predictive systems
Reanalysis
Regional climate
Relative operating characteristics
ROC curves
Sensitivity tests
Skill Score
Spatial domains
Weather patterns
Weather stations
Moisture
Pattern recognition systems
Time domain analysis
calibration
diurnal variation
downscaling
precipitation (climatology)
regional climate
statistical analysis
weather forecasting
weather station
Argentina
spellingShingle Atmospheric variables
Clustering techniques
Different domains
Domain size
Down-scaling
Dynamical variables
K-means
Local scale
Precipitation data
Predictive systems
Reanalysis
Regional climate
Relative operating characteristics
ROC curves
Sensitivity tests
Skill Score
Spatial domains
Weather patterns
Weather stations
Moisture
Pattern recognition systems
Time domain analysis
calibration
diurnal variation
downscaling
precipitation (climatology)
regional climate
statistical analysis
weather forecasting
weather station
Argentina
D'Onofrio, Alejandro
Segura, Enrique Carlos
CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation
topic_facet Atmospheric variables
Clustering techniques
Different domains
Domain size
Down-scaling
Dynamical variables
K-means
Local scale
Precipitation data
Predictive systems
Reanalysis
Regional climate
Relative operating characteristics
ROC curves
Sensitivity tests
Skill Score
Spatial domains
Weather patterns
Weather stations
Moisture
Pattern recognition systems
Time domain analysis
calibration
diurnal variation
downscaling
precipitation (climatology)
regional climate
statistical analysis
weather forecasting
weather station
Argentina
description A weather pattern clustering method is applied and calibrated to Argentinean daily weather stations in order to predict daily precipitation data. The clustering technique is based on k-means and is applied to a set of 17 atmospheric variables from the ERA-40 reanalysis covering the period 1979-1999. The set of atmospheric variables represent the different components of the atmosphere (dynamical, thermal and moisture). Different sensitivity tests are applied to optimize (1) the number of observations (weather patterns) per cluster, (2) the spatial domain size of the weather pattern around the station and (3) the number of members of the ensembles. All the sensitivity tests are compared using the ROC (Relative Operating Characteristic) Skill Score (RSS) derived from the ROC curve used to assess the performance of a predictive system. First, we found the number of observations per cluster to be optimum for values larger than 39. Second, the spatial domain size (̃4° × 4°) was found to be closer to a local scale than to a synoptic scale, certainly due to a dominant role of the moisture components in the optimization of the transfer function. Indeed, when reducing the set of variables to the subset of dynamical variables, the predictive skill of the method is significantly reduced, but at the same time the domain size must be increased. A potential improvement of the method may therefore be to consider different domains for dynamical and non-dynamical variables. Third, the number of members per ensembles of simulations was estimated to be always two to three times larger than the mean number of observations per cluster (meaning that at least all the observed weather patterns are selected by one member). The skill of the statistical method to predict daily precipitation is found to be relatively homogeneous all over the country for different thresholds of precipitation. © Springer Science + Business Media B.V. 2009.
author D'Onofrio, Alejandro
Segura, Enrique Carlos
author_facet D'Onofrio, Alejandro
Segura, Enrique Carlos
author_sort D'Onofrio, Alejandro
title CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation
title_short CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation
title_full CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation
title_fullStr CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation
title_full_unstemmed CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation
title_sort chac: a weather pattern classification system for regional climate downscaling of daily precipitation
publishDate 2010
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650009_v98_n3_p405_Donofrio
http://hdl.handle.net/20.500.12110/paper_01650009_v98_n3_p405_Donofrio
work_keys_str_mv AT donofrioalejandro chacaweatherpatternclassificationsystemforregionalclimatedownscalingofdailyprecipitation
AT seguraenriquecarlos chacaweatherpatternclassificationsystemforregionalclimatedownscalingofdailyprecipitation
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