Navegar

Colección

Datos Estadísticas

Artículo

La versión final de este artículo es de uso interno de la institución.
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

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.

Registro:

Documento: Artículo
Título:CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation
Autor:D'onofrio, A.; Boulanger, J.-P.; Segura, E.C.
Filiación:Departamento de Ciencias de la Computación, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina
LOCEAN, UMR CNRS/IRD/UPMC, Tour 45-55/Etage 4/Case 100, UPMC, 4 Place Jussieu, 75252 Paris, France
Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina
Palabras clave: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
Año:2010
Volumen:98
Número:3
Página de inicio:405
Página de fin:427
DOI: http://dx.doi.org/10.1007/s10584-009-9738-4
Título revista:Climatic Change
Título revista abreviado:Clim. Change
ISSN:01650009
CODEN:CLCHD
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650009_v98_n3_p405_Donofrio

Referencias:

  • Bellman, R., (1961) Adaptive Control Process: A Guided Tour, , Princeton: Princeton University Press
  • Boulanger, J.-P., Martinez, F., Penalba, O., Segura, E.C., Neural network based daily precipitation generator (NNGEN-P) (2007) Clim Dyn, 28, pp. 307-324. , doi:10.1007/s00382-006-0184-y
  • Buishand, T.A., Some methods for testing the homogeneit of rainfall records (1982) J Hidrol, 58, pp. 11-27
  • Cavazos, T., Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Balkans (2000) J Clim, 13, pp. 1718-1732
  • Cavazos, T., Hewitson, B.C., Performance of NCEP-NCAR reanalysis variables in statistical downscaling of daily precipitation (2005) Clim Res, 28, pp. 95-107
  • Gutiérrez, J.M., Cano, R., Cofiño, A.S., Sordo, C., Analysis and downscaling multi-model seasonal forecast using self-organizing maps (2004) Tellus A, 57, pp. 435-447
  • Hewitson, B.C., Crane, R.G., Consensus between GCM climate change projections with empirical downscaling: Precipitation downscaling over South Africa (2006) Int J Climatol, 26, pp. 1315-1337
  • Hughes, J., Lettemaier, D.P., Guttorp, P., A stochastic approach for assessing the effect of changes in regional circulation patterns on local precipitation (1993) Water Resour Res, 29, pp. 3303-3315
  • Jain, A.K., Murty, M.N., Flynn, P.J., Data clustering: A review (1999) ACM Comput Surv, 31 (3), pp. 265-322
  • Kharin, V.V., Zwiers, F.W., On the ROC score of probability forecasts (2003) J Clim, 16 (24), p. 4145
  • Kohonen, T., Self-organizing formation of topologically correct feature maps (1982) Biol Cybern, 43 (1), pp. 59-69
  • Lorenz, E.N., Atmospheric predictability as revealed by naturally occurring analogues (1969) J Atmos Sci, 26, pp. 636-646
  • Magrin, G.O., Travasso, M.I., Rodriguez, G.R., Changes in climate and crop production during the 20th century in Argentina (2005) Clim Change, 72, pp. 229-249
  • Mehrotra, R., Sharma, A., A nonparametric nonhomogeneous hidden Markov model for downscaling of multisite daily rainfall occurrences (2005) J Geophys Res, 110, p. 16108. , doi:10.1029/2004JD005677
  • Menéndez, C.G., de castro, M., Boulanger, J.-P., D'onofrio, A., Sanchez, E., Sörensson, A.A., Blazquez, J., Teichmann, C., Downscaling extreme month-long anomalies in southern South America (2009) Clim Change, , doi:10.1007/s10584-009-9739-3
  • Smit, B., Skinner, M., Adaptation options in agriculture to climate change: A typology (2004) Migr Adapt Strateg Glob Change, 7 (1), pp. 85-114
  • Solman, S.A., Nuñez, M.N., Local estimates of global climate change: A statistical downscaling approach (1999) Int J Climatol, 19, pp. 835-861
  • Vrac, M., Stein, M.L., Hayhoe, K., Liang, X.-Z., A general method for validating statistical downscaling methods under future climate change (2007) Geophys Res Lett, 34, p. 18701. , doi:10.1029/2007GL030295
  • Wilks, D.S., Interannual variability and extreme-value characteristics of several stochastic daily precipitation models (1999) Agric For Meteorol, 93, pp. 153-169

Citas:

---------- APA ----------
D'onofrio, A., Boulanger, J.-P. & Segura, E.C. (2010) . CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation. Climatic Change, 98(3), 405-427.
http://dx.doi.org/10.1007/s10584-009-9738-4
---------- CHICAGO ----------
D'onofrio, A., Boulanger, J.-P., Segura, E.C. "CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation" . Climatic Change 98, no. 3 (2010) : 405-427.
http://dx.doi.org/10.1007/s10584-009-9738-4
---------- MLA ----------
D'onofrio, A., Boulanger, J.-P., Segura, E.C. "CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation" . Climatic Change, vol. 98, no. 3, 2010, pp. 405-427.
http://dx.doi.org/10.1007/s10584-009-9738-4
---------- VANCOUVER ----------
D'onofrio, A., Boulanger, J.-P., Segura, E.C. CHAC: A weather pattern classification system for regional climate downscaling of daily precipitation. Clim. Change. 2010;98(3):405-427.
http://dx.doi.org/10.1007/s10584-009-9738-4