LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Gutiérrez, J. M.; Cano, R.; Cofiño, A. S.; Sordo, C. (2005)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects: DEMETER project, Predicción estacional, Ensemble seasonal forecasts, Self-organizing maps

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics, Physics::Geophysics
We present an application of self-organizing maps (SOMs) for analysing multi-model ensemble seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors (cluster centroids). Moreover, it has outstanding analysis and visualization properties, because the reference vectors can be projected into a two-dimensional lattice, preserving their high-dimensional topology. In the first part of the paper, the SOM is applied to analyse both atmospheric patterns over Peru and local precipitation observations at two nearby stations. First, the method is applied to cluster the ERA40 reanalysis patterns on the area of study (Northern Peru), obtaining a two-dimensional lattice which represents the climatology. Then, each particular phenomenon or event (e.g. El Niño or La Niña) is shown to define a probability density function (PDF) on the lattice, which represents its characteristic ‘location’ within the climatology. On the other hand, the climatological lattice is also used to represent the local precipitation regime associated with a given station. For instance, we show that the precipitation regime is strongly associated with El Niño events for one station, whereas it is more uniform for the other. The second part of the paper is devoted to downscaling seasonal ensemble forecasts from the multi-model DEMETER ensemble to local stations. To this aim, the PDF generated on the lattice by the patterns predicted for a particular season is combined with the local precipitation lattice for a given station. Thus, a probabilistic or numeric local forecast is easily obtained from the resulting PDF. Moreover, a measure of predictability for the downscaled forecast can be computed in terms of the entropy of the ensemble PDF.We present some evidence that accurate local predictions for accumulated seasonal precipitation can be obtained some months in advance for strong El Niño episodes. Finally, we compare the multi-model ensemble with single-model ensembles, and show that the best results correspond to different models for different years; however, the best global performance over the whole period corresponds to the multi-model ensemble.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Allan, R. J., Nicholls, N., Jones, P. D. and Butterworth, I. J. 1991. A further extension of the Tahiti-Darwin SOI, early SOI results and Darwin pressure. J. Climate 4, 743-749.
    • Anderson, D. L. T., Stockdale, T., Balmaseda, M. A., Ferranti, L., Vitart, F. and co-authors. 2003. Comparison of the ECMWF seasonal forecast systems 1 and 2, including the relative performance for the 1997/8 El Nin˜o. Technical Memoranda no. 404, ECMWF.
    • Cavazos, T. 1999. Large-scale circulation anomalies conductive to extreme precipitation events and derivation of daily rainfall in northeastern Mexico and south-eastern Texas. J. Climate 12, 1506-1523.
    • Cavazos, T. 2000. Using self-organizing maps to investigate extreme climate event: an application to wintertime precipitation in the Balkans. J. Climate 13, 1718-1732.
    • Cofin˜o, A. S., Gutie´rrez, J. M., Jakubiak, B. and Melonek, M. 2003. Implementation of data mining techniques for meteorological applications. In Realizing Teracomputing (eds W. Zwieflhofer, and N. Kreitz). World Scientific, Singapore, 215-240.
    • D´ıez, E., Primo, C., Garc´ıa-Moya, J. A., Gutie´rrez, J. M. and Orfila, B. 2005. Statistical and dynamical downscaling of precipitation over Spain from DEMETER seasonal forecasts. Tellus 57A, 409-423.
    • Eckert, P., Cattani, A. and Ambu¨hl, J. 1996. Classification of ensemble forecasts by means of an artificial neural network. Meteorol. Appl. 3, 169-178.
    • Feddersen, H. and Andersen, U. 2005. A method for statistical downscaling of seasonal ensemble predictions. Tellus 57A, 398-408.
    • Gutie´rrez, J. M., Cano, R., Cofin˜o, A. S. and Rodr´ıguez, M. A. 2004. Clustering methods for statistical downscaling in short-range weather forecast. Mon. Wea. Rev. 132, 2169-2183.
    • Han, J. and Kamber, M. 2000. Data Mining: Concepts and Techniques. Morgan Kaufmann, San Mateo, CA, pp. 500.
    • Hewitson, B. C. and Crane, R. G. 2002. Self-organizing maps: applications to synoptic climatology. Climate Res. 22, 13-26.
    • Jolliffe, I. T. and Stephenson, D. B. 2003. Forecast Verification: A Practitioner's Guide in Atmospheric Science. Wiley, New York.
    • Kohonen, T. 1995. Self-Organizing Maps. No. 30 in Springer Series in Information Sciences. Springer-Verlag, Berlin, pp. 521.
    • Oja, E. and Kaski, S. eds. 1999. Kohonen Maps. Elsevier, Amsterdam, pp. 400.
    • Palmer, T. N., Alessandri, A., Andersen, U., Cantelaube, P., Davey, M. and co-authors. 2004. Development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER). Bull. Am. Meteorol. Soc. 85, 853-872.
    • Pen˜a, J. M., Lozano, J. A. and Larran˜aga, P. 1999. An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters 20, 1027-1040.
    • Shannon, C. E. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 623-656.
    • Wilby, R. L. and Wigley, T. M. L. 1997. Downscaling general circulation model output: a review of methods and limitations. Prog. Phys. Geog. 21, 530-548.
    • Zorita, E. and von Storch, H. 1999. The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J. Climate 12, 2474-2489.
  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

    Title Trust
    41
    41%
  • No similar publications.

Share - Bookmark

Cite this article