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
Dilmi , Djallel; Mallet , Cécile; Barthès , Laurent; Chazottes , Aymeric (2017)
Publisher: European Geosciences Union
Journal: Atmospheric Measurement Techniques
Languages: English
Types: Article
Subjects: TA170-171, [ SDU.STU.ME ] Sciences of the Universe [physics]/Earth Sciences/Meteorology, Earthwork. Foundations, [ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean, Atmosphere, Environmental engineering, TA715-787
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 rain events. 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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Akrour, N., Chazottes, A., Verrier, S., Mallet, C., and Barthes, L.: Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution, Water Resour. Res., 51, 7417-7435, 2015.
    • Atlas, D., Ulbrich, C. W., Marks, F. D., Amitai, E., and Williams, C. R.: Systematic variation of drop size and radar-rainfall relations, J. Geophys. Res.-Atmos., 104, 6155-6169, 1999.
    • Balme, M., Vischel, T., Lebel, T., Peugeot, C., and Galle, S.: Assessing the water balance in the Sahel: impact of small scale rainfall variability on runoff Part 1: rainfall variability analysis, J. Hydrol., 331, 336-348, 2006.
    • Bringi, V. N., Chandrasekar, V., Hubbert, J., Gorgucci, E., Randeu, W. L., and Schoenhuber, M.: Raindrop size distribution in different climatic regimes from disdrometer and dual-polarized radar analysis, J. Atmos. Sci., 60, 354-365, 2003.
    • Brown, B. G., Katz, R. W., and Murphy, A. H.: Statistical analysis of climatological data to characterize erosion potential: 1. Precipitation Events in Western Oregon. Oregon Agricultural Experiment Station Spec. Rep. No. 689, Oregon State University, 1983.
    • Brown, B. G., Katz, R. W., and Murphy, A. H.: Statistical analysis of climatological data to characterize erosion potential: 4. Freezing events in eastern Oregon/Washington. Oregon Agricultural Experiment Station Spec. Rep. No. 689, Oregon State University, 1984.
    • Brown, B. G., Katz, R. W., and Murphy, A. H.: Exploratory Analysis of Precipitation events with Implications for Stochastic Modeling, J. Clim. Appl. Meteorol., 57-67, 1985.
    • Cosgrove, C. M. and Garstang, M.: Simulation of rain events from rain-gauge measurements, Int. J. Climatol., 15, 1021-1029, 1995.
    • Coutinho, J. V., Almeida, C. Das, N., Leal. A. M. F., and Barbarosa, L. R.: Characterization of sub-daily rainfall properties in three rain gauges located in northeast Brazil. Evolving Water Resources Systems: Understanding, Predicting and Managing Water-Society Interactions Proceedings of ICAR 2014, Bologna, Italy, 345-350, 2014.
    • Daumas, F.: Méthodes de normalisation de données, Revue de statistique appliquée, 30, 23-38, 1982.
    • Delahaye, J.-Y., Barthès, L., Golé, P., Lavergnat J., and Vinson, J. P.: a dual beam spectropluviometer concept, J. Hydrol., 328, 110- 120, 2006.
    • de Montera, L., Barthes, L., and Mallet, C.: The effect of rain-no rain intermittency on the estimation of the Universal Multifractal model parameters, J. Hydrometeorol., 10, 493-506, 2009.
    • Driscoll, E. D., Palhegyi, G. E., Strecker, E. W., and Shelley, P. E.: Analysis of storm events characteristics for selected rainfall gauges throughout the United States, US Environmental Protection Agency, Washington, DC, 1989.
    • Dunkerley, D.: Rain event properties in nature and in rainfall simulation experiments: a comparative review with recommendations for increasingly systematic study and reporting, Hydrol. Process., 22, 4415-4435, 2008a.
    • Dunkerley, D.: Identifying individual rain events from pluviograph records: a review with analysis of data from an Australian dryland site, Hydrol. Process., 22, 5024-5036, 2008b.
    • Eagleson, P. S.: Dynamic Hydrology, McGraw-Hill, 1970.
    • Everitt, B.: Cluster Analysis, London: Heinemann Educ. Books, 1974.
    • Gargouri, E. and Chebchoub, A.: Modélisation de la structure de dépendance hauteur-durée d'événements pluvieux par la copule de Gumbel, Hydrological Sciences-Journal-des Sciences Hydrologiques, 53, 802-817, 2010.
    • Grazioli, J., Tuia, D., and Berne, A.: Hydrometeor classification from polarimetric radar measurements: a clustering approach, Atmos. Meas. Tech., 8, 149-170, doi:10.5194/amt-8-149-2015, 2015.
    • Guyon, I. and Elisseeff, A.: An Introduction to Variable and Feature Selection, Kernel Machines Section, 3, 1157-1182, 2003.
    • Haile, A. T., Rientjes, T. H. M., Habib, E., Jetten, V., and Gebremichael, M.: Rain event properties at the source of the Blue Nile River, Hydrol. Earth Syst. Sci., 15, 1023-1034, doi:10.5194/hess-15-1023-2011, 2011.
    • Holland, J. H.: Adaptation In Natural And Artificial Systems, University of Michigan Press, 1975.
    • Iguchi, T., Kozu, T., Kwiatkowski, J., Meneghini, R., Awaka, J., and Okamoto, K.: Uncertainties in the rain profiling algorithm for the TRMM precipitation radark, J. Meteorol. Soc. Jpn., 87A, 1-30, 2009.
    • Kohonen, T.: Self-organizing formation of topologically correct feature maps, Biological Cybernetics, 46, 59-69, 1982.
    • Kohonen, T.: Self-Organizing Maps. Springer-Verlag, ISBN 3-540- 67921-9, New York, Berlin, Heidelberg, 2001.
    • Larsen, M. L. and Teves, J. B.: Identifying Individual Rain Events with a Dense Disdrometer Network, Adv. Meteorol., 2015, ID582782, doi:10.1155/2015/582782, 2015.
    • Lavergnat, J. and Golé, P.: A Stochastic Raindrop Time Distribution Model, J. Appl. Meteorol., 37, 805-818, 1998.
    • Lavergnat, J. and Golé, P.: A stochastic model of raindrop release: Application to the simulation of point rain observations, J. Hydrol., 328, 8-19, 2006.
    • Liu, Y. and Weisberg R. H.: A review of self-organizing map applications in meteorology and oceanography, in: Self-Organizing Maps-Applications and Novel Algorithm Design, 253-272, 2011.
    • Liu, Y., Weisberg, R. H., and Mooers, C. N. K.: Performance evaluation of the self- organizing map for feature extraction, J. Geophys. Res., 111, C05018, doi:10.1029/2005JC003117, 2006.
    • Llasat, M. C.: An objective classification of rainfall events on the basis of their convective features. Application to rainfall intensity in the north east of Spain, Int. J. Climatol., 21, 1385-1400, 2001.
    • Marzuki, M., Hashiguchi, H., Yamamoto, M. K., Mori, S., and Yamanaka, M. D.: Regional variability of raindrop size distribution over Indonesia, Ann. Geophys., 31, 1941-1948, 2013.
    • Molini, L., Parodi, A., Rebora, N., and Craig, G. C.: Classifying severe rainfall events over Italy by hydrometeorological and dynamical criteria, Q. J. Roy. Meteorol. Soc., 137, 148-154, 2011.
    • Moussa, R. and Bocquillon, C.: Caractérisation fractale d'une série chronologique d'intensité de pluie. Rencontres hydrologiques Franco-Romaines, 363-370, 1991.
    • Suh, S.-H., You, C.-H., and Lee, D.-I.: Climatological characteristics of raindrop size distributions in Busan, Republic of Korea, Hydrol. Earth Syst. Sci., 20, 193-207, doi:10.5194/hess-20-193- 2016, 2016.
    • Tapiador, F. J., Checa, R., and de Castro, M.: An experiment to measure the spatial variability of rain drop size distribution using sixteen laser disdrometers, Geophys. Res. Lett., 37, L16803, doi:10.1029/2010GL044120, 2010.
    • Testud, J. S., Oury, P., Amayenc, and Black, R. A.: The concept of “normalized” distributions to describe raindrop spectra: A tool for cloud physics and cloud remote sensing, J. Appl. Meteorol., 40, 1118-1140, 2001.
    • Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing: Fundamentals and Radiometry, Vol. I. Artech House, 321-327, 1981.
    • Uriarte, E. A. and Martín, F. D., Topology Preservation in SOM, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering 2, 9, 2008.
    • Verrier, S., Barthès, L., and Mallet, C.: Theoretical and empirical scale dependency of Z-R relationships: Evidence, impacts, and correction, J. Geophys. Res.-Atmos., 118, 7435-7449, 2013.
    • Vesanto, J. and Alhoniemi, E.: Clustering of the self-organizing map, IEEE Transactions on Neural Networks, 11, 586-600, 2000.
  • No related research data.
  • No similar publications.