INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.6, No.4, Aug. 2014

Analysis of the Time Trends of Precipitation over Mediterranean Region

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Author(s)

Mourad Lazri, Soltane Ameur, Jean Michel Brucker

Index Terms

Rainfall;Meteorological Radar;Markov Chain;Transition Probabilities

Abstract

Time trends of precipitation in the north of Algeria from meteorological radar are analysed. A probabilistic approach presented here proposes to study the evolution of the rainfall phenomenon in two distinct study areas, one located in sea and other located in ground. A decision criterion is established and based on radar reflectivity in order to classify the precipitation events located in both areas. At each radar observation, a state of precipitation is classified, either convective (heavy precipitation) or stratiform (average precipitation) both for the "sea" and for the "ground". In all, a time series of precipitation composed of three states; no raining, stratiform precipitation and convective precipitation, is obtained for each of the two areas. Thereby, we studied and characterized the behavior of precipitation in time by a Markov chain of order one with three states. Transition probabilities are calculated. The results show that rainfall is well described by a Markov chain of order one with three states. Indeed, the stationary probabilities, which are calculated by using the Markovian model, and the actual probabilities are almost identical.

Cite This Paper

Mourad Lazri, Soltane Ameur, Jean Michel Brucker,"Analysis of the Time Trends of Precipitation over Mediterranean Region", IJIEEB, vol.6, no.4, pp.38-44, 2014. DOI: 10.5815/ijieeb.2014.04.06

Reference

[1]Lazri M., Ameur Z., Ameur S., Mohia Y., Brucker J. M., Testud J., 2013. Rainfall estimation over a Mediterranean region using a method based on various spectral parameters of SEVIRI-MSG. J. Adv. Space Res. http://dx.doi.org/10.1016/j.asr.2013.07.036.

[2]Nanda S. K., Tripathy, D. P. Nayak, S. K. Mohapatra, S. Prediction of Rainfall in India using Artificial Neural Network (ANN) Models", IJISA, vol.5, no.12, pp.1-22, 2013. DOI: 10.5815/ijisa.2013.12.01

[3]Hess, G.D., Leslie, L.M., Guymer, A.E., and Fraedrich, K., 1989. Application of a Markov technique to the operational, short-term forecasting of rainfall, Australian Meteorological Magazine, 37, 2, 83-91.

[4]Lennartsson, J., Anastassia, B., Deliang, C., , 2008. Modelling precipitation in Sweden using multiple step markov chains and a composite model, Journal of Hydrology, 363, 42– 59.

[5]Ratnasingham, S., Geoffrey, G., Pegram, S., 2009. A nested multisite daily rainfall stochastic generation model, Journal of Hydrology, 371, 142–153.

[6]Lazri M., Ameur S., Haddad, B., 2007. Analyse de Données de Précipitations par Approche Markovienne,” Larhyss Journal, 6, 7-20. 

[7]Hughes, J.P., Guttorp, P., Charles, S., 1999. A non-homogeneous hidden Markov model for precipitation occurrence, Appl. Stat. 48 (1), 15–30.

[8]Charles, C., Gafni, A., and Whelan, T., 1999. Decision-making in the physician–patient encounter: revisiting treatment decision-making model, Social Science & Medicine, Vol. 49, Issue 5, 651-661.

[9]Mehrotra, R., Sharma, A., 2005. A non-parametric non-homogeneous hidden Markov model for downscaling of multi-site daily rainfall occurrences, J. Geophys. Res. Vol, 110, 13 PP, D16108. doi:10.1029/2004JD00567.

[10]Talagrand, M., 1996. The Glivenko-Cantelli Problem, Ten Years Later, Journal of Theoretical probability, Vol. 9, No. 2, 371-384, DOI: 10.1007/BF02214655.

[11]Koutsoyiannis, D., 2010. A random walk on water, Hydrol. Earth Syst. Sci., 14, 585–601.

[12]Todorovic, P., Woolhiser, D.A. : A stochastic model of n-day precipitation, J. Appl. Prob. 12, 488–497, 1975.

[13]Arruda, H.V., Pinto, H.S. , 1980. An alternative model for dry-spell probability analysis, Monthly Weather Rev. 108, 823–825.

[14]Srikanthan, R., and McMahon, T.A., 2001. Stochastic generation of annual, monthly and daily climate data: A review, Hydrology and Earth System Sciences, 5(4), 653–670.

[15]Gabriel, K.R., Neumann, J. , 1962. A Markov chain model for daily rainfall occurrence at Tel Aviv, Q. J. R. Meterol. Soc. 88, 90–95.

[16]Gates, P., Tong, H., 1976. On Markov chain modeling to some weather data. Journal of Applied Meteorology, 15, 1145-1151.

[17]Caskey, J.E., 1963. Markov Chain Model for the probability of precipitation occurrence in intervals of various length, Monthly Weather Rev. 91, 298–301. 

[18]Weiss, L.L., 1964. Sequences of wet or dry days described by a Markov chain probability model, Monthly Weather Rev. 92, 169–176.

[19]Jimoh, O., Webster, P., 1996. The optimum order of a Markov chain for daily rainfall in Nigeria, Journal of Hydrology, 185, 45-69.

[20]Moon, S.E., Ryoo, S.B., Known, J.G., 1994. A Markov chain model for daily precipitation occurrence in South Korea. Int. J. Climatol. 14, 1009–1016.

[21]Gômez Narvaro, L., 1996. Calcul par les chaînes de Markov des probabilités de durée des séquences sèches et pluvieuses en Espagne, Publ. Assoc. Int. Climatol. 9, 203–209.

[22]Johnson, J. T., MacKeen, P. L., Witt, A., Mitchell, E. D., Stumpf, G. J., Eilts, M. D., Thomas, K. W., , 1998. The Storm Cell Identification and Tracking (SCIT) algorithm: An enhanced WSR-88D algorithm, Weather Forecasting, 13, 263–276.

[23]Santosh Kumar Nanda, Debi Prasad Tripathy, Simanta Kumar Nayak, Subhasis Mohapatra,"Prediction of Rainfall in India using Artificial Neural Network (ANN) Models", IJISA, vol.5, no.12, pp.1-22, 2013. DOI: 10.5815/ijisa.2013.12.01