Influence of atmospheric circulation types in space-time distribution of intense rainfall

Mamassis, N., and D. Koutsoyiannis, Influence of atmospheric circulation types in space-time distribution of intense rainfall, Journal of Geophysical Research-Atmospheres, 101 (D21), 26267–26276, 1996.



The influence of the prevailing weather situation on the temporal evolution and geographical distribution of intense rainfall is studied, as a potential tool to improve rainfall prediction. A classification scheme of the atmospheric circulation over the east Mediterranean territory is used for the analysis. The study area is the Sterea Hellas region (central Greece) with an area of about 25,000 km2. Daily data from 71 rain gages and hourly data from three rain recorders over a 20 year period are used. From these data sets, the intense rainfall events were extracted and analyzed. Several empirical and statistical methods (also including the available tools of a Geographical Information System) are used for the analysis and comparison of rainfall distribution both in time and in space. The analysis shows that the contribution of the concept of weather types to the quantitative point rainfall prediction in short timescale is small, and only the estimation of the probability of occurrence of an intense event is feasible. On the contrary, the relation between the spatial distribution of rainfall and the atmospheric circulation patterns is significant and may be used for improving the forecasting of the geographical distribution of rainfall.

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Our works referenced by this work:

1. Mamassis, N., et D. Koutsoyiannis, Structure stochastique de pluies intenses par type de temps, Publications de l'Association Internationale de Climatologie, 6eme Colloque International de Climatologie, edité par P. Maheras, Thessaloniki, 6, 301–313, Association Internationale de Climatologie, Aix-en-Provence Cedex, France, 1993.
2. Mamassis, N., D. Koutsoyiannis, and I. Nalbantis, Intense rainfall and flood event classification by weather type, 19th General Assembly of the European Geophysical Society, Annales Geophysicae, Vol. 12, Supplement II, Part II, Grenoble, 440, European Geophysical Society, 1994.

Our works that reference this work:

1. Cudennec, C., C. Leduc, and D. Koutsoyiannis, Dryland hydrology in Mediterranean regions -- a review, Hydrological Sciences Journal, 52 (6), 1077–1087, 2007.
2. Koutsoyiannis, D., N. Mamassis, A. Efstratiadis, N. Zarkadoulas, and Y. Markonis, Floods in Greece, Changes of Flood Risk in Europe, edited by Z. W. Kundzewicz, Chapter 12, 238–256, IAHS Press, Wallingford – International Association of Hydrological Sciences, 2012.

Other works that reference this work:

1. Nalbantis, I., Real-time flood forecasting with the use of inadequate data, Hydrological Sciences Journal, 45(2), 269-284, 2000.
2. Stehlik, J., and A. Bardossy, Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation, Journal of Hydrology, 256(1-2), 120-141, 2002.
3. Anderson, B.T., Regional simulation of intraseasonal variations in the summertime hydrologic cycle over the southwestern United States, J. Climate, 15 (17), 2282-2300, 2002.
4. Anderson, B.T., and J.O. Roads, Regional simulation of summertime precipitation over the southwestern United States, J. Climate, 15 (23), 3321-3342, 2002
5. Rudari, R., D. Entekhabi and G. Roth, Terrain and multiple-scale interactions as factors in generating extreme precipitation events, Journal of Hydrometeorology, 5 (3), 390-404, 2004.
6. Rudari, R., D. Entekhabi and G. Roth, Large-scale atmospheric patterns associated with mesoscale features leading to extreme precipitation events in Northwestern Italy, Advances in Water Resources, 28(6), 601-614, 2005.
7. Boni, G., A. Parodi and R. Rudari, Extreme rainfall events: Learning from raingauge time series, Journal of Hydrology, 327(3-4), 304-314, 2006.
8. Vrac, M. and P. Naveau, Stochastic downscaling of precipitation: From dry events to heavy rainfalls, Water Resources Research, 43(7), W07402, 2007.
9. Troin, M., M. Vrac, M. Khodri, C. Vallet-Coulomb, E. Piovano and F. Sylvestre, Coupling statistically downscaled GCM outputs with a basin-lake hydrological model in subtropical South America: evaluation of the influence of large-scale precipitation changes on regional hydroclimate variability, Hydrol. Earth Syst. Sci. Discuss., 7, 9523-9565, doi: 10.5194/hessd-7-9523-2010, 2010.
10. Karagiorgos, K., S. Fuchs, T. Thaler, M. Chiari, F. Maris and J. Hübl, A flood hazard database for Greece, Wildbach- und Lawinenverbau, 77 (170), 264-277, 2013.
11. Panagoulia, D., P. Economou and C. Caroni, Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change, Environmetrics, 25 (1), 29-43, 2014.

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