Investigation on a new dynamic state index in heavy precipitation events in the southern coasts of Caspian Sea

Document Type : Research Article

Author

• Hamid Nouri

Malayer University

10.22067/geo.v1i2.14228

Abstract

Introduction
The Dynamic State Index (DSI) is being introduced on the basis of a newly developed theory termed the Energy-Vorticity Theory (EVT). As a weather index, the DSI can describe the temporal sequence and intensity of areas of high and low pressure; but as a climate index, it can also diagnose climate change based on the variation in solar radiation or the influences of friction. In physical terms, the index is a parameter that combines the retention of energy and the atmospheric vorticity with each other. If the index is at zero, the atmosphere is in a stable basic energy-vorticity state. The new idea is to determine the variability of the weather and the climate as deviations from this characteristic status. If the index is positive, the large-scale weather pattern is characterized by extended areas of high pressure. If, however, it takes on a negative value, atmospheric activity features mainly low-pressure vortices (cyclones) instead. Hot summers, such as that experienced in Europe in 1997, are characterized by especially low fluctuations in this index. This research investigates relationships between heavy precipitation events and DSI.

Study Area

The north of Iran including Gilan, Mazandaran and Golestan provinces is the study area for surface data analyses. Also a region between 0° to 80°N in latitude and -20° to 120°E in longitude is the study area for upper levels analyses. The Caspian Sea is located in this region. The Caspian Sea is the largest closed body of water on the surface of the Earth. The sea has a surface area of 371,000 square kilometers and a volume of 78,200 cubic kilometers. Its basin has no outflows and is bounded by northern Iran, southern Russia, western Kazakhstan and Turkmenistan, and eastern Azerbaijan.
Material and Methods
On the basis of daily precipitation events and with regard to 25 and 50 percent probability, precipitation events were divided into two groups of heavy and super heavy precipitation. The precipitation was grouped into two classes including convective and non-convective clouds based on clouds synoptic indexes. The DSI was computed in different levels and different precipitation groups including heavy, super-heavy, convective and non-convective events using density, potential vorticity, potential temperature and Bernoulli-Stream function. These atmospheric characteristics were calculated using temperature, Geopotential height and wind velocity (ERA40 database). Spearman’s correlation coefficient between mean daily of DSI and mean of precipitation was computed in different levels and precipitation groups. The mean SLP and mean DSI maps were compared in different precipitation groups.


Results and Discussion

investigation on correlation coefficient between DSI and mean precipitation in different groups (super-heavy and convective, heavy and convective, super-heavy and non-convective, heavy and non-convective) and different levels show that the super-heavy and convective precipitation events in 310° Kelvin level show the highest positive correlation coefficient (40%) in the north east of Caspian Sea and the highest negative correlation coefficient (-32%) in south west of Caspian Sea among the groups significantly. The north and the south of Caspian Sea have positive and negative DSI respectively in different levels and precipitation groups (figures 1-4).


Conclusion

The results reveal that the north of Caspian Sea have positive DSI(subsidence) and negative DSI(air lifting) in all precipitation groups and in 280 to 310 Kelvin. In super-heavy precipitation group when convective clouds are in the sky, there are just a small area with strong positive DSI (strong subsidence) in the north of Caspian Sea. The other parts of the Sea have negative DSI or air lifting. DSI in heavy precipitation group is less positive and less negative than super precipitation group both convective and non-convective events. Subsidence and airlifting area are different in each precipitation group. Despite the good role of the DSI for recognition of subsidence and airlifting area, correlation coefficient between precipitation and the absolute DSI is more in Europe than the north of Iran.

Keywords

• : Dynamic
• heavy precipitation
• convective
• non-convective
• Caspian Sea

---

References

Alijani, B.,2002. Synoptic climatology, SAMT Publications, Tehran, Iran. 1:112-114.

Blessmann, D., and Nevir, P., 2005. Analysing the vertical structure of the atmosphere using the Dynamic-State-Index (DSI), Geophysical Research Abstracts, Vol. 7, 08744 SRef-ID: 1607-7962/gra/EGU05-A-08744

Schartner, T., Nevir, P., Leckebusch, G. C., Ulbrich, U., 2009. Analysis of thunderstorms with the dynamic state index (DSI) in a limited area high resolution model. 5th European Conference on Severe Storms 12 - 16 October 2009 - Landshut Germany, 112-134.

Claußnitzer, A., Nevir, P., Langer, I., Reimer, E., Cubasch, U., 2008. Scale-dependent analyses of precipitation forecasts and cloud properties using the Dynamic State Index, Meteorol. Zeitschrift, 17, 813-825.

Dong-Kyou L., Jeong-Gyun P., and Joo-Wan K. 2008. Heavy rainfall events lasting days from July 31 to August 17, 1998, over Korea, Journal of the Meteorological Society of Japan, VOL, 86, NO. 2, pp. 313-333, 2008.

Ghayoor, H., Masoodian, A., Azadi, M., Nouri, H., 2011. Temporal And Spatial Analysis Of Heavy And Superheavy Precipitations In The Southern Coastal Of Caspian Sea, 100:21-50

Kumar, A., Dudhia, J., Rotunno, R., Niyogi, D. and Mohanty, U., 2008. Analysis of the 26 July 2005 heavy rain event over Mumbai, India using the Weather Research and Forecasting(WRF), Q.J.R. Meteoral. Soc. 134:1897-1910.

Kyou, L., Gyun, P. and Wan, K., 2008, Heavy rainfall events lasting 18 days from July 31 to August 17, 1998, over Korea, J. of the Meteorogical Society of Japan, Vol, 86, NO.2, PP.313-333.

Langer, I., E. Reimer, A. Oestreich, 2008. First results: Cloud classification from Meteosat data for separation of convective and stratiform precipitation. – Meteor. Z. 17, 19–27.

Matthies, A., Schartner, T., Leckebusch, G.C., Rohlfing, G., Nevir, P. and Ulbrich, U., 2008. Extreme weather events in southern Germany – Climatological risk and development of a nowcasting procedure, www.geo.fu-berlin.de/met/, 32-65.

Masoodian, S. A., 2003.Climatic regions of Iran. J. Geography and Development. 2:171-183.

Moradi H., 2003. The role of Caspian Sea in the southern coasts precipitation, J. of Marin sciences in Iran. 2:14-15.

Nouri, H., Ildoromi, A., 2012. Synoptic and Dynamic Analysis of Heavy Rainfall Events in the Southern Coasts of Caspian Sea in Comparison with the rest of Iran. J. of Geography and planning.38: 12-56

Vaidya, S. and Kulkarni, J., 2007. Simulation of heavy precipitation over Santascruz, Mumbai on 26 July 2005, using Meseoscale model, Meteorol Atmos Phys, DOI 10.1007/s00703-006-0233-4.

Weber, T., Nevir, P., 2008. Storm Tracks and Cyclone Development using the Theoretical Concept of the Dynamic State Index (DSI), Tellus, 60(A), 1-10