Dynamical Analysis of Severe Rain Events over Iraq

Authors

  • shatha issa Department of Atmospheric Sciences, College of Science Mustansiriyah University, Baghdad, Iraq
  • Jasim Kadhum Department of Atmospheric Sciences, College of Science Mustansiriyah University, Baghdad, Iraq

DOI:

https://doi.org/10.23851/mjs.v30i1.586

Keywords:

rainfall, meteorological elements, dynamical analysis, period

Abstract

Abstract Rain is one of the most important meteorological elements for the various forms of life. Over time, because of the changes in the Earth's climate, patterns of precipitation have changed. Many areas have suffered from lack of water resources. Others have become completely dry and turned into arid deserts. For these reasons, increased interest in climate studies, especially those related to rainfall. In this study, the statistical indicators was showed for the mean annual rainfall is about 225.84 mm and rainfall fluctuates around this mean, and the trend of rainfall was decreasing for period (1983-2017). The mean of monthly rainfall indicate that the highest amount is less than40 mm/month. The histograms of monthly rain shows the highest counts of(40-50) mm, (30-40) mm, (20-30) mm, ( 10-20)mm, (0-10)mm intervals for same period. By using TRMM daily rainfall maps to study the dynamical analysis of severe rainfall cases was conducted in Iraq for four individual study cases. The highest values are ranged between (80- 160) mm. Eleven meteorological elements were selected to study their behavior in the process of severe rainfall in these study cases as the (1000-500) mb thickness, mean sea level pressure, the 850 hPa (relative humidity, temperature and streamlines), the 500 hPa (vorticity and geopotential height), the 200 hPa (streamlines and isotaches), the Convective Available Potential Energy (CAPE) and the Total Cloud Water Vapor (TCWV) founding some of the results that were illustrated in this paper.

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References

References

Strangeways I.C., (2007): Precipitation Theory, Measurement and

Distribution. Cambridge University Press, 302 p.

Levizzani, V., et al., (2007): Measuring Precipitation from Space.

Springer, 724 p.

Andronache, C., (2018): Remote Sensing of Clouds and Precipitation.

Springer, 282 p.

Bellerby, T., M., et al., (2000): Rainfall Estimation from a

Combination of TRMM Precipitation Radar and GOES Multispectral

Satellite Imagery through the Use of an Artificial Neural Network,

Journal of Applied Meteorology, vol. 39, pp. 2115-2128.

Ikai, J. and K. Nakamura, (2003): Comparison of Rain Rates over the

Ocean Derived from TRMM Microwave Imager and Precipitation

Radar, Journal of Atmospheric and Oceanic Technology, vol. 20, pp.

-1726, Dec

Gruber, A., et al., (2000): The Comparison of Two Merged Rain

Gauge-Satellite Precipitation Datasets, Bulletin of the American

Meteorological Society, vol. 81, no. 11, Nov.

Chen, F. W., (2004): Global estimation of precipitation using opaque

microwave bands. Ph. D. Thesis, MIT, USA.

Teo, C. K., (2006): Application of satellite-based rainfall estimates to

crop yield forecasting in Africa. Ph. D. Thesis, University of Reading,

UK.

Artan, G., et al., (2007): Adequacy of satellite derived rainfall data

for stream flow modeling. Nat. Hazard, 43, 167-185.

Jamil, L. T., and K. J. Al-Jumaily, (2015): Study and analysis of

convective rain in Iraq: Case studies. Mustansiriyah J. Sc., 26 (2),

-55, (in Arabic).

Abdulrida, M. A., and K. J. Al-Jumaily, (2016): Comparisons of

monthly rainfall data with satellite estimates of TRMM 3B42 over

Iraq. IJSR, 6(1), 494-499.

Al-Zuhairi, M. F., K. J. AL-Jumaily, and A. M. AL-Salihi, (2016):

Analysis of TRMM precipitation radar measurements over Iraq. IJSR,

(12), 1-9.

Al-Falahi, A.A., 2008: Middle East Water and Livelihoods Initiative.

ICARDA, Aleppo 7-9 July, 2008.

Gelaro, R., et al., 2017: The Modern-Era Retrospective Analysis for

Research and, Version 2 (MERRA-2). J. Climate, 30, 5419-5454.

www.ecmwf.int

Wang, Y.Q., Zhang, X.Y. and Draxler, R., (2009): TrajStat: GIS-

based software that uses various trajectory statistical analysis

methods to identify potential sources from long-term air pollution

measurement data. Environmental Modelling & Software, 24: 938-

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Key Dates

Published

15-08-2019

Issue

Section

Original Article

How to Cite

[1]
shatha issa and J. Kadhum, “Dynamical Analysis of Severe Rain Events over Iraq”, Al-Mustansiriyah Journal of Science, vol. 30, no. 1, pp. 15–22, Aug. 2019, doi: 10.23851/mjs.v30i1.586.

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