Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

Analysis of Thunderstorm Instability Indexes in Iran using Reanalysis Data

Authors
Firouz Mojarrad 1
Samira Koshki 1
Jafar Masompour 1
Morteza Miri 2
1 Geography Dept., Faculty of Literature and Humanities, Razi Univ., Kermanshah
2 Clhmatology Dept., Geography Faculty, Tehran Univ., Tehran
Abstract
Thunderstorm is a destructive atmospheric phenomenon, which annually causes a lot of damage to various parts of human activities. Due to the accompaniment of thunderstorm with rainstorm and hail and its effective role in creating sudden floods, the analysis of the behavior of this hazard has been widely studied both in terms of agriculture and in terms of financial and life damages throughout the world. The study of thunderstorm as a hazardous atmospheric phenomenon using instability indexes in Iran has been less considered due to lack of observation stations. Convective Available Potential Energy (CAPE) and Vertical Wind Shear (VWS) are two indexes that are often used to describe and detect thunderstorm environments. This study evaluates the thunderstorms in Iran with reanalysis data using CAPE and VWS indexes.

Thunderstorm data in 7 different conditions at 8 times a day for 42 synoptic and upper air stations during a 37-year common period (1980-2016) was received from the Iranian Meteorological Organization. At first, frequency, trend and time of occurrence of thunderstorms in Iran were investigated during the statistical period. Then, the ERA-Interim reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) with spatial resolution of 0.5 ° was used for the analysis of thunderstorms. To evaluate the ERA-Interim dataset, the CAPE and VWS values for the 80 selected thunderstorm events that were calculated using the RAOB software were compared with ERA data and their accuracy was confirmed. After confirming the accuracy of ERA data, the average values of CAPE and VWS indexes in 42 stations of the country were calculated based on 4,542 thunderstorm events at 00 and 12 GMT during the study period, and the maps of these two indexes were drawn up using the IDW method. Then, using an equation, the thunderstorm severity thresholds across the country were determined using ERA data with 4,542 thunderstorm events to distinguish between mild, severe and very severe storms. To ensure the selection of important storms, storms with CAPE values of less than 50 were removed to exclude poor environments for convection occurrence. As a result, out of 4,542 thunderstorms, 535 events were eliminated and 4007 events remained. On this basis, a "2 x 2 contingency table" was prepared that compares thunderstorm events and forecasts. This table provides the information required to compute warning performance statistics including POD (Probability of Detection), FAR (False Alarm Ratio) and CSI (Critical Success Index). But the results of these statistics did not match well with the conditions of thunderstorm events in Iran. Therefore, the discriminant analysis was used to differentiate the intensity of thunderstorms and to discriminate mild, severe and extremely severe thunderstorms.
The results of the study showed that thunderstorms in Iran are increasing during the statistical period with a regression slope of 0.23 events per year (8.5 events in the statistical period). The highest frequency of thunderstorms was observed in the month of May with an annual number of 111, and the lowest was observed in January with 12 events. Most thunderstorms occur around 21:30. The highest average frequency of annual events at stations was related to the stations of Urmia, Tabriz, Khorramabad and Bushehr respectively. The proper capability of ERA data to estimate instability indexes in Iran was proved. ERA data provides a very near estimate for VWS, but estimates for the CAPE index are slightly more than observational values. The highest values of the CAPE index are observed in southern provinces, as well as in the southwest of the Caspian Sea coasts, and the highest values of the VWS index are found on the Persian Gulf coasts. When the storm severity breakdown equation for the 400 selected storm events was obtained and the "2 x 2 contingency table" was prepared, it was found that this equation was not satisfactory with respect to the POD, FAR, and CSI indexes. Hence, using the discriminant analysis, the storm severity breakdown relationships and their discriminant equations were obtained. These equations categorized 60% of the surveyed thunderstorms correctly. There is no significant difference between the mean values of CAPE and VWS in the three storm intensity groups. The role of the VWS index was higher in determining the type of storm.
Keywords
thunderstorm
Iran
ECMWF
ERA-Interim Reanalysis Data
CAPE Index
VWS Index
10- Adams, D. K. and E. P. Souza. 2008. CAPE and Convective Events in the Southwest during the North American Monsoon. Monthly Weather Review, 137 (1): 83-98.
11- Allen, J. T.; D. J. Karoly and G. A. Mills. 2011. A Severe Thunderstorm Climatology for Australia and Associated Thunderstorm Environments. Australian Meteorological and Oceanographic Journal, 61(3): 143–158.
12- Allen, J. T.; D. J. Karoly and K. J. Walsh. 2014. Future Australian Severe Thunderstorm Environments. Part I: A Novel Evaluation and Climatology of Convective Parameters from Two Climate Models for the Late Twentieth Century. Journal of Climate, 27 (10): 3848- 3868.
13- Allen, J. T. and D. J. Karoly. 2014. A Climatology of Australian Severe Thunderstorm Environments 1979–2011: Inter-annual Variability and ENSO Influence. International Journal of Climatology, 34 (1): 81–97.
14- Bunkers, M. J. 2002. Vertical Wind Shear Associated with Left-Moving Supercells. Weather and Forecasting, 17: 845-855.
15- Brooks, H. E.; C. A. Doswell and J. Cooper. 1994. On the Environments of
Tornadic and Nontornadic Mesocyclones. Weather and Forecasting, 9 (4): 606–618.
16- Brooks, H. E.; J. W. Lee and J. P. Craven. 2003. The Spatial Distribution of Severe Thunderstorm and Tornado Environments from Global Reanalysis Data. Atmospheric Research, 67-68: 73–94.
17- Chaudhuri, S. and A. Middey. 2012. A Composite Stability Index for Dichotomous Forecast of Thunderstorms. Theoretical and Applied Climatology, 110 (3): 457- 469.
18- Craven, J. P.; R. E. Jewell and H. E. Brooks. 2002. Comparison between Observed Convective Cloud-Base Heights and Lifting Condensation Level for two Different Lifted Parcels. Weather and Forecasting, 17: 885 – 890.
19- Craven, J. P. and H. E. Brooks. 2004. Baseline Climatology of Sounding Derived Parameters Associated with Deep Moist Convection. National Weather Digest, 28: 13-24.
20- Del Genio, A. D.; M. S. Yao and J. Jonas. 2007. Will Moist Convection be Stronger in a Warmer Climate? Geophysical Research Letters, 34 (16): 1-5.
21- Diffenbaugh, N. S.; R. J. Trapp and H. E. Brooks. 2008. Does Global Warming Influence Tornado Activity? Eos Transactions American Geophysical ::::::::::::::union::::::::::::::, 89 (53): 553–554.
22- Galway, J. G. 1956. The Lifted Index as a Predictor of Latent Instability. Bulletin of the American Meteorological Society, 37: 528–529.
23- Gottlieb, R. J. 2009. Analysis of Stability Indices for Severe Thunderstorms in the Northeastern United States, M.Sc. Thesis, Cornell University, Ithaca, USA.
24- Keene, K.; P. Schlatter, J. Hales and H. Brooks. 2008. Evaluation of NWS Watch and Warning Performance related to Tornadic Events. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc. P.
25- Klotzsche, S. 2013. Changes of Meteorological Parameters that Influence Tornadoes and Thunderstorms in Climate Simulations with Models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), M. Sc. Thesis in Meteorology, advisor: J. Quaas.
26- Manzato, A. and J. Morgan. 2003. Evaluating the Sounding Instability with the Lifted Parcel Theory. Atmospheric Research, 67: 455-473.
27- Marinaki, A.; M. Spiliotopoulos and H. Michalopoulou. 2006. Evaluation of Atmospheric Instability Indices in Greece. Advances in Geosciences, European Geosciences ::::::::::::::union::::::::::::::, 7: 131–135.
28- Markowski, P. and Y. Richardson. 2010. Mesoscale Meteorology in Midlatitudes, John Wiley & Sons, 430 p.
29- Marsh, P. T.; H. E. Brooks and D. J. Karoly. 2009. Preliminary Investigation into the Severe Thunderstorm Environment of Europe Simulated by the Community Climate System Model 3. Atmospheric Research, 93 (1-3): 607–618.
30- Moncrieff, M. W. and M. J. Miller. 1976. The dynamics and Simulation of Tropical Cumulonimbus and Squall Lines. Royal Meteorological Society, 102: 373-394.
31- Niall, S. and K. Walsh. 2005. The Impact of Climate Change on Hailstorms in Southeastern Australia. International Journal of Climatology, 25 (14): 1933–1952.
32- Rasmussen, E. N. 2003. Refined Supercell and Tornado Forecast Parameters. Weather and Forecasting, 18 (3): 530–535.
33- Rasmussen, E. N. and D. O. Blanchard. 1998. A Baseline Climatology of Sounding-Derived Supercell and Tornado Forecast Parameters. Weather and Forecasting, 13 (4): 1148–1164.
34- Schaefer, J. T. 1990. The Critical Success Index as an Indicator of Warning Skill. Weather and Forecasting, 5 (4): 570-575.
35- Seeley J. T. and D. M. Romps. 2015. The Effect of Global Warming on Severe Thunderstorms in the United States. Journal of Climate, 28 (6): 2443- 2458.
36- Showalter, A. K. 1953. A Stability Index for Thunderstorm Forecasting. Bulletin of the American Meteorological Society, 34 (6): pp. 250-252.
37- Turcotte, V. and D. Vigneux. 1987. Severe Thunderstorms and Hail Forecasting using Derived Parameters from Standard RAOBS Data. Preprints, Second Workshop on Operational Meteorology. Halifax, Nova Scotia, Canada, Atmos. Environ. Service/Canadian Meteorol. and Oceanogr., pp. 142–153.