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Dr. Firouz Mojarrad, Mrs. Samira Koshki, Dr. Jafar Masompour, Dr. Morteza Miri,
Volume 4, Issue 4 (1-2018)

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.

Mr. Kaveh Bapirzadeh, Mr. Hesam Seyedkaboli, Miss Leila Najafi,
Volume 9, Issue 2 (9-2022)

 A comparative study of quantitative mapping methods for bias correction of ERA5 reanalysis precipitation data

Kaveh Bapirzadeh1, Hesam Seyed kaboli*2, Leila Najafi3
1 MSc student, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran.
*2 Associate Professor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran. Corresponding Author: Email:
3 Instructor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran.
This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected synoptic stations in climates with different topographic characteristics were received daily from the European Centre for Medium-Range Weather Forecasts (ECMWF) website. Bias correction of these data was performed using 5 quantitative mapping methods based on observational data in R software environment. Two-part evaluation and Taylor diagram were used to compare the performance of different methods. The results showed that the performance of the quantification mapping method depends on the performance functions, set of parameters and climatic conditions. In general, non-parametric methods of multiple mapping have better performance than parametric methods, so that the best performance is related to QUANT and RQUANT methods, among which DIST method has the weakest performance.

Keywords: Quantitative mapping, Bias correction, ERA5, ECMWF

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