STATISTICAL ANALYSIS OF CONVECTIVE STORMS BASED ON ORADEA AND BOBOHALMA WSR-98D RADAR IN JULY - N. MAIER 1, T. TUDOSE 2 ABSTRACT. - Statistical analysis of convective storms based on Oradea and Bobohalma WSR-98D radar in July -. Radar data usage for the statistics of convective storms is very useful due to high temporal and spatial distribution. On the other hand, July month is characterized by intense convective processes as a consequence of pronounced radiative heating from the earth's surface so that favors the development of convective storms. The present analyses uses data from Oradea and Bobohalma radars of July month in the - period and the processing concerns the extent of atmospheric instability in the north-west half of Romania, based on reflectivity, vertical integrated liquid (VIL), hail diameter and convective wind speed parameters. Also, it has been determined the diurnal variation, daily number and time duration of convective storms. The graphic materials presents spatial distribution of significant hailstorms (storms producing hail 5 cm or larger in diameter) and storms that cause strong wind speeds (over 90 km/h). It was also considered the fact that the degree of instability of a day is not given only by the number of convective storms, but also the intensity of the phenomena that is generated. Keywords: convective storm, hail, convective wind, atmospheric instability 1. INTRODUCTION Hail genesis, updraft and downdraft flow theories associated to convective clouds are complex and incomplete so that makes the detection and prediction difficult. On the other hand, the feedback regarding the intensity of the phenomena its slow and most of the time difficult to make, taking into account the fact that some meteorological phenomena as hail cover a small area compared to area of the storm. In some cases, local climatological data and studies related to hail or strong winds are often subject of interest for damages that occur, especially for agriculture and construction. In Romania, the spatial distribution of hail and wind is based on data from weather stations (Clima României, p 327 and 332). To cover the lack of information regarding this issue the present study uses weather radar data for a detailed image of the areas where hail has a high probability to fall, especially the large one (Maier, ). Downdraft flow for each storm was analyzed in order to determine the intensity of winds within convective origin, also known as convective winds. Stewart (1991) uses and improves a method developed by the Air Weather Service 1 NAM Bucharest - RMC North Transylvania, Cluj-Napoca, Romania, e-mail: mcis73@yahoo.com; 2 "Babeş-Bolyai" University, Faculty of Geography, 400006 Cluj-Napoca, Romania, e-mail: ttraiant@gmail.com 92
meteorologists (AWS 1996) in order to determine the capacity of a convective cell to cause an intense downdraft flow (downburst). According to it, downdraft flow potential of intense winds depends on maximum height of a storm cloud (Echo Top-ET) and VIL, both of them being estimated by WSR-98D radars. The spatial distribution of hail and convective winds associated to storm clouds can be done using radar data taking into account that these meteorological phenomena are generated by synoptic distribution and their spatial manifestation is influenced by mesoscale conditions (Maier, ). The main purpose of this analysis is to determine the areas where the convective strong wind speeds and significant hailstorms occur, using the statistical methods. 2. DATA AND METHODS Doppler radar S-band WSR-98D Bobohalma (RDBB) and Oradea (RDOD) data from July month of - period was used for the present study having a coverage area of 166106 sq. km for each and an individual radius of 230 km. The WSR radar application identifies whether a storm has reached a different stage of development by analyzing certain storm parameters such as reflectivity, echo height, the presence of hail etc. If a cloud system has the characteristics of a storm the radar application labels it and the evolution of its parameters is recorded and displayed every 6 minutes. The main features of the convective storms recorded by radar application are: name, polar coordinates (distance and azimuth), system type (mesocyclone, tornado vortex etc.), and hail probability (POSH - probability of severe hail and POH- probability of normal hail), hail diameter, VIL, maximum reflectivity and its height, ET, forecasted speed and direction of the storm. The polar coordinates were transformed into Cartesian coordinates and convective wind speed was calculated using the following formula (AWS, 1996): Where W represents convective wind speed, VIL is Vertical Integrated Liquid and ET stands for Echo Top. 3. STATISTICAL ANALYSIS The total number of storms observed into the studied period was 214703, 121435 by RDBB and 93268 by RDOD, as shown in Table 1. Storms detected outside the radar range (230 km away) were not taken into account. The temporal evolution of the storms parameters identified by the radar (reflectivity, VIL, ET etc.) are recorded and displayed every 6 minutes. Statistical analyses of the storms shows that the annual average duration of a storm is 29 minutes for RDBB and 27 minutes in the case of RDOD, the longest average duration being recorded in (Table 1). (1) 93
storm number of detected in Iuly Table 1. Numbers of storms detected by RDBB and RDOD and their average duration for the period under study STORMS TOTAL RDBB 1 10021 8149 6941 11523 6868 13076 11426 9827 6657 17091 7847 121435 Average duration 29 25 31 25 26 27 30 28 31 34 27 30 29 [minutes] RDOD 8749 9207 5289 4199 7997 6343 10633 8153 9281 4637 12354 6426 93268 Average duration [minutes] 26 27 28 23 24 29 27 29 27 29 27 28 27 Daily analysis points out that the maximum number of storms recorded in one day was 1550 on the 27.07., by RDOD and 1502 on the 22.07., by RDBB, in both cases with an average duration of 22 minutes. Furthermore the year with the largest number of daily storms was when RDBB recorded more than 500 storms per day for a period of 18 days, and RDOD recorded the same amount for 10 days. The highest annual number of convective cells (a storm is often composed of several convective cells) was recorded in, over 79000 being identified by 90000 RDBB RDOD RDBB and more 79052 80000 than 57000 by 66153 70000 60000 RDOD, 54494 57647 and the 58137 50671 50958 52386 50000 lowest was 44747 40000 41930 43235 43322 (fig. 1), with a 38869 42654 32859 41134 30000 37795 31744 25504 28616 31662 weak convective 20000 31650 22837 activity and the 10000 % 10.0 0 16182 Fig. 1. Number of annual convective cells identified by RDBB and RDOD for the period under study 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 RDBB 0 1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 1819 20 21 22 23 LT Fig. 2. Diurnal variability of convective activity RDOD smallest duration of storms (table 1), due to synoptic conditions. The highest diurnal convective a c t i v i t y i s recorded between 1300 and 1900 LC (Local Time), 65% of the storms being identified by RDBB and 55% of them by RDOD, with a maximum at 1600 LT in both cases (fig. 2). 94
The intensity of convective wind associated to convective storms shows the absence of it in almost 20% of the cases. The development stage of the storms is characterized by convective winds between 0 and 30 km/h (43%), and gusts of 30 to 60 km/h are present in 26% to 28% of cases; 11% of storms generate convective winds between 60 and 90 km/h and less than 1% of them over 90 km/h (table 2). Table 2. Convective wind speed of storms detected by RDBB and RDOD for the period under study Wind Wind RDBB % RDOD % [km/h] [km/h] RDBB % RDOD % No wind 100971 17.2 87347 20.0 58 14158 2.4 9966 2.3 <4 5069 0.9 4002 0.9 61 13210 2.3 9236 2.1 4 14612 2.5 11598 2.7 65 12049 2.1 8463 1.9 7 24795 4.2 19647 4.5 68 10980 1.9 7685 1.8 11 34367 5.9 26141 6.0 72 9061 1.5 6751 1.5 14 35876 6.1 27001 6.2 76 7323 1.3 5679 1.3 18 38937 6.7 27849 6.4 79 5802 1.0 4459 1.0 22 36137 6.2 26022 6.0 83 4274 0.7 3146 0.7 25 34005 5.8 23779 5.4 86 2822 0.5 1939 0.4 29 30563 5.2 21004 4.8 90 1440 0.3 890 0.2 32 28439 4.9 19759 4.5 94 816 0.1 554 0.1 36 25069 4.3 17537 4.0 97 461 0.1 297 0.1 40 22814 3.9 15955 3.7 101 183 0.03 139 0.03 43 20653 3.5 14233 3.3 104 77 0.01 47 0.01 47 18509 3.2 13094 3.0 108 10 0.002 7 0.002 50 16651 2.8 11651 2.7 >108 1 0.0002 3 0.0007 54 15205 2.6 10829 2.5 Diurnal variation of wind gust over 90 km/h associated with convective cells has the highest frequency in the afternoon and evening hours, with a maximum recorded at 1600 LT by RDBB and at 1800 LT by RDOD; a second maximum with a lower frequency is recorded in the second part of the night (fig. 3), usually due to supercell storms present mostly over the Tisa Plain. 18 16 14 12 10 8 6 4 2 0 RDBB RDOD 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Fig. 3. Diurnal variation of convective cells generating wind gusts over 90 km/h 95
The hail analyses shows the lack of it in a large number of convective cells (66.9% in the case of RDBB, and 71.4% in that of RDOD), 2.4% of RDBB and 3.8% of RDOD s convective cells being considered as false (erroneous data). The hail was detected in more than a quarter of cases (24.8% by RDOD, and 30.7% by RDBB), it s size distribution being as follows: small hail (diameter under 1.5 cm) was recorded in 24.1% of the storms detected by RDBB, and 19.6% in those detected by RDOD; medium and large hail (diameter between 1.5 cm and 5 cm) was detected in 6.4% of storms of RDBB, and 5.5% of RDOD; very large hail (diameter over 5 cm), was present only in 0.3% of storms detected by RDBB and 0.1% in those detected by RDOD (table 3). Table 3. Hail size frequency into convective storms detected by RDBB and RDOD HAIL Erroneous <1.3 1.3 1.9 2.5 3.2 3.8 4.4 5 5.7 6.3 7 7.6 8.2 8.9 9.5 10.2 No data cm cm cm cm cm cm cm cm cm cm cm cm cm cm cm cm hail RDBB 13748 91738 49099 20520 9090 4319 2227 1181 646 404 222 145 84 68 36 21 43 391847 % 2.35 15.67 8.39 3.51 1.55 0.74 0.38 0.20 0.11 0.07 0.038 0.025 0.014 0.012 0.006 0.004 0.007 66.9 RDOD 16691 54997 30789 12487 5297 2480 1096 541 297 176 84 34 29 11 9 7 5 311755 % 3.82 12.59 7.05 2.86 1.21 0.57 0.25 0.12 0.07 0.04 0.019 0.008 0.007 0.003 0.002 0.002 0.001 71.4 Diurnal variation of hailstorms has the highest frequency in the afternoon and evening, related to storms generating intense wind gusts, with a maximum at around 1600 LT for 18 16 14 12 10 8 6 4 2 RDBB RDOD Bobohalma radar and at 1800 LT for Oradea. It can be noticed a time difference between the two radars, most of mature storms that form over the Tisa Plain and detected by RDOD need more time to develop than those that appear in the 0 mountains that surround -2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Transylvanian Basin, Fig. 4. Diurnal variation of hailstorms detected by RDBB and RDOD detected by RDBB (fig. 4). 4. SPATIAL ANALYSIS The data analysis shows a dispersed distribution of hailstorms in the study area and a low number of convective cells containing very large hail, 652 being detected by RDOD, and 1660 by RDBB. The difference between the two radars is determined by the areas under the surveillance and the obstruction of the radar beam by the mountain area. In the case of Oradea radar, the scanning is performed over plain areas in the north, south and west part of it, while Bobohalma radar is surrounded by mountain area. Usually, thermal convection initiates due to warming 96
of the mountain slopes or mesoscale convergence determined by mountain breezes or by other storm s downdraft. It has been observed that areas with high frequency of appearance of daily first convective storms are next to mountain slopes or near water sources like lakes or river basins (Maier, ). The statistics of the very large hail distribution shows a high frequency in the Şimleu Fig. 5. Spatial distribution of very large hail in the case of RDOD (a) and RDBB (b) Depression (for the years,, and ), Almaş-Agrij Depression (years,,, and ), Transylvanian Plain (especially Ţaga and Geaca lake area, in,,, and ) and the west border of it, along the Moldoveneşti-Turda- Viişoara alignment (,,,, 2, ), Târnave and Făgăraş Depressions (fig. 5). The terrain configuration with a high frequency of large hailstorms reveals the presence of depressions and valley corridors, next to water sources, such as Someş River, lower Basin of Arieş River, etc. The yearly highest frequency of hailstorms detected by RDBB was in,,,, and that detected by RDOD was in,, (fig. 5). 97
Spatial distribution of convective wind shows some differences that of the very large hail. In the case of RDOD, the high frequency of convective cells containing very large hail is in the east part of it, over the Apuseni Mountains while intense convective winds are present over the Tisa Plain too (fig. 6, a). Also, it can be noted a difference of frequency between the two radars: Fig. 6. Spatial distribution of convective wind in the case of RDOD (a) and RDBB (b) B o b o h a l ma r a d a r r e c o r d e d 2 9 8 8 c o n ve c t i ve c e l l s generating convective winds while Oradea radar identified only 1937 such cells. As the radar data shows, the most favorable places for convective wind gusts are depression and valley areas, and those located near the mountain slopes or hills. Taking into account this fact, the most vulnerable areas to this phenomena are Şimleu Depression (the highest frequency in,,,,, ), Almaş- Agrij Depression (the highest frequency in,,,, ), Transylvania Plain (most of the years), as shown in figure 6. The most intense storms generating convective wind gusts were recorded in,,, and. 98
5. CONCLUSIONS Statistical analysis of radar data for the July month of - period can be used to reveal surface distribution of severe weather events such as very large hail storms and intense convective winds and their diurnal variability. On the other hand some specifications about the months with high air instability can be done not only in terms of number of storms but also regarding their intensity: if July month of was the most unstable due to large number of storms it had a small number of severe storms; another example is the year being the 8 th in number of storms, but among the years with most severe ones. Radar analysis shows that the highest air instability generating convective storms was in,,, and. Doppler radar WSR-98D Bobohalma and Oradea can provide data to a good spatial and temporal resolution for determining spatial structure of severe weather events such as hail, rainfall and wind gusts. REFERENCES 1. Maier, N. (), Estimarea vântului convectiv la ieşirea din furtună, Sesiunea anuală de comunicări ştiinţifice a ANM Bucureşti, 17-18 noiembrie, ISBN: 978-973-0-11713-4, PV37-PV45. 2. Maier N., Lăcătuş D. () Determinarea cu ajutorul radarului Doppler a zonelor cu potenţial ridicat în iniţierea convecţiei în funcţie de circulaţia la mezoscară, Sesiunea anuală de comunicări ştiinţifice. 10-11 noiembrie ANM Bucureşti, ISBN: 978-973-0-09341-4, p PV47-PV53. 3. Maier, N., Haidu, I. (), Radar climatology of hail in the Apuseni Mountains, Aerul şi Apa Componente ale Mediului, ISSN:2067-743X, Presa Universitara Clujeană, Cluj-Napoca, 247-252. 4. Maier, N., Haidu, I. (), Determination of Maximum Gust in Convection Storms, Aerul şi Apa Componente ale Mediului, ISSN:2067-743X, Presa Universitara Clujeană, Cluj-Napoca, 344-350. 5. Maier, N., Tatiana, Rusu, Denisa, Lăcătuş (), Hail in the area covered distribution of WSR-98D radar from Bobohalma, Aerul şi Apa Componente ale Mediului, ISSN: 2067-743X, Presa Universitară Clujeană, Cluj-Napoca, 404-411. 6. Maier, N., Tatiana, Mureşan, Denisa, Lăcătuş (), Statistical indices derived from the Bobohalma WSR-88D radar data useful in forecasting hail, Romanian Journal of Meteorology nr.1. 7. Stewart, S. R. (1991), The prediction of pulse-yype thunderstorm gusts using Vertically Integrated Liquid water content (VIL) and the cloud top penetrative downdraft mechanism, NOAA Tech. Memo, NWS SR-136. 8. Wakimoto, R. M. (2001), Convectively driven high wind events. Severe Convective Storms, American Meteorological Society, 28, 255-298. 9. ***(1996) Air Weather Service (AWS), Echoes: Operational use of vertically integrated liquid (VIL), No. 16, Scott AFB: Air Weather Service. 10. *** (), Clima României, Editura Academiei Române, Bucureşti, ISBN 978-973-27-1674-8. 99