Termodinamik İndekslerin ve Atmosferik Kararlılık Parametrelerinin Oraj Hadiselerinin Tahmininde Performans Analizi: Samsun Örneği

Abstract

Bu çalışmada Samsun radyozonde istasyonu verileri ve Samsun Çarşamba Havalimanı havacılık rasatları kullanılarak, 2018-2022 yılları arası beş yıllık periyot için termodinamik indeks ve atmosferik kararlılık parametrelerinin oraj tahminindeki performansını analiz etmek amaçlanmıştır. Çalışmada kullanılan termodinamik indeks ve atmosferik kararlılık parametreleri; Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN) ve Bulk Richardson Number (BRN)’dir. İndeks ve parametrelerin oraj olaylarını tahmin etme başarısını ölçmek için Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS) ve True Skill Score (TSS) gibi istatistiksel başarım testleri uygulanmıştır. Analizler R programlama dili ve Excel kullanılarak yapılmıştır. Sonuçlara göre; oraj tahmininde en başarılı indeks SI olurken, CAPE ve BRN parametrelerinin başarısı en düşük olmuştur. Tüm bu analizler sonucunda orajların çoğunluğunun yılın en sıcak aylarında ve günün en sıcak saatlerinde gerçekleştiği görülmektedir. Bu durum orajların çoğunluğunun ısınma nedeniyle gerçekleştiğini gösterir. Bazı olaylarda indeks ve parametreler oraj oluşumu için belirlenen eşik değerine dahi ulaşamamıştır. İndeks ve parametrelerin, eşik değerleri Samsun için optimize edildiği takdirde tahmin başarısının daha da artacağı ortadadır. Ayrıca bu indeks ve parametreleri tek başına kullanarak oraj tahmini yapmak yerine birden fazla indeks ve parametre değerlendirilerek yapılacak tahminler tutarlılığı artıracak ve daha doğru sonuçlar alınmasını sağlayacaktır.

Keywords

• Oraj
• Kararlılık Parametreleri
• Termodinamik İndeksler
• CAPE
• BRN
• Samsun

Performance Analysis of Thermodynamic Indices and Atmospheric Stability Parameters in Thunderstorm Prediction: The Case of Samsun

Abstract

In this study, the performance of thermodynamic indices and atmospheric stability parameters in thunderstorm prediction was analyzed for a five-year period between 2018 and 2022 using data from Samsun radiosonde station and aviation observations from Samsun Çarşamba Airport. The thermodynamic indices and atmospheric stability parameters used in the study are Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Bulk Richardson Number (BRN). Statistical performance tests such as Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS), and True Skill Score (TSS) were used to measure the success of the indices and parameters in predicting thunderstorm events. The analyses were performed using R and Excel. According to the results, the most successful index in thunderstorm prediction was SI, while the performance of CAPE and BRN parameters was the lowest. Based on all these analyses, it is observed that the majority of thunderstorms occur during the hottest months of the year and the hottest hours of the day. This indicates that most thunderstorms occur due to heating. In some events, the indices and parameters did not even reach the threshold value determined for thunderstorm formation. It is obvious that the prediction accuracy will be further improved if the thresholds of the indices and parameters are optimized for Samsun. Furthermore, evaluating multiple indices and parameters instead of relying on a single index or parameter for thunderstorm prediction will increase consistency and lead to more accurate results.

Keywords

• Thunderstorm
• Stability Indices
• Thermodynamic Indices
• CAPE
• BRN
• Samsun

References

1. Arora, K., Ray, K., Ram, S., & Mehajan, R. (2023). The role of instability ındices in forecasting thunderstorm and non-thunderstorm Ddys across six cities in India. Climate, 11(1), Article 14. https://doi.org/10.3390/cli11010014
2. Galway, J. G. (1956). The lifted index as a predictor of latent instability. Bulletin of the American Meteorological Society, 37(10), 528-529.
3. George, J. J. (1960). Weather forecasting for aeronautics. Academic Press.
4. Iowa State University. (2023, February 24) Environmental mesonet: ASOS-AWOS-METAR data. Iowa Environmental Mesonet. 24 Şubat 2023 tarihinde https://mesonet.agron.iastate.edu/request/download.phtml adresinden alındı
5. Kosif, K. (2001). Samsun ilinde iklim trendleri. DSİ Teknik Bülteni, 98, 3-13.
6. Koutavarapu, R., Umakanth, N., Madhav, B. T. P., Rao, M. C., & Shim, J. (2021a). Influence of thermodynamic indices during severe convection over Andaman Nicobar and Lakshadweep islands. Thalassas: An International Journal of Marine Sciences, 37, 593-619. https://doi.org/10.1007/s41208-021-00335-y
7. Koutavarapu, R., Umakanth, N., Satyanarayana, T., Kumar, M. S., Rao, M. C., Lee, D. Y., & Shim, J. (2021b). Study of statistical estimated parameters using ERA5 reanalysis data over Khulna region during monsoon season. Acta Geophysica, 69(5), 1963-1978.
8. Kunz, M. (2007). The skill of convective parameters and indices to predict isolated and severe thunderstorms. Natural Hazards and Earth System Sciences, 7(2), 327-342.

9. Lucas, C., Zipser, E. J., & LeMone, M. A. (1994). Convective available potential energy in the environment of oceanic and continental clouds: Correction and comments. Journal of the Atmospheric Sciences, 51, Article 95A67914. https://doi.org/10.1175/1520-0469(1994)051<3829:CAPEIT>2.0.CO;2
10. Miller, R. C. (1967). Notes on analysis and severe-storm forecasting procedures of the Military Weather Warning Center (AWS Tech. Rep. 200). Air Weather Service (MAC), United States Air Force.
11. Miller, R. C., Bidner, A., Maddox, R. A. (1972). The use of computer products in severe weather forecasting (the SWEAT index) (AWS RP 105-2, No: 72-1, 2-9). Air Weather Service.
12. NWS. (2023, April 27). National Weather Service: Definition of atmospheric variables, atmospheric stability indices. National Weather Service (NWS). 27 Nisan 2023’de https://www.weather.gov/ffc/gloss2 adresinden alındı
13. Özdemir, E. T., Deniz, A., Sezen, İ., Aslan, Z., & Yavuz, V. (2017). Investigation of thunderstorms over Ataturk International Airport (LTBA), Istanbul. Mausam, 68(1), 175-180.
14. Peppler, R. A., & Lamb, P. J. (1989). Tropospheric static stability and central North American growing season rainfall. Monthly weather review, 117(6), 1156-1180.
15. Sahu, R. K., Dadich, J., Tyagi, B., Vissa, N. K., & Singh, J. (2020a). Evaluating the impact of climate change in threshold values of thermodynamic indices during pre-monsoon thunderstorm season over Eastern India. Natural Hazards, 102, 1541-1569. https://doi.org/10.1007/s11069-020-03978-x
16. Sahu, R. K., Dadich, J., Tyagi, B., & Vissa, N. K. (2020b). Trends of thermodynamic indices thresholds over two tropical stations of north-east India during pre-monsoon thunderstorms. Journal of Atmospheric and Solar-Terrestrial Physics, 211, Article 105472. https://doi.org/10.1016/j.jastp.2020.105472
17. Showalter, A. K. (1953). A stability index for thunderstorm forecasting. Bulletin of the American Meteorological Society, 34(6), 250-252.
18. Türkeş, M. (2015). Gökgürültülü fırtınalar ve oluşum düzenekleri. Bilim ve Gelecek, 131(2015), 84-87.
19. Tyagi, B., Naresh Krishna, V., & Satyanarayana, A. N. V. (2011). Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006-2008. Natural Hazards, 56, 681-698. https://doi.org/10.1007/s11069-010-9582-x
20. Umakanth, N., Satyanarayana, G. C., Simon, B., Rao, M. C., Kumar, M. T., & Babu, N. R. (2020). Analysis of various thermodynamic instability parameters and their association with the rainfall during thunderstorm events over Anakapalle (Visakhapatnam district), India. Acta Geophysica, 68, 1549-1564. https://doi.org/10.1007/s11600-020-00478-1
21. University of Wyoming. (2023, March 4). Upper-level atmospheric charts. University of Wyoming, College of Engineering. 4 Mart 2023’de http://weather.uwyo.edu/upperair/sounding.html adresinden alındı
22. Weisman, M. L., & Klemp, J. B. (1982). The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Monthly Weather Review, 110(6), 504-520.
23. Yavuz, V., Deniz, A., Özdemir, E. T., Karan, H., & Temiz, C. (2022). Long-term thunderstorm analysis at airports in the Marmara Region: types and favourable atmospheric conditions. International Journal of Global Warming, 28(1), 81-95. https://doi.org/10.1504/IJGW.2022.125083
24. Yavuz, V., Özdemir, E. T., Deniz, A. (2020). Nowcasting of a thunderstorm: the case study of 2 February, 2015 at Istanbul Ataturk International Airport. Mausam, 71(1), 21-32.
25. Yavuz, V. (2023). Performance analyzes of thermodynamic indices and atmospheric parameters in thunderstorm and non-thunderstorm days in Istanbul, Turkey. Research Square. https://doi.org/10.21203/rs.3.rs-2544300/v1
26. Ye, B., Del Genio, A. D., & Lo, K. K. (1998). CAPE variations in the current climate and in a climate change. Journal of Climate, 11(8), 1997-2015.
27. Zhang, G. J. (2003, March 31-April 4). Diurnal cycle of convection at the ARM SGP Site: Role of large-scale forcing, surface fluxes, and convective inhibition [Conference presentation]. Thirteenth ARM Science Team Meeting, Broomfield, Colorado.