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Evaluation of Atmospheric Instability Indices in Greece A Evaluation of atmospheric instability indices in Greece A. Marinaki, M. Spiliotopoulos, H. Michalopoulou To cite this version: A. Marinaki, M. Spiliotopoulos, H. Michalopoulou. Evaluation of atmospheric instability indices in Greece. Advances in Geosciences, European Geosciences Union, 2006, 7, pp.131-135. hal-00296864 HAL Id: hal-00296864 https://hal.archives-ouvertes.fr/hal-00296864 Submitted on 16 Feb 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Advances in Geosciences, 7, 131–135, 2006 SRef-ID: 1680-7359/adgeo/2006-7-131 Advances in European Geosciences Union Geosciences © 2006 Author(s). This work is licensed under a Creative Commons License. Evaluation of atmospheric instability indices in Greece A. Marinaki1, M. Spiliotopoulos1,2, and H. Michalopoulou1 1Department of Applied Physics, Laboratory of Meteorology, University of Athens, Greece 2Department of Management of Environment and Natural Resources, University of Thessaly, Volos, Greece Received: 10 November 2005 – Revised: 21 December 2005 – Accepted: 16 January 2006 – Published: 16 February 2006 Abstract. The potential of instability indices in assessing The estimation of atmospheric instability with the use atmospheric instability is examined for the areas of Attica, of instability indices generally requires computation based Thessaly and Central Aegean and Crete, Greece. Gener- on several thermodynamic parameters (Showalter, 1953; ally, many indices have been developed to estimate the tro- George, 1960; Boyden, 1963; Jefferson, 1963a, b; Miller, posphere’s stability for forecasting purposes. At this study 1967; Litynska et al., 1976; Peppler, 1988; Peppler and several instability indices, commonly used in Meteorology, Lamb, 1989; Jacovides and Yonetani, 1990; Reuter and Ak- are computed based on radiosonde data. Firstly, the indices tary, 1993). Instability indices have been developed and used are computed for several months based on the 00:00 and to aid both research and operational forecasting of severe 12:00 UTC radiosonde data during the period 1981–2003. weather and thunderstorms by quantifying the thermody- Statistical methods were used to compare and test the ef- namic instability with the aid of radiosonde data. A thorough fectiveness of these indices in the described area using me- comparison of instability indices for the Greek peninsula has teorological data from seventeen meteorological stations of been carried out in the near past (Dalezios and Papamanolis, Greece. Thus, the potential of monitoring the atmospheric 1991; Michalopoulou and Karadana, 1996; Sioutas and Flo- instability conditions is examined. The next stage of this cas, 2003; Chrysoulakis et al., 2005). All these studies suf- study is an effort to test the thresholds of the existing indices fer from data deficiency and reliability that comes from the in order to improve the results of these indices. It seems that sparse existing radiosonde network. In general, it should be this effort can make a good simulation to the assessment of noticed that all the instability indices describe the potential of instability, contributing to local level weather forecasting. convection but the referred threshold values are not definite, but may vary with geographical location, season and synoptic situation (Michalopoulou and Jacovides, 1987; Prezerakos, 1989; Dalezios and Papamanolis, 1991; Haklander and Van 1 Introduction Delden, 2003). The objective of this study is to investigate which index and where is the most appropriate for instability Thunderstorms and showers are very important phenomena monitoring as well as to improve the prevailing thresholds affecting every aspect of human life. It is already known according to each area’s profile. that in order to estimate the possibility of the development of thunderstorms, atmospheric instability is a major deter- minant especially in warm period weather. Generally, an unstable atmosphere tends to fire up showers or thunder- 2 Data and methodology storms while a stable atmosphere usually brings sunny skies. The analysis of the atmosphere during the thunderstorms Upper air data from the three Greek upper air stations: leads the scientific community to develop parameters that Elliniko (Athens), Mikra (Thessaloniki) and Heraklion would indicate whether or not the conditions are favourable (Crete) are utilized at this study. Several months are exam- for thunderstorm development. Even if the development of ined based on the 00:00 and 12:00 UTC radiosonde data dur- medium scale numerical models enables forecasting of thun- ing the period 1981–2003. Attention is given especially to derstorms it is still worth to estimate instability with such a spring and summer months where convective thunderstorms traditional way like the instability indices. are more usual to occur. Radiosonde data such as Temper- ature T and Dew Point Temperature Td for basic isobaric Correspondence to: M. Spiliotopoulos levels of 850, 700 and 500 hPa are recorded and analyzed. ([email protected]) Additionally geodynamic height Z for the (1000–700)hPa 132 A. Marinaki et al.: Evaluation of atmospheric instability indices in Greece Table 1. Consistency table. Forecast/Observed Yes No Total Yes a b a+b No c d c+d Total a+c b+d a+b+c+d the best performance is chosen to be associated with that par- ticular predictor (Haklander and Van Delden, 2003). Every meteorological station of the study region gives the observed information of yes or no event. The objective of this study is to investigate which index and where is the most appropriate for instability monitoring as well as to improve the prevailing thresholds according to each area’s profile. For this reason the consistency table (Table 1) is constructed. Fig. 1. Map of the study area, showing the related regions of For the examination of atmospheric instability seven insta- Greece. bility indices are examined. The indices used are described as follows: thickness, Potential Temperature θ for each isobaric level and – Showalter Index (Showalter,1953): wet bulb Potential Temperature θw are computed. Finally, SI = T500 hPa − T850 hPa−500 hPa (1) lapse rate 0, saturated lapse rate 0s and Relative Humidity RH are considered for each case. – K Index (George, 1960): Each day is recorded as thunderstorm day if a thunder- storm event is observed at least for one station of each area. KI = (T850 − T500) + Td850 − (T700 − Td700) (2) Rainfall data are observed from 17 Greek meteorological sta- tions located at three regions (Fig. 1): – Boyden Index (Boyden, 1963): – Attica (Elliniko, N. Philadelphia, National Observatory BI = Z − T700 − 200 (3) of Athens, Dekeleia and Eleysina). – Thessaly and Central Aegean (Aghialos, Trikala, Lar- where, Z is the difference between the geopotential height isa, Skiathos and Skyros) between 700 hPa and 1000 hPa – Jefferson Index (Jefferson, 1963a;b): – Crete (Chania, Heraklio, Tympaki, Rethimno, Kasteli, Ierapetra and Siteia). 1 JI = 1.60θw850 − T500 − (T − Td )700 − 8 (4) Usually, instability indices are employed to alert the me- 2 teorologist on thunderstorm possibility. Generally, a certain – Total Totals Index (Miller, 1967) : threshold value is defined above (or below) which the pos- sibility of thunderstorms is considered (Haklander and Van TT = (T850 − T500) + (Td850 − Td500) (5) Delden, 2003). Another aim of this study has been to derive threshold values that perform best in a dichotomous forecast- – Humidity Index (Litynska et al, 1976) : ing scheme that is to divide its range of values into two parts in which a thunderstorm event is either forecast or not fore- HI = (T − Td )850 + (T − Td )700 + (T − Td )500 (6) cast. Thus, the thunderstorm probability increases monoton- ically with either decreasing or increasing index values, ac- – Yonetani Index (Jacovides and Yonetani, 1990): cording to a benchmark value to distinguish between “thun- YI = 0.9660 + 2.41(0 − 0 ) derstorm” or “no-thunderstorm” forecasts. This benchmark (900−800)hPa (850−800)hPa S850 hPa RH value is called an upper threshold value or a lower thresh- +9.66( ) − 15.5, if RH > 57 : (7a) old value according to if a thundery case is forecasting when 100 the index value lies at or below that benchmark value (a non- RH thundery case is forecast otherwise). When using the various YI = 0.9660i + 2.41(0a − 0s) + 9.66 − 17, thunderstorm indices and parameters in such a two-class cat- 100 egorical forecasting scheme, it makes sense to associate them if RH < 57 (7b) with their optimal threshold values. The threshold value with A. Marinaki et al.: Evaluation of atmospheric instability indices in Greece 133 Table 2. General Results. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec SI ATTICA threshold ≤6 ≤6 ≤6 Yule 0,14 0,13 0,27 THESSALY threshold ≤5 ≤6 ≤50 ≤3 ≤3 ≤5 ≤5 Yule 0,19 0,12 0,15 0,19 0,18 0,22 0,26 KI ATTICA threshold ≤20 ≤10 ≤20 Yule 0,14 0,19 0,26 THESSALY thresholds ≤25 ≤10 ≤10 ≤15 ≤15 ≤10 ≤10 Yule 0,30 0,19 0,20 0,23 0,23 0,23 0,26 CRETE thresholds ≤26 ≤31 ≤34 ≤25 ≤29 ≤35 ≤33 ≤35 ≤35 ≤35 ≤30 ≤25 Yule 0,34 0,42 0,46 0,30 0,40 0,34 0,42 0,46 0,38
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