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Fig.9 Same as Fig. 8, but for Hit Rate and False Alert Ratio distribution for all evaluated sub-regions provided by (Blitzortung.org).” The verified period taken dates from May Ist, 2019, through to October Ist, 2019, including some historic very severe weather cases. The observational data are carefully evaluated case by case using the comprehensive ESWD archive by defining the following severe weather elements: heavy rain, large hail, tornado, and severe wind gust. In addition, the spatial distribution o ightning is evaluated together with radar reflectivity patterns. For NOTHAS, the forecast event is the expected occurrence of severe weather conditions somewhere in the forecast subdomains (in our case fifteen selected countries) sometime during the valid area and period of the forecast and warning. It should be pointed out that in the verification process, categorical levels are taken (in particular, 3 to 5), which indicate the occurrence of an enhanced, severe and extreme weather. Furthermore, it is considered that the size and the signature of the patterns which indicate the type of the case (e.g. single case-isolated case, widespread affected area or other specific forms). Four significant elements are defined in the verification process:

Figure 9 Same as Fig. 8, but for Hit Rate and False Alert Ratio distribution for all evaluated sub-regions provided by (Blitzortung.org).” The verified period taken dates from May Ist, 2019, through to October Ist, 2019, including some historic very severe weather cases. The observational data are carefully evaluated case by case using the comprehensive ESWD archive by defining the following severe weather elements: heavy rain, large hail, tornado, and severe wind gust. In addition, the spatial distribution o ightning is evaluated together with radar reflectivity patterns. For NOTHAS, the forecast event is the expected occurrence of severe weather conditions somewhere in the forecast subdomains (in our case fifteen selected countries) sometime during the valid area and period of the forecast and warning. It should be pointed out that in the verification process, categorical levels are taken (in particular, 3 to 5), which indicate the occurrence of an enhanced, severe and extreme weather. Furthermore, it is considered that the size and the signature of the patterns which indicate the type of the case (e.g. single case-isolated case, widespread affected area or other specific forms). Four significant elements are defined in the verification process:

Related Figures (13)

Fig. 1 Model integration domain with landscape for Southeast Europe
Fig. 1 Model integration domain with landscape for Southeast Europe
A Novel Thunderstorm Alert System (NOTHAS) is an ad- vanced forecast and diagnostic system for early assessment of storm intensity and alert category of severe thunderstorm threats across the selected area. It consists of WRF model the ingredients proposed by Doswell III and Schultz (2006). A daily issued report (valid for 24 h) encompasses weather
A Novel Thunderstorm Alert System (NOTHAS) is an ad- vanced forecast and diagnostic system for early assessment of storm intensity and alert category of severe thunderstorm threats across the selected area. It consists of WRF model the ingredients proposed by Doswell III and Schultz (2006). A daily issued report (valid for 24 h) encompasses weather
w whic here CDF represents a cumulative distribution function h estimates the probability of a given variable to be less or equal to its threshold value. X; in Eq.1 is threshold value for the a ppropriate variable for i= 1, N; Xo is constant that deter- mines the start of the distribution on the x-axis, Z; defines where the bulk of the distribution lies, and Y; varies from grid point to grid point within the model run and X gives the pattern of the distribution. MCDF can be written in the developed form as: This new approach allows the use of multiple key output variables, in which a probability can be determined for any threshold X; in Eqn. (2). More specifically, diagnostic algo- rithm provides one output variable to describe a given specific uncertainty, and their sum to determine the overall (integral) uncertainty for occurrence of the severe convective weather within one grid box. The method enables flexibility in our post-processing algorithm as the result of relatively simple diagnosis of uncertainty which can be determined for any threshold value X;~1, y. NOTHAS diagnostic package is comprised with a set of combined physical and potential con- vective instability parameters expressed in terms of the multi- variate cumulative distribution function as a diagnostic tool with defined threshold values for given severe convective weather indices and physical parameters listed in Table 2. A more detailed description of all convective instability terms and their relations are given in the Appendix. The Weibull curve for the precipitation (as the most significant parameter)
w whic here CDF represents a cumulative distribution function h estimates the probability of a given variable to be less or equal to its threshold value. X; in Eq.1 is threshold value for the a ppropriate variable for i= 1, N; Xo is constant that deter- mines the start of the distribution on the x-axis, Z; defines where the bulk of the distribution lies, and Y; varies from grid point to grid point within the model run and X gives the pattern of the distribution. MCDF can be written in the developed form as: This new approach allows the use of multiple key output variables, in which a probability can be determined for any threshold X; in Eqn. (2). More specifically, diagnostic algo- rithm provides one output variable to describe a given specific uncertainty, and their sum to determine the overall (integral) uncertainty for occurrence of the severe convective weather within one grid box. The method enables flexibility in our post-processing algorithm as the result of relatively simple diagnosis of uncertainty which can be determined for any threshold value X;~1, y. NOTHAS diagnostic package is comprised with a set of combined physical and potential con- vective instability parameters expressed in terms of the multi- variate cumulative distribution function as a diagnostic tool with defined threshold values for given severe convective weather indices and physical parameters listed in Table 2. A more detailed description of all convective instability terms and their relations are given in the Appendix. The Weibull curve for the precipitation (as the most significant parameter)
Table 2. Convective instability and Weibull parameters
Table 2. Convective instability and Weibull parameters
Fig. 2 NOTHAS storm category and alert levels ensemble approach is not always suitable for real-time run- ning (especially when the available computing resources are limited), the flexibility of the system with a new diagnostic algorithm allows the use of a deterministic method given that the diagnostic package incorporates a set of key convective parameters as important elements for triggering convection. In general, the system provides a general overview of the weath- er patterns, emphasizing features that will influence the severe and general thunderstorm threats as useful information for severe weather forecast centres which issue their own official warnings. The graphical outputs on an hourly basis depict non-severe thunderstorm areas and severe thunderstorm threats across the selected model domain, along with a meso- scale discussion and a text narrative displayed 1—3 days ahead. Areas with categorical risk are plotted in standard weather maps with shaded patterns and numbers on an hourly basis.
Fig. 2 NOTHAS storm category and alert levels ensemble approach is not always suitable for real-time run- ning (especially when the available computing resources are limited), the flexibility of the system with a new diagnostic algorithm allows the use of a deterministic method given that the diagnostic package incorporates a set of key convective parameters as important elements for triggering convection. In general, the system provides a general overview of the weath- er patterns, emphasizing features that will influence the severe and general thunderstorm threats as useful information for severe weather forecast centres which issue their own official warnings. The graphical outputs on an hourly basis depict non-severe thunderstorm areas and severe thunderstorm threats across the selected model domain, along with a meso- scale discussion and a text narrative displayed 1—3 days ahead. Areas with categorical risk are plotted in standard weather maps with shaded patterns and numbers on an hourly basis.
Balkans at 500 hPa level surface in collision with warm 850 hPa level contributed to the unsettled atmosphere over Belgrade area on June 23rd, 2019, resulting in excessive rain- fall capable of producing widespread flooding. The 12Z atmo- spheric sounding measurements at Belgrade-KoSutnjak sta- tion showed clear signals of persistently increased unsettled- ness for many sounding indices, including total-totals (TT), CAPE, K, SWEAT, BRN and lifted index (LI). Southerly to south-westerly (SSW) circulation in the upper troposphere levels prevailed (reaching the height as high as 17,6 km), with cooler and humid air, while warmth was the dominant feature at the surface. Due to the unsettled atmospheric conditions, the moist adiabatic lapse rate (MALR) prevailed, continuously causing the air parcel to lift up. The point at which the air parcel reached the saturation point occurred at nearly 2,24 km above the ground. The lowest temperature of —58,5 °C was measured at 18,6 km above the ground, while the equilibrium state was reached at roughly 13,4 km, which are the level boundaries for the vertical cloud development.
Balkans at 500 hPa level surface in collision with warm 850 hPa level contributed to the unsettled atmosphere over Belgrade area on June 23rd, 2019, resulting in excessive rain- fall capable of producing widespread flooding. The 12Z atmo- spheric sounding measurements at Belgrade-KoSutnjak sta- tion showed clear signals of persistently increased unsettled- ness for many sounding indices, including total-totals (TT), CAPE, K, SWEAT, BRN and lifted index (LI). Southerly to south-westerly (SSW) circulation in the upper troposphere levels prevailed (reaching the height as high as 17,6 km), with cooler and humid air, while warmth was the dominant feature at the surface. Due to the unsettled atmospheric conditions, the moist adiabatic lapse rate (MALR) prevailed, continuously causing the air parcel to lift up. The point at which the air parcel reached the saturation point occurred at nearly 2,24 km above the ground. The lowest temperature of —58,5 °C was measured at 18,6 km above the ground, while the equilibrium state was reached at roughly 13,4 km, which are the level boundaries for the vertical cloud development.
Table 3 Contingency table with scores for southeast Europe - May 1st — October Ist, 2019
Table 3 Contingency table with scores for southeast Europe - May 1st — October Ist, 2019
Fig.8 Verification scores for NOTHAS severe weather forecast and Alert for South-East-Europe domain for 24 h during mid-latitude convective season (May—October 2019). The solid straight line in each distribution represents the averaged rate
Fig.8 Verification scores for NOTHAS severe weather forecast and Alert for South-East-Europe domain for 24 h during mid-latitude convective season (May—October 2019). The solid straight line in each distribution represents the averaged rate
Heat Rate which has a range of 0 to 1 (with | representing a perfect warning of severe weather) and its sensitive to miss events has shown an average rate of 0,82 which indicate high accuracy. It is usually suggested that HR be used in conjunc- tion with False Alert Ratio (FAR) or the false Alert rate (FA) (Fig. 9). While the averaged false Alert ratio is about 0,25, a high accuracy is compensated by Hit Rate, whose weighted calculated rate is about 0,84. The Threat Score CSI (critical
Heat Rate which has a range of 0 to 1 (with | representing a perfect warning of severe weather) and its sensitive to miss events has shown an average rate of 0,82 which indicate high accuracy. It is usually suggested that HR be used in conjunc- tion with False Alert Ratio (FAR) or the false Alert rate (FA) (Fig. 9). While the averaged false Alert ratio is about 0,25, a high accuracy is compensated by Hit Rate, whose weighted calculated rate is about 0,84. The Threat Score CSI (critical
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