The use of the simple 1D steady-state convective cloud model in the decision tree for determining the probability of thunderstorm occurrence

Резюме

К нaбору nре¶rt;uкmоров, включaющему uн¶rt;ексы неусmоŭчuвосmu u резульmamы о¶rt;номерноŭ сmaцuонaрноŭ мо¶rt;елu кучево¶rt;о облaкa, был nрuменен aл¶rt;орumм ¶rt;воuчно¶rt;о рaзрешaюще¶rt;о ¶rt;еревa [1–3], оnре¶rt;еляющuŭ верояmносmь nоявленuя ¶rt;розы. Пре¶rt;uкmaнmы А, В, С, D=1/0 сооmвеmсmвуюm рaзному временному u nросmрaнсmвенному оnре¶rt;еленuю nоявленuя ¶rt;розы, зaре¶rt;uсmрuровaнному сuноnmuческuмu сmaнцuямu в 100-кuломеmровоŭ окресmносmu Прa¶rt;u. Пре¶rt;uкmоры былu оnре¶rt;елены нa основе ¶rt;aнных ТЕМР 12.00 ГМТ с сmaнцuu Прa¶rt;a Лuбущ ¶rt;ля месяцев V–VIII 1981–1985. Первые mесmы нanрaвлены нa срaвненuе кaчесmвa рaзрешенuя ¶rt;ля рaзных munов nре¶rt;uкmaнmов u ¶rt;ля нaборов nре¶rt;uкmоров, сосmоящuх mолько uз uн¶rt;ексов неусmоŭчuвосmu, mолько uз мо¶rt;ельных nре¶rt;uкmоров u uз велuчuн о¶rt;оuх munов. Нauлучшuе резульmamы uмеюm рaзрешaющuе ¶rt;еревья, nосmроенные нa нaборaх всех nре¶rt;uкmоров, nрuчем мо¶rt;ельные nре¶rt;uкmоры ¶rt;еŭсmвуюm nреж¶rt;е все¶rt;о в неусmоŭчuвоŭ веmвu ¶rt;еревьев. Нauвысше¶rt;о кaчесmвa рaзрешенuя было ¶rt;осmu¶rt;нуmо ¶rt;ля nре¶rt;uкmaнma В, чmо нaхо¶rt;umся в со¶rt;лaсuu с nоняmuем о сaмоŭ сuльноŭ связu меж¶rt;у mермо¶rt;uнaмuческuм сосmоянuем amмосферы u nоверхносmью, нa коmороŭ uмееm шuрокое nоявленuе ¶rt;роз.

Summary

The algorithm of a binary decision tree [1–3] determining the probability of thunderstorm occurrence was applied to a set of predictors including the instability indices and results of the 1D steady-state convective cloud model. Predictants A, B, C, D=1/0 correspond to various temporal and spatial limits for thunderstorm occurrence recorded at synoptic stations in an area 100km around Prague. The predictors were found on the basis of TEMP 12.00 GMT data from the Prague Libuš station for the months V–VIII in the years 1981–1985. The first tests were carried out to compare the decision quality for various types of predictants and for sets of predictors consisting only of the instability indices, only of model predictors and of both quantities. The best results were obtained using a decision tree constructed on the basis of sets of all the predictors, where the model predictors were employed primarily in the unstable branch of the tree. The best decision quality was obtained for predictant B, in agreement with the concept of the strongest connection between the thermodynamic state of the atmosphere and the area of the extent of thunderstorm occurrence.