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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">dan</journal-id><journal-title-group><journal-title xml:lang="ru">Доклады Национальной академии наук Беларуси</journal-title><trans-title-group xml:lang="en"><trans-title>Doklady of the National Academy of Sciences of Belarus</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1561-8323</issn><issn pub-type="epub">2524-2431</issn><publisher><publisher-name>The Republican Unitary Enterprise Publishing House "Belaruskaya Navuka"</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.29235/1561-8323-2023-67-6-499-507</article-id><article-id custom-type="elpub" pub-id-type="custom">dan-1166</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>НАУКИ О ЗЕМЛЕ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>EARTH SCIENCES</subject></subj-group></article-categories><title-group><article-title>Пространственно-временная когерентность квазипериодических компонент метеорологических полей как основа долгосрочных прогнозов погоды</article-title><trans-title-group xml:lang="en"><trans-title>Spatial and temporal coherence of quasi-periodic components of meteorological fields as a basis for long-term weather forecasts</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Лысенко</surname><given-names>С. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Lysenko</surname><given-names>S. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Лысенко Сергей Александрович – д-р физ.-мат. наук, профессор, директор.</p><p>Ул. Ф. Скорины, 10, 220114, Минск</p></bio><bio xml:lang="en"><p>Lysenko Sergey A. – D. Sc. (Physics and Mathematics), Professor, Director.</p><p>10, F. Skorina Str., 220114, Minsk</p></bio><email xlink:type="simple">lysenko.nature@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Логинов</surname><given-names>В. Ф.</given-names></name><name name-style="western" xml:lang="en"><surname>Loginov</surname><given-names>V. F.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Логинов Владимир Федорович – академик, д-р географ. наук, профессор, гл. науч. сотрудник.</p><p>Ул. Ф. Скорины, 10, 220114, Минск</p></bio><bio xml:lang="en"><p>Loginov Vladimir F. – Academician, D. Sc. (Geography), Professor, Chief Researcher.</p><p>10, F. Skorina Str., 220114, Minsk</p></bio><email xlink:type="simple">nature@ecology.basnet.by</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Институт природопользования Национальной академии наук Беларуси</institution></aff><aff xml:lang="en"><institution>Institute of Nature Management of the National Academy of Sciences of Belarus</institution></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>07</day><month>01</month><year>2024</year></pub-date><volume>67</volume><issue>6</issue><fpage>499</fpage><lpage>507</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Лысенко С.А., Логинов В.Ф., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Лысенко С.А., Логинов В.Ф.</copyright-holder><copyright-holder xml:lang="en">Lysenko S.A., Loginov V.F.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://doklady.belnauka.by/jour/article/view/1166">https://doklady.belnauka.by/jour/article/view/1166</self-uri><abstract><p>Предложен новый метод исследования дальних (телеконнекционных) связей в климате Земли, основанный на выделении в глобальных метеорологических полях температуры и давления климатически сопряженных районов по характерному для них когерентному квазипериодическому колебанию. Данный метод реализован с целью отбора предикторов зимней температуры воздуха в Беларуси с заблаговременностью 2 месяца. В качестве критерия отбора предикторов рассматривалась степень когерентности давления на уровне моря и зимней температуры в Беларуси на квази-8-летнем цикле. Прогноз реализован с использованием передовой модели глубокого машинного обучения TimesNet и показал достаточно высокие для сезонного метеорологического прогноза метрики качества: коэффициент корреляции фактических и предсказанных значений температуры составил 0,66, а взвешенные макросредние значения точности и полноты прогноза в градации «норма», «выше нормы» и «ниже нормы» составили 0,61 и 0,56 соответственно.</p></abstract><trans-abstract xml:lang="en"><p>A new method of teleconnections studding is proposed which is based on the identification of conjugate regions in the global meteorological fields of temperature and pressure by their characteristic coherent quasi-periodic oscillation. This method was implemented in order to select predictors of winter air temperature in Belarus with an advance of 2 months. The degree of coherence of sea level pressure and winter temperature in Belarus on a quasi-8-year cycle was considered as a criterion for the selection of predictors. The forecast was implemented using the advanced deep machine learning model TimesNet and showed rather high metrics of quality for seasonal meteorological forecasting: the correlation coefficient between actual and predicted temperature values was 0.66, and the weighted macro-average values of precision and recall of the forecast in the gradations “normal”, “above normal” and “below normal” were 0.61 and 0.56, respectively.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>долгосрочный метеорологический прогноз</kwd><kwd>квазипериодические колебания</kwd><kwd>когерентность метеорологических полей</kwd></kwd-group><kwd-group xml:lang="en"><kwd>long-term meteorological forecast</kwd><kwd>quasi-periodic oscillations</kwd><kwd>coherence of meteorological fields</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Исследование выполнено при финансовой поддержке Белорусского республиканского фонда фундаментальных исследований (грант № Х23РНФ-122)</funding-statement><funding-statement xml:lang="en">The study was carried out with financial support from the Belarusian Republican Foundation for Fundamental Research (grant no. 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