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Abstract. In order to study climate change impacts on the regional scale, meteorological data on a virtually continuous spatial scale is required. In this paper were applied a simple linear scaling bias correction method to adjust maximum and minimum temperature data in 2000, 2010 and 2018. The results show that linear scaling bias correction works better in plain areas than in mountain areas if the elevation effect is not taken into consideration. However, the adjustment is done in five of six points successfully.

  • Web Address
  • DOI
  • Date of creation in the UzSCI system 23-12-2024
  • Read count 83
  • Date of publication 29-12-2022
  • Main LanguageIngliz
  • Pages27-31
English

Abstract. In order to study climate change impacts on the regional scale, meteorological data on a virtually continuous spatial scale is required. In this paper were applied a simple linear scaling bias correction method to adjust maximum and minimum temperature data in 2000, 2010 and 2018. The results show that linear scaling bias correction works better in plain areas than in mountain areas if the elevation effect is not taken into consideration. However, the adjustment is done in five of six points successfully.

Ўзбек

Annotatsiya. Iqlim o'zgarishining mintaqaviy miqyosdagi ta'sirini o'rganish uchun uzluksiz fazoviy meteorologik ma'lumotlar talab qilinadi. Ushbu maqolada 2000, 2010 va 2018-yillarda maksimal va minimal havo harorati ma'lumotlarini sozlash uchun oddiy chiziqli to'g'rilash usuli qo'llanilgan. Natijalar shuni ko'rsatadiki, agar balandlik effekti hisobga olinmasa, tog'li hududlarga qaraganda tekislikdagi joylarida to`g`irlash samarali natija beradi. Shunga qaramasdan, to`g`irlash oltita nuqtadan beshtasida muvaffaqiyatli amalga oshirildi.

Русский

Аннотация. Для изучения последствий изменения климата в региональном масштабе тре буются метеорологические данные в практически непрерывном пространственном масштабе. В этой статье был применен простой метод линейной коррекции для корректировки данных о максимальной и минимальной температуре в 2000, 2010 и 2018 годах. Результаты показывают, что линейная коррекция работает лучше в равнинных районах, чем в горных районах, если не при нимать во внимание влияние высоты. Однако, в пяти из шести пунктов корректировка успешно выполнено.

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