Advances in Space Research, Год журнала: 2022, Номер 71(1), С. 964 - 974
Опубликована: Сен. 2, 2022
Язык: Английский
Advances in Space Research, Год журнала: 2022, Номер 71(1), С. 964 - 974
Опубликована: Сен. 2, 2022
Язык: Английский
Advances in Atmospheric Sciences, Год журнала: 2023, Номер 41(2), С. 341 - 354
Опубликована: Дек. 6, 2023
Язык: Английский
Процитировано
5International Journal of Climatology, Год журнала: 2024, Номер 44(7), С. 2484 - 2504
Опубликована: Апрель 25, 2024
Abstract Climate classification is a commonly used tool to simplify, visualize and make sense of an otherwise unwieldy amount climate data in applied science research. Typically, these classifications have stemmed from one two perspectives, either circulation‐to‐environment (C2E) approach, or environment‐to‐circulation approach (E2C), each with advantages drawbacks. This research discusses novel environment‐to‐circulation‐to‐environment (ECE) perspective classification, develops specific ECE methodology that utilizes canonical correlation discriminant analysis—the CANDECE method. The benefits the generally, specifically, are demonstrated applying aid modelling anomalous water levels (AWLs) along portions East West coasts United States. Results show method performs better than traditional methods ( k ‐means self‐organizing maps [SOMs]) at relating AWLs their broad‐scale atmospheric setups, especially regard both high low extreme AWLs. It further that, compared coast, particularly advantageous southeastern US where primary modes variability (which drive produced by SOMs ‐means) do not align relevant circulation‐based factors driving AWL variability. While were utilized for demonstrating proof‐of‐concept herein, designed be useful any application.
Язык: Английский
Процитировано
1Advances in Space Research, Год журнала: 2022, Номер 71(1), С. 964 - 974
Опубликована: Сен. 2, 2022
Язык: Английский
Процитировано
1