Advances in Space Research, Journal Year: 2022, Volume and Issue: 71(1), P. 964 - 974
Published: Sept. 2, 2022
Language: Английский
Advances in Space Research, Journal Year: 2022, Volume and Issue: 71(1), P. 964 - 974
Published: Sept. 2, 2022
Language: Английский
Advances in Atmospheric Sciences, Journal Year: 2023, Volume and Issue: 41(2), P. 341 - 354
Published: Dec. 6, 2023
Language: Английский
Citations
5International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(7), P. 2484 - 2504
Published: April 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.
Language: Английский
Citations
1Advances in Space Research, Journal Year: 2022, Volume and Issue: 71(1), P. 964 - 974
Published: Sept. 2, 2022
Language: Английский
Citations
1