A147-0021
Tying large-scale meteorological patterns to northeastern U.S. extratropical cyclones with climate data and self-organizing maps
Tying large-scale meteorological patterns to northeastern U.S. extratropical cyclones with climate data and self-organizing maps
Monday, 14 December 2020
Poster
Abstract:
Extratropical cyclones (ETCs) play an important role in the regional climate of the northeastern United States (NEUS), often leading to high-impact weather conditions which can have wide-ranging socioeconomic impacts. Traditionally, ETCs are studied using techniques such as case studies and synoptic typing. However, these approaches can require subjective analysis and do not necessarily identify the coincident large-scale meteorological patterns (LSMPs). Here, we apply self-organizing maps (SOMs) as an objective approach to characterize the LSMPs over the NEUS and the associated frequency and intensity of discrete ETC storm events in gridded climate data.
In this presentation we will discuss the dominant patterns of geopotential height variability identified through SOM analysis of several reanalysis products (ERA5, JRA-55, MERRA2, CFSR, and the 20CRv3) during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify storm properties associated with each pattern. In particular, we consider ETC frequency, location, and intensity. We then apply reanalysis-derived SOMs as a reference in order to evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events over NEUS. We will also discuss the potential of this methodology to assess extreme precipitation events, specifically those involving sleet, freezing rain, and snow.
In this presentation we will discuss the dominant patterns of geopotential height variability identified through SOM analysis of several reanalysis products (ERA5, JRA-55, MERRA2, CFSR, and the 20CRv3) during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify storm properties associated with each pattern. In particular, we consider ETC frequency, location, and intensity. We then apply reanalysis-derived SOMs as a reference in order to evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events over NEUS. We will also discuss the potential of this methodology to assess extreme precipitation events, specifically those involving sleet, freezing rain, and snow.