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Singular vectors (SVs) have long been employed in the initialization of ensemble numerical weather prediction (NWP) in order to capture the structural organization and growth rates of those perturbations or "errors" associated with initial condition errors and instability processes of the large scale flow. Due to their (super) exponential growth rates and spatial scales, initial SVs are typically combined empirically with evolved SVs in order to generate forecast perturbations whose structures and growth rates are tuned for specified lead-times. Here, we present a systematic approach to generating finite time or "mixed" SVs (MSVs) based on a method for the calculation of covariant Lyapunov vectors and appropriate choices of the matrix cocycle. We first derive a data-driven reduced-order model to characterize persistent geopotential height anomalies over Europe and Western Asia (Eurasia) over the period 1979–present from the National Centers for Environmental Prediction v1 reanalysis. We then characterize and compare the MSVs and SVs of each persistent state over Eurasia for particular lead-times from a day to over a week. Finally, we compare the spatiotemporal properties of SVs and MSVs in an examination of the dynamics of the 2010 Russian heatwave. We show that MSVs provide a systematic approach to generate initial forecast perturbations projected onto relevant expanding directions in phase space for typical NWP forecast lead-times. Persistent atmospheric events, often coherent over thousands of kilometers, can have a large impact on daily weather conditions yet remain challenging to forecast. Most weather prediction centers now routinely use ensemble forecasts to estimate the range of uncertainties in weather forecasts of the near future. This involves initializing multiple forecasts in the directions of greatest instability in order to capture transitional behavior. Here, we first develop a reduced model for atmospheric regimes in the continental Europe–Asia sector of the Northern Hemisphere from which we explore different methods for identifying directions of unstable growth based on local (finite time) and global (asymptotic) dynamical vectors. We compute the local perturbation vectors optimized over an increasing number of days to compare their respective ability to project onto the synoptic time and spatial scales of interest typically associated with blocking. We then consider the specific case study of the 2010 Russian heat wave.

Item Type:	Refereed Article
Keywords:	singular vectors, Numerical Weather Prediction, clustering, atmospheric structures
Research Division:	Mathematical Sciences
Research Group:	Applied mathematics
Research Field:	Dynamical systems in applications
Objective Division:	Environmental Policy, Climate Change and Natural Hazards
Objective Group:	Understanding climate change
Objective Field:	Climate variability (excl. social impacts)
UTAS Author:	Quinn, C (Dr Courtney Quinn)
ID Code:	149471
Year Published:	2022
Web of Science® Times Cited:	1
Deposited By:	Mathematics
Deposited On:	2022-03-31
Last Modified:	2022-05-13
Downloads:	11 View Download Statistics