Identifying efficient ensemble perturbations for initializing subseasonal-to-seasonal prediction Journal Article uri icon

Overview

abstract

  • The prediction of the weather at subseasonal-to-seasonal (S2S); timescales is dependent on both initial and boundary conditions. An open; question is how to best initialize a relatively small-sized ensemble of; numerical model integrations to produce reliable forecasts at these; timescales. Reliability in this case means that the statistical; properties of the ensemble forecast are consistent with the actual; uncertainties about the future state of the geophysical system under; investigation. In the present work, a method is introduced to construct; initial conditions that produce reliable ensemble forecasts by; projecting onto the eigenfunctions of the Koopman or the; Perron-Frobenius operators, which describe the time-evolution of; observables and probability distributions of the system dynamics,; respectively. These eigenfunctions can be approximated from data by; using the Dynamic Mode Decomposition (DMD) algorithm. The effectiveness; of this approach is illustrated in the framework of a low-order; ocean-atmosphere model exhibiting multiple characteristic timescales,; and is compared to other ensemble initialization methods based on the; Empirical Orthogonal Functions (EOFs) of the model trajectory and on the; backward and covariant Lyapunov vectors of the model dynamics.; Projecting initial conditions onto a subset of the Koopman or; Perron-Frobenius eigenfunctions that are characterized by time scales; with fast-decaying oscillations is found to produce highly reliable; forecasts at all lead times investigated, ranging from one week to two; months. Reliable forecasts are also obtained with the adjoint covariant; Lyapunov vectors, which are the eigenfunctions of the Koopman operator; in the tangent space. The advantages of these different methods are; discussed.

publication date

  • September 22, 2021

has restriction

  • hybrid

Date in CU Experts

  • September 28, 2021 1:30 AM

Full Author List

  • Demaeyer J; Penny SG; Vannitsem S

author count

  • 3

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