English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Many problems in the geophysical sciences demand the ability to calibrate theparameters and predict the time evolution of complex dynamical models usingsequentially-collected data. Here we introduce a general methodology for thejoint estimation of the static parameters and the forecasting of the statevariables of nonlinear, and possibly chaotic, dynamical models. The proposedscheme is essentially probabilistic. It aims at recursively computing thesequence of joint posterior probability distributions of the unknown modelparameters and its (time varying) state variables conditional on the availableobservations. The latter are possibly partial and contaminated by noise. Thenew framework combines a Monte Carlo scheme to approximate the posteriordistribution of the fixed parameters with filtering (or {\em dataassimilation}) techniques to track and predict the distribution of the statevariables. For this reason, we refer to the proposed methodology as {\em nestedfiltering}. In this paper we specifically explore the use of Gaussian filteringmethods, but other approaches fit naturally within the new framework. As anillustrative example, we apply three different implementations of themethodology to the tracking of the state, and the estimation of the fixedparameters, of a stochastic two-scale Lorenz 96 system. This model is commonlyused to assess data assimilation procedures in meteorology. For this example,we compare different nested filters and show estimation and forecasting resultsfor a 4,000-dimensional system.

A probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems