The ensemble algorithm for estimation of model parameters in the problem of assessing greenhouse gases fluxes

Abstract

The problem of assessing the greenhouse gases fluxes from the Earth’s surface based on observations is currently very urgent. To solve it, it is customary to use data assimilation systems (or a more general concept — inverse modeling), which include the observations on the concentration of greenhouse gases and models of the transport and diffusion. Since such problems involve large volumes of satellite data and the global model of transport and diffusion, it has a huge dimension. For this reason, the development of effective algorithms to enable the practical implementation of the task is required. The paper discusses data assimilation algorithms based on the ensemble Kalman filter and ensemble Kalman smoothing, which can be used to solve the problem of estimating greenhouse gases fluxes. Economical algorithms for estimating a parameter that is constant over a given time interval are proposed.