Advances in Meteorology

Volume 2016, Article ID 9372786, 18 pages

http://dx.doi.org/10.1155/2016/9372786

## From the Kalman Filter to the Particle Filter: A Geometrical Perspective of the Curse of Dimensionality

^{1}CNRM, UMR 3589, 42 Av. Coriolis, 31057 Toulouse, France^{2}INPT-ENM, 42 Av. Coriolis, 31057 Toulouse, France

Received 28 June 2016; Accepted 20 October 2016

Academic Editor: James Cleverly

Copyright © 2016 Olivier Pannekoucke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The aim of this contribution is to provide a description of the difference between Kalman filter and particle filter when the state space is of high dimension. In the Gaussian framework, KF and PF give the same theoretical result. However, in high dimension and using finite sampling for the Gaussian distribution, the PF is not able to reproduce the solution produced by the KF. This discrepancy is highlighted from the convergence property of the Gaussian law toward a hypersphere: in high dimension, any finite sample of a Gaussian law lies within a hypersphere centered in the mean of the Gaussian law and of radius square-root of the trace of the covariance matrix. This concentration of probability suggests the use of norm as a criterium that discriminates whether a forecast sample can be compatible or not with a given analysis state. The contribution illustrates important characteristics that have to be considered for the high dimension but does not introduce a new approach to face the curse of dimensionality.

#### 1. Introduction

From the work of Bengtsson et al. [1] and Snyder et al. [2], the particle filter is known to suffer from the “curse of dimensionality” [3, 4], in the sense that when the problem is in high dimension, the size of the particle ensemble should be exponentially large leading to the failure of direct Monte-Carlo strategies.

More precisely, in the particle filter, a weight is computed for each particle from the observational error distribution. This weight measures the proximity of the particle to a given observations set. If the ensemble size does not follow an exponential increase with the dimension, then the maximum weight is systematically close to 1, meaning that only one particle is compatible with the observations (or at least the less far from the observation considering the other particles): this leads to the particle degeneracy [5]. While the observation error distribution is often assumed Gaussian in data assimilation, a change of observation error distribution by a larger tail distribution may be introduced to limit the particle degeneracy in realistic application [6]. But this replacement should not be used for theoretical study where it would introduce incoherence between the way the observations are supposed to be and the way they are assimilated. Moreover, Snyder et al. [2] have shown that the particle degeneracy still occurs with larger tail distributions, for example, for the Cauchy distribution.

In the demonstration proposed by Snyder et al. [2], to study the particle filter, the curse of dimensionality is described in terms of weight, related to the observational space. Hence, it is still difficult to understand what happens in the state space, and also what is the real difference between the ensemble version of the Kalman equations (EnKF), introduced by Evensen [7], and the particle filter approach (PF), introduced by Gordon et al. [8]. This question is not to know whether the EnKF converges toward the PF in the general framework, where known results are existing for this issue: (a) the PF is known to converge toward the nonlinear filter in the limit of large number of particles in interaction, Del Moral [9]; (b) the EnKF is known to converge toward a different solution than the nonlinear filter [10, 11]. How can we feel the paradox that the two algorithms, the EnKF and the PF under Gaussian assumption, should deliver the same conclusion while, in the practical high dimension case, they do not?

In the present contribution, important characteristics are illustrated that have to be considered for the high dimension, but no new approach is introduced to face the curse of dimensionality. This work investigates another point of view that relies on the multidimensional spheres or hyperspheres. A similar but different approach has been considered by Chorin and Morzfeld [12]. This perspective, mentioned in the conclusion of Snyder et al. [2], helps to understand in a direct way the effect of the dimension on the difference between the Kalman filter and the particle approximation of the Bayes rule under Gaussian assumption. The main result used in this work is that a normalized Gaussian law in high dimension is not a simple extension of higher dimension of the usual scatter plot in 2d but converges toward the uniform law on a sphere in dimension, a phenomenon known as the Poincaré Lemma [13]. This phenomenon occurs even from the “high dimension” that is very small compared with the degrees of freedom encountered in geophysical applications: the constraints due to the dimension are quite strong for this area.

The understanding of the behaviour in high dimension is required when using alternative to classical data assimilation to update probabilistic information contained within an ensemble. Such an update can be motivated in order to increase the ensemble size by merging lagged ensemble [14, 15] with the question of how to merge ensemble from different forecast times [16]. This can also be due to the real time constraints: ensemble prediction system can now rely on an analysis ensemble [17], but in practice there can be an important delay between the operational analysis production (that relies on Kalman filter formalism) and the ensemble analysis production. As a result an analysis state exists that could be used to update the last forecast ensemble available, for example, by using the Particle filter approach [18, 19] where an adaptation of the metric in the computation of the weight is also introduced in the application of the particle filter strategy. This kind of situation is a motivation to clarify the differences between Kalman filter and particle filter, at least under Gaussian assumption.

To tackle this issue we first recall the behaviour of Gaussian distribution in high dimension in Section 2. In Section 3, we review the description of the filtering theory under Gaussian assumption, then detail the asymptotic consequences of the high dimension, and provide the constraints implied onto the background and the analysis distribution that constitutes the difference between the EnKF and the PF. The conclusions are reported in Section 4.

#### 2. Behaviour of Gaussian Distribution in High Dimension

##### 2.1. Concentration of Gaussian Law in High Dimension

Human intuition of the high dimension behaviour is often an extrapolation of the low dimension experiences. This leads us to assume that graphical representations in low dimensions are also verified in high dimensions. This is particularly true for Gaussian distribution where the similitude of scatter plots in dimensions lower than 3 invites us to speculate that the distribution in high dimensions should not be so different (see Figure 1). But actually this is completely wrong and leads to a misleading interpretation of what a Gaussian distribution is really, despite its intensive use in large systems as encountered in geophysical sciences and especially in data assimilation.