In statistical data assimilation one evaluates the conditional expected values, conditioned on measurements, of interesting quantities on the path of a model through observation and prediction windows. This often requires working with very high dimensional integrals in the discrete time descriptions of the observations and model dynamics, which become functional integrals in the continuous-time limit. Two familiar methods for performing these integrals include (1) Monte Carlo calculations and (2) variational approximations using the method of Laplace plus perturbative corrections to the dominant contributions. We attend here to aspects of the Laplace approximation and develop an annealing method for locating the variational path satisfying the Euler-Lagrange equations that comprises the major contribution to the integrals. This begins with the identification of the minimum action path starting with a situation where the model dynamics is totally unresolved in state space, and the consistent minimum of the variational problem is known. We then proceed to slowly increase the model resolution, seeking to remain in the basin of the minimum action path, until a path that gives the dominant contribution to the integral is identified. After a discussion of some general issues, we give examples of the assimilation process for some simple, instructive models from the geophysical literature. Then we explore a slightly richer model of the same type with two distinct time scales. This is followed by a model characterizing the biophysics of individual neurons.

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Information in measurements of a nonlinear dynamical system can be transferred to a quantitative model of the observed system to establish its fixed parameters and unobserved state variables. After this learning period is complete, one may predict the model response to new forces and, when successful, these predictions will match additional observations. This adjustment process encounters problems when the model is nonlinear and chaotic because dynamical instability impedes the transfer of information from the data to the model when the number of measurements at each observation time is insufficient. We discuss the use of information in the waveform of the data, realized through a time delayed collection of measurements, to provide additional stability and accuracy to this search procedure. Several examples are explored, including a few familiar nonlinear dynamical systems and small networks of Colpitts oscillators.

Most data based state and parameter estimation methods require suitable initial values or guesses to achieve convergence to the desired solution, which typically is a global minimum of some cost function. Unfortunately, however, other stable solutions (e.g., local minima) may exist and provide suboptimal or even wrong estimates. Here we demonstrate for a 9-dimensional Lorenz-96 model how to characterize the basin size of the global minimum when applying some particular optimization based estimation algorithm. We compare three different strategies for generating suitable initial guesses and we investigate the dependence of the solution on the given trajectory segment (underlying the measured time series). To address the question of how many state variables have to be measured for optimal performance, different types of multivariate time series are considered consisting of 1, 2, or 3 variables. Based on these time series the local observability of state variables and parameters of the Lorenz-96 model is investigated and confirmed using delay coordinates. This result is in good agreement with the observation that correct state and parameter estimation results are obtained if the optimization algorithm is initialized with initial guesses close to the true solution. In contrast, initialization with other exact solutions of the model equations (different from the true solution used to generate the time series) typically fails, i.e. the optimization procedure ends up in local minima different from the true solution. Initialization using random values in a box around the attractor exhibits success rates depending on the number of observables and the available time series (trajectory segment).Keywords: Data assimilation, parameter estimation, nonlinear modeling, observability, basin size For many physical processes dynamical models are available but often not all their state variables and (fixed) parameters are known or easily accessible. In meteorology, for example, sophisticated large scale models exist, which have to be continuously adapted to the true temporal changes of temperatures, wind speed, humidity, and other relevant physical quantities. In quantitative biology mathematical models of single neural or cardiac cells or networks may contain many state variables and parameters whose values are not easy to measure (without destroying the system). In such cases, data based estimation methods can be used to determine these unknown states and a parameters by adapting a suitable model to reproduce and predict the measured time series. This approach can be successful only if two conditions are fulfilled: (i) the available data have to a) Electronic mail: ulrich.parlitz@ds.mpg.de b) Electronic mail: habarbanel@ucsd.edu c) Electronic mail: stefan.luther@ds.mpg.de provide sufficient information, i.e. the unknown state variables and parameters have to be observable and (ii) the estimation algorithm has to be properly initialized with initial guesses sufficiently close to the true solution. Here, we consider both problems f...

We present a high-pressure fluid handling system based around a simple-to-construct seal for applications in the biologically relevant kiloatmosphere range. Connectors are compact and finger tightened, as compared to the wrench tightening required of cone-type seals commonly used. The seal relies on an O-ring compression, and the system has been tested up to 2000 atm. While the system was designed for biological studies, it should be versatile enough for a wide range of applications, thus contributing finger-tightened convenience to the kiloatmosphere range.