75 research outputs found
SOFT feature-tracking software handbook
This handbook (SOFT_WP31_handbook.pdf) describes the suite of MATLAB
programs developed within Work Package 3, task 3.1 of the SOFT Project, for the
tracking of large-scale, westward propagating features (planetary waves or
westward-travelling eddies) in altimeter data and the removal of the identified features
from the datasets. The suite has been applied to TOPEX/POSEIDON data over the
Azores region (one of the SOFT study regions) but its modularity makes it adaptable in
a straightforward way to other datasets and other regions.
The companion to this handbook is the progress report on task 3.1 released in
January 2003 (SOFT_WP31_report.pdf), which presents the rationale to the study and
gives ample details on the scheme adopted for the fitting of elementary waves
(according to a Gaussian wave shape model) to altimeter data. A synopsis of the fitting
scheme is briefly recalled in the following sections of this document, for the benefit of
the reader. All the code listings are in the appendix.
The forecasting of the westward-propagating fields (which is the object of task
3.2 in Work Package 3 id described in version 1 of another report,
SOFT_WP32_rep1.pdf
SOFT Wave forecasting report - v.1.0
This report (SOFT_WP32_rep1.pdf) describes the first version of the wave forecasting code developed within Work Package 3, task 3.2 (implementation of a hybrid SOFT tracking system) of the SOFT Project. The forecasting of westward propagating signals (planetary waves or westward-travelling eddies), using the fields of tracked wave from Work Package 3, task 3.1, is one of the two components of the hybrid system which is the overall deliverable of task 3.2. The results presented here are provisional and are likely to be replaced as research proceeds. Related to this report are two other documents:- the progress report on task 3.1 released in January 2003(SOFT_WP31_report.pdf), which presents the rationale to the study and gives ample details on the scheme adopted for the fitting of elementary waves (according to a Gaussian wave shape model) to altimeter data (see also the paper by Cipollini, 2003);- the handbook SOFT_WP31_handbook.pdf describing the suite of MATLAB programs developed within Work Package 3, task 3.1 of the SOFTProject, for the tracking of large-scale, westward propagating features (planetary waves or westward-travelling eddies) in altimeter data and the removal of the identified features from the datasets. The suite has been applied to TOPEX/POSEIDON data over the Azores region (one of the SOFTstudy regions) and the output results have been used for the forecast
SOFT Development of feature tracking methods
The present report describes the work carried out within task 3.1 of Work Package 3 of the SOFT Project. The above task is âDevelopment of feature tracking methodsâ and consists of the development of a software to track large-scale, westward propagating features (planetary waves or westward-travelling eddies) in the altimetric datasets, and in the removal of the identified features from the datasets. The residual field (that is the original dataset minus the tracked features) is then made available to the other work packages in the Project
Emulating dynamic non-linear simulators using Gaussian processes
The dynamic emulation of non-linear deterministic computer codes where the
output is a time series, possibly multivariate, is examined. Such computer
models simulate the evolution of some real-world phenomenon over time, for
example models of the climate or the functioning of the human brain. The models
we are interested in are highly non-linear and exhibit tipping points,
bifurcations and chaotic behaviour. However, each simulation run could be too
time-consuming to perform analyses that require many runs, including
quantifying the variation in model output with respect to changes in the
inputs. Therefore, Gaussian process emulators are used to approximate the
output of the code. To do this, the flow map of the system under study is
emulated over a short time period. Then, it is used in an iterative way to
predict the whole time series. A number of ways are proposed to take into
account the uncertainty of inputs to the emulators, after fixed initial
conditions, and the correlation between them through the time series. The
methodology is illustrated with two examples: the highly non-linear dynamical
systems described by the Lorenz and Van der Pol equations. In both cases, the
predictive performance is relatively high and the measure of uncertainty
provided by the method reflects the extent of predictability in each system
Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
The analysis of computer models can be aided by the construction of surrogate
models, or emulators, that statistically model the numerical computer model.
Increasingly, computer models are becoming stochastic, yielding different
outputs each time they are run, even if the same input values are used.
Stochastic computer models are more difficult to analyse and more difficult to
emulate - often requiring substantially more computer model runs to fit. We
present a method of using deterministic approximations of the computer model to
better construct an emulator. The method is applied to numerous toy examples,
as well as an idealistic epidemiology model, and a model from the building
performance field
Super-parameterisation of ocean deep convection
Technical report on the use of emulators in the parameterisation of ocean convectionSub-grid scale processes play an important role in ocean and climate modelling. Typical examples include clouds in atmospheric models, flows over restricted topographies or the resolution of con- vective plumes in ocean models. Detailed numerical models of these sub-grid scale processes exist, but embedding them in a Global Circulation Model (GCM) for example, would be computationally prohibitive. In the present work we investigate the applicability of emulators for representing the sub-grid scale processes within a GCM simulation. Emulators can be thought as encapsulating our beliefs about the sub-grid dynamical model, derived from a designed computer experiment using a Bayesian framework. In particular, we propose to employ an emulator for parameterising the sub-grid scale process, and embed this within a GCM as a surrogate for the actual sub-grid scale model. The result of combining the GCM with the emulator will be a Super-parameterised model, which will also be computationally efficient, since the emulator incurs a very small computational overhead. The example we chose to illustrate the proposed methodology is deep ocean convection. The sub-grid scale dynamical model simulates deep convective plumes, while the large scale dy- namics simulate the geostrophic eddy scale. We present details on building the emulator of the convective plumes and its coupling with the large scale process model. We also discuss whether the emulator should be run as a deterministic or stochastic parameterisation
Modelling Numerical Systems with Two Distinct Labelled Output Classes
We present a new method of modelling numerical systems where there are two
distinct output solution classes, for example tipping points or bifurcations.
Gaussian process emulation is a useful tool in understanding these complex
systems and provides estimates of uncertainty, but we aim to include systems
where there are discontinuities between the two output solutions. Due to
continuity assumptions, we consider current methods of classification to split
our input space into two output regions. Classification and logistic regression
methods currently rely on drawing from an independent Bernoulli distribution,
which neglects any information known in the neighbouring area. We build on this
by including correlation between our input points. Gaussian processes are still
a vital element, but used in latent space to model the two regions. Using the
input values and an associated output class label, the latent variable is
estimated using MCMC sampling and a unique likelihood. A threshold (usually at
zero) defines the boundary. We apply our method to a motivating example
provided by the hormones associated with the reproductive system in mammals,
where the two solutions are associated with high and low rates of reproduction
Emulating complex dynamical simulators with random Fourier features
A Gaussian process (GP)-based methodology is proposed to emulate complex
dynamical computer models (or simulators). The method relies on emulating the
short-time numerical flow map of the system, where the flow map is a function
that returns the solution of a dynamical system at a certain time point, given
initial conditions. Here, the flow map is emulated via a GP whose kernel is
approximated with random Fourier features. This yields a random predictor whose
realisations are approximations to the flow map. In order to predict a given
time series (i.e., the model output), a single realisation of the approximate
flow map is taken and used to iterate from the initial condition ahead in time.
Repeating this procedure with multiple realisations from the distribution of
approximate flow maps creates a distribution over the time series whose mean
and variance serve as the model output prediction and the associated
uncertainty, respectively. The proposed method is applied to emulate several
dynamic nonlinear simulators including the well-known Lorenz and van der Pol
models. The results suggest that our approach has a high predictive performance
and the associated uncertainty can capture the dynamics of the system
accurately. Additionally, our approach has potential for "embarrassingly"
parallel implementations where one can conduct the iterative predictions
performed by a realisation on a single computing node
The implications of transporting architecture on human health
This is the author accepted manuscript.Where modern buildings are unable to maintain the internal environment to within comfort levels they often rely on mechanical systems to become habitable. This could be due to bad design or putting the building in an environment for which it is not suited. Due to climate change it is likely that all buildings will in effect and time be moved to an environment for which it is not suited. In this work the effects of changes in climate on the internal environment will be explored and an index to define how moveable a construction might be, will be developed.The authors would like to thank the EPSRC for their support [grant ref: EP/J002380/1
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