Stochastic simulation of climatic data with the Direct Sampling method
| Responsable du projet |
Philippe Renard
Julien Straubhaar |
| Collaborateur | Fabio Oriani |
| Résumé |
A wealth of historic climatic data has been made available in the
last decades, and this is especially true for rainfall data for
which records of more than a century exist for a number of
locations. The statistics based on such long records are quite
robust and reliable. However, most methods proceed by first
deriving parametric, low order statistics from these data, and in a
second step use this model for prediction. The statistical model is
a simplification of reality, including possibly restrictive
assumptions. Therefore the statistical models traditionally
employed can only represent models up to a certain degree of
complexity, and are limited by their underlying hypotheses. We
argue that this does not allow using the full richness of the data
sets. Training-image based methods and especially multiple-point
statistics simulation have been initially developed for subsurface
applications (Boucher, 2009, De Vries, et al., 2009, Guardiano and
Srivastava, 1993, Hu and Chugunova, 2008, Strebelle, 2002, Wu, et
al., 2008). In this field, the major limitation of most numerical
models resides in the lack of data on the subsurface.
Multiple-point geostatistics introduced the concept of a training
image that represents the typical structures that a geologist would
expect. High-order statistics of the training image are then
reproduced in the simulation domain. One of the major drawbacks of
the multiple-point approach is the lack of availability of relevant
training data. This problem does not exist with climatic
applications where data are available in large amounts. In the
recent years, the stochastic hydrogeology group of the University
of Neuchâtel has developed a multiple-point simulation method
(Direct Sampling, DS) that allows exploiting historical data (or
training data) in their whole complexity using non-parametric
high-order statistics (Mariethoz, et al., 2010). Contrarily to
existing training-image based methods, the Direct Sampling can
simulate either categorical or continuous variables, or a
combination of both in a multivariate framework. We believe this
ability is of tremendous potential for applications related to
climatic time series. In particular, the method reproduces the
statistical properties of the training data set up to a very high
order, and regardless of the complexity of these statistics. If the
example data inform several variables, all variables are jointly
simulated and the multivariate statistics are reproduced as well,
including possibly non-linear or even multi-modal correlations. The
objective of this project is therefore to extend the current range
of applications of the Direct Sampling method to climatic data sets
and to evaluate its performances as compared to other well
established or newly developed methods such as copulas based
geostatistics. We expect that this work will lead to improve
stochastic modeling of climatic data by increasing the realism of
the simulations accounting for the physical complexity of the
phenomena while simplifying the work of the modeler. |
| Mots-clés |
Spatial and temporal statistics, Climatic variables |
| Type de projet | Recherche fondamentale |
| Domaine de recherche | Hydrologie, limnologie, glaciologie |
| Source de financement | FNS - Encouragement de projets (Div. I-III) |
| Etat | Terminé |
| Début de projet | 1-8-2011 |
| Fin du projet | 31-7-2015 |
| Budget alloué | 237'903.00 |
| Autre information |
http://p3.snf.ch/projects-134614# |
| Contact | Philippe Renard |