Data assimilation algorithms
Data assimilation is a mathematical process by which a numerical evolution model
is constrained to stay close to a partially observed reality. Here, it is a set of
techniques to prepare the initial forecast states of ARPEGE, ALADIN and AROME using
observations of the atmosphere. Most activities concentrate on understanding
sequential variational assimilation techniques, called 3D-Var and 4D-Var, and tuning
their most important ingredient, the covariances of estimation errors of the
background states.
This activity has many quick and useful applications to improving the numerical
weather forecasts. It is supported by research grants such as INSU/LEFE (from CNRS)
and ANR.
References
- Berre, L., Pannekoucke, O., Desroziers, G., and Martel, C., 2006: A spectral
diagnosis and filtering of sampling noise in ensemble background error standard
deviations, Submitted to Quart. Jour. Roy. Meteor. Soc.
- Pannekoucke, O., Berre, L. and Desroziers, G., 2006: Wavelets expansion
and its use as an adaptive filter.
- Accepted for publication in
Quart. Jour. Roy. Meteor. Soc.
- Desroziers G., L. Berre, B. Chapnik and P. Poli, 2006: Diagnosis of
observation, background and analysis error statistics in observation space.
Quart. Jour. Roy. Meteor. Soc., 131, pp.3385-3396.
- Chapnik, B., G. Desroziers, F. Rabier, and O. Talagrand, 2004 : Properties and first applications of an error statistics tuning method in variational assimilation. Quart. Jour. Roy. Meteor. Soc., 130, pp. 2253-2275.
- Chapnik, B., G. Desroziers, F. Rabier and O. Talagrand, 2006: Diagnosis and
tuning of observational error in a quasi-operational data assimilation setting.
Quart. Jour. Roy. Meteor. Soc., 132, pp.543-565.
- Deckmyn, A., and Berre L., 2004: A wavelet approach to representing
background error covariances in a limited area model. Mon. Wea. Rev.,
133, pp.1279-1294.
- Desroziers, G., P. Brousseau and B. Chapnik, 2005: Use of randomization to
diagnose the impact of observations on analyses and forecasts.
Quart. Jour. Roy. Meteor. Soc., 131, pp.2821-2837.
- Chapnik, B., G. Desroziers, F. Rabier, and O. Talagrand, 2004:
Properties and first applications of an error statistics tuning method in
variational assimilation. Quart. Jour. Roy. Meteor. Soc., 130,
pp.2253.
- Gauthier, P. and J.-N. Thépaut, 2001: Impact of the digital filter as a
weak constraint in the pre-operational 4D-Var assimilation system of
Météo-France. Monthly Weather Review, 129, pp.2089-2102.
- Hello, G. and F. Bouttier, 2001: Using adjoint sensitivity as a local
structure function in variational data assimilation. Nonlinear Processes
in Geophysics, vol 8, n.6, pp.347-355.
[link]
- Rabier, F., 2006: Importance of data: a meteorological perspective. Chapitre 12 du livre de Chassignet, E.P., and J. Verron (Eds.), 2006: Ocean Weather Forecasting: An Integrated View of Oceanography. Springer, 577 pp.
- Rabier, F., 2005: Overview of data assimilation developments in Numerical Weather Prediction centres . Quart. Jour. Roy. Meteor. Soc., 131, pp. 3215-3233.
- Veersé, F. and J.N. Thépaut, 1998 : Multiple-truncation incremental approach
for four-dimensional variational data assimilation.
Quart. Jour. Roy. Meteor. Soc., 124, pp.1889-1908.