8 research outputs found
A reduced-order strategy for 4D-Var data assimilation
This paper presents a reduced-order approach for four-dimensional variational
data assimilation, based on a prior EO F analysis of a model trajectory. This
method implies two main advantages: a natural model-based definition of a mul
tivariate background error covariance matrix , and an important
decrease of the computational burden o f the method, due to the drastic
reduction of the dimension of the control space. % An illustration of the
feasibility and the effectiveness of this method is given in the academic
framework of twin experiments for a model of the equatorial Pacific ocean. It
is shown that the multivariate aspect of brings additional
information which substantially improves the identification procedure. Moreover
the computational cost can be decreased by one order of magnitude with regard
to the full-space 4D-Var method
Control of lateral boundary conditions in four-dimensional variational data assimilation for a limited area model
The limited area model forecasting problem is a lateral boundary condition (LBC) problem in addition to the initial condition problem. The data assimilation has traditionally been considered as a process for estimation of the initial condition only, while for the limited area data assimilation this estimation may be extended to include also the LBCs, at least during the data assimilation time window when observations are available. A procedure for such a control of the LBCs has been included in the four-dimensional variational data assimilation (4D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system. A description of this procedure is provided together with results from idealised as well as real data experiments. The results indicate that control of LBCs may be important with small forecast domains and in particular for weather disturbances moving quickly into and through the forecast domain
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Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments
During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean
forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has
been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French
MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate
a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and
salinity profiles, focusing on high-resolution scales of the ocean dynamics.
The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication
including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed
through the Syst`eme dâAssimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal
interpolation which can be operated using âaltimetry-onlyâ or âmulti-dataâ set-ups; it relies on the concept of
separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions.
The second release, SAM-2, is being developed to include new features from the singular evolutive extended
Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third
one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm.
Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation
assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the
European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring
and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations
of the current systems. This paper provides an overview of the developments conducted in MERSEA with
the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter
Data assimilation for geophysical fluids: the Diffusive Back and Forth Nudging
International audienc
Seasonal evaluation of evapotranspiration fluxes from MODIS satellite and mesoscale model downscaled global reanalysis datasets
Evaluation of the AROME model's ability to represent ice crystal icing using in situ observations from the HAIC 2015 field campaign
International audienceSince pilots generally avoid intense convective areas, ice crystals icing (ICI) is an aeronautical weather incident that mainly occurs in the anvil of tropical deep convective clouds. Samples of favorable conditions for the occurrence of ICI and data from the High Altitude Ice Crystals (HAIC) 2015 field campaign in French Guiana are investigated and compared with simulations of the French operational mesoscale forecast system Application of Research to Operations at Mesoscales (AROME). To this end, a contextualization of convective systems into convective, stratiform, and cirriform regions is employed for both observations and AROME. General features of the microphysics of deep tropical convective systems are identified. The number concentration of crystals larger than 125âÎŒm and total water content (TWC) are strongly correlated at each temperature level, and both decrease with increasing distance from convective cores. AROME can reproduce the general behavior of the observed microphysics, especially TWC, but seems unable to simulate extreme ICI events. Reasons are sought in the assumptions performed in the microphysical scheme ICE3, and guidelines are proposed to enhance its skills in the context of ICI. In particular, the representation of the snow particle size distribution is adjusted across observations using a generalized gamma shape. This shape is found to outperform the usual MarshallâPalmer and gamma shapes. Additionally, a temperature and snow content dependence of generalized gamma parameters is found. These changes are found to significantly improve the snow concentration diagnostic of ICE3, and these modifications open the way for improvements in the ICE3 schem