A 3D ensemble variational data assimilation scheme for the limited-area AROME model: Formulation and preliminary results

2018 ◽  
Vol 144 (716) ◽  
pp. 2196-2215 ◽  
Author(s):  
Thibaut Montmerle ◽  
Yann Michel ◽  
Etienne Arbogast ◽  
Benjamin Ménétrier ◽  
Pierre Brousseau
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Limited Area ◽  
Model Formulation ◽  
Preliminary Results ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

The Met. Office global three-dimensional variational data assimilation scheme

2000 ◽  
Vol 126 (570) ◽  
pp. 2991-3012 ◽  
Author(s):  
A. C. Lorenc ◽  
S. P. Ballard ◽  
R. S. Bell ◽  
N. B. Ingleby ◽  
P. L. F. Andrews ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

A Parallel Three-dimensional Variational Data Assimilation Scheme

2011 ◽  
Author(s):  
L. D’Amore ◽  
R. Arcucci ◽  
L. Marcellino ◽  
A. Murli ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

scholarly journals Assimilation of Tropical Cyclone Observations: Improving the Assimilation of TCVitals, Scatterometer Winds, and Dropwindsonde Observations

2015 ◽  
Vol 143 (10) ◽  
pp. 3956-3980 ◽  
Author(s):  
Christina Holt ◽  
Istvan Szunyogh ◽  
Gyorgyi Gyarmati ◽  
S. Mark Leidner ◽  
Ross N. Hoffman
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Statistical Model ◽  
Global Analysis ◽  
Second Order ◽  
Statistical Moments ◽  
Limited Area ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme ◽  
Normally Distributed

Abstract The standard statistical model of data assimilation assumes that the background and observation errors are normally distributed, and the first- and second-order statistical moments of the two distributions are known or can be accurately estimated. Because these assumptions are never satisfied completely in practice, data assimilation schemes must be robust to errors in the underlying statistical model. This paper tests simple approaches to improving the robustness of data assimilation in tropical cyclone (TC) regions. Analysis–forecast experiments are carried out with three types of data—Tropical Cyclone Vitals (TCVitals), DOTSTAR, and QuikSCAT—that are particularly relevant for TCs and with an ensemble-based data assimilation scheme that prepares a global analysis and a limited-area analysis in a TC basin simultaneously. The results of the experiments demonstrate that significant analysis and forecast improvements can be achieved for TCs that are category 1 and higher by improving the robustness of the data assimilation scheme.

scholarly journals Displacing Potential Vorticity Structures by the Assimilation of Pseudo-Observations

2011 ◽  
Vol 139 (2) ◽  
pp. 549-565 ◽  
Author(s):  
Yann Michel
Keyword(s):  
Data Assimilation ◽  
Potential Vorticity ◽  
Variational Data Assimilation ◽  
Tuning Parameter ◽  
Data Assimilation Scheme ◽  
The Difference ◽  
Upper Level ◽  
Meteorological Features ◽  
Assimilation Scheme ◽  
Dry Intrusions

Abstract Classic formulations of variational data assimilation in amplitude space are not able to directly handle observations that measure the geographical positions of meteorological features like fronts and vortices. These observations can be derived from satellite images, as is already the case for tropical cyclones. Although some advanced data assimilation algorithms have been specifically designed to tackle the problem, a widespread way of dealing with this information is to use so-called bogussing pseudo-observations: user-specified artificial observations are inserted in a traditional data assimilation scheme. At the midlatitudes, there is a relationship between dry intrusions in water vapor images and upper-level potential vorticity structures. Some prior work has also shown that it was possible to automatically identify dry intrusions with tracking algorithms. The difference of positions between model and image dry intrusions could therefore be used as observations of the misplacement of potential vorticity structures. One strategy to achieve the displacement of potential vorticity anomalies is to sample them, and assimilate the values at displaced locations. The uncertainty of these pseudo-observations is left as a tuning parameter to try to make the displacement both effective and robust. A simple one-dimensional assimilation model is used to study the displacement of curves defined by Gaussian humps. The concept is then illustrated in realistic examples from real synoptic systems, where pseudo-observations of potential vorticity are incorporated in a global variational data assimilation scheme. Overall and despite reasonable optimization, the results contain artifacts. This suggests that the use of pseudo-observations to displace identifiable structures is not an effective strategy.

Sign in / Sign up

Export Citation Format

Share Document