Changeset 214

Timestamp:

12/14/15 12:53:42 (8 years ago)

Author:

jbrlod

Message:

few correction

Location:

altifloat/doc/ocean_modelling

Files:

• Draft1.tex (modified) (6 diffs)
• mybib.bib (modified) (1 diff)

altifloat/doc/ocean_modelling/Draft1.tex

@@ -238 +238 @@
 
 \section{\label{sec:data}Data}
-All the data detailed in this section were extracted from two target periods: the data associated with the NEMED project~\footnote{\url{http://nettuno.ogs.trieste.it/sire/drifter/nemed/nemed_main.html}} was from 25 August 2009 to 3 September 2009, and the data associated with the AltiFloat project was from 28 August 2013 to 4 September 2013.
+All the data detailed in this section were extracted from two target periods: on one hand the data associated with the NEMED project~\footnote{\url{http://nettuno.ogs.trieste.it/sire/drifter/nemed/nemed_main.html}} from 25 August 2009 to 3 September 2009, and on the other hand the data associated with the AltiFloat project from 28 August 2013 to 4 September 2013.
 \subsection {\label{sec:aviso}Altimetry data}
-Geostrophic surface velocity fields used as a background in the study were produced by Ssalt/\textit{Duacs} and distributed by AVISO~\footnote{www.aviso.altimetry.fr}. Altimetric mission used were  Saral, Cryosat-2, Jason-1\&2. The geostrophic absolute velocity fields were deduced from Maps of Absolute Dynamic Topography (MADT) of the regional Mediterranean Sea product using the recently released Mean Dynamic Topography by~\citet{rio2014}.
+Geostrophic surface velocity fields used as a background in the study were produced by Ssalt/\textit{Duacs} and distributed by AVISO~\footnote{\url{www.aviso.altimetry.fr}}. Altimetric mission used were  Saral, Cryosat-2, Jason-1\&2. The geostrophic absolute velocity fields were deduced from Maps of Absolute Dynamic Topography (MADT) of the regional Mediterranean Sea product using the recently released Mean Dynamic Topography by~\citet{rio2014}.
 
 Data were mapped daily at a resolution of 1/8$^o$. Data were linearly interpolated every hour at the advection model time step.
@@ -385 +385 @@
 a numerical tool very well adapted to variational assimilation problems that simplifies the computation and implementation of the adjoint needed in the optimization. 
 
-The solution was found by using the M1QN3 minimiser \citep{gilbert1989some} linked with the YAO tool. The convergence of the assimilation in a typical time window of $24$ h takes $20$ seconds on a sequential code compiled on a CPU Intel(R) Core(TM) at 3.40GHz.
+The solution was found by using the M1QN3 minimiser \citep{gilbert1989some} linked with the YAO tool. The convergence of the assimilation in a typical time window $T_w=24$ h takes $20$ seconds on a sequential code compiled on a CPU Intel(R) Core(TM) at 3.40GHz.
 
 
@@ -449 +449 @@
 
 \subsection{Sensitivity to the time sampling size}
-Finally, we show the effect of the time sampling parameter $\Delta t$ of the observations in Fig.~\ref{fig:time}. Curves after correction correspond to $\Delta t=6, 4$ and $2$ hours and as we see from the figure, the difference between these cases is not too large. The realistic scenario of  $\Delta t=6$ h still yields a very good correction.
+We show the effect of the time sampling parameter $\Delta t$ of the observations in Fig.~\ref{fig:time}. Curves after correction correspond to $\Delta t=6, 4$ and $2$ hours and as we see from the figure, the difference between these cases is not too large. The realistic scenario of  $\Delta t=6$ h still yields a very good correction.
 
 \begin{figure}[htbp]
@@ -462 +462 @@
 \subsection{Sensitivity to the effect of the divergence constraint}
 
-\textcolor{red}{The role of the divergence constraint in the optimization is determined by a delicate balance between the various terms. This term should be non negligible because as mentioned earlier, it forces the correction to be in the direction tangent to the coast, making the component perpendicular to the coast small. However, it cannot be too strong as to interfere with the regularization term, because that would make the optimization ill-conditionned. To show its effect on the correction, we conduct a sensitivity experiment where we compare the results (in the same setting as the previous experiments) with and without this term. As seen from Fig.~\ref{fig:div}, we obtain an improvement of about $10\%$ in the overall error if we have this term. This is expected because we are correcting the velocity in a region close to the coast.}
+\textcolor{red}{The role of the divergence constraint in the optimization is determined by a delicate balance between the various terms. This term should be non negligible because as mentioned earlier, it forces the correction to be in the direction tangent to the coast, making the component perpendicular to the coast small. However, it cannot be too strong as to interfere with the regularization term, because that would make the optimization ill-conditionned. To show its effect on the correction, we conduct a sensitivity experiment where we compare the results (in the same setting as the previous experiments) with and without this term. As seen from Fig.~\ref{fig:div}, we obtain an improvement of about $10\%$ in the overall error if this term is present in the cost function. This is expected because we are correcting the velocity in a region close to the coast.}
 
 
@@ -535 +535 @@
 \subsection{\label{sec:lebanon}Improvement of velocity field near the coast}
 
-Three drifters were launched on August 28 2013 from the South of Beirut, at the positions shown in circles in Fig.~\ref{fig:leb1}. They provide their position every $\Delta t= 6$ h and stay within $20$ km of the coast for the duration of the experiment. 
+Three drifters were launched on 28 August 2013 from the South of Beirut, at the positions shown in circles in Fig.~\ref{fig:leb1}. They provide their position every $\Delta t= 6$ h and stay within $20$ km of the coast for the duration of the experiment. 
 The experiment considered here lasts for six days (a time frame where the three drifters are still spatially close before two of them hit the shore). The window size is $T_w=24$ h. The smoothing parameter $\sigma=6$ h. 
 Fig.~\ref{fig:leb1}, shows the trajectories simulated with the corrected field on top of the observed ones, 
@@ -605 +605 @@
 
 \section{Conclusion}
-We presented a simple and efficient algorithm to blend drifter Lagrangian data with altimetry Eulerian velocities in the Eastern Levantine Mediterranean. The method has a cheap implementation and is quick to converge, so it is well fitted for near-real time applications. Assimilating two successive drifter positions produces a correction of the velocity field within a radius of 20km and for approximatively 24h before and after the measurement.
+We presented a simple and efficient algorithm to blend drifter Lagrangian data with altimetry Eulerian velocities in the Eastern Levantine Mediterranean. After implementation, the method needs very few computing ressource and is quick to converge, so it is well fitted for near-real time applications. Assimilating two successive drifter positions produces a correction of the velocity field within a radius of 20km and for approximatively 24h before and after the measurement.
 
 This algorithm was able to correct some typical weaknesses of altimetric fields, in particular the estimation of velocity near the coast and accurate estimations of eddies dimensions and intensity.

altifloat/doc/ocean_modelling/mybib.bib

@@ -192 +192 @@
 
 @article{dimet1986variational,
-        Author = {DIMET, FRAN{\c{C}}OIS-XAVIER LE and Talagrand, Olivier},
+        Author = {Le Dimet, Fran{\c{c}}ois-Xavier and Talagrand, Olivier},
         Date-Added = {2015-10-28 09:57:04 +0000},
         Date-Modified = {2015-10-28 09:57:04 +0000},