Changeset 189 for altifloat/doc

Timestamp:

10/26/15 10:15:52 (9 years ago)

Author:

jbrlod

Message:

modif intro

File:

• altifloat/doc/ocean_modelling/Draft1.tex (modified) (1 diff)

altifloat/doc/ocean_modelling/Draft1.tex

@@ -137 +137 @@
 \section{Introduction}
 \label{}
-
-Recurrent marine pollution like the ones observed near the heavily populated coastal regions of the Eastern Levantine Mediterranean basin is a major threat to the marine environment and halieutic resources. Polluting agents, being transported through currents to either deep waters or to another part of the coast, have not only an immediate local effect, but also a long term, large scale one.
-It is clear that a good knowledge of the underlying surface velocity field is necessary to understand the dynamics of this transport process. 
- Geostrophic velocities have been widely used to predict the large mesoscale features of the ocean resolving typically lengths on the order of $100$ km [Refs]. There are however limitations to their usage. They are inaccurate in resolving short temporal and spatial scales of some physical processes, like eddies, which results in blurring these structures. Further errors and inaccuracies occur near the coastal areas (within 20-50 km from land),
-where satellite information is degraded; this is due to various factors such as ``land contamination, inaccurate tidal and geophysical
+An accurate estimation of mesoscale to sub-mesoscale surface dynamics of the ocean is critical in several application regarding the Eastern Levantine Mediterranean basin. For instance, the study of the pollutant dispersion in this heavily populated region. A good knowledge of surface velocity field is a great challenge, considering that direct observations are relatively sparse in this region. 
+
+%Recurrent marine pollution like the ones observed near the heavily populated coastal regions of the Eastern Levantine Mediterranean basin is a major threat to the marine environment and halieutic resources. Polluting agents, being transported through currents to either deep waters or to another part of the coast, have not only an immediate local effect, but also a long term, large scale one.
+%It is clear that a good knowledge of the underlying surface velocity field is necessary to understand the dynamics of this transport process. 
+Altimetry has been widely used to predict the large mesoscale features of the ocean resolving typically lengths on the order of $100$ km [Refs]. There are however limitations to its usage. It is inaccurate in resolving short temporal and spatial scales of some physical processes, like eddies, which results in blurring these structures. Further errors and inaccuracies occur near the coastal areas (within 20-50 km from land),
+where satellite information is degraded; this is due to various factors such as land contamination, inaccurate tidal and geophysical
 corrections and incorrect removal
-of high frequency atmospheric effects at the sea surface." [Caballero]. 
-
-
-To improve geostrophic velocities, especially near the coast, several types of data can be combined. In situ observations [Bouffard et al., 2010; Ruiz et al., 2009] provided by drifters are particularly useful. Drifters follow the currents and when numerous, they allow for an extensive spatial coverage of the region of interest. They are relatively not very expensive, easily deployable and provide accurate information on their position and other
-environmental parameters [Lumpkin and Pazos, 2007]. 
-Figure~\ref{fig:cnrs} shows the real-time positions of three drifters launched south of Beirut on August 28 2013, in the context of the ALTIFLOAT* project. We observe that unlike the corresponding positions simulated by the geostrophic field (provided by AVISO), the drifters stay within 10-20 km from the coast. The background field shown in that figure is the geostrophic field, averaged over a period of 6 days. The drifters' data render a more precise image of the surface velocity than the altimetric one, because it includes geostrophic and non-geostrophic components; however, this is only possible along the path following their trajectory. These two forms of data are therefore complementary. In this work, we propose a new algorithm that blends geostrophic and drifters data in an optimal way, taking into account the wind effect. The algorithm is then used to estimate the surface velocity field in the 
+of high frequency atmospheric effects at the sea surface. [Caballero]. 
+
+To improve geostrophic velocities, especially near the coast, in situ observations, [Bouffard et al., 2010; Ruiz et al., 2009] provided by drifters, can be considered. Drifters follow the currents and when numerous, they allow for an extensive spatial coverage of the region of interest. They are relatively not very expensive, easily deployable and provide accurate information on their position and other
+environmental parameters [Lumpkin and Pazos, 2007].
+
+To illustrate the information bring by drifters data, Figure~\ref{fig:cnrs} shows the real-time positions of three drifters launched south of Beirut on August 28 2013. These positions can be compared with the position that would have be obtained if the drifters were advected by the altimetric velocity field. We observe that unlike the corresponding positions simulated by the altimetric field (provided by AVISO), the drifters stay within 10-20 km from the coast. The background velocity field shown in that figure is the geostrophic field, averaged over a period of 6 days. The drifters' in situ data render a more precise image of the local surface velocity than the altimetric one; however, this only possible along the path following their trajectory. These types of data are therefore complementary. In this work, we propose a new algorithm that blends these types of data in an optimal way, in order to estimate the surface velocity field in the 
+Eastern Levantine basin, taking into account the wind effect. complementary. In this work, we propose a new algorithm that blends geostrophic and drifters data in an optimal way, taking into account the wind effect. The algorithm is then used to estimate the surface velocity field in the 
 Eastern Levantine basin, in order to shed light on the region between Cyprus and the Syrio-Lebanese coast, which has not been so well studied in the literature before. 
-%" not completely reliable when they However, they can be sparse and heterogeneous in space and time, rendering time averages over a mesoscale global grid fraught with possible sampling bias."
-
 
 
 From the methodological point of view, combining altimetric and drifters data has been done using statistical approaches, with availability of extensive data sets. A common approach is to use regression models to combine geostrophic, wind and drifters components, with the drifters' velocity component being computed from drifters' positions using a pseudo-Lagrangian approach. When large data sets are available, this approach produces an unbiased refinement of the geostrophic circulation maps, with better spatial resolution. [Poulain et al. 2012, Mena et al. 2012, Niller 2003, Centurino 2008, Uchida and Imawaki, 2003 ].
 Another approach relies on variational assimilation, a method classically used in weather predictions [Courtier, Talagrand, etc...]. 
-In the context of blending altimetric and drifters' data, the method was used by [Taillander 2006] (should cite other people as well? look at Taillandier's intro) and it is based on a simple advection model for the drifters' positions, matched to observations via optimisation. The implementation of the method relies on the time-independent approximation of the velocity correction during a time interval shorter than the typical time scale of the mesoscale field. The method is then improved by considering  inertial oscillations superimposed on the mesoscale field. This work 
-led to the development of the LAVA algorithm [Refs],  initially tested and applied to correct model velocity fields using drifter trajectories [Taillandier et al., 2006b, 2008] and later 
+In the context of bending altimetric and drifters' data, the method was used by [Taillander 2006] (should cite other people as well? look at Taillandier's intro) and it is based on a simple advection model for the drifters' positions, matched to observations via optimisation. The implementation of the method relies on the time-independent approximation of the velocity correction during a time interval shorter than the typical time scale of the mesoscale field. 
+%Improvement of the method consists of considering  inertial oscillations superimposed on the mesoscale field. 
+These varationnal technics had
+led to the development of the so called "Lagragian Variationel analysis" (LAVA) ,  initially tested and applied to correct model velocity fields using drifter trajectories [Taillandier et al., 2006b, 2008] and later 
  customised to several other applications such as model assimilation [Chang et al., 2011; Taillandier et al., 2010]. Recently,  [Berta
 et al.] applied it to estimate surface currents in the Gulf of Mexico, where they also added a measure of performance consisting of skill scores, that compare 
 the separation between observed and hindcast trajectories to the observed absolute dispersion.
-
-
-