OSOcean ScienceOSOcean Sci.1812-0792Copernicus PublicationsGöttingen, Germany10.5194/os-14-1069-2018Dense CTD survey versus glider fleet sampling: comparing data assimilation performance in a regional ocean model west of SardiniaDense CTD survey versus glider fleet samplingHernandez-LasherasJaimejhernandez@socib.esMourreBaptisteBalearic Islands Coastal Observing and Forecasting System
– SOCIB, Palma de Mallorca, SpainJaime Hernandez-Lasheras (jhernandez@socib.es)20September20181451069108428March20185April201817August201824August2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://os.copernicus.org/articles/14/1069/2018/os-14-1069-2018.htmlThe full text article is available as a PDF file from https://os.copernicus.org/articles/14/1069/2018/os-14-1069-2018.pdf
The REP14-MED sea trial carried out off the west coast of
Sardinia in June 2014 provided a rich set of observations from both
ship-based conductivity–temperature–depth (CTD) probes and a fleet of underwater gliders. We present the results of
several simulations assimilating data either from CTDs or from different
subsets of glider data, including up to eight vehicles, in addition to
satellite sea level anomalies, surface temperature and Argo profiles. The
Western Mediterranean OPerational forcasting system
(WMOP) regional ocean model is used with a local multi-model ensemble optimal
interpolation scheme to recursively ingest both lower-resolution large-scale
and dense local observations over the whole sea trial duration. Results show
the capacity of the system to ingest both types of data, leading to
improvements in the representation of all assimilated variables. These
improvements persist during the 3-day periods separating two analyses. At
the same time, the system presents some limitations in properly representing
the smaller-scale structures, which are smoothed out by the model error
covariances provided by the ensemble. An evaluation of the forecasts using
independent measurements from shipborne CTDs and a towed ScanFish deployed at
the end of the sea trial shows that the simulations assimilating initial CTD
data reduce the error by 39 % on average with respect to the simulation
without data assimilation. In the glider-data-assimilative experiments, the
forecast error is reduced as the number of vehicles increases. The simulation
assimilating CTDs outperforms the simulations assimilating data from one to
four gliders. A fleet of eight gliders provides similar performance to the
10 km spaced CTD initialization survey in these experiments, with an overall
40 % model error reduction capacity with respect to the simulation without
data assimilation when comparing against independent campaign observations.
Introduction
Short-term regional ocean prediction is important to respond to
maritime emergencies related to search-and-rescue or accidental
contamination, for maritime security or as a support to naval operations.
High-resolution regional ocean circulation models are used to downscale the
conditions provided by operational large-scale models, so as to represent
mesoscale and coastal processes which are not properly resolved in the
large-scale simulations but play a major role in ocean transport of relevance for
practical applications. Data assimilation (DA), which aims at optimally
combining dynamical ocean models with in situ and remotely sensed
observations, constitutes an essential component of the prediction systems
since it helps to recursively improve the initial conditions used for the
prediction phases.
In order to constrain errors and remain as close as possible to reality,
models must be fed with different kinds of observations. Satellites play a
key role, providing regular near-real-time data of surface variables such as
temperature and sea surface height. Water column measurements are more
scarce. The Argo program provides routine temperature and salinity profiles
at regular intervals, but the distance between floats is insufficient to
monitor the mesoscale and finer-scale variability .
Dedicated campaigns providing underwater measurements from ship data or
glider measurements provide complementary data over specific areas. Efficient
DA systems should be able to advantageously combine large-scale observations
over a large domain with more dense, high-resolution observations in specific
areas. Traditionally, campaigns aboard research vessels (RVs) have been
carried out to collect dense conductivity–temperature–depth (CTD) data to initialize regional ocean prediction
systems. However, campaigns are not always possible. They depend on ship
availability, weather and access to the area of interest, and they remain very
expensive. Recent evolutions in technology allow to deploy autonomous
underwater vehicles (AUVs) such as gliders to collect dense hydrographic data
over specific areas of interest
. Gliders are able to
operate under hard maritime situations and to reach difficult access areas,
with an overall cost reduced compared to traditional ship campaigns. Glider
missions are typically planned to reach a series of locations commonly called
waypoints, in order to track areas of interest and adapt to safety conditions
. Their controllability also permits adaptive sampling
procedures, changing their route along the mission with the objective to
collect data at optimal locations to maximize their information content
(e.g., ).
The potential of gliders to sample fine-scale processes and to identify
different water masses has been demonstrated ,
as well as their capability to improve ocean model predictions via DA (e.g.,
). The question arises
whether the sampling offered by a fleet of several gliders is as useful as a
traditional ship-based CTD survey for regional ocean forecasting
applications. This is the question we are addressing in this paper.
The rich dataset collected during the REP14-MED campaign is used for this
purpose. REP14-MED took place in June 2014 off the western coast of
Sardinia . Two RVs tracked in parallel a 100×100 km2 area during a 20-day period, providing dense CTD sampling with a 10 km
separation and continuous towed CTD measurements for limited periods of time.
In addition, a fleet of 11 gliders was deployed performing back-and-forth
sections perpendicular to the coast with a 10 km vehicle intertrack distance.
The Sardinian Sea is a region of the so-called Algero-Provençal basin of the
western Mediterranean Sea. In this region, the surface layer is characterized
by a water mass of Atlantic origin and a strong mesoscale activity. The
region is one of the most dynamic areas of the entire Mediterranean Sea
, since it is strongly influenced by
instabilities of the Algerian current. This generates intense anticyclonic
eddies which can propagate northward towards the western Sardinian coast
. Such eddies can last from
weeks to months. They are responsible for an intense mesoscale activity in
the study region . A southward current flowing along
the southern part of the Sardinian coast has also been evidenced in long-term
numerical studies and in field campaigns contributing to
episodically wind-induced advection of coastal water . At
depth, eddies are also generated from the interaction between the Algerian
Gyre and inflows of Levantine Intermediate Water (LIW) and Tyrrhenian Deep
Water (TDW) coming from the Sardinia Channel .
The assimilation system used in this work follows an EnOI (ensemble optimal
interpolation) scheme . This method provides a
cost-effective approach when compared with more advanced methods such as EnKF
(ensemble Kalman filter) or 4D-Var , which is suitable for
operational implementations in regional ocean models. EnOI is a three-dimensional
sequential DA method. A stationary ensemble of model simulations is used to
calculate background covariances. Contrary to the EnKF, which requires
evolving an ensemble of simulations, a single model integration is only
necessary between two analysis steps in the EnOI, making the method
numerically efficient. The EnOI provides a suboptimal solution compared to
the EnKF . However, it represents a good alternative
allowing to use a large ensemble size together with localization when
necessary . In this work, a local multi-model EnOI scheme is
implemented. “Multi-model” represents the fact that the library of ocean
states is built using different long-term model simulations. “Local” means
that the EnOI analysis comprises some domain localization to reduce the
impact of potential significant covariances associated with remote
observations.
The paper is organized as follows: Sect. 2 presents the observing and
modeling frameworks, as well as the specific forecast experiment. Section 3
details the results, which are further discussed in Sect. 4. Finally,
Sect. 5 concludes the paper.
Data and methods
For this study, several simulations were produced assimilating different
datasets from the REP14-MED campaign. This section describes the model and
data used and the methodology followed in this work.
REP14-MED experiment
The REP14-MED sea trial was conducted in the framework of
the EKOE (environmental knowledge and operational effectiveness)
research program of the Centre for Maritime Research and Experimentation
(CMRE, Science and Technology Organization – NATO). It is part of a series of
sea trials dedicated to rapid environmental assessment (REA),
denoted by the acronym REP (recognized environmental picture). Led
by CMRE and supported by 20 partners, the trial took place for 20 days in
June 2014, with RVs Alliance and Planet conducting a joint
survey over an approximately 100×100 km2 area off the west coast of
Sardinia (Fig. ). A massive amount of data was
collected during the campaign with various oceanographic instruments,
including CTD stations, towed ScanFish and CTD chain, ship-mounted acoustic Doppler current profiler (ADCP),
shallow and deep underwater gliders, moorings, surface drifters and profiling
floats. The sampling was divided into three legs. The time distribution of
the collection of observations used in the present work is illustrated in
Fig. .
Sea surface height annual mean for the year 2014 and corresponding
surface geostrophic currents from the Western Mediterranean OPerational
forcasting system (WMOP) model. The REP14-MED sea trial area is
highlighted in red.
During Leg 1, both RVs conducted a parallel sampling of the target area,
collecting CTD data with a 10 km distance between stations over a 5-day
period. During Leg 3, CTD data were collected with the same density, yet over
a reduced spatial extension, providing very valuable data to validate the
forecast experiments. CTD casts reached down to 1000 m deep when possible. A
few profiles even get deeper in order to characterize deep water masses.
Additional towed ScanFish measurements of temperature and salinity down to
200 m depth allowed to complete the characterization of the area during Leg 3.
At the same time, and during the whole duration of the campaign, eight
gliders were considered, traveling continuously along back-and-forth
transects perpendicularly to the Sardinian coast. Five of these gliders were
deep gliders submerging to depths down to 800 m; the remaining three were
shallow water platforms collecting data in the upper 200 m only. Each of
these single transects was completed in about 3 days for each way. Notice
that three additional gliders were deployed during the sea trial, but due to
technical problems, duplication of the track and lack of processed data, they
were discarded here. All glider tracks are approximately parallel to each
other, with an intertrack distance around 10 km, thus covering the target
area. Figure shows the position of CTD, glider and
ScanFish data during Legs 1 and 3 of the sea trial.
Model
The model used in this work is the WMOP () model, covering a domain extending from Strait of Gibraltar
to the Sardinia Channel. WMOP is based on ROMS , a
three-dimensional free-surface, sigma-coordinate, primitive equations model
using split-explicit time stepping with Boussinesq and hydrostatic
approximations. WMOP is set up with 32 vertical levels and a 2 km spatial
resolution. It is forced at the surface using the 3-hourly and 5 km
resolution HIRLAM atmospheric model provided by the Spanish meteorological
agency (AEMET). In this work, the initial state of the forecast experiments
is provided by the simulation fields on 1 June 2014 of a 7-year-long
free-run WMOP simulation spanning the period 2009–2015. This simulation uses
initial state and boundary conditions from the Copernicus Marine Environment
Monitoring Service (CMEMS) Mediterranean (MED) reanalysis
. In addition, several WMOP free-run hindcast simulations were
generated, including modifications of the parent model used as initial and
boundary conditions and some model parameters. These different simulations
provide the library of ocean states used by the DA system.
The model has been evaluated using satellite and in situ observations
(). The mean circulation of the free-run
simulation over the year 2014 is illustrated in
Fig. ). It is found to properly represent the mean
surface geostrophic circulation over the basin, in particular the main
features which are the Alboran gyres, the Algerian Current along the African
coast and its associated instabilities, the Northern Current along the French
and Spanish coasts and the Balearic Current flowing northeastwards north of
the Balearic Islands. Close to Sardinia, the mean circulation in the model is
characterized by a southeastward flow centered around
40∘ N, which separates
into two branches flowing northward and southward, respectively, when
approaching the Sardinian coast, also giving rise to small eddies in the
REP14-MED area. This is in agreement with both the historically established
regional surface circulation and the more recent average
estimates provided by the mean dynamic topography .
Moreover, we illustrate here the sea surface temperature (SST) maps derived
from the model and satellite data at the beginning of the REP14 period
(Fig. ). The model is found to properly represent
the large-scale spatial variability of the SST, with lower temperatures around
the Gulf of Lion, warmer waters in the Algerian basin south of the Balearic
Islands and some lower temperature inflow of Atlantic Water from the Strait
of Gibraltar. Finer details associated with mesoscale eddies and filaments do
not generally coincide between the free-run model and observations. In
particular, local differences are found in the REP14-MED area, with the model
slightly overestimating surface temperatures due to an apparent more
pronounced advection of higher temperature waters from the southwest.
(a) Sampling schedule in June 2014 of the REP14-MED
sea trial data employed in the present work. The spatial distribution of
observations is illustrated in the two bottom panels. CTDs from Leg 1 (red)
and gliders (black) observations used for assimilation are shown in
panel (b). Independent CTDs (red) and ScanFish (blue) gathered during Leg
3 and used for the validation are shown in panel (c). The color bar
indicates depth (m) and the white contour indicates the 200 m isobath.
SST for 31 May 2014 from (a) Group for High Resolution
Sea Surface Temperature (GHRSST) Jet Propulsion Laboratory (JPL) Multiscale Ultrahigh Resolution (MUR)
satellite-derived product and (b) the free-run WMOP model.
Data assimilation system
The WMOP DA system is based on a local multi-model ensemble optimal
interpolation (EnOI) scheme. It consists of a sequence of analyses (model
updates given a set of observations) and model forward simulations. During
the analysis step, the state vector xa is updated according to
Eq. (), where xf is the background model state vector,
H is the linear observation operator projecting the model state onto
the observation space, and K̃ is the Kalman gain estimated
from the sample covariances (Eq. ). y is the vector
of observations. Matrices P̃f and R are the
error covariance matrices of the model and the observations, respectively.
xa=xf+K̃(y-Hxf)K̃=P̃fHT(HP̃fHT+R)-1
P̃f contains the background error covariances (BECs)
estimated by sampling three long-run simulations of the WMOP system with
different initial/boundary forcing (coming from CMEMS MED and GLOBAL models)
and momentum diffusion parameters. More concretely, for each analysis, a
80-realization ensemble is generated to calculate the BECs. Ensemble
realizations are multivariate model fields sampled from the three simulations
during the same season, with a time window of 90 days centered on the
analysis date. The seasonal cycle is removed from the ensemble anomalies to
discard the corresponding large-scale correlations mainly affecting
temperature. Following this procedure, the computed BECs reflect the spatial
variability and anisotropy of the ocean mesoscale circulation. They also
represent dynamically consistent covariances between different model
variables and depths. Moreover, a domain localization strategy
is used to dampen the impact of remote observations. A 200 km localization radius is used, determined by both the size of mesoscale
structures and the approximate distance between two Argo platforms in the
western Mediterranean basin. Here, the domain localization consists of
computing independent analyses for each water column of the WMOP domain,
considering only the observations located within a 200 km radius. It allows
to locally dampen the impact of remote observations in the presence of
spurious long-range correlations. The code used in this study is written in C
and is an adaptation of the EnKF version used in and . It was also previously used during
the Alborex experiment carried out in the Alboran Sea .
In the EnOI, as in any other sequential DA scheme, special care needs to be
brought to the model initialization after analysis updates .
When restarting the simulation from an analysis field, the multivariate
initial fields may be violating some physical constrains, such as mass
conservation. The model response to balance this state may generate some
spurious waves and introduce noise into the system. To minimize such effects,
a nudging strategy has been implemented to restart the model after the
analysis. In concrete terms, after an analysis is computed, the model is
restarted 24 h before the analysis date, applying a strong nudging term in
the temperature, salinity and sea level equations towards analyzed values.
The timescale associated with this nudging term is 1 day. The nudging
procedure reduces the model correction but guarantees updated multivariate
fields closer to the model equation balances, which limits instabilities.
The assimilation system implemented here uses a 3-day cycle (Fig. ),
which was determined according to the time spent by
the gliders to complete one zonal transect. All measurements collected during
these 3 days are considered synoptic in the DA process. Altimeter sea
level anomalies (SLAs), SST and Argo temperature and salinity profiles are
assimilated over the whole WMOP domain. For each analysis, a 5-day time
window in the past is defined to select Argo observations. This window
corresponds to the interval between two profiles provided by a single
platform. This ensures that every model point is bounded by at least one Argo
profile within the 200 km localization radius during each analysis.
Concerning altimetry, the last 72 h CMEMS along-track reprocessed filtered
sea level observations are considered for the analysis. The SST field is
given by the daily L4 near-real-time Group for High Resolution
Sea Surface Temperature (GHRSST) Jet Propulsion Laboratory (JPL) Multiscale Ultrahigh Resolution (MUR) satellite-derived
interpolated product
(https://podaac.jpl.nasa.gov/dataset/JPL-L4UHfnd-GLOB-MUR, last access: 28 August 2018).
The last available field before analysis is considered. The
original 1 km resolution data are smoothed and interpolated onto a 10 km
resolution grid to limit the number of observations considered for each
analysis. The selected resolution is considered to be sufficient to represent
the main circulation features and mesoscale structures present in this SST
product, permitting at the same time an affordable computational cost.
The glider profiles are considered as vertical. The corresponding
observations are binned vertically and a single value is given for each model
grid cell. The representation error is the addition of vertical and
horizontal components. For each vertical level, the observed variance in the
vertical grid cell is used as an approximation of the vertical representation
error. In addition, the horizontal representation error variance is assumed
to be 0.0625 K2 and 0.0025 for temperature and salinity
measurements, respectively. CTD observations are binned vertically in a
similar way before assimilation, considering the representation error in an
analogous way.
Scheme of the 3-day DA cycles.
Figure illustrates the innovations (differences
between the observations and the background model) computed for the first
analysis carried out on 31 May. It shows the absence of significant biases in
the model prior to DA, which is a prerequisite for an effective assimilation
of the observations. Moreover, the magnitude of the standard deviation of
innovations of the surface variables (0.42 K for SST and 0.042 m for SLA)
properly matches that of the observation error (0.56 K for SST and 0.036 m for SLA) and the ensemble spread (1.10 K for SST and 0.056 m for SLA for the
analysis on 31 May), which guarantees the necessary overlap between the
probability density functions of model and data.
(a) SST misfits between the observations and the free-run model on
31 May 2014. (b) Histograms of the innovations for the different sources
of observation ingested by the assimilation system. The corresponding mean
and standard deviation are provided in each panel.
Experiments
In addition to the background simulation without any DA (hereafter
NO_ASSIM), seven simulations were produced spanning the period 1–24 June 2014, assimilating different sets of observations. The first simulation (GNR)
assimilated generic observations from satellite along-track SLA, satellite
SST and Argo temperature and salinity profiles. The second simulation
(GNR_CTD) assimilated these generic observations plus all CTD temperature
and salinity profiles collected during Leg 1. The five remaining simulations
assimilated the generic observations plus glider temperature and salinity
data from one to eight vehicles (GNR_1G, GNR_2G, GNR_3G, GNR_4G,
GNR_8G), selected among the available platforms to optimally cover the area
of interest. For example, GNR_1G considers the glider which travels in the
center part of the domain, GNR_2G selects the two gliders which divide the
study region in three areas of similar dimensions, etc. The different sets
of vehicles selected for these simulations are illustrated in Fig. .
The whole timeline of the numerical experiments is described in
Fig. . A spin-up period of 9 days was imposed for all
these data-assimilative simulations, during which only the generic
observations were assimilated. As explained previously, the 3-day
assimilation cycle implemented in this study corresponds to the time spent by
a glider to complete a zonal transect. In the case of the CTDs, as the
duration of the data collection in Leg 1 was 6 days, the data were
assimilated during two cycles.
Illustration of the different sets of gliders selected in the
different data-assimilative experiments. The positions of all glider
measurements are shown in black dots. The red zonal lines indicate the
selected glider tracks in each of the experiments. The name of the
corresponding simulation is specified in each panel.
Timeline of the seven data-assimilative simulations. The analyses dates are highlighted in color, indicating the assimilated datasets.
After the last analysis, both Leg 3 CTDs and ScanFish temperature and
salinity measurements are used as independent observations to evaluate the
performance of the simulations.
Results
We evaluate in this section the performance of the DA following three
successive steps. We first verify that the data from the different sources
are properly ingested in the system over the whole modeling area both during
the spin-up period and subsequent assimilation phase. Then, we examine the
impact of the assimilation of the local and dense observations datasets onto
the temperature, salinity and density fields in the REP14-MED area. Finally,
we assess the performance of the simulations against independent data from
CTDs and ScanFish observations collected during Leg 3.
Data ingestion and performance over the whole modeling area
We first assess here the performance of the assimilation during the spin-up
period by analyzing the evolution of the root mean square difference (RMSD)
between the model simulations and satellite SLA, SST observations and Argo
profiles. For each source and variable, the RMSD is calculated as expressed
in Eq. () below, where oi and mi take the values of
the observations and their model equivalents, respectively. n is the number
of observations. To better highlight relative simulation improvements, the
RMSD for each specific day is normalized by dividing the RMSD by that of the
simulation without any DA for that specific day. A reduction of the
normalized RMSD indicates that the analyzed field is closer to the
observations than the background field without assimilation.
The normalized RMSD is computed every day from 1 to 9 June. For the days
including an analysis, the observations assimilated during this analysis are
used to compute the normalized RMSD. This includes the different assimilation
windows (5 days for Argo, 3 days for SLA and 1 day for SST in
particular). For the remaining days, we consider the observations that the
system would have ingested if we had performed the analysis on that date, so
considering similar time windows. Model equivalents to the observations are
obtained through linear interpolation in space of the average daily model
fields onto the position of the measurements.
The results are presented in Fig. . They show a
satisfactory and continuous reduction of the RMSD for all the sources of data
and variables, indicating a good system performance. The normalized RMSD is
significantly reduced during the first analysis (between 20 % and 60 %
depending on the analyzed variable); it then tends to slightly increase until
the next analysis 3 days later, which reduces it again in most of the
cases. In some occurrences, the RMSD continues decreasing during 2 days
after the analysis. The overall persistence of the correction between two
assimilation dates is especially remarkable. It reveals the general proper
performance of the assimilation system, which is able to recursively correct
the multivariate fields without introducing spurious structures and
instabilities which would significantly alter the system.
Evolution of the normalized RMSD against observations for the spin-up simulation.
After this verification of the overall satisfactory performance of the DA
during the spin-up period in terms of RMSD, the same kind of assessment was
performed for the seven subsequent simulations, the GNR control simulation
and the ones assimilating either CTDs or glider observations during the field
experiment besides the generic observations. We only show here results from
the GNR_CTD simulation (Fig. ), since the behavior is
very similar for the rest of the simulations. The system still properly
reduces the normalized RMSD in terms of SST, SLA and T-S profiles at Argo
locations, with similar reductions to those observed during the spin-up period
(from 20 % to 60 % of error reduction).
An important aspect in this comparison is that the assimilation of
high-resolution T-S profiles data in the REP14-MED area does not negatively
affect the overall performance of the system over the whole modeling area.
This could happen through the generation of spurious structures in the
densely observed area which could then propagate over the domain. Moreover,
the reduction of the normalized RMSD with respect to CTD observations shows
that the local observations have also been properly ingested in the system.
Notice that the relatively larger SLA RMSD found during the period 10–23 June
compared to the spin-up period also affects the GNR simulation. Therefore, it
is not due to the incorporation of CTD observations but rather related to
the natural evolution of SLA errors.
Evolution of the normalized RMSD against observations for the GNR_CTD simulation.
Temperature, salinity and density fields in the REP14-MED area
To complement these statistical diagnostics based on the normalized RMSD, we
analyze here the temperature and salinity fields in the REP14-MED trial area
on 13 June. This corresponds to the first day after the second analysis of
CTD and glider DA cycles. At that time, either all CTD data from Leg 1 or
one back-and-forth transect from the gliders have been introduced into the
system. Figure shows temperature, salinity and
potential density daily average fields at 50 m depth for four of the
simulations (NO_ASSIM, GNR, GNR_CTD and GNR_8G) on 13 June. The
temperature and salinity data assimilated until that date are also
represented as colored dots on the panels corresponding to GNR_CTD and
GNR_8G.
As illustrated by the potential density maps, two different water masses are
represented in the NO_ASSIM simulation. While the northern part of the
domain is mostly occupied by a denser water mass with a salinity over 38,
lower density waters are relatively fresher, and higher
temperature characteristics are found in the southern part of the REP14-MED
domain. The GNR simulation redistributes these water masses over the domain,
representing patches of denser water in the central, southwestern,
northwestern and northern coastal parts of the domain. These patches,
characterized by a higher temperature and salinity, are associated with
cyclonic circulations.
Model temperature (a), salinity (b) and potential
density and currents (c) at 50 m depth on 13 June. From left to
right: simulations NO_ASSIM, GNR, GNR_CTD and GNR_8G. The assimilated data
are superimposed as colored dots in the temperature and salinity panels for
the two simulations (GNR_CTD and GNR_8G).
The additional assimilation of dense local data from CTDs and gliders further
modulates these patterns, producing smaller-scale patches and filaments. Two
main high temperature anomalies are detected in both CTD and glider
observations at 50 m depth, associated with relatively small salty anomalies.
The strongest one, located around 39.3∘ N, 7.7∘ E, is somehow
represented in both simulations (GNR_CTD and GNR_8G), with a more pronounced
signature in GNR_8G. Notice that this signature is not fully coincident with
the observed location displayed in Fig. due to
the evolution of the model from the first analysis on 10 June to the time of
the plot 3 days later. The second relatively high temperature patch, found
around 40∘ N, 7.5∘ E, is less marked that the first one. Again, it
is somehow better reproduced in GNR_8G than GNR_CTD. The observations, from
both CTDs and gliders, are characterized by an energetic small-scale
variability, which translates into small scales and filamented structures in
the model after DA. Notice also the improvements in the relatively higher
salinity along the coast after assimilation of the measurements from the CTDs
and the gliders. The relatively high salinity patch around 39.5∘ N seen
in the GNR simulation is strongly attenuated in both GNR_CTD and GNR_8G.
The density fields of GNR_8G exhibit two areas of lower potential density
associated with these two anomalies, in good qualitative agrement with the
observations even if the magnitude of the gradients is reduced compared to
the measurements. These density anomalies are less clear in GNR_CTD. Both
simulations show denser water on the northeastern part of the domain and
similar overall circulation patterns which significantly differ from
NO_ASSIM and have also marked differences with GNR. A common property
observed in both simulations is the current flowing northeastwards in the
central part of the sampled area and bifurcating near the coast, with one
branch directed southwards and other northwards, giving also rise to cyclonic
and anticyclonic eddies with dimensions around 30–40 km.
(a) Potential density field reconstruction from ScanFish and CTD data
collected between 20 and 23 June (kgm-3). (b–h) Potential density (kgm-3) and model currents
at 50 m depth on 22 June for the seven simulations (NO_ASSIM, GNR, GNR_1G, GNR_2G, GNR_4G, GNR_8G and GNR_CTD).
Performance assessment using independent data during Leg 3
As a third step, we analyze here the realism of the simulations during Leg 3
using independent observations which have not been assimilated in the
experiments. More specifically, we compared the model outputs on 22 June
(after all assimilation cycles have been completed) with CTD and ScanFish
temperature and salinity observations collected between 20 and 23 June. A
qualitative analysis is first performed, based on the potential density
fields reconstructed from both CTD and ScanFish observations at 50 m depth.
The DIVA software (Data-Interpolating Variational Analysis;
) and its web interface
(http://ec.oceanbrowser.net/emodnet/diva.html, last access: 28 August 2018) have been used to generate the
interpolated density field. Figure compares the density fields at 50 m depth from
the different simulations with this density field derived from the
observations. Model currents at that depth are shown as well as the CTD and
ScanFish observations.
The main features represented in the reconstructed density field derived from
the observations include a marked negative density anomaly, centered around
39.4∘ N, 7.8∘ E, with a spatial extension around 40 km, a coastal
fringe with relatively denser waters and a second patch of denser water
between 39.5 and 40∘ N on the western side of the domain.
These features were somehow already present during Leg 1 (see Fig. and Sect. 3.2).
All the data-assimilative simulations represent the denser coastal fringe and
the associated southward flow, yet with different characteristics. It extends
offshore, associated with a cyclonic eddy, in GNR, GNR_CTD, GNR_1G and
GNR_2G. GNR_4G and GNR_8G qualitatively provide a more accurate shape of
this coastal feature. In addition, these two simulations better represent the
secondary relatively denser patch on the western side. Lower density
anomalies south of 39.5∘ N are also present in all the simulations.
GNR, GNR_CTD and GNR_1G seem to qualitatively better match the
reconstructed field by representing an anticyclonic eddy around a local
density minimum, with an approximate 40 km diameter. However, the exact shape
of this anomaly, and in particular its meridional extension, was not properly
observed during Leg 3, which only provided a single ScanFish zonal section at
39.4∘ N across this anomaly. While it is represented as a close eddy in
the reconstructed field due to the interpolation method, the more elongated
shape in the meridional direction provided by GNR_4G and GNR_8G is also
consistent with the ScanFish observations. Notice that the data-assimilative
simulations all qualitatively improve the solution without DA. Among them,
GNR_4G and GNR_8G provide a particularly remarkable pattern agreement with
the ScanFish and CTD observations.
To quantify the improvement, we now present the normalized RMSD, both
considering the type of large-scale observations which were assimilated over
the whole domain and the independent sea trial observations used in this
section. We computed the normalized RMSD for each of the seven
data-assimilative simulations on 22 June and for the different sources of
observation (Fig. ). CTD and ScanFish observations
between 20 and 23 June were considered synoptic for this purpose.
Normalized RMSD against observations on 22 June for the seven
numerical simulations. Dashed bounding boxes delimitate on the one side the
observations assimilated over the whole domain and on the other side the
independent campaign observations within the REP14-MED domain.
As already described in Sect. 3.1, the generic assimilation (SLA
along-track, SST and Argo) provides similar results over the whole domain to
that obtained during the spin-up period, when no dense profile data are
assimilated in the REP14-MED domain. It reduces significantly the RMSD
compared to the NO_ASSIM simulation. Moreover, it also allows to reduce by
around 10 % the RMSD against independent CTD observations both in temperature
and salinity. While it improves the comparison with ScanFish temperature
observations, it slightly degrades salinity comparisons.
The ingestion of high-resolution local data from the REP14-MED campaign
further reduces the RMSD with SST, SLA and Argo computed over the whole
domain, with similar results when assimilating observations from CTDs and
gliders (with the exception of the SLA which is not improved when a single
glider is assimilated). In the REP14-MED domain, the assimilation of CTDs
allows a reduction of the RMSD against independent observations between 30 %
and 40 %, both in temperature and salinity, with respect to the simulation
without any DA. The assimilation of glider observations also reduces the
RMSD, with an overall enhanced performance as the number of platforms
increases. The comparison with different platforms and variables provides
slightly different rankings of the simulations. For instance, in this
comparison, GNR_1G provides similar performance to GNR_CTD against
independent CTD temperature data but a worse performance against CTD
salinity and ScanFish measurements. The assimilation of data from four
gliders improves the performance with respect to the assimilation of CTDs
when comparing to ScanFish salinity data, but the performance is worse when
comparing to the other sources. GNR_CTD provides the best RMSD reduction
when comparing to CTD salinity and ScanFish temperature, but it is GNR_8G
which shows the best performance when considering CTD temperature and
ScanFish salinity data. These variations are probably due to the specific
spatial sampling of the CTDs and ScanFish (see Fig. ) combined with the high spatial oceanic variability in
the area.
An average RMSD reduction number is obtained here by computing the square
root of the average normalized mean square difference over the four
comparisons (CTD and ScanFish temperature and salinity) in the REP14-MED
domain. These synthetic average normalized RMSD scores are presented in
Fig. . This average RMSD gives scores of
39 % and 40 % of error reduction for the GNR_CTD and GNR_8G simulations,
respectively. According to this overall metric, the CTD survey is more
performant than a sampling using four gliders and shows very close
performance to that obtained with eight gliders. Notice that the average
normalized RMSD illustrates the progressive gain obtained when increasing the
number of gliders considered in these experiments.
Average normalized RMSD against independent observations in the REP14-MED areas on 22 June for the seven numerical simulations.
Discussion
EnOI temperature ensemble correlations for a temperature observation
within the REP14-MED domain at 50 m depth (position indicated by the white
dot on panel b). (a) Correlations along the vertical.
(b) Horizontal correlations at 50 m depth.
The assimilation of observations is crucial to improve forecasts. Regional
forecasting systems should be able to efficiently combine high-resolution
local profile data and larger-scale satellite observations over an extended
modeling domain. The recursive ensemble optimal interpolation scheme
employed in this study is shown to be able to ingest both types of data and
to systematically reduce the errors when compared to a control free-run
simulation. Even if the EnOI is theoretically inferior to more advanced DA
schemes such as the ensemble Kalman filter or 4D-Var, its numerical
efficiency makes it a good compromise for operational and practical
implementations with high-resolution models.
The domain localization approach, which does not take into account the
observations further than a given radius to correct the field at a given
location, guarantees that the assimilation of dense profile observations from
gliders and CTDs over a reduced area does not degrade the results over the
whole modeling domain. Moreover, it allows to reduce the RMSD and to improve
the representation of local water masses and the associated circulation in
the reduced REP14-MED area which has dimensions around 100 km. The
corrections introduced by the assimilation of CTD data during Leg 1 are found
to remain in time, providing a very positive and significant error reduction
when comparing to independent measurements 10 days after the initial CTD data
collection.
As a limitation, we notice that the oceanographic structures of small
horizontal and vertical dimensions, which have a strong signature in the
dense observation datasets, are only approximately represented in the
temperature and salinity fields just after assimilation, as shown in
Fig. . This is the case, for instance, for the strong
positive temperature anomaly around 39.3∘ N, 7.7∘ E, which is the
signature of an eddy with an horizontal diameter around 40 km and a vertical
dimension around 50 m. We attribute this limitation on the one hand to the
smoothing effect of the background error covariances, which impact both
along the horizontal and vertical directions, and on the other hand to the
nudging initialization procedure, which attenuates the model correction with
the aim to provide more dynamically consistent fields. To illustrate the
error covariances of our EnOI implementation, Fig.
shows both the horizontal and vertical model ensemble correlations generated
from the ensemble for the analysis on 22 June.
A spatial smoothing takes place during the assimilation, affecting the area
with significant correlations with the observed locations. The ensemble
correlation distances are found to be around 100 m in the vertical and
75 km in the horizontal, being then larger than the smaller-scale structures
observed in the CTD and glider surveys. Two-step assimilation strategies
separating long- and short-distance correlation scales might allow to improve
the representation of these finer patterns in more sophisticated DA systems
(e.g., ).
The second factor explaining this limitation is related to the nudging
initialization strategy, which has the advantage of limiting undesired model
shocks after the analysis but also attenuates the corrections and therefore
the agreement with observations. The simple nudging technique used in this
work is easy to implement and cost-effective. It could be improved in the
future by considering more advanced approaches (e.g., ).
In spite of these limitations, the EnOI scheme implemented in this study is
shown to be able to properly ingest the multi-scale observations, which leads
to improved representations of the mesoscale structures in the REP14-MED area
and enhanced forecasting skills persisting several cycles after the
assimilation of the dense observations.
While CTDs allow a relatively fast comprehensive description of a specific
study area, gliders provide a slower sampling but also allow a repetition of
specific monitoring tracks over a longer period. In this study, the CTD
initialization survey results in a similar forecast performance after DA in
terms of RMSD reduction as an eight-glider continuous monitoring of the area
flying along predefined paths with regular spacing. It should be highlighted
that the meridional spacing in the case of the eight-glider fleet is the same as
for the CTD casts (∼10 km). The improvement provided by the higher spatial
resolution offered by gliders in the zonal direction might be limited by the
spatial resolution of the model, which does not allow to ingest the very
fine-scale features observed by the gliders. In that sense, it is likely that
glider DA would further benefit from an increase of the model resolution. It
should be mentioned that while glider platforms are considered autonomous,
their operation still implies a very significant effort in terms of platform
deployment, recovery, piloting and maintenance. The models could also highly
benefit from the near-real-time controllability of gliders, allowing to
continuously adjust their path along optimal routes in the study area. In
this framework, efficient adaptive sampling procedures should theoretically
allow to use a reduced number of gliders to reach the same level of
performance and lead to a better description of
specific targeted features, as long as their representation is permitted by
the model resolution. The definition of optimal collective behaviors based,
for instance, on glider fleet coordination or cooperation (e.g.,
) also constitutes an interesting field of research in that
direction.
Conclusions
We presented in this work the results of several simulations assimilating
different multi-platform observations in the context of the REP14-MED sea
trial carried out in June 2014 off the west coast of Sardinia. The
experiments were designed to assimilate intensive campaign data from CTDs and
gliders, along with satellite SST and SLA, as well as Argo profile
observations over the whole model domain covering the western Mediterranean
Sea from Gibraltar to the Sardinia Channel. The objective was to explore the
performance of different sampling strategies based on either a dense CTD
initialization survey or a glider fleet sampling, in improving model
forecasting capabilities in a specific area.
The DA system was shown to perform correctly. The local multi-model EnOI
scheme, following 3-day recursive cycles with a 1-day nudging
initialization phase after analysis, allows to properly ingest both
large-scale data over the whole western Mediterranean domain and high density
temperature and salinity profiles collected during the sampling experiment
over a limited area. In spite of the limitations associated with the
smoothing effect of ensemble covariances, which do not allow to exactly
represent the smaller-scale features present in the observations, this system
enables a significant improvement of the forecasting skill of the model with
respect to the simulation without assimilation, and that assimilating only
satellite and Argo data. Its reduced cost makes it a good option for
operational implementations.
While the assimilation of generic observations from SST, SLA and Argo leads
to an average error reduction of 15 % when comparing to independent
measurements collected during Leg 3 of the sea trial in the REP14-MED area,
the assimilation of glider and CTD data allows an additional significant
improvement. Gliders, which provide a continuous sampling of the area along
regularly spaced zonal tracks, allow to reduce the forecast error as the
number of platforms increases. The consideration of one glider leads to a
24 % average error reduction with respect to the simulation without
assimilation. This percentage increases to 28 %, 33 %, 35 % and finally 40 %
for the two-glider, three-glider, four-glider and eight-glider fleet
configurations, respectively. Incrementing the number of gliders results in a
better representation of the ocean state captured by observations, with a
most accurate representation of the mesoscale structures and associated
circulation.
The assimilation of the observations from the dense initialization survey
based on 10 km spaced CTD stations leads to an average error reduction of
39 %: it outperforms the four-glider configuration and provides very similar
results in terms of RMSD to the eight-glider fleet configuration. The
10 km spacing offered by both sampling strategies is essential here to improve the
representation of the mesoscale variability in the study area. In view of
these results, gliders certainly provide a very interesting alternative to
traditional CTD surveys used to initialize high-resolution regional ocean
models, provided that a fleet of vehicles can be deployed at sea. Moreover,
an increased performance can certainly still be expected by optimizing the
regular track sampling carried out in this experiment through adaptive
sampling procedures.
All data from the REP14-MED experiment are available on the
CMRE ftp server at ftp://ftp.cmre.nato.int/. Requests for access may be
directed at registry@cmre.nato.int or pao@cmre.nato.in. Simulations are
archived on the SOCIB server and are available upon request to info@socib.es.
Both authors contributed to the numerical experimental
set-up, generation of the simulations, analysis and writing of the paper.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “REP14-MED: A Glider
Fleet Experiment in a Limited Marine Area”. It is not associated with a
conference.
Acknowledgements
The authors especially thank Reiner Onken for leading the experiment. They
also acknowledge all the partners, scientists and technicians having
participated to the REP14-MED sea trial, allowing the collection and
processing of this very valuable dataset. They especially thank Ines Borrione
and Michaela Knoll for their help in providing and interpreting ADCP data.
This work uses CMEMS products from the Mediterranean – Monitoring
Forecasting Centre (MED-MFC) which produces the Mediterranean Forecasting
System. The HIRLAM atmospheric fields used to force the ocean forecasts are
provided by the Spanish National Meteorological Agency. SOCIB DA system
developments were partly supported by the Medclic project funded by La Caixa
foundation and the JERICO-NEXT European project.
Edited by: Reiner Onken
Reviewed by: two anonymous referees
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