Assessment of Skill for Coastal Ocean Transients
ASCOT-02
Tyrrhenian Sea/Ligurian Sea/Elba/Procchio Bay
May 2002
An Experiment for Ocean Coastal Prediction and
NATO Rapid Environmental Assessment Skills Evaluation
A.R. Robinson, W. G. Leslie, R. Onken, P.J. Haley, Jr.
Harvard University
NATO SACLANT Undersea Research Centre
DRAFT
January 2002
Table of Contents
1. Introduction
2. Goals and Objectives
3. Geographic and Oceanographic Context
4. Forecast Methodology
5. Nested Modeling Domains
6. Schedule and Sample Tracks
7. Adaptive Sampling
8. Distributed Systems and Logistics
Sections of the document pertaining to operations are highlighted above in red.
1. Introduction
Coastal Predictive Skill Experimentation (CPSE) measures the ability of a forecast system to
combine model results and observations in coastal domains or regimes and to accurately define
the present state and predict the future state. Rapid Environmental Assessment (REA) is defined
in the military environment as "the acquisition, compilation and release of tactically relevant
environmental information in a tactically relevant time frame". Ocean forecasting is essential for
effective and efficient REA operations. A REA CPSE must be designed to determine forecast
skill on the basis of minimal and covertly attainable observations and thus may be most
efficiently carried out in the context of the definitive over-sampling provided by a CPSE.
Environmental observations are a necessity for initialization and updating of ocean forecasts.
Numerical ocean forecast capabilities in general consist of observational networks, data
assimilation schemes and dynamic forecast models. Since observations are the most expensive
part of the forecast and are often difficult to achieve, methods that would reduce the requirements
are highly desirable. Knowledge of features, structures and the dynamics which evolves them is
necessary for successful forecasting. Adaptive sampling of the observations of greatest impact
increases efficiency and can drastically reduce the observational requirements, i.e. by one or two
orders of magnitude. This project will develop methodology for ocean forecasting using
minimum input.
The Assessment of Skill for Coastal Ocean Transients (ASCOT) project is a series of real-time
CPSE/REA experiments and simulations focussed on quantitative skill evaluation and costeffective forecast system development. ASCOT-01, carried out in Massachusetts Bay/Gulf of
Maine in June 2001, was the first such experiment. ASCOT-02 is planned for the Eastern
Ligurian Sea in May 2002.
2. Goals and Objectives
ASCOT Overall Goal: to enhance the efficiency, improve the accuracy and extend the scope of
nowcasting and forecasting of oceanic fields for Coastal Predictive Skill Experimentation and for
Rapid Environmental Assessment in the coastal ocean and to quantify such CPSE and REA
capabilities.
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ASCOT General Objectives:
 obtain data sets adequate for: 1) definitive real-time verification of regional coastal ocean
predictive skills, with and without REA constraints; 2) CPSE and REA Observational System
Simulation Experiments (OSSEs), both for ASCOT design and more generally; and, 3)
definitive knowledge of dynamics
 define useful skill metrics and real-time forecast validation and verification procedures for
REA
 assemble, calibrate, exercise in real-time, evaluate and improve a generic, portable, scalable
advanced ocean forecast system (dynamical models and data analysis, management and
assimilation schemes) applicable for CPSE in general and NATO REA in particular.
ASCOT-02 Objectives:
 carry out and quantitatively evaluate in the Eastern Ligurian Sea in the region of the island of




Elba a multiscale real-time forecast experiment
obtain a data set adequate to define coupled dynamical processes (submeso-, meso-, bay-,
gulf- scales) that govern the formation and evolution of structures and events, including
generic processes and the coupling of wind-forced events and buoyancy currents
obtain an intensive data set adequate for definitive quantitative skill assessment and suitable
for the design of minimal data requirements for both REA and for an efficient regional
monitoring and prediction system
support AUV exercises
provide a rigorous real time test of a distributed ocean prediction system technology.
REA requires multiscale capabilities for different kinds of warfare (e.g. anti-submarine (ASW),
mine warfare (MW), etc.). An experiment which is to assess the predictive skill of a forecast
system must therefore measure and evaluate on multiple scales. Knowledge of the multiscale
dynamics is essential. Skill metrics will be designed to take the coupling of scales into account.
All coastal regions require both generic and regional-specific metrics for the dominant
variabilities. For example, upwelling is a generic process, however, the location and time of
occurrence of upwelling is specific to the region.
As a predictive skill experiment, ASCOT-02 will include oversampling, in order that sources of
error can be tracked. During the verification survey a significant fraction of the initialization
survey will be repeated. Specific goals of the adaptive sampling will include: specification of the
sampling patterns for the AUVs; sampling in regions not yet covered to locate local structures;
sampling in regions not recently covered to understand the evolution of structures; sampling to
determine the strength and structure of the anticyclone north of Elba; sampling to determine the
general nature of the flow in vicinity of Procchio Bay (e.g.. is it from the north or the result of
flow through the Corsican Channel from the Tyrrhenian turning around the island?); sampling to
evaluate the structure and evolution of the flow between Corsica and Elba; sampling to determine
the impact of the flow between Elba and the coast of Italy; and, sampling to determine the nature
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of conditions in Procchio Bay; coupling of wind-response and buoyancy currents; and, reduction
of multi-variate forecast errors.
3. Geographic and Oceanographic Context
The island of Elba lies off the western coast of Italy in the Corsican Channel, which connects the
Ligurian Sea to the north with the Tyrrhenian Sea to the south (Figure 1). The dominant large
scale general circulation features of these two seas in late September 2000 are indicated in the sea
surface temperature of the figure as an elongated cool pool of water (Ligurian Sea cyclonic gyre)
and a pair of cool (cyclonic) and warm (anti-cyclonic) gyres in the Tyrrhenian. The circulation
and variabilities of the Corsican Channel are driven interactively by the circulation and
variabilities of the south-eastern Ligurian and northern Tyrrhenian, as well as by direct local
atmospheric fluxes. Bonifazio Strait, between the islands of Corsica and Sardinia, is famous for
strong wind (Moen, 1984). Also, directly east of the strait, westerly wind is stronger than in the
shadow of the islands. An area of enhanced turbulence is reflected by lower temperatures. The
prevailing westerly wind through the strait induces a pair of eddies in the northern Tyrrhenian
Sea (Artale et al., 1994). The cyclonic eddy in the north lifts up the thermocline with the
consequence of a cool surface signature (e.g. Philippe and Harang, 1982). On both sides of
Corsica currents are directed to the north (Astraldi et al., 1990; Vignudelli et al., 1999). North of
Corsica, the combined flow sets along the Riviera coast, thus closing the loop for the Ligurian
Sea cyclone (Krivosheva, 1983). The Harvard Ocean Prediction System will be used to carry out
real time ocean forecasts for the Corsican Channel with a focus on the Gulf of Procchio, an
embayment of approximately 2km x 4km located along the northern coast of the island of Elba.
This section needs possible updating for May conditions.
Fig. 1 - Satellite sea surface temperature in the Ligurian-Provencal Basin and the Tyrrhenian Sea during
September 2000.
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4. Forecast Methodology
The Harvard Ocean Prediction System (HOPS, Figure 2) consists of data analysis and
assimilation schemes, and a suite of coupled interdisciplinary (physical, acoustical, optical,
biogeochemical-ecosystem) dynamical models (Robinson et al., 1998; Robinson, 1999;
Robinson and Glenn, 1999). HOPS employs a primitive equation (PE) physical dynamical
circulation model. Boundary layers (top and bottom) and isopycnal and diapycnal turbulence are
modeled through process parameterization, turbulence closure schemes, or scale-dependent
filters. Multiple sigma vertical coordinates are calibrated for accurate modelling of steep
topography. Multiple two-way nests are an existing option for the horizontal grids. HOPS is used
to build and maintain a regional synoptic description through nowcasting, forecasting and
assimilation.
HOPS data assimilation, which melds observations with dynamics, provides the most feasible
basis for obtaining accurate synoptic mesoscale realizations over the space-time scales and
domains of interest (Robinson et al., 1998). Data assimilation methods used by HOPS include a
robust (suboptimal) optimal interpolation (OI) scheme as well as a quasi-optimal scheme called
Error Subspace Statistical Estimation (ESSE) (Lermusiaux, 1997; Lermusiaux, 1999;
Lermusiaux and Robinson, 1999). The ESSE method determines the nonlinear evolution of the
oceanic state and its uncertainties by minimizing the most energetic errors under the constraints
of the dynamical and measurement models and their errors. Real-time efficiency is achieved by
reducing the error covariance to its dominant eigen-decomposition. Data assimilation is utilized
for a variety of purposes. Among them: control of predictability error; correction of model
dynamical deficiencies; parameter estimation; process studies; simulations, Observation System
Simulation Experiments (OSSEs) and sensitivity analysis.
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Fig. 2 - Harvard Ocean Prediction System (HOPS).
Each working day, data will be analyzed and gridded into fields, model runs performed, adaptive
sampling plans designed and disseminated, and nowcasts and forecasts issued via the GOATS
web site. The HOPS modelling is performed under the guideline: "assimilate yesterday's data
today for tomorrow's forecast". In other words, each morning, data collected during the previous
day and night was available to be processed and analyzed. The data is utilized in model runs that
produced nowcasts and forecasts to be issued the next day. The HOPS model system will be run
at Harvard University on a set of Sun Ultra-80 quad-processor workstations.
5. Nested Modeling Domains
The design of the modelling domains was accomplished by first reviewing the dynamical
processes in the region and examining the topographic influences on these processes. These
issues are balanced against the computational resources required for the simulations. The model
system was tuned by investigating objective analysis (OA) parameters in local (such as around
Elba) and far-field (in the Ligurian Sea) regions, temporal correlation parameters, various
dynamical model parameters, assimilation criteria, vertical resolution, assimilation
methodologies, etc, through a series of OSSEs. The domains were further refined based on these
results and the sensitivity studies redone until a robust oceanographic observation and prediction
system emerged. The model tuning was validated by comparing model output fields with
objective analysis maps of data, AVHRR imagery, and documented circulation patterns in the
region.
A two-way nested domain pair consists of a dynamical model defined in two domains, one with
coarser resolution containing the other with finer resolution. Information from the finer
resolution domain is used to replace information in the coarser resolution domain areas which
intersects with the finer resolution domain (up-scale). Information from the coarser resolution
domain around the boundaries of the finer resolution domain is interpolated to improve boundary
information in the finer resolution domain (down-scale).
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Fig. 3 - Schematic of the HOPS operational nested modelling domains.
The operational nested modelling domains for real-time nowcasting and forecasting are known as
the "Channel" and "Elba" domains. These HOPS domains are pictured in Figure 3. Figure 4
depicts the process of the two-way nesting. At each time step, independent estimates are made in
each domain. The left-hand panel indicates how the coarse grid (green circles) is interpolated
using bi-cubic interpolation onto the fine grid boundaries (green highlighted plus signs). The
right-hand panel demonstrates how the fine grid data (red highlighted plus signs) is averaged
onto a coarse grid node (circle at the center of the red square). Collocation of the two grids
simplifies navigation between the coarse nodes and supporting fine grid nodes. A 3:1 horizontal
resolution ratio is used for scale matching. The domains to be used continue to be refined.
Extension of the Channel Domain to the south and the Elba Domain to the east is being explored.
Considerations include model run time and computational resources.
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Fig. 4 - 2-way nesting for modelling domains. (left) Channel to Elba (coarse to fine), (right) Elba to
Channel (fine to coarse).
Modeling Domains
DOMAIN
Channel
Elba
DESCRIPTION/ SPECIFICATION
Resolution: 675m
Size: 203x120x20 (nx x ny x nz)
Domain center: 42.85N, 10.26E
Extent: 136x80km
Resolution: 225m
Size: 173x138x20 (nx x ny x nz)
Domain center: 42.80N, 10.27E
Extent: 39x31km
Products will be issued for the entire "Channel" domain and for a sub-region of the "Elba"
domain that included only the area of Procchio Bay. This Procchio Bay product focuses on the
AUV area of interest and operations. The standard suite of GOATS/MEANS products includes:
 A description of the forecast products
 A discussion of the current model forcing (e.g., winds, oceanographic features) and the
analysis/forecast fields
 Comments on forecast methodology
 Synoptic (nowcast) maps for:
- Temperature (surface and 50m)
- Salinity (surface and 50m)
- Current speed (sub-tidal velocity vectors at the surface and 50m)
 Twenty-four (24) and 48-hour forecast maps for:
- Temperature (surface and 50m)
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-
Salinity (surface and 50m)
Current speed (sub-tidal velocity vectors at the surface and 50m)
6. Schedule and Sample Tracks
The "Battlespace Preparation with AUVs" (BP02) experiment will take place on the NRV
Alliance from 6 May to 2 June 2002. The ASCOT-02 component will take place from 8-17 May.
There will be three periods devoted to sampling for ASCOT-02: 8-10 May - initialization; 11-13
May - adaptive sampling; and, 14-17 May - verification. In the 72 hours allotted to initialization,
a survey similar to the one indicated in Figure 5 could be accomplished. This set of stations was
performed during GOATS-00. There may be sufficient time to add an additional row of stations
to the south. As an alternative to adding another row of stations, it may be useful to perform
high-resolution sampling in the area near Procchio Bay.
Fig. 5 - CTD stations collected during GOATS-00. A similar set of stations will be collected during
ASCOT-02.
7. Adaptive Sampling
Uniformly sampled observations are generally utilized for initialization and assimilation as
forecasts advance in time. Sampled uniformly over a predetermined space-time grid, these
observations are usually adequate to resolve most scales of interest. However, only a small
subset of the observations has a significant impact on the accuracy of the forecasts. The impact
subset is related to intermittent energetic synoptic dynamical events. Adaptively sampled
observations are preferable for initialization and assimilation as forecasts advance in time. The
sampling scheme is tailored to the ocean state to be observed based on knowledge of the ocean
state from ongoing observations, nowcasts and forecasts. Adaptive sampling targets observations
that will have the greatest impact on knowledge, resolve scales of interest, and improve forecast
accuracy. Efficient adaptive sampling can reduce observational requirements by one or two
orders of magnitude (Robinson and Glenn, 1999).
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Adaptive sampling schemes can be either subjective, objective or a combination of both.
Sampling can be based on environmental forecasts or error forecasts. Forecast information is
combined with a priori experience to intuitively choose future sampling. Objectively, the
forecast serves as input to a quantitative sampling criterion whose optimization predicts the
adapted sampling. A goal is automated objective adaptive sampling.
The adaptive sampling patterns in Figure 6 were designed on a nearly-daily basis during
GOATS-00 and sent to the NRV Alliance. Patterns were designed both for large scale adaptive
sampling carried out by the Alliance and for the AUV sampling in Procchio Bay. The efficacy of
the distributed system is illustrated by the fact that scientists local to the sampling area and on
board the vessels performing the sampling requested adaptive sampling patterns from scientists
located thousands of kilometers away who were performing the detailed studies of the fields and
their evolution.
Fig. 6 - Adaptive sampling tracks designed on a real-time basis during GOATS-00. a) for the Channel
Domain; b) for the AUV in Procchio Bay.
8. Distributed Systems and Logistics
A distributed system relying on the Internet and satellite communication between NRV Alliance
and SACLANTCEN was established (Robinson and Sellschopp, 2001) for GOATS-00. A
similar distributed system is anticipated for ASCOT-02. Processing of the measured ocean
profiles was collocated with data acquisition on board. A dial-up satellite connection with a
computer at SACLANTCEN was used to send and receive electronic mail and to store processed
data on an Internet server, from where exercise participants would download. The server
examines the authorisation of connecting computers and persons, before it provides access to its
contents.
The ship data were acquired during the night and after processing transferred to shore before
noon. The time difference between Europe and New England allowed the modelling team to find
the data on the server early in the morning for assimilation into the model run. Model results
were required by early afternoon in order to arrive on Alliance in the evening together with a
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suggested station plan for sampling during night. Graphical representations of the model output
were incorporated into pages with the pertinent documentation and uploaded to the server at
SACLANTCEN for access by trials participants. Table 1 shows the daily time-table for the time
critical activities on Alliance and at Harvard. It is expected that a similar process will be
followed for ASCOT-02.
Table 1
Elba time
11:00
19:00
20:00
00:00
04:00
08:00
08:00
11:00
Activity
Transmit data of the night survey
Final editing of input data
Process data received overnight
Plot and analyze completed forecast
Grid data, prepare for assimilation into HOPS run
Begin HOPS run
Plan adaptive sampling tracks
Transmit forecast and sampling plan
Start CTD survey on Alliance
Apply CUPOM boundary conditions
Continue HOPS forecast to completion
Replace CTD by XCTD and XBT
Continue with CTD
Survey ends close to Elba
Process data of last night
Transmit data of the survey
Boston time
05:00
06:00
09:00
10:00
10:00
11:00
12:00
13:00
16:00
17:00
05:00
Contact Information
Personnel and tasks to be updated following the January 2002 meeting.
Prof. Allan R. Robinson
robinson@pacific.deas.harvard.edu
Wayne Leslie
leslie@pacific.deas.harvard.edu
Pat Haley
Pierre Lermusiaux
haley@pacific.deas.harvard.edu
pierrel@pacific.deas.harvard.edu
617-495-2819 (Office)
617-267-4359 (Home)
617-512-6437 (Cell)
617-495-4569 (Office)
781-665-5170 (Home)
617-495-5192 (Fax)
781-718-1856 (Cell)
617-495-2827 (Office)
617-495-0378 (Office)
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Oleg Logoutov
Patricia Moreno
Gioia Sweetland
oleg@pacific.deas.harvard.edu
pmoreno@pacific.deas.harvard.edu
gioia@pacific.deas.harvard.edu
617-496-4939 (Office)
617-495-8051 (Office)
617-495-2919 (Office)
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