AOSN-II in Monterey Bay: data assimilation, adaptive sampling and dynamics

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Ocean Prediction Systems Advanced Concepts and
Research Issues
Allan R. Robinson
Harvard University Division of Engineering and
Applied Sciences Department of Earth and
Planetary Sciences
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• Interdisciplinary System Concept
• Harvard Ocean Prediction System Research
• Multi-Scale Examples
• Wind-Induced Upwelling
• Episodic Mass. Bay/GOM
• Sustained Monterey Bay
• Conclusions

Harvard University Patrick J. Haley, Jr., Pierre
F.J. Lermusiaux, Wayne G. Leslie, X. San
Liang, Oleg Logutov, Patricia Moreno Avijit
Gangopadhyay (Umass.-Dartmouth)
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Interdisciplinary Ocean Science Today

• Research underway on coupled physical,
  biological, chemical, sedimentological,
  acoustical, optical processes
• Ocean prediction for science and operational
  applications has now been initiated on basin and
  regional scales
• Interdisciplinary processes are now known to
  occur on multiple interactive scales in space and
  time with bi-directional feedbacks

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System Concept

• The concept of Ocean Observing and Prediction
  Systems for field and parameter estimations has
  crystallized with three major components
• An observational network a suite of platforms
  and sensors for specific tasks
• A suite of interdisciplinary dynamical models
• Data assimilation schemes
• Systems are modular, based on distributed
  information providing shareable, scalable,
  flexible and efficient workflow and management

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Interdisciplinary Data Assimilation

• Data assimilation can contribute powerfully to
  understanding and modeling physical-acoustical-bio
  logical processes and is essential for ocean
  field prediction and parameter estimation
• Model-model, data-data and data-model
  compatibilities are essential and dedicated
  interdisciplinary research is needed

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Interdisciplinary Processes - Biological-Physical-
Acoustical Interactions
Physics - Density
Biology Fluorescence (Phytoplankton)
Acoustics Backscatter (Zooplankton)
Griffiths et al, Vol 12, THE SEA
Almeira-Oran front in Mediterranean Sea Fielding
et al, JMS, 2001
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Biological-Physical-Acoustical Interactions

• Distribution of zooplankton is influenced by both
  animal behavior (diel vertical migration) and the
  physical environment.
• Fluorescence coincident with subducted surface
  waters indicates that phytoplankton were drawn
  down and along isopycnals, by cross-front
  ageostrophic motion, to depths of 200 m.
• Sound-scattering layers (SSL) show a layer of
  zooplankton coincident with the drawn-down
  phytoplankton. Layer persists during and despite
  diel vertical migration.
• Periodic vertical velocities of 20 m/day,
  associated with the propagation of wave-like
  meanders along the front, have a significant
  effect on the vertical distribution of
  zooplankton across the front despite their
  ability to migrate at greater speeds.

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Coupled Interdisciplinary Data Assimilation
x xA xO xB
Unified interdisciplinary state vector
Physics xO T, S, U, V, W Biology xB
Ni, Pi, Zi, Bi, Di, Ci Acoustics xA
Pressure (p), Phase (?)
Coupled error covariance with off-diagonal terms
PAA PAO PAB P POA POO POB
PBA PBO PBB
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Data Assimilation in Advanced Ocean Prediction
Systems
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THREE PHASES OF (ITERATIVE)COUPLED MODELS SKILL
ASSESSMENT

• VALIDATION
• Structures of models generally capable of
  representation of relevant dynamical processes
• Adequacy and compatibility of physical/biological
  data for process identification
• Dynamical models with data assimilation robustly
  represent events and identify processes

• CALIBRATION
• Tuning of models parameters dynamical rates,
  subgridscale processes, computational protocols
• Dynamical models without data assimilation
  represent events that are validated by data
  previously assimilated

• VERIFICATION
• Real-time prediction verified by skill assessment
  quantities within a priori specified bounds
• Hindcasts and simulations with quantitative
  statistical dynamical behavior

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HOPS/ESSE Long-Term Research Goal
To develop, validate, and demonstrate an advanced
relocatable regional ocean prediction system for
real-time ensemble forecasting and simulation of
interdisciplinary multiscale oceanic fields and
their associated errors and uncertainties, which
incorporates both autonomous adaptive modeling
and autonomous adaptive optimal sampling
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HOPS/ESSE System
Error Subspace Statistical Estimation
Harvard Ocean Prediction System
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Harvard Generalized Adaptable Biological Model
(R.C. Tian, P.F.J. Lermusiaux, J.J. McCarthy and
A.R. Robinson, HU, 2004)
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Approach
To achieve regional field estimates as realistic
and valid as possible

• every effort is made to acquire and assimilate
  both remotely sensed and in situ synoptic
  multiscale data from a variety of sensors and
  platforms in real time or for the simulation
  period, and a combination of historical synoptic
  data and feature models are used for system
  initialization
• fine-tune the model to the region, processes
  and variabilities examine model output, modify
  set-up (e.g. grids, etc.) and alter structure and
  values of parameters (e.g. SGS, boundary
  conditions, etc.)
• continuously evaluate and iterate tuning as
  necessary

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Ongoing Research Objectives
To extend the HOPS-ESSE assimilation, real-time
forecast and simulation capabilities to a single
interdisciplinary state vector of ocean
physical-acoustical-biological fields. To
continue to develop and to demonstrate the
capability of multiscale simulations and
forecasts for shorter space and time scales via
multiple space-time nests (Mini-HOPS), and for
longer scales via the nesting of HOPS into other
basin scale models. To achieve a multi-model
ensemble forecast capability.
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Mini-HOPS Corsican Channel 2003

• Designed to locally solve the problem of accurate
  representation of sub-mesoscale synopticity,
  including inertial motions
• Involves rapid real-time assimilation of
  high-resolution data in a high-resolution model
  domain nested in a regional model

• Produces locally more accurate oceanographic
  field estimates and short-term forecasts and
  improves the impact of local field
  high-resolution data assimilation
• Dynamically interpolated and extrapolated
  high-resolution fields are assimilated through
  2-way nesting into large domain models

Modeling Domains
In collaboration with Dr. Emanuel Coelho (NATO
Undersea Research Centre)
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Mini-HOPS for MREA-03
Prior to experiment, several configurations were
tested leading to selection of 2-way nesting with
super-mini at Harvard

• During experiment
• Daily runs of regional and super mini at Harvard
• Daily transmission of updated IC/BC fields for
  mini-HOPS domains
• Mini-HOPS successfully run aboard NRV Alliance

Mini-HOPS simulation run aboard NRV Alliance in
Central mini-HOPS domain (surface temperature and
velocity)
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Results of MREA03 Re-analysis and Model Tuning
Real-time Model/Data Comparison
Re-analysis Model/Data Comparison
Model Temp. Observed Temp.
Bias residue lt .25oC

• Tuned parameters for stability and agreement with
  profiles (especially vertical mixing)
• Improved vertical resolution in surface and
  thermocline
• Corrected input net heat flux
• Improved initialization and synoptic assimilation
  in dynamically tuned model

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Error Analyses and Optimal (Multi) Model Estimates
Real-Time Forecast Training via
Maximum-Likelihood Correction
Model Temp. Observed Temp.
Uncorrected
Training Full Data Set
Training 1 Profile
Training 3 Profiles
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Nesting HOPS in a Coarse-Resolution Climate PCM
A.R. Robinson, P.J. Haley, Jr., W.G. Leslie
Concept

• Nest a high-resolution hydrodynamic model within
  a coarse-resolution Global Circulation Model
  (GCM) to provide high-resolution forecasts of
  mesoscale features in the Gulf of Maine
• Results applicable to cod and lobster
  temperature-dependent behavior change, including
  recruitment

Setup

• Parallel Climate Model (PCM) outputs for 2000 and
  2085 for coarse circulation (1 degree)
• Gulf of Maine Feature Model provides
  higher-resolution synoptic circulation (5km)
• Harvard Ocean Prediction System (HOPS)
  dynamically adjusts Feature Model output

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Gulf of Maine Feature Model

• Prevalent circulation features are identified
• Synoptic water-mass structures are characterized
  and parameterized to develop T-S feature models
• Temperature and Salinity feature model profiles
  are placed on a regional circulation template

Gangopadhyay, A., A.R. Robinson, et al., 2002.
Feature-oriented regional modeling and
simulations (FORMS) in the Gulf of Maine and
Georges Bank, Continental Shelf Research, 23
(3-4), 317-353
Parallel Climate Model (PCM) Ocean Fields
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Synoptic Estimate of Surface Salinity
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Nesting High Resolution HOPS in a Climate Model
A.R. Robinson, P.J. Haley, Jr., W.G. Leslie

• In order to arrive at future state, calculate the
  difference between the global model fields in
  2000 and 2085 on the shelf and in deep water
• Perturb present-day observed fields by the
  climate change profile to achieve the 2085
  fields

Deep
Shelf
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Compare September 2000 and September 2085
Dynamically adjusted fields for September 2000

• Temperature increases from 2000-2085
• Salinity increases from 2000-2085
• Details under study
• Fields provided to Union of Concerned Scientists
  (UCS) for studies on regional climate change
  effects

Dynamically adjusted fields for September 2085
Temperature
Salinity
http//oceans.deas.harvard.edu/UCS
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Wind-Induced Upwelling
Massachusetts Bay Episodic upwelling
Monterey Bay Sustained Upwelling
Red Wind, Blue Upwelling
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Dominant circulation and bio-physical dynamics
for trophic enrichment and accumulation

• Patterns are not present at all times
• Most common patterns (solid), less common
  (dashed)
• Patterns drawn correspond to main currents in the
  upper layers of the pycnocline where the buoyancy
  driven component of the horizontal flow is often
  the largest

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ASCOT-01 (6-26 June 2001) Positions of data
collected and fed into models
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ASCOT-01 Sample Real-Time Forecast Products
Massachusetts Bay
Gulf of Maine
2m Temp.
10m Temp.
3m Temp.
25m Temp.
5m Chlorophyll
15m Nitrate
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Validation Upwelling event in Massachusetts Bay

• Moderate southerly winds lead to upwelling on the
  western side of Cape Cod Bay
• Near the surface temperature decreases from 17oC
  to 12oC
• Near the surface chlorophyll increases from 1.4
  mg Chl/m3 to 2.3
• One-half day later, chlorophyll
• continues to increase near the surface
• decreases between 5-10m
• Between 3-10m there is maximum primary production
• Advective effects are stronger, bringing the
  newly produced chlorophyll closer to the surface
• Primary production during the upwelling event is
  mainly due to ammonium uptake
• Nitrate acts as a passive tracer

Upwelling signature in T (top) and chlorophyll
(bottom)
P.A. Moreno
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Calibration Nutrient uptake parameters

• 20 sets of nutrient uptake parameters tried
  followed by 3 model runs.
• Nutrient uptake data is used to determine the
  photosynthesis and nutrient limitation
  parameters.
• Guided by MWRA primary productivity data and
  literature survey.

Red - data Black - model
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Zooplankton grazing parameters
Data Nutrient uptake rate in the euphotic zone
for June 12-13, 2001
Phytoplankton equation
Grazing
Photosynthesis
Nutrient limitation

• Choose zooplankton grazing parameters such that
  the nutrient uptake is approximately balanced by
  grazing.

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Prediction - towards verificationEpisodic
upwelling events in Massachusetts Bay

• Historical wind data (May-June 1985-2005)
  indicates strongest wind events of 1-2 days and
  maximum wind stress 2-4 dyn/cm2
• Design two feature wind events duration 2 days,
  2 and 4 dyn/cm2

Winds Observed at Mass. Bay Buoy
Feature Wind Event Replacing Observations
Simulation begins 6 June 2001 and lasts 20 days.
Feature event starts four days into simulation.
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Prediction For strong winds the chlorophyll is
upwelled and advected away from the coast work
in progress
June 2001 Wind
Chlorophyll
June 2001 Wind
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Integrated Ocean Observing and Prediction Systems
Platforms, sensors and integrative models
HOPS-ROMS real-time forecasting and re-analyses
AOSN II
AOSN II
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Coastal upwelling system sustained upwelling
relaxation re-establishment
30m Temperature 6 August 3 September (4 day
intervals)
6 Aug
10 Aug
14 Aug
18 Aug
22 Aug
26 Aug
30 Aug
3 Sep
Descriptive oceanography of re-analysis fields
and and real-time error fields initiated at the
mesoscale. Description includes Upwelling and
relaxation stages and transitions, Cyclonic
circulation in Monterey Bay, Diurnal scales,
Topography-induced small scales, etc.
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HOPS AOSN-II Re-Analysis
22 August
18 August
Ano Nuevo
Monterey Bay
Point Sur
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Which sampling on Aug 26 optimally reduces
uncertainties on Aug 27?
4 candidate tracks, overlaid on surface T fcst
for Aug 26

• Based on nonlinear error covariance evolution
• For every choice of adaptive strategy, an
  ensemble is computed

Best predicted relative error reduction track 1
ESSE fcsts after DA of each track
DA
IC(nowcast)
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Bayesian Adaptive Multi-Model Forecasting
ROMS and HOPS SST forecasts for August 28, 2003
with track of validating NPS aircraft SST data
taken on August 29, 2003
Model-data misfits is the source of information
that is utilized to estimate the uncertainty
parameters in models via Maximum-Likelihood. The
models are then combined based on the uncertainty
parameters, as
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Bayesian Adaptive Multi-Model Forecasting
ROMS and HOPS individual SST forecasts and the
NPS aircraft SST data are combined based on their
estimated uncertainties to form the central
forecast

• A new batch of model-data misfits and priors on
  uncertainty parameters determine via the Bayesian
  principle uncertainty parameter values that are
  employed to combine the forecasts.
• The Bayesian model fusion technique that we
  advocate treats forecast errors from different
  models as uncorrelated in order to gain its
  capability to work with a small sample of past
  validating events, however, accounts for spatial
  structure in forecast error covariances.

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Multi-Scale Energy and Vorticity Analysis
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Multi-Scale Energy and Vorticity Analysis

• MS-EVA is a new methodology utilizing multiple
  scale window decomposition
• in space and time for the investigation of
  processes which are
• multi-scale interactive
• nonlinear
• intermittent in space
• episodic in time

• Through exploring
• pattern generation and
• energy and enstrophy
• transfers
• transports, and
• conversions

MS-EVA helps unravel the intricate relationships
between events on different scales and locations
in phase and physical space. Dr. X.
San Liang
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Multi-Scale Energy and Vorticity Analysis
Window-Window Interactions MS-EVA-based
Localized Instability Theory
Perfect transfer A process that exchanges energy
among distinct scale windows which does not
create nor destroy energy as a whole. In the
MS-EVA framework, the perfect transfers are
represented as field-like variables. They are of
particular use for real ocean processes which in
nature are non-linear and intermittent in space
and time.

• Localized instability theory
• BC Total perfect transfer of APE from
  large-scale window to meso-scale window.
• BT Total perfect transfer of KE from large-scale
  window to meso-scale window.
• BT BC gt 0 gt system locally unstable otherwise
  stable
• If BT BC gt 0, and
• BC ? 0 gt barotropic instability
• BT ? 0 gt baroclinic instability
• BT gt 0 and BC gt 0 gt mixed instability

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Wavelet Spectra
Monterey Bay
Surface Temperature
Pt. Sur
Pt. AN
Surface Velocity
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Multi-Scale Energy and Vorticity Analysis
Multi-Scale Window Decomposition in AOSN-II
Reanalysis
The reconstructed large-scale and meso-scale
fields are filtered in the horizontal with
features lt 5km removed.
Time windows Large scale gt 8 days Meso-scale
0.5-8 days Sub-mesoscale lt 0.5 day
Question How does the large-scale flow lose
stability to generate the meso-scale structures?
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Multi-Scale Energy and Vorticity Analysis

• Decomposition in space and time (wavelet-based)
  of energy/vorticity eqns.

Large-scale Available Potential Energy (APE)
Large-scale Kinetic Energy (KE)

• Both APE and KE decrease during the relaxation
  period
• Transfer from large-scale window to mesoscale
  window occurs to account for decrease in
  large-scale energies (as confirmed by transfer
  and mesoscale terms)

Windows Large-scale (gt 8days gt 30km),
mesoscale (0.5-8 days), and sub-mesoscale (lt 0.5
days)
Dr. X. San Liang
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Multi-Scale Energy and Vorticity Analysis
MS-EVA Analysis 11-27 August 2003
Transfer of APE from large-scale to meso-scale
Transfer of KE from large-scale to meso-scale
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Multi-Scale Energy and Vorticity Analysis
Process Schematic
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Multi-Scale Energy and Vorticity Analysis
Multi-Scale Dynamics

• Two distinct centers of instability both of
  mixed type but different in cause.
• Center west of Pt. Sur winds destabilize the
  ocean directly during upwelling.
• Center near the Bay winds enter the balance on
  the large-scale window and release energy to the
  mesoscale window during relaxation.
• Monterey Bay is source region of perturbation and
  when the wind is relaxed, the generated mesoscale
  structures propagate northward along the
  coastline in a surface-intensified free mode of
  coastal trapped waves.
• Sub-mesoscale processes and their role in the
  overall large, mesoscale, sub-mesoscale dynamics
  are under study.

Energy transfer from meso-scale window to
sub-mesoscale window.
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Monterey Bay August 2006 Adaptive Sampling and
Prediction (ASAP) Assessing the Effects of
Submesoscale Ocean Parameterizations
(AESOP) Undersea Persistent Surveillance
(UPS) Layered Organization in the Coastal Ocean
(LOCO)
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CONCLUSIONS

• Entering a new era of fully interdisciplinary
  ocean science physical-biological-acoustical-biog
  eochemical
• Advanced ocean prediction systems for science,
  operations and management interdisciplinary,
  multi-scale, multi-model ensembles
• Interdisciplinary estimation of state variables
  and error fields via multivariate
  physical-biological-acoustical data assimilation

http//www.deas.harvard.edu/robinson