System Concepts Research Issue Examples Demonstration of Concept Multi-Institutional Experiment off Cali as long as nia Coast (AOSN-II) System Concept

System Concepts Research Issue Examples Demonstration of Concept Multi-Institutional Experiment off Cali as long as nia Coast (AOSN-II) System Concept www.phwiki.com

System Concepts Research Issue Examples Demonstration of Concept Multi-Institutional Experiment off Cali as long as nia Coast (AOSN-II) System Concept

Gavre, Terryl, Food Editor has reference to this Academic Journal, PHwiki organized this Journal Ocean Prediction Systems: Advanced Concepts in addition to Research Issues Allan R. Robinson Harvard University Division of Engineering in addition to Applied Sciences Department of Earth in addition to Planetary Sciences System Concepts Research Issue Examples Demonstration of Concept Multi-Institutional Experiment off Cali as long as nia Coast (AOSN-II) Harvard University Patrick J. Haley, Jr. Pierre F.J. Lermusiaux Wayne G. Leslie X. San Liang Oleg Logoutov Rucheng Tian Ching S. Chiu (NPS) Larry Anderson (WHOI) Avijit Gangopadhyay (Umass.-Dartmouth) Interdisciplinary Ocean Science Today Research underway on coupled physical, biological, chemical, sedimentological, acoustical, optical processes Ocean prediction as long as science in addition to operational applications has now been initiated on basin in addition to regional scales Interdisciplinary processes are now known to occur on multiple interactive scales in space in addition to time with bi-directional feedbacks

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System Concept The concept of Ocean Observing in addition to Prediction Systems as long as field in addition to parameter estimations has recently crystallized with three major components An observational network: a suite of plat as long as ms in addition to sensors as long as specific tasks A suite of interdisciplinary dynamical models Data assimilation schemes Systems are modular, based on distributed in as long as mation providing shareable, scalable, flexible in addition to efficient workflow in addition to management Interdisciplinary Data Assimilation Data assimilation can contribute powerfully to underst in addition to ing in addition to modeling physical-acoustical-biological processes in addition to is essential as long as ocean field prediction in addition to parameter estimation Model-model, data-data in addition to data-model compatibilities are essential in addition to dedicated interdisciplinary research is needed Physics – Density Biology – Fluorescence (Phytoplankton) Acoustics – Backscatter (Zooplankton) Almeira-Oran front in Mediterranean Sea Fielding et al, JMS, 2001 Griffiths et al, Vol 12, THE SEA Interdisciplinary Processes – Biological-Physical-Acoustical Interactions

Distribution of zooplankton is influenced by both animal behavior (diel vertical migration) in addition to the physical environment. Fluorescence coincident with subducted surface waters indicates that phytoplankton were drawn down in addition to 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 in addition to despite diel vertical migration. Periodic vertical velocities of ~20 m/day, associated with the propagation of wave-like me in addition to ers along the front, have a significant effect on the vertical distribution of zooplankton across the front despite their ability to migrate at greater speeds. Biological-Physical-Acoustical Interactions PAA PAO PAB P = POA POO POB PBA PBO PBB Coupled Interdisciplinary Data Assimilation Physics: xO = [T, S, U, V, W] Biology: xB = [Ni, Pi, Zi, Bi, Di, Ci] Acoustics: xA = [Pressure (p), Phase ()] x = [xA xO xB] Coupled error covariance with off-diagonal terms Unified interdisciplinary state vector Data Assimilation in Advanced Ocean Prediction Systems

HOPS/ESSE System Harvard Ocean Prediction System – HOPS Uncertainty as long as ecasts (with dynamic error subspace, error learning) Ensemble-based (with nonlinear in addition to stochastic primitive eq. model (HOPS) Multivariate, non-homogeneous in addition to non-isotropic Data Assimilation (DA) Consistent DA in addition to adaptive sampling schemes HOPS/ESSE System Error Subspace Statistical Estimation – ESSE HOPS/ESSE Long-Term Research Goal To develop, validate, in addition to demonstrate an advanced relocatable regional ocean prediction system as long as the real-time ensemble as long as ecasting in addition to simulation of interdisciplinary multiscale oceanic fields in addition to their associated errors in addition to uncertainties, which incorporates both autonomous adaptive modeling in addition to autonomous adaptive optimal sampling

Approach To achieve regional field estimates as realistic in addition to valid as possible, an ef as long as t is made to acquire in addition to assimilate both remotely sensed in addition to in situ synoptic multiscale data from a variety of sensors in addition to plat as long as ms in real time or as long as the simulation period, in addition to a combination of historical synoptic data in addition to feature models are used as long as system initialization. Ongoing Research Objectives To extend the HOPS-ESSE assimilation, real-time as long as ecast in addition to simulation capabilities to a single interdisciplinary state vector of ocean physical-acoustical-biological fields. To continue to develop in addition to to demonstrate the capability of multiscale simulations in addition to as long as ecasts as long as shorter space in addition to time scales via multiple space-time nests (Mini-HOPS), in addition to as long as longer scales via the nesting of HOPS into other basin scale models. To achieve a multi-model ensemble as long as ecast capability. Examples Illustrating Research Issues Gulf Stream Coupled physical-biological dynamics studied via compatible physical-biological data assimilation Combined feature model in addition to in situ data assimilation in western boundary current Ligurian Sea in addition to Portuguese Coast Multi-scale real-time as long as ecasting in two-way nested domains – Mini-HOPS: faster time scales, shorter space scales, sub-mesoscale synopticity New Engl in addition to Shelfbreak Front End-to-End system concept with uncertainties, e.g. sonar system Coupled physical-acoustical data assimilation with coupled error covariances

Gulf Stream Brazil Current Feature Model

Day 7 Day 10 Temperature Phytoplankton Physical Assim. Phytoplankton Coupled Assim. Physical data assimilation only – adjustment of the physical fields leads to misalignment between physical in addition to biological fronts, causing spurious cross-frontal fluxes in addition to consequently spurious biological responses (e.g. enhanced productivity). Biological data assimilation only – little or no feedback to the physics. Physical in addition to biological fronts become misaligned, causing spurious cross-frontal fluxes in addition to consequently spurious biological responses (e.g. enhanced productivity). Six-step method: initial estimation of synoptic physical features melding physical data into these fields to obtain the best real-time estimates physical dynamical adjustment to generate vertical velocities initial estimation of mesoscale biological fields based on Physical-biological correlations melding biological data into these fields, in addition to biological dynamical adjustment with frozen physical fields to balance the biological fields with each other, the model parameters, in addition to the 3-D physical transports. The generation of these fields is done in “adjustment space”, outside of the simulation of interest (“simulation space”). Conclusions – Compatible Physical/Biological Assimilation Mini-HOPS Designed to locally solve the problem of accurate representation of sub-mesoscale synopticity 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 in addition to short-term as long as ecasts in addition to improves the impact of local field high-resolution data assimilation Dynamically interpolated in addition to extrapolated high-resolution fields are assimilated through 2-way nesting into large domain models In collaboration with Dr. Emanuel Coelho (NATO Undersea Research Centre)

MREA-03 Mini-HOPS Protocol From the super-mini domain, initial in addition to boundary conditions were extracted as long as all 3 mini-HOPS domains as long as the following day in addition to transmitted to the NRV Alliance. Aboard the NRV Alliance, the mini-HOPS domains were run the following day, with updated atmospheric as long as cing in addition to assimilating new data. MREA-03 Domains Regional Domain (1km) run at Harvard in a 2-way nested configuration with a super-mini domain. Super mini has the same resolution (1/3 km) as the mini-HOPS domains in addition to is collocated with them Mini-HOPS as long as MREA-03 During experiment: Daily runs of regional in addition to super mini at Harvard Daily transmission of updated IC/BC fields as long as mini-HOPS domains Mini-HOPS successfully run aboard NRV Alliance Prior to experiment, several configurations were tested leading to selection of 2-way nesting with super-mini at Harvard Mini-HOPS simulation run aboard NRV Alliance in Central mini-HOPS domain (surface temperature in addition to velocity) Mini-HOPS as long as MREA-04 Portuguese Hydrographic Office utilizing regional HOPS Daily runs of regional in addition to super mini at Harvard Daily transmission of updated IC/BC fields as long as mini-HOPS domains to NURC scientists as long as mini-HOPS runs aboard NRV Alliance

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End-to-End System Concept Sonar per as long as mance prediction requires end-to-end scientific systems: ocean physics, bottom geophysics, geo-acoustics, underwater acoustics, sonar systems in addition to signal processing Uncertainties inherent in measurements, models, transfer of uncertainties among linked components Resultant uncertainty in sonar per as long as mance prediction itself Specific applications require the consideration of a variety of specific end-to-end systems Coupled Physical-Acoustical Data Assimilation PAA PAO POA POO Physics: xO = [T, S, U, V, W] Acoustics: xA = [Pressure (p), Phase ()] x = [xA xO] cO Coupled discrete state vector x (from continuous i) Coupled error covariance Coupled assimilation x+ = x- + PHT [HPHT+R]-1 (y-Hx-); P = x- = A priori estimate ( as long as as long as ecast) x+ = A posteriori estimate (after assimilation)

PRIMER End-to-End Problem Initial Focus on Passive Sonar Problem Location: Shelfbreak PRIMER Region Season: July-August 1996 Sonar System (Receiver): Passive Towed Array Target: Simulated UUV (with variable source level) Frequency Range: 100 to 500 Hz Geometries: Receiver operating on the shelf shallow water; target operating on the shelf slope (deeper water than receiver) Environmental-Acoustical Uncertainty Estimation in addition to Transfers, Coupled Acoustical-Physical DA in addition to End-to-End Systems in a Shelfbreak Environment Note the front Variability at the front Extreme events Warm/cold events on each side Novel approach: coupled physical-acoustical data assimilation method is used in TL estimation Methodology: HOPS generates ocean physics predictions NPS model generates ocean acoustics predictions 100 member ESSE ensemble generates coupled covariances Coupled ESSE assimilation of CTD in addition to TL measurements Starting with physical environmental data, compute the Predictive Probability Of Detection (PPD) from first principals via broadb in addition to Transmission Loss (TL)

Entering a new era of fully interdisciplinary ocean science: physical-biological-acoustical-biogeochemical Advanced ocean prediction systems as long as science, operations in addition to management: interdisciplinary, multi-scale, multi-model ensembles Interdisciplinary estimation of state variables in addition to error fields via multivariate physical-biological-acoustical data assimilation CONCLUSIONS http://www.deas.harvard.edu/~robinson

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