aosn-ii in monterey bay: data assimilation, adaptive sampling and dynamics allan r. robinson pierre...
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AOSN-II in Monterey Bay:
Data Assimilation, Adaptive Sampling and Dynamics
Allan R. RobinsonPierre F.J. Lermusiaux
Harvard University
Harvard Contributors: Patrick J. Haley, Jr., Wayne G. Leslie, X. San Liang,
Oleg Logoutov, Patricia Moreno, Gianpiero Cossarini (Trieste)
HOPS – AOSN-II Accomplishments• 23 sets of real-time nowcasts and forecasts of temperature, salinity
and velocity released from 4 August to 3 September- Assimilated ship (Pt. Sur, Martin, Pt. Lobos), glider (WHOI and Scripps) and
aircraft SST data, within 24 hours of appearance on data server (after quality control)
- Forecasts forced by 3km and hourly COAMPS flux predictions
• Data analyzed and quality controlled daily for real-time forecasts
• Web: http://www.deas.harvard.edu/~leslie/AOSNII/index.html for daily distribution of field and error forecasts, scientific analyses, data analyses, special products and control-room presentations
• Features analyzed and described daily. Formed basis for adaptive sampling recommendations
• Boundary conditions and model parameters for atmospheric forcing calibrated and modified in real-time to adapt to evolving conditions
HOPS – AOSN-II Accomplishments (cont.)
• 10 sets of real-time ESSE forecasts issued from 4 Aug. to 3 Sep. – total of 4323 ensemble members (stochastic model, BCs and forcings), 270 – 500 members per day
- ESSE fields included: central forecasts, ensemble means, forecast errors, a priori and a posteriori error estimates, dominant singular vectors and covariance fields
• ESSE text products included: descriptions of uncertainty initialization and forecast procedures, analyses of ESSE prediction results (uncertainties and dynamics) and adaptive sampling recommendations
• Real-time research work on: multi-scale energy and vorticity analysis, coupled physics-biology, tides, free-surface PE model
This sequence of snapshots spans the time period 6 August to 6 September. The frames are at one-day intervals. The three columns represent total velocity (left), regional-scale (center) and meso-scale (right) velocity.
This animation is a sequence of concatenated snapshots from the real-time simulations. As such there are jumps in the field evolution due to the sequence of forecast restarts. However, it is possible to follow, especially in the meso-scale velocity plots, the movement of features over the passage of time.
A smooth animation is being constructed.
Oceanic responses and atmospheric forcings during August 2003
Upwelling Relaxation
Oceanic responses and atmospheric forcings during August 2003
Aug 10: Upwelling Aug 16: Upwelled
Aug 20: Relaxation Aug 23: Relaxed
Forecast RMS Error Estimate– Temperature (left), Salinity (right)
Blue – 12 AugGreen – 13 Aug
Solid – ForecastDash – Persistence
T Difference for 13 August
Persistence – Data Forecast – Data
Bias EstimateHorizontally-averaged data-model differences
Verification data time: Aug 13 Nowcast (Persistence forecast): Aug 11 1-day/2-day forecasts: Aug 12/Aug 13
• Real-time predictions of forecast errors with a significant number of ensemble members and ESSE assimilation is possible
• Novel group of experimentalists, modelers and managers provided an unprecedented picture of a variety of Monterey Bay processes and phenomena
• Effective communication among and integration of assets requires a cooperative effort greater than AOSN-II but can only be accomplished with real-time practice
• Need for a priori and ongoing regular inter-calibration between assets
• Initialization survey needed to be completed prior to start of real-time forecasting period
• Maintenance of background circulation with adequate coverage critical
• Anomalous conditions in 2003 proved challenging for HOPS set-up methodology
• Model details (e.g. BC, response to forcing) must be anticipated to be adapted in real-time. Resources for such adaptation must be included in logistics.
• High temporal resolution forcing lends greater importance to diurnal cycle – impacts data assimilation protocol
HOPS – AOSN-II Lessons Learned
• Generate consistent 4-D simulations of the physical (and coupled physical-biological fields) for August 2003 (re-analysis fields)
o Improve the forecast protocol for hindcasting – data compatibility, assimilation methodology and domains to be re-evaluated
o Hindcast additions – T/S feature model, tides• Summarize kinematic and dynamical findings of Monterey Bay, as well as
California Current System interactions and fluxes (balance of terms, etc)• Multi-Scale Energy and Vorticity Analysis of the dynamical evolution• Complete forecast skill evaluation of fields/errors with classic and new
generic/specific metrics. Issues include: definition of “persistence”, accounting for phase error, etc.
• Predictability studies, ensemble properties (mean, mpf, std, sv, etc), energetics, improve stochastic forcings (data/model errors)
• Develop and carry out interdisciplinary adaptive sampling OSSEs on multiple scales
HOPS – AOSN-II Research Tasks
Multi-Scale Energy and Vorticity AnalysisMS-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 spaces.
Multi-Scale Energy and Vorticity Analysis (Cont.)
Temperature DecompositionLeft – Total TemperatureCenter – Regional Scale (> 75km)Right – Mesoscale (8-75km)
(a) Rate of potential energy transfer from large-scale window to meso-scale window
(b) Rate of kinetic energy transfer from large-scale window to meso-scale window
(c) Meso-scale buoyancy conversion rate
(d) Vertical pressure working rate on the meso-scale window
These plots are for 30m on 17 August.
Interdisciplinary Adaptive Sampling
• Use forecasts and their uncertainties to alter the observational system in space (locations/paths) and time (frequencies) for physics, biology and acoustics.
• Predict most useful regions/variables to sample, based on:
• Uncertainty predictions (error variance, higher moments, pdf’s)
• Interesting physical/biological/acoustical phenomena predictions (feature extraction, Multi-Scale Energy and Vorticity analysis)
• Synoptic accuracy/coverage predictions
• Plan observations under operational, time and cost constraints to maximize information content (e.g. minimize uncertainty at final time or over the observation period).
Real-time Adaptive Sampling – Pt. Lobos
• Large uncertainty forecast on 26 Aug. related to predicted meander of the coastal current which advected warm and fresh waters towards Monterey Bay Peninsula.
• Position and strength of meander were very uncertain (e.g. T and S error St. Dev., based on 450 2-day fcsts).
• Different ensemble members showed that the meander could be very weak (almost not present) or further north than in the central forecast
• Sampling plan designed to investigate position and strength of meander and region of high forecast uncertainty.
Temperature Error Fcst. Salinity Error Fcst.
Surf. Temperature Fcst.
Real-time 3-day forecast of cross-sections along 1 ship-track (all the way back to Moss Landing)
Such sections were provided to R/V Pt Lobos, in advance of its survey
Real-time 3-day forecast of the expected errors in cross-sections along 1 ship-track (all the way back to Moss Landing)
Such error sections were provided to R/V Pt Lobos, in advance of its survey
Error Covariance: dPe/dt = A Pe + Pe AT + Q – Ke R KeT
Dynamics-misfits covariance: dPd/dt = D Pd + Pd DT – Kd R Kd
T
Coverage-misfits covariance: dPc/dt = C Pc + Pc CT – Kc R KcT
where: all K’s = K(H,R), with Ke= Pe H R-1
Metric or Cost function: e.g.
Find Hi and Ri
Dynamics: dx/dt =Ax + ~ (0, Q)Measurement: y = H x + ~ (0, R)
Definition of metric for adaptive sampling:Illustration for linear systems
dtt
ttPtrMinortPtrMin
f
RiHif
RiHi 0
,,))(())((
Current Quantitative Adaptive Sampling Developments
• In realistic cases, need to account for:- Nonlinear systems and large covariances => ESSE- Operational constraints
- Multiple objectives and integration, e.g. Min tr(Pe +Pd + Pc)
• With nonlinear systems, posterior pdf (and error covariances) are a function of data values and pdfs
• Quantitative adaptive sampling via ESSE (Mark 1 software written)- Select sets of candidate sampling regions and variables that satisfy
operational constraints - Forecast reduction of errors for each set based on a tree structure of
ensembles and data assimilation- Sampling path optimization: select sequence of sub-regions/variables
which maximize the nonlinear error reduction at tf (trace of ``information matrix’’ at final time) or over [t0 , tf ]
ESSE DA properties: Error covariance function predicted for 28 August
ESSE T error-Sv
ESSE Field and Error Modes Forecast for August 28 (all at 10m)
ESSE S error-Sv
T S
Observed Tidal Effects
Temperature at M1 CODAR Velocity
Tidal-series least-square fit to data: • For T at 300m, 10-30% total amp.• For U,V at 0m, 20-40% total amp.
Modeling of tidal effects in HOPS
• Obtain first estimate of principal tidal constituents via a shallow water model1. Global TPXO5 fields (Egbert, Bennett et al.)
2. Nested regional OTIS inversion using tidal-gauges and TPX05 at open-boundary
• Used to estimate hierarchy of tidal parametrizations :i. Vertical tidal Reynolds stresses (diff., visc.) KT = ||uT||2 and K=max(KS, KT)
ii. Modification of bottom stress =CD ||uS+ uT || uS
iii. Horiz. momentum tidal Reyn. stresses (Reyn. stresses averaged over own T)
iv. Horiz. tidal advection of tracers ½ free surface
v. Forcing for free-surface HOPS full free surface
T section across Monterey-BayTemp. at 10 m
No-tides
Two 6-day runs
Tidal effects• Vert. Rey.
Str.• Horiz.
Momen. Str.
Dominant dynamical balances for initial biogeochemical fields/parameters
Balance subject to observed variables and parameters constraints
Dominant dynamical balances for initial biogeochemical fields/parameters(Cont.)
Observed fields Balanced fields
• Monterey Bay-CCS in August 2003: Daily real-time predictions of field and errors, DA, adaptive sampling and dynamical analyses
• Preliminary kinematic and dynamical processes:
– Two successions of upwelling and relaxed states: Pt AN << Pt Sur, but in phase
– Local upwellings at Pts. AN/Sur join, along-shelf upwelling, warm croissant along Monterey Bay coastline: this favors a cyclonic circulation in the Bay (occurs without tides, hence tidal effects likely not a cause)
– Relaxation process very interesting:
• Release of KE, possible increase of APE due to N(z) profile variation
• Since KE/APE ~ (R/L)2 : more geostrophic turbulence, baroclinic instability potential, more internal jets, squirts, filaments, eddies
– Daily cycles matter: e.g. modulate downwelling-driven northward coastal jets
– Observed bifurcation (separatrix /LCS front) at Monterey Bay Peninsula
– Some tidal effects matter: regional-scale offshore, (sub)-mesoscale in the Bay
CONCLUSIONS