Title: 4DVar Assimilation (physics) in ROMS ESPreSSO* John Wilkin, Julia Levin, Javier Zavala-Garay
14DVar Assimilation (physics) in ROMS
ESPreSSOJohn Wilkin, Julia Levin, Javier
Zavala-Garay
- 2006 reanalysis (SW06)
- Operational system for OOI CI OSSE (ongoing)
- Assimilating altimeter SLA satellite IR SST
CODAR surface currents climatology glider
T,S T,S from XBT/CTD, Argo, NDBC (via GTS)
- Use methodology developed for spring 2006 LaTTE
reanalysis Zhang et al., Ocean
Modelling, submitted 2009 - Skill assessed in forecast window
- several days for T,S
- 1-2 days for velocity
Experimental System for Predicting Shelf and
Slope Optics
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3ROMS ESPreSSO configuration
- 5 km horizontal resolution Cape Cod to Cape
Hatteras - 36 levels in traditional ROMS s-coordinate
(stretching1) - 4th order Akima T,S advection 3rd order upwind
u,v advection - Bathymetry, land-sea mask from NGDC Coastal
Relief Model - Open boundary data HyCOMNCODA (no bias
correction) - stiff boundary nudging in forward simulations
- Meteorology forcing 3-hourly 12-km NCEP NAM-WRF
- 72-hour forecast window
- sea level atmospheric conditions bulk formulae
fluxes - use NCEP NARR in 2006 reanalysis
- River daily average discharge USGS gauges
- adjusted for ungauged fraction of watershed
- Tides TPXO0.7 tides (5 harmonics)
We are working in a data rich location for 4DVar
assimilation
4Mid-Atlantic Regional Coastal Ocean Observing
System (MARCOOS) CODAR, gliders, moorings,
tide gauges, drifters, satellites
5IS4DVAR
- Given a first guess (the forward trajectory)
- and given the available data
Incremental Strong Constraint 4-Dimensional
Variational data assimilation
6IS4DVAR
- Given a first guess (the forward trajectory)
- and given the available data
- what change (or increment) to the initial
conditions (IC) produces a new forward trajectory
that better fits the observations?
7The best fit becomes the analysis
assimilation window
ti analysis initial time
tf analysis final time
The strong constraint requires the trajectory
satisfies the physics in ROMS. The Adjoint
enforces the consistency among state variables.
8The final analysis state becomes the IC for the
forecast window
assimilation window
forecast
tf analysis final time
tf t forecast horizon
9Forecast verification is with respect to data not
yet assimilated
assimilation window
forecast
verification
tf t forecast horizon
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13LaTTE 2006 reanalysis (60 days)
LaTTE domain and observation locations.
Bathymetry of the New York Bight is in
grayscale and dashed contours. yellow star is
location of Ambrose Tower green squares are
CODAR HF Radar sites
14LaTTE 2006 reanalysis (60 days)
Comparison of observed and modeled sea surface
temperature and current at 0700 UTC 20 April 2006.
15LaTTE 2006 reanalysis (60 days)
2-D histograms comparing observed and modeled
temperature, salinity, and u-component of
velocity model before (control simulation) and
after (analysis) data assimilation. Color
indicates the log10 of the number of
observations.
16Ensemble average of the DA skill for analysis and
forecast periods for different data withheld from
systemVertical bars are 95 confidence.Vertic
al dashed line is boundary between analysis and
forecast.
LaTTE 2006 reanalysis (60 days)
Removing any data from the analysis system has
more or less predictable negative impact on the
forecast. More data is always better.
17LaTTE 2006 reanalysis (60 days)
- Ensemble average of the DA skill for analysis and
forecast periods for different data withheld from
system, evaluated with respect to - glider-measured temperature and
- satellite-measured SST Vertical bars are 95
confidence.Vertical dashed line is boundary
between analysis and forecast.
Satellite SST is crucial to forecast skill for
this skill metric namely, comparison to new
observations in the forecast window.
184DVar Assimilation (physics) in ROMS ESPreSSO
- 2006 reanalysis (SW06)
- Basis for experiments with ecosystem and
bio-optical modeling - 2009- operational System for OOI CI OSSE
(ongoing) - 72-hour forecast (NAM-WRF meteorology)
- tides, rivers, OBC HyCOM NCODA etc.
- assimilates
- altimeter along-track SLA
- satellite IR SST
- CODAR surface currents
- climatology
- glider T,S
- GTS XBT/CTD, Argo, NDBC
19- Work flow for operational ESPreSSO/MARCOOS 4DVar
- Analysis interval is 0000
2400 UTC - Input data preparation commences 0100 EST (0500
UT) - RU CODAR is hourly - but with 4-hour delay
- RU glider T,S where available (approx 1 hour
delay) - USGS daily average flow available 1100 EST
- persist in forecast
- AVHRR IR passes (approx 2 hour delay)
- HyCOM NCODA forecast updated daily
- Jason-2 along-track SLA via RADS (4 to 16 hour
delay) - GTS XBT/CTD, Argo, NDBC from AOML (intermittent)
- T,S climatology (MOCHA)
20- Work flow for operational ESPreSSO/MARCOOS 4DVar
- Input
preprocessing - RU CODAR de-tided (harmonic analysis) and binned
to 5km - variance within bins and OI combiner expected
u_err used for QC - ROMS tide solution added to de-tided CODAR this
approach reduces tide phase error contribution to
cost function - RU glider T,S averaged to 5 km horiz. and 5 m
vertical bins - developed thermal lag salinity correction using
constrained parametric fit to minimize statically
unstable profiles - AVHRR IR individual passes 6-8 per day
- use Matt Olivers cloud mask bin to 5 km
resolution - 2006 reanalysis uses REMSS daily SST OI
combination of AVHRR, GOES, AMSR - Jason-2 alongtrack 5 km bins (no coastal
corrections) - MDT from 4DVAR on mean model (climatology 3D
T,S, usurface, twind)
21Comparison of HF Radar observed and modeled M2
tide in LaTTE.
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23Work flow for operational ESPreSSO/MARCOOS
- Input preprocessing completes approximately 0500
EST - 4DVAR analysis completes approx 0800 EST
- 24-hour analysis is followed by 72-hour forecast
using NCEP NAM 00Z cycle available from NOMADS
OPeNDAP at 0230 UT (1030 pm EST) - Forecast complete and transferred to OPeNDAP by
0900 EST - Effective forecast is 60 hours
OPeNDAP http//tashtego.marine.rutgers.edu8080/t
hredds/catalog.html ncWMS http//tashtego.ma
rine.rutgers.edu8081/ncWMS/godiva2.html
24Output OPeNDAP http//tashtego.marine.rutgers.edu
8080/thredds/catalog.html ncWMS
http//tashtego.marine.rutgers.edu8081/ncWMS/god
iva2.html
25ESPreSSO operational system
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30Lessons from operational IS4DVAR for ESPreSSO
- More and diverse data is better
- use all available observations and platforms
- Quality control
- outliers in CODAR
- cloud clearing from IR
- coastal altimetry
- High resolution regional climatology
- removes bias from Open Boundary Condition
- improves representation of dynamic modes and
adjoint-based increments - IR SST individual passes work best with 4DVAR
- time variability is explicitly resolved
- implications for optics
- Useful skill for operational applications
- glider reachability forecast
- Physics analysis affects ecosystem/optics model
skill
31Small scales may be important to large scale
dynamics (and ecosystem)
RU Endurance Line glider transect May 18-24, 2006
32assimilation window
tf analysis final time
33The analysis final hours becomes the data for the
high-res 4DVAR, then forecast
high-res assimilationwindow
high-res forecast
tf t forecast horizon
assimilation window
tf analysis final time
34Issues/Tasks ahead for 4DVAR ESPreSSO
- High frequencies
- Filter inertial oscillations/tides in increment
when updating outer loop - High frequencies in coastal altimetry (keep or
remove IB correction?) - Background error covariance
- Can we use multi-variate balance constraint in
coastal ocean? - Wide shelf, steep slope, anisotropic variability
- Ecosystem / bio-optics?
- Ecosystem/bio-optics assimilation in 4DVar
- have Adjoint for NZPD model (ecosystem emphasis)
- have Adjoint for simple bio-optical model (IOP
emphasis) - Climatology? Initialization?
- need dense data set for assimilation development
- twin experiments?
- optics in Community Sediment Transport Model?
- Couple ecosystem/optics with thermodynamics
- interaction is significant on NJ inner shelf
(just do it) - Downscaling
- 1 km resolution grid (better bathymetry and
land/sea mask)