Wilkin-NOS-2013-MAB-DA

Report
Overview of Rutgers Ocean Modeling
Group activities with 4DVar data
assimilation in the Mid-Atlantic Bight
John Wilkin
NOS Silver Spring
Feb 28-29, 2012
Apr 24-25, 2013
LaTTE 2006 Data Assimilative 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
Zhang, W., J. Wilkin and H. Arango (2010a), Towards an
integrated observation and modeling system in the
New York Bight using variational methods, Part I:
4DVAR Data Assimilation, Ocean Modelling, 35, 119133, doi: 10.1016/j.ocemod.2010.08.003
Zhang, W., J. Wilkin and J. Levin (2010b), Towards an
…, Part II: Representer-based observing system design,
Ocean Modelling, 35, 134-145,
10.1016/j.ocemod.2010.06.006.
We overlap 3-day
analysis cycles,
performing a new
analysis and new
forecast every day
LaTTE 2006 reanalysis (60 days)
Comparison of observed
and modeled sea
surface temperature and
current at 0700 UTC 20
April 2006.
Zhang, W., J. Wilkin and H. Arango (2010a), Towards an
integrated observation and modeling system in the
New York Bight using variational methods, Part I:
4DVAR Data Assimilation, Ocean Modelling, 35, 119133, doi: 10.1016/j.ocemod.2010.08.003
Zhang, W., J. Wilkin and J. Levin (2010b), Towards an
…, Part II: Representer-based observing system design,
Ocean Modelling, 35, 134-145,
10.1016/j.ocemod.2010.06.006.
LaTTE 2006 reanalysis (60 days)
2-D histograms comparing
observed and modeled
temperature, salinity, and ucomponent of velocity model
before (control simulation) and
after (analysis) data
assimilation.
Color indicates the log10 of the
number of observations.
Zhang, W., J. Wilkin and H. Arango (2010a), Towards an
integrated observation and modeling system in the
New York Bight using variational methods, Part I:
4DVAR Data Assimilation, Ocean Modelling, 35, 119133, doi: 10.1016/j.ocemod.2010.08.003
Zhang, W., J. Wilkin and J. Levin (2010b), Towards an
…, Part II: Representer-based observing system design,
Ocean Modelling, 35, 134-145,
10.1016/j.ocemod.2010.06.006.
Surface velocity forecast skill improves with assimilation
of CODAR
This analysis for ROMS LaTTE domain (NY Bight). 2-day forecast skill significantly
improved for cross-correlation (submesoscale pattern variability)
MSE = mean squared error
CC = cross-correlation; Sm and So are std. dev. of model and obs
no assimilation of CODAR data
analysis
forecast window
after assimilating CODAR data
analysis
forecast window
• 2009- operational system for OOI CI OSSE (ongoing)
– 72-hour forecast (NAM-WRF meteorology)
– tides, rivers, OBC HyCOM NCODA
– assimilates:
• altimeter along-track SLA
• satellite IR SST
• CODAR surface currents
• climatology
• glider T,S
• GTS: XBT/CTD, Argo, NDBC
ESPreSSO
4DVar Assimilation (physics) in ROMS ESPreSSO*
*Experimental System for
Predicting Shelf and
Slope Optics
www.myroms.org/espresso
7
Work flow for operational ESPreSSO/MARCOOS 4DVar
ROMS 4DVAR Analysis and Forecast
Analysis interval is 00:00 – 24:00 UTC
ESPreSSO
• Input pre-processing starts 01:00 EST
• Input preprocessing completes approximately 05:00 EST
• 4DVAR analysis completes approx 08:00 EST
• 24-hour analysis is followed by 72-hour forecast using NCEP NAM 0Z
cycle available from NOMADS OPeNDAP at 02:30 UT (10:30 pm EST)
• Forecast complete and transferred to OPeNDAP by 09:00 EST
• Effective forecast is ~ 60 hours
*Experimental System for
Predicting Shelf and
Slope Optics
www.myroms.org/espresso
Work flow for operational ESPreSSO/MARCOOS 4DVar
Data used:
•
•
•
•
•
•
•
•
•
72-hour forecast (NAM-WRF meteorology 0Z cycle at 2 am EST)
RU CODAR is hourly - but with 4-hour latency delay
RU glider T,S when available (seldom) (~ 1 hour delay)
USGS daily average flow available 11:00 EST
– persist in forecast
AVHRR IR passes 6-8 per day (~ 2 hour delay)
HyCOM NCODA 7-day forecast updated daily
Jason-2 along-track SLA via RADS (4 to 16 hour delay for OGDR)
– Also ENVISAT and Jason-1 NRT data (OGDR and IGDR)
SOOP XBT/CTD, Argo floats, NDBC buoys via GTS from AOML
Regional high-resolution T,S climatology (MOCHA*)
*Mid-Atlantic Ocean Climatology Hydrographic Analysis
Work flow for operational ESPreSSO/MARCOOS 4DVar
Input pre-processing
• RU CODAR de-tided (harmonic analysis) and binned to 5km
– variance within bin & OI combiner expected u_err (GDOP) used for QC
>> ROMS tide added to de-tided CODAR – reduces tide phase error contribution to cost function
• RU glider T,S averaged to ~5 km horiz. and 5 m vertical bins
– need thermal lag salinity correction to statically unstable profiles
• SOOP XBT and Argo – not used at present
• AVHRR IR individual passes 6-8 per day
– U. Del cloud mask; bin to 5 km resolution
– REMSS daily SST OI combination of AVHRR, GOES, AMSR-E
• Jason-2 along-track 5 km bins (with coastal corrections) from RADS
– MDT from 4DVAR on “mean model” (climatology 3D T,S, uCODAR, τwind)
>> add ROMS tide solution to SSH
• USGS daily river flow is scaled to account for un-gauged watershed
ESPreSSO operational system
11
ESPreSSO operational system
Skill of DA analysis at hind-casting mesoscale SST
14
Skill of DA analysis at hind-casting along-track SSHA
Jason-2
Envisat
15
Comparison to withheld T and S observations from CTD, gliders and XBT
16
16
Skill at hind-casting vertical structure of salinity.
These observations were not assimilated
17
Skill at hind-casting vertical structure of temperature.
These observations were not assimilated
18
Summary
• ROMS LaTTE and ESPreSSO 4DVAR use all available data from a modern
coastal ocean observing system
– satellites, HF-radar, moorings, AUV (glider, Argo …), XBT/CTD;
IR SST individual passes work best; time variability resolved
– more and diverse data is better
– climatology assimilation: removes OBC bias; improves representation
of dynamic modes and adjoint-based increments
• Useful skill for operational applications
– 5-7 days for temperature and salinity
– 1-2 days for velocity
– improved short-term ecosystem prediction
– observing system operation … glider path planning
• Variational methods for observing system design
– adjoint sensitivity and representer-based observing system design
(see W. Zhang et al. papers in Ocean Modelling, 2010); observation
impact analysis (see A. Moore et al. papers in Prog. Oceanog. 2011)
Future
Switch to ROMS 4DVAR formulation based on
weak constraint/dual space formulation
• Solution is in “observation” space – typically much much smaller than
model space
• Uses same forward, adjoint and tangent linear models, and observation
input format
• Comparable convergence and speed
• Two variants:
– #define W4DVAR - Indirect Representer algorithm (Egbert et al. 1994)
– #define W4DPSAS - Physical Space Statistical Analysis System (Da Silva
et al. 1995)
• Allows for adjustment to time-varying forcing and boundary conditions,
explicit acknowledge of model errors, and posterior analysis of e.g.
observation impact (and more)
DOPPIO model domain configuration
with local shelf and estuarine nests
Doppio …
Proposed Doppio domain with Rio09 Mean Dynamic Topography
Grid cell
resolution
7 km
Proposed IODA domain (version 3) (only every 3rd grid cell) to nest within Doppio
Grid cell
resolution
2.33 km
Lm x Mm
96 x 144
the resolution challenge …
Small scales –
important to large
scale dynamics (and
ecosystem)?
RU Endurance Line glider
transect May 18-24, 2006

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