estimating and predicting ocean currents in the u.s. coastal oceans john d. farrara*, yi chao,...
TRANSCRIPT
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Estimating and Predicting Ocean Currents in the U.S. coastal oceans
John D. Farrara*, Yi Chao, Zhijin Li, Xiaochun Wang*,
Hongchun Zhang*, Peggy Li, Quoc Vu
NASA Jet Propulsion Laboratory
*Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles
Ocean Currents and End User Feedback Workshop, May 5-6, 2011, Atlanta 1
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DataAssimilation
Model
Products
UsersObservations
Feedback
Forecasting
Ocean Hindcast/Nowcast/Forecast
Ocean observing is rapidly expanding, ocean models are maturing; To what extent can the regional oceans be predicted from synoptic weather (days) to climate (seasons, interannual, decadal) time scales?
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A Portable, Data-Assimilative Coastal Ocean Nowcast/Forecast/Hindcast System
- Based on the ROMS regional ocean model A typical configuration has a horizontal resolution of a few km or less,
and extends several hundred kilometers offshore
Nowcasts typically generated every 6 hours, with a daily 48 or 72 hour forecast
- A multi-scale 3DVAR data assimilation scheme
Large and small spatial scales separately assimilated
Scale-dependent error covariances
Scale-dependent dynamic balance constraints
Typical data assimilated includes satellite SSTs, HF radar surface currents
and glider/mooring temperature and salinity vertical profiles
- Tides: Oregon State University global tidal forcing
- Atmospheric Forcing: Regional atmospheric models (WRF, NAM)
- Ensemble Forecasting methodology
- Interactive trajectory tool, real-time execution is fully automated3
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U. S. Coastal Regions where the system has been applied
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15-km 5-km 1.5-km
Multi-level Nested Regional Ocean Modeling System (ROMS)
A multi-scale (or “nested”) ROMS modeling approach has been developed in order to simulate the 3D ocean at the spatial scale (e.g., 1-km) measured by satellites and coastal HF
radars in a way that is computationally efficient enough to allow real-time operations.
5
.
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x is obtained by minimizing the Cost Function
J = (x)T B-1 (x) + (h x-y)T R-1 (h x-y)
xa = xf + x
3DVAR Data Assimilation(3-dimensional variational)
x: model (with error); xft+1=M(xa
t)
f: forecast (model alone)a: analysis/nowcast (with data)
y: observation (with error); h: map model to data
References: Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008a: A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090.Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation schemefor the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002, doi:10.1029/2006JC004042.
Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2011: A multi-scale three-dimensional variational data assimilation scheme and its application to coastal
oceans. Quart. J. Roy. Meteorol. Soc., submitted.
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3DVAR Data Assimilation With Geostrophic Constraint
xuv
xuvf Gdx
TSF
adx
a yc
Min[(Xuvo-Xuv
f)2]
uvG
TSGeostrophic Balance
HF Radar
Current Obs.
Geostrophic vs. Non-geostrophic
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3DVAR Data Assimilation With Hydrostatic Constraint
Min[(Xo - Xf)2]
Hydrostatic Balance dxzS Pdx
TS
xV
xVf Pdx
TSdx
aV
Satellite
Altimetric
Sea Level Obs.
Steric vs non-steric
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Satellite SST/SSH HF Radar
LR-
3DVAR
xLf
Forecast
Smoothed
x f
Start
HR-
3DVAR
Low-Res
Increment La xa x f dx a
xLa x f dx
La
End
Glider/Argo/Mooring
Smoothed
Multi-Scale 3DVAR Data Assimilation
High-Res
Increment dx a
t+1
Low-Res. Observations High-Res. Observations
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Impact of Surface Current Data Assimilation on
Nowcast
RMS Correlation
ROMS w/o
HF radar data
ROMS with
HF radar data1
0
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Impact of Surface Current Data Assimilation on
Forecast
ROMS forecast w/o
sfc current data Persistence
ROMS forecast with
sfc current data
1
1
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HF Radial Current Data Assimilation
Quality Control:
STD<10 cm/sec
MapError<0.95
JPL/ROMS
DAS
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L0 10km, 40 layersL1 3.3.km, 40 layersL2 1.1km, 40 layers
Atmospheric Forcing:L2: UAA, 4km WRF
L1, L0: 0.5o GFS
Tides forced on lateral boundaryof L0 domain byOSU global tide model output.
Gulf of Alaska/Prince William Sound Configuration
1
3
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ROMS vs. Independent Data:Drifter Trajectories
ROMS Daily Mean Surface Currents for
July 26, 2009
Observed Drifter Trajectories –
July 25 – 27, 2009
Sound Predictions 2009 Field
Experiment, Prince William Sound, Alaska
1
4
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July 31 – Aug 3, 2009
July 20 – 26, 2009
July 27 – 30, 2009
ROMS vs. Assimilated DataHF Radar Surface Currents
1
5
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ROMS vs. Assimilated Data: HF Radar Surface Currents
RM
S (
cm/s
)S
pati
al C
orre
lati
on
Num
ber
of O
bser
vati
ons
(Lig
ht B
lue
Bar
s)
Julian Day 2008 1
6
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.
ROMS Ensemble Forecasting: Surface Current Speed Spreads (cm/s)
Hour 6 Hour 48
.
1
7
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SVP 10m Drifter, Release time: July 25 th, 02 GMT, End time: July 31st, 02 GMT.
Ensemble of ROMS simulated drifter trajectories, release time and location corresponding to that of the SVP
drifter shown above.
1
8
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ROMS Forecast Performance vs. Persistence(Spatial correlation / RMS Difference)
Forecast Field ROMS Single Fcst ROMS Ensemble Fcst Persistence
24 hr Sfc Currents 0.90 / 7.4 cm/s 0.91 / 7.2 cm/s 0.72 / 14.1 cm/s
48 hr Sfc Currents 0.80 / 10.6 cm/s 0.82 / 9.9 cm/s 0.57 / 17.2 cm/s
PWS
1
9
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Subsurface Currents in the PWS
Julian Day 2009 2
0
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California (3 km) and Southern California Bight (1 km) domains
2
1
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ROMS Forecast Performance vs. Persistence(Spatial correlation / RMS Difference)
Forecast Field ROMS Single Fcst ROMS Ensemble Fcst Persistence
24 hr Sfc Currents 0.78 / 12 cm/s 0.66 / 18 cm/s
48 hr Sfc Currents 0.66 / 17 cm/s 0.46 / 23 cm/s
SCB
2
2
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SCB ROMS Forecasts vs. HF radar observed
surface currents
2
3
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Comparison of Glider-Derived Currents
(vertically integrated current)
Black: glider
Red: ROMS
2
4
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RMS: 8.5 cm/s, Correlation: 0.46
Subsurface currents in California:ROMS CA-3km vs Independent Data: Vertically-averaged glider currents
2
5
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2006 MB06/ASAP/AESOP
Baroclinic Tides
2
6
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Summary1) A portable ROMS-based nowcast/forecast/hindcast nested modeling system
assimilating coastal HF radar surface current measurements, satellite SSTs and in-situ
glider/mooring data capable of routinely producing near real-time nowcasts every 6
hours and daily 2-3 day forecasts for coastal ocean regions at a horizontal resolution of
1km was presented.
2) A key component of the system is a multi-scale 3DVAR assimilation methodology
that incorporates spatially varying and scale-dependent error covariances, scale-
dependent dynamic balance constraints and can simultaneously assimilate all types of
ocean observations.
3) Surface circulation patterns are quantitatively reproduced (RMS differences of 5-15
cm/s). Subsurface currents are qualitatively reproduced.
4) One and two day forecasts of surface current forecasts more skillful than persistence
forecasts and clearly show the positive impact of the assimilation of HF radar surface
currents at the surface and below.
ourocean.jpl.nasa.gov/{PWS, CA, SCB}2
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All slides that follow are 'extras'
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Depth-Integrated Baroclinic Tide Energy/Energy Flux (M2)
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Barotropic Tide Energy Flux (M2)
3
0
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Barotropic Tide Current (M2)
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Data Assimilation to enable Forecasting& estimate Uncertainty
Time
Stat
e of
Oce
an (e
.g.,
T, S
, Cur
rent
)
T T+ 6 hours T+ 72 hours
Forecast
True Ocean
Observations
EnsembleForecast
Error
Nowcast
3
2
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HF Radar Total vs Radial Current Data Assimilation
Total currents data assimilation
(circle)
1st Guess(blue)
Reanalysis(red)
Radial current data assimilation
(triangle)
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Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008a: A Three-Dimensional Variational Data Assimilation Scheme for
the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090.
Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation scheme
for the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002,
doi:10.1029/2006JC004042.
Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2011: A multi-scale three-dimensional variational data assimilation
scheme and its application to coastal oceans. Quart. J. Roy. Meteorol. Soc., submitted.
Global to Regional&
Physics to Biology
Multi-scale (or “nested”) ROMS modeling approach
has been developed in order to simulate the 3D ocean at the spatial scale (e.g., 1-km) measured by
satellites
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Impact of HF Radar Current Data on
Nowcast/ForecastNowcast
RMSROMS w/oCurrent DA
ROMS withCurrent DA
48-hour forecast
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Surface Current Forecasting in the PWS: Two case studies
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Mean distance between ROMS nowcast and Observed drifter trajectories
Hours since Deployed
Dis
tanc
e (k
m)
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Data Assimilation Formulation
J=1
2x x f T B 1 x x f 1
2Hx y T R 1 Hx y
prescribed B optimization algorithm
Variational methods (3Dvar/4Dvar):
Sequential methods (Kalman filter/smoother)
dynamically evolved B analytical solution
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Distance Offshore (km)
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