Transcript Operational use of regional high

Status and plans for assimilation of
satellite data in coupled ocean-ice
models
Jon Albretsen and Lars-Anders Breivik
Outline:
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Input from satellites
Ice-Ocean model configuration
Assimilation scheme
Results and validation statistics
Challenges in the future
Norwegian Meteorological Institute met.no
Input from satellites, SST
OSI SAF product: 10 km resolution SST fields
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composed every 12th hour
At high latitudes these products are based on 1.5 km
resolution polar orbiting NOAA satellite passes.
Each SST value have corresponding quality flags. The most
important properties when it comes to SST data
assimilation, are:
1. Confidence level (from excellent to erroneous or
unprocessed value).
2. Mean coverage of the pixel (i.e. the average of the
coverage of the primary 1.5 km resolution SST values
used in the 12 hour composite).
3. No. of primary SST values used in the 12 hour
composite.
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OSI SAF 12 hour SST composite, valid 09-01-2003 at 00 UTC
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The current SST assimilation scheme needs a complete SST
data set covering the total model area.
Weekly
SST field
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The current SST assimilation scheme needs a complete SST
data set covering the total model area.
SST field
successively
overwritten
by OSI SAF
SST
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Input from satellites, Sea Ice
The OSI SAF Sea Ice Concentration product is assimilated. This is a
daily product presented on a 10km resolution grid, covering the
same area as the SST product.
SSM/I ice concentration product is an analysis derived in 2 steps:
1.
Using the OSI SAF SSM/I hybrid algorithm sea ice concentration is
estimated for each observation node during the analysis interval
(1 day).
2.
In the next step, these results are analyzed on the 10 km SAF
grid:
Several SSM/I observation nodes with estimated concentrations
influence on each analysis grid point.
The radius of influence r, for each SSM/I observation is 18 km.
The weight assigned to each SSM/I observation in the analysis is
dependent on:
1. sn2 : the variance of the SSM/I concentration estimate.
2. dn : the distance between the centre of the SSM/I node and
the grid point.
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met.no
OSI SAF sea ice concentration, valid 08-01-2003 at 12 UTC
Norwegian Meteorological Institute met.no
Model configuration
The coupled sea ice – ocean model consists of:
• MIPOM, met.no’s operational ocean model, a
version of the sigma coordinate ocean model, POM
(Princeton Ocean Model).
• MI-IM (Meteorological Institute’s Ice Model), a
state-of-art dynamic-thermodynamic sea ice
model.
A net heatflux is calculated in MI-IM, both in icecovered and ice-free areas, and used in MIPOM as
the surface boundary condition for temperature.
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Model area for the coupled sea ice – ocean model
• Grid mesh 20 km
• Relaxation along open
horizontal boundaries,
at depths greater than
1000 m and in surface
salinity towards
climatological monthly
means
• Atmospheric forcing
from the ECMWF
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Assimilation scheme
Before each daily 10 days forecast is produced, the models
are run 30 hours in hindcast.
A heat flux nudging method is used to force the model
towards the “observed” SST, which is the complete
weekly SST chart successively overlaid by 10 km
resolution OSI SAF SST fields valid the same period.
Forecast
ice conc. ass.
-30
-24
0
+240
Analysis time
sst ass.
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Mathematically, the assimilation method can be expressed as,
Fh  wFc  (1  w) Fi ,
where
Fc  k (Ta  Tm ).
w is the weight between the flux correction, Fc, and the heat flux
calculated in MI-IM, Fi.
Fc is the difference between the analyzed (“observed”) SST, Ta,
and the SST from the ocean model, Tm, multiplied by a flux
coefficient, k.
The value of k and the thickness of the upper vertical grid cell decides how fast
the model temperature approaches Ta. The time scale for the nudging
method is typically 20 minutes or 3 hours where the total depth is 100 m or
1000 m, respectively.
During prognosis time, only the heat flux from MI-IM, Fi, is used as boundary
condition for temperature in MIPOM (w=0).
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MI-IM receives an updated field with the OSI SAF sea ice
concentration valid 24 hours before model analysis time. A
simple nudging scheme is then executed the first 6 hours of
the hindcast period.
The corrected sea ice concentration in the model can be
expressed as,
ccorr  cm  K (cana  cm )
cm and cana are the model and analyzed sea ice concentrations,
respectively, and the nudging coefficient, K, varies linearly
from 0 to 0.5 through the first 6 hours assimilation period.
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Results
The following results demonstrate some of the impacts the
SST and sea ice assimilation has on the forecast.
A control run without any kind of assimilation is executed
parallel with the main run, and some differences are
presented here.
The results are retrieved from a model run performed with
analysis time 8th of January 2003 at 12 UTC.
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Analyzed SST and model SST from the control run
at analysis time (+00)
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Analyzed SST and model SST from the assimilation run
at analysis time (+00)
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SST from the model run with assimilation and the
model run without assimilation at analysis time (+00)
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SST from the model run with assimilation and the
model run without assimilation at +120 hours prognosis time
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Depth (m)
Sea temperature at 65N,2E from run with assimilation
and run without assimilation
Prognosis (hours)
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Forecast impact, SST: Root Mean Square error
Red line: Control run
Blue line: Assimilation run
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Sea ice concentration from the model run with assimilation
(black lines) and corresponding values from OSI SAF product
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Forecast impact, ice concentration: Root Mean Square error
Red line: Control run
Blue line: Assimilation run
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Forecast impact, ice concentration: Bias (model-observation)
Red line: Control run
Blue line: Assimilation run
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Conclusions
• Assimilation of SST and sea ice concentration has a
positive impact on the forecast.
• The positive impact on the ocean model’s SST is
maintained through the forecast period.
• This is not the case for the sea ice concentration where
the positive impact is deteriorated after about 5 days
forecast.
• The impacts of the SST assimilation propagates
downwards during prognosis time.
Challenges SST
• Automatic full cover SST analysis based on microwave
(e.g. AMSR) and infra-red
• more advanced assimilation techniques
EU FP-5 and -6 projects
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Challenges Sea Ice:
1. Achieve a multivariate well balanced
analyzed field ( today by nudging
techniques)
2. Optimal choice of ice related
parameter to be assimilated.
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Challenges Sea Ice:
Sea Ice analysis methods
• Further development of the OSI SAF
multi sensor analysis
• Automatic high resolution analysis based
on SAR
• Multivariate assimilation schemes for
high resolution sea ice models
ESA GMES, EU FP-6, national projects
Cooperation within IICWG !
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References
Engedahl, H. (1995): Implementation of the Princeton Ocean
Model (POM/ECOM-3D) at The Norwegian Meteorological
Institute (DNMI). Research Report NO. 5, The Norwegian
Meteorological Institute
Blumberg, A.F. and G.L. Mellor (1987): A description of a threedimensional coastal ocean circulation model. Threedimensional Coastal Ocean Models, Vol. 4, N. Heaps (Ed.),
American Geophysical Union, Washington D.C.,1-16.
Sætra, Ø., L.P. Røed and J. Albretsen (1999): The DNMI RegClim
Ice Model. RegClim general technical report, No. 3. (Available
from Norwegian Institute for Air Research, P.O. Box 100, N2007 Kjeller, Norway).
Brieivk, L.-A., S. Eastwood, Ø. Godøy, H. Schyberg, S. Andersen,
and R. Tonboe (2001), Sea ice products for EUMETSAT Satellite
Application Facility, Canadian Journal of Remote Sensing, Vol.
27, no. 5
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