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1. Motivation and initial objectives2. Overview of FOAM system3. Data to be assimilated4. Sea-ice model and configurations being used5. Assimilation methods6. Plans for validation and testing

Sea-Ice Data Assimilation and Modelling at UK Met Office

John Stark, Mike Bell, Adrian Hines, Matt Martin, Jeff Ridley, Alistair Sellar

18 month study funded by ESA started 5 Jan 2004

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Motivation improve operational forecasts of sea-ice and ocean improve seasonal and decadal forecasts improve NWP forecasts by using better sea-ice data reduce uncertainties in sea-ice models by detailed

intercomparison of models & data

Short-term Objectives (18 months) design, build, trial and assess performance of a system for assimilating satellite & in situ sea-ice data

– Assessment to be as quantitative as possible

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The FOAM system Operational daily 5-day forecasts

of temperature, currents & sea-ice Driven by atmosphere, assimilates

satellite & in situ observations Relocatable high resolution nested

model capability Hindcast capability (back to 1997) 1° (operational since 1997)

1/3° operational April 20011/9° pre-operational April 2002

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We will initially use – SSM/I as “work-horse” data set– ASI algorithm by Kaleschke et al. (2001)

» uses low frequency NTA with weather filter as an ice mask.» polarisation difference at 85 GHz

– swath data directly (rather than a gridded product)

We aim to – retain flexibility in choice of algorithm – develop algorithm to detect formation & freezing of melt ponds

Sea ice concentration data to be assimilated

SSMI using ASI 15 Jan 2001

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Cross-correlation methods

– passive microwave (SSM/I) from NSIDC web-site

– scatterometer (QuikSCAT) from CERSAT web-site

– intend to use 3-day average displacements initially

– cross-correlation products are relatively coarse

SAR data will be used for validation where possible (RGPS)

Sea ice motion data to be assimilated

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Other important data which will not be used initially

AMSR(E)– higher resolution but only available from 5 April 2003

Scatterometer – valuable for sea-ice edge – could use as mask for

passive microwave

SAR data – narrow swath & expensive (computation and financial)

– but accurate and high resolution

– will use for validation

– may be useful to improve “tie-points” or as unbiased data in future

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Sea-ice model and configurations Will use sea-ice model developed for HadGEM1

– zero layer thermodynamics (Semtner 1976)– elastic viscous plastic rheology (Hunke & Dukowicz 1997) – ice thickness distribution (Lipscomb 2001) – derived from the CICE model (Hunke & Lipscomb, LANL)

Configurations for Arctic and Antarctic nested within global

Mean Ice Velocities from proto-HadGEM1

March & October Mean Ice Concentration.

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FOAM Assimilation scheme (1)

– scheme is statistical: “expected” errors in model and observations are specified

– expected errors in model can have more than one spatial correlation scale (e.g. mesoscale and synoptic scale) and variance of each can vary spatially

– scheme iterates towards “optimal” solution. Each iteration uses only one group of observations (e.g. ice concentrations)

– for each group, balancing increments in other fields are calculated; e.g. to conserve salt when ice forms

– Observations make impact close to time of validity then retain weight which decays with time (extension of first-guess at appropriate time).

– The quick brown fox jumped over some rather significant results and was surprised.

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Assimilation scheme (2)

Issues for concentration data– whether to assimilate first-year/multi-year information

– how to adjust ice thickness distribution using ice concentration measurements

– how to apply smooth analysis increments without diffusing the ice edge.

Issues for velocity data – the fraction of velocity increments projected into the

divergent flow will vary spatially (should depend on sea-ice strength ?)

– the model velocities used to make increments need to be appropriate time and space averages

– specification of velocity increments near to coast

Issues in common– lack of data for summer melt.

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Assimilation of Ice Velocity Minimise spurious divergence through use of

stream fn. and velocity potential.

Analysis Field

IncrementsNSIDC Analysis

Initial results after 1 day of assimilation.

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Plans for testing and validation

Planned integrations– One month tests: January 2000

– One year integration: 1 Oct 1999 – 31 Oct 2000

Independent products for intercomparison – RGPS (Kwok & Cunningham) based on RADARSAT

– IAPB (Arctic) and IPAB (Antarctic) buoys

Alternative analyses– NIC and NSIDC ice concentration analyses

– Weekly mean ice velocity analyses from NSIDC

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Next highest priority sea-ice data

Ice freeboard– altimeters (ICESAT and Cryosat)

– not yet mature

– useful to close momentum balance and improve

– advection of ice concentration

Surface temperature and snow depth – AMSR (6 & 10 GHz) passive microwave

– assimilation of surface temperature would have to be introduced into atmosphere model and take account of diurnal variations

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Impact on ATOVS/AMSU Assimilation in NWP

Lack of knowledge of microwave freq. sea ice emissivity results in large data voids in sounding data over polar regions. Sea ice fraction is an important part of the current emissivity parameterization.

The output from the new products will be compared with the operational sea ice fraction to determine:

– whether the ability of the ice-model to fill in data gaps and represent a more realistic sea ice field will benefit the AMSU processing

– whether increased accuracy of sea-ice fraction improves the fit of forward-modelled AMSU radiances to the observed data

– the task is to repeat work of Schyberg & English using the new sea-ice fields as input to the boundary conditions for the profile retrieval processing

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Summary

The FOAM system now includes an EVP and ITD sea-ice model.

We are in the early stages of development of a sea-ice concentration and velocity data assimilation scheme for FOAM.– Principal data sources are SSM/I (ASI) , QuikSCAT

(CERSAT), and SSM/I (NSIDC).

We aim to use the sea-ice model to improve the ice concentration retrievals during the melt season and improve data quality for NWP assimilation.