An International Collaborative Effort Towards Automated Sea Ice Chart Production

1
An International Collaborative Effort Towards
Automated Sea Ice Chart Production

• Tom Carrieres
• Ice Modelling Manager
• Applied Science Division
• Canadian Ice Service

2
Contributors

• Tom Carrieres
• Doug Lamb
• Lars-Anders Breivik
• Rashpal Gill
• Dean Flett
• Mark Buehner
• Bruce Ramsay
• Mike Van Woert
• Mike Manore

3
Outline

• Background
• State of the Art Review
• Data
• Models
• Data Assimilation
• Research and Development Steps

4
Background

• Current Ice Chart Production
• - ice analysts with extensive experience
• - increasing problem of information overload
• - visible, infrared and passive microwave data
• - SAR, scatterometers, aerial reconnaissance
• - ice drift from beacons, models, algorithms
• - derived geophysical fields very limited
• - operational time constraints
• subjective labour intensive analyses

5
Background

• NWP/data assimilation benefits
• - objective, optimal integration of varied data
• - allows forecasters more time to focus on
  critical areas
• IICWG 3
• - are similar benefits feasible for sea ice?
• - provide first guess for analysts to build on
• - expert judgement focuses on critical areas
• - objective weighting of data leads to more
  consistent products
• - possible avenue for international collaboration
• - science workshop and White Paper on data
  assimilation

6
Requirements

• - observations that resolve phenomena
• - models that adequately predict future state
• - consistent, inclusive, objective analysis

7
State of the Art Review - DataSatellite Data
8
State of the Art Review - Data

• Ice motion
• - beacon/buoy derived motion is sparse but almost
  continuous
• - sequential image derived motion is sensor
  dependent with variable resolution and interval
• - algorithms perform poorly in marginal or
  featureless ice areas

9
State of the Art Review - Data

• Data Issues
• - should revisit algorithm development within the
  context of objective, automated use
• - characterization of data errors almost
  non-existent
• - variety of data sources is optimal
• - errors can offset each other
• - take advantage of higher resolution data
• - data management becomes an issue
• - mix of derived fields and direct satellite
  measurements may provide the most useful
  combination of information

10
State of the Art Review - Models

• Components of an ice model
• - drift
• - thickness distribution and redistribution
• - strength and rheology
• - thermodynamics
• - ocean coupling
• Typical model
• - resolution 5-20 km
• - forecast period hours to days
• - areal extent hundreds to thousands of km

11
State of the Art Review - Models

• Issues
• - less direct coordination/cooperation has
  occurred in model development
• - most models designed for climate change and or
  engineering design
• - operational ice forecasting is more of an
  initial value problem and scale is different
• - many complex processes have been modelled but
  very few ice characteristics are observed
• - focus on assimilative models?
• - simple models?
• - constrained by data
• - for use in 4Dvar?
• - model skill is dependent on accuracy of forcing
  fields

12
State of the Art Review Data Assimilation

• - combines observed and model data
• - statistically optimal manner
• - constrained by model physics
• - accounts for relative errors
• - constrains models to reality overcoming
• - uncertainties in forcing
• - parameterization limitations
• - finite spatial and temporal scale
• - improves on data by
• - filling observation gaps
• - combining disparate data into coherent
  products
• - adding temporal consistency

13
State of the Art Review Data Assimilation

• Analysis segment
• - simplest technique is to accept observations
  from individual sources as truth and interpolate
  or average the data onto a grid
• - to combine observations from different sources,
  statistical analysis or optimal interpolation
  methods are employed, making use of the error
  variances of each of the data sources
• - model forecasts may be used as complete
  background fields with their own applicable error
  statistics

14
State of the Art Review Data Assimilation

• Model initialization segment
• - simplest way to initialize a model with
  analysis fields is by insertion or replacing
  model fields but this can cause numerical
  instabilities
• - nudging techniques reduce these problems by
  inserting data in an asymptotic process over a
  number of model timesteps
• - NWP has moved far beyond this with variational
  techniques or Ensemble Kalman filters

15
State of the Art Review Data Assimilation

• NWP situation is different
• - analysis is an interpolation and filtering
  process with analysis weights to determine the
  relative contribution from the various
  observations and the model first guess
• - few observations compared to the degrees of
  freedom in the models, and variations in scales
  between what is resolved by the models and what
  is actually observed
• - weights are defined in terms of the expected
  errors variances and error correlations of the
  model first guess and the observations
• - horizontal error correlations are assumed to be
  smooth, isotropic and homogenous
• - multivariate error correlations are defined by
  assuming balanced constraints on the synoptic
  scale (e.g. geostrophy).

16
State of the Art Review Data Assimilation

• Issues
• - lack of in-situ/direct observations
• - incomplete and inconsistent data sets
• - difficulties of air/sea/ice interface
• - interactions
• - time and space scales
• - ice is a discontinuous, deformable medium so
  assumption of isotropy and homogeneity in the
  error variance fields is less valid
• - multivariate treatment is important e.g. sea
  temperature consistent with ice extent
• - observation operator that relates model
  variables to quantities observed by satellites is
  key

17
Research and Development StepsRequirements
Definition - Canada
18
Research and Development Steps Data

• - co-development of better passive microwave ice
  algorithms using digital ice charts as validation
  and tuning in conjunction with use of more
  detailed unique datasets
• - development of forward and inverse algorithms
  to mesh direct observations (eg. radiance) with
  model predictions
• - development of simpler information extraction
  algorithms for SAR
• - for all of the above, it is essential to
  characterize the errors in a manner suitable for
  data assimilation

19
Research and Development Steps

• Models
• - development of adequate or modification of
  existing models for a data assimilation system,
  with investigation of appropriate level of model
  complexity
• - characterization of model errors
• Data Assimilation
• - determination of the best techniques for
  automated analysis of ice data
• - determination of best techniques for
  incorporating automated and manual analysis
  fields into models
• - determination of most useful data to be used
  within a sea ice data assimilation system

20
Research and Development Steps

• Other Issues
• - can/should we develop a common framework (with
  each other and with NWP community)
• - expertise in all areas does not exist at
  individual Ice Centers and may not exist within
  the entire operational ice community
• - resources this work will reduce the workload
  of analysts in the future but where do the
  resources come from now in order to develop such
  systems?
• - what is the best way to engage the entire ice
  community in a collaborative effort?
• - how do we engage other expertise particularly
  in the NWP, oceanography and climate change
  communities?
• - verification has not been specifically
  discussed but is important and methodologies
  should be shared

21
Research and Development Steps

• Strategies
• - information exchange
• - joint research (project based)
• - operational model and/or data exchange
• - ice integrated in national NWP programs
• - common system approach (regional, hemispheric)
• - others

22
Research and Development Steps

• Next Steps
• - IICWG science meeting on modelling/data
  assimilation
• - IICWG approved/funded workshops on data
  assimilation etc.
• - identification of national leads
• Longer Term
• - Coordinated "working groups"?
• - Data Group - develop data products and assess
  data errors
• - Model Group - assemble model components
• - Assimilation Group - develop and test
  assimilation approaches
• - Evaluation Group - obtain comparison data for
  models, data, and assimilated products
  implement and test operationally
• - Coordinate to share data, models, assimilation
  algorithms, and results
• - Consistent grids for models, data, forcing
• - Interchangeable modular components
• - Follow example of model intercomparison
  projects