reanalysis of sea ice concentration from the smmr and ssm/i records søren andersen, lars-anders...
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REANALYSIS OF SEA ICE CONCENTRATION FROM THE SMMR AND SSM/I RECORDS
Søren Andersen, Lars-Anders Breivik, Mary J. Brodzik, Craig Donlon, Steinar Eastwood, Florence Fetterer, Jacob Høyer, Walter N. Meier, Leif Toudal Pedersen, Nick Rayner, John Stark, Julienne Stroeve, Rasmus
Tonboe
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Outline
• History and background• Scientific advances
– Algorithm selection– Atmospheric correction– Error estimates
• Processing and data set outline • Plans and schedule
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History
• Present sea ice concentration time series products are extremely simple and contain little to no meta data and uncertainty information
• Products are based on level 3 radiances precluding detailed scrutiny and satellite intercomparison
• A number of products exist but there is currently no consensus on relative merits or best practices.
• Following OSISAF activitites to reprocess the SSM/I time series, a workshop was held at NSIDC in March 2007 to:– Exchange views and results– Review and adapt OSISAF plans – Define a shared state of the art, traceable data set including the SMMR
time series (1978-1987)
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Background
• Error sources– Atmospheric– Ice/snow emissivity– Mixing of footprints– Sensor noise (<2%)
Thin iceThin iceComiso et al., 1997Comiso et al., 1997
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Variation of Geophysical Errors
Atmospheric errors are important at low ice concentrations
Ice emissivity errors are important at high ice concentrations
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Algorithm selection
• Algorithm selection is mainly based on:
– Comparison to 59 classified SAR scenes, comparison of high concentration variability and outcome of tiepoint study
– Sensitivity study based on radiative transfer modelling over Open Water
– Comparison to AVHRR over the Arctic marginal seas
– Taking into account 8 algorithms
• Combination of – Bristol (high conc., 1978 ff.) – TUD (85 GHz high res, 1991 ff.)– Bootstrap (low conc. 1978 ff.)
Atm. stdev
Bristol
Bootstrap
+Mainly: Meier (2005); Toudal (2006); Andersen et al. (2006+7)
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Atmospheric correction
• Based on estimates of wind, water vapour, surface temperature and potentially cloud liquid water. Currently based on ECMWF ERA-40 data.
• Calculates corrections to all brightness temperatures and ice concentrations
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Tie points
• Tie points are derived with error bars:
– Prerequisite to estimating uncertainty in ice concentrations
• Tie points are determined dynamically:
– Offers a consistent way to reconcile intersensor differences
– Takes into account interannual and seasonal signature variations
Melt onset
Interannualvariation
Uncertainty
Water
Ice
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Error estimates
Spatially and temporally varying error estimates:
• Error due to atmospheric contribution, estimated from ERA-40 1987-2003 data
• Error due to sea ice emissivity uncertainty
• Error due to footprint mixing and resolution artefacts in marginal ice zone, estimated empirically from local gradient
B
Atm. stdev
Bristol
Bootstrap
A
Empirical
AtmosphereIce tiepoint
Combined
C
Uncertainty
Water
Ice
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Processing
• Processing is aimed at maximum transparency:
– All output is based on netcdf following the cf convention where applicable
– Level2 chain contains no irreversible processing steps. All changes and additions are appended as new variables to netcdf orbit ”super” files
– Level3 processing is highly customisable.
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Output products• Level 2 one file per orbit:
Ice concentrations (3 base algorithms), Brightness temperatures, atm. correction, melt flags, weather filter, sea ice (type) probabilities, error estimates platform metadata.
– Inclusion of ice and ice type probabilities to allow for later extension in a Bayesian scheme with e.g. scatterometer records.
– Brightness temperatures may be stripped prior to general distribution due to copyright restrictions.
• Level 3, two daily products:1. Common climate oriented data set,
common distributionOne product per satellite in EASE grid:
Ice concentration, atm. correction, sea ice probability, combined error estimate, weather filter flag, melt flag, surface temp., wind, water vapour
2. OSISAF operational typeFor use in model testing, hindcasts, etc.
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Schedule
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