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Challenges and Solutions for Cloud/Ice Masking and SST Algorithms in the Arctic

This session discusses problems and plans for improving cloud/ice masking and SST algorithms in the Arctic, including issues with night separation, ice detection, simulation comparisons, biases, and error covariances.

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Challenges and Solutions for Cloud/Ice Masking and SST Algorithms in the Arctic

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Presentation Transcript

  1. Session 1 Input for discussion

  2. Cloud/ice masking (Steinar / Owen) • Problems : • Night separation of clouds and ice (Steinar); ice detection using only IR channels (Owen) • Difference in capability to mask water from ice/clouds on daytime, twilight and nighttime (Steinar) • Warm water from rivers outflows where the water temperature can be 10 K warmer than the prior SST (Owen) • Plans/solutions : • Add an ice class to the Bayesian cloud detection (Owen) • Improve the Bayesian cloud detection so it is less reliant on the prior/NWP SST, change the assumptions about prior SST error in coastal regions (Owen) • (difference between Steinar and Owen’s Bayesian cloud/ice detection methods : use of clear air RT simulations ?)

  3. Use of NWP-based RT simulations in the Arctic (Pierre) • Problems : • Main problem in our OSI SAF METOP prototype : adjusting brightness temperature simulations • Simulations based on OSTIA foundation SSTs are very difficult to compare with observations, since foundation SST is very scarcely observed from space in low wind and permanent solar illumination conditions • Plans/solutions : • Use a drastic wind filtering before adjusting brightness temperature simulations in the Arctic • CMS to write a paper on 7 years of METOP-A Arctic results • X (?) to build up a profile data base adapted to Arctic algorithms • CMS to prepare regional high latitude algorithms as a back-up to simulation-derived BTs for METOP-B • CMS and DMI to compare METOP biases (derived from NWP simulations and from Jacob’s SST correction method)

  4. SST algorithms in the Arctic (Jacob) • Problems : • SST products (AATSR, AVHRRs and AMSR-E) have in general larger errors in the Arctic, compared to Global and Southern Ocean performance • Biases generally depend upon Solar Zenith angle and TWVC • AVHRR biases found in operational products as well as CCI products • Regional AVHRR coefficients can improve biases, largest improvements in daytime algorithms • Arctic NWP profiles with “large SST errors” are more humid and warmer than profiles with “low SST errors” • Plans/solutions : • Write up the paper on CCI high latitude algorithms • Use MMS to further examine NWP in relation to SST algorithms • Collect in-situ data set for high latitude Arctic validation • Develop MW OE Sea Ice and SST processor for AMSR-E and AMSR2

  5. Physics-based SST retrieval in the Arctic (Chris) • Problems : • Errors in RT simulations can arise from surface temperature errors, RT model errors and/or NWP profiles errors (idea about NWP errors in the Arctic ?) • Error covariance of RT simulations is not well known • Plans/solutions : • “bias tolerant” approaches for SST retrieval : • Modified Total Least Square (Prabhat Koner) • Make the OE retrieval “bias aware”

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