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Objective of this study

Selecting a first-guess sea surface temperature field as input to forward radiative transfer model. Objective of this study. To cross-evaluate* eleven L4 SST fields (as potential first-guess SST input to CRTM), using ACSPO L2 as a “transfer standard”

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Objective of this study

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  1. Selecting a first-guess sea surface temperature field as input to forward radiative transfer model Objective of this study • To cross-evaluate* eleven L4 SST fields (as potential first-guess SST input to CRTM), using ACSPO L2 as a “transfer standard” • * Using “L4-L2” SST biases rather than BTs M-O biases • Avoids running computationally challenging CRTM • Maximizes contrast between different L4 SST fields Input Data L2 SST Global Area Coverage (GAC) 4 Km data for the following AVHRR sensors: NOAA-19 (N19) NOAA-18 (N18) METOP-A (MA) For MA the 1 Km Full Resolution Area Coverage (FRAC) data is sub-sampled to look like GAC • Four Metrics used to rank the L4 SSTs • μ(μΔε) – average-in-time of the spatial-mean (L4-L2) bias • σ(μΔε) – variability-in-time of the spatial-mean (L4-L2) bias • μ(σΔε) – average-in-time of the variability-in-space of the (L4-L2) bias • σ(σΔε) – variability-in-time of the variability-in-space of the (L4-L2) bias L4 SST Reynolds (AVHRR): DOI_AV Reynolds (AVHRR+AMSR-E): DOI_AA RTG high resolution: RTG_HR RTG low resolution: RTG_LR NAVO K10 NESDIS POESGOES blended OSTIA, UK Met Office CMC 0.2o, Environment Canada GAMSSA 28km, Australian BOM ODYSSEA, MERSEA France GHRSST Median Ensemble: GMPE

  2. Major results of this study (a) The time series of (a) global median (μΔε) for ΔTL4L2, (b) global RSD (σΔε) for ΔTL4L2 ,with L2 SST derived from Metop-A GAC data. • Typically, μ(μΔε) ~±70 mK and σ(μΔε) ~35mK (except ODYSSEA) • Typically, μ(σΔε) ~<500 mK and σ(σΔε) ~25mK • The GHRSST Multi-Product Ensemble (GMPE) and Canadian Meteorological Centre analysis (CMC-0.2o), show better consistency with ACSPO L2 SST Saha, K., A. Ignatov, X. M. Liang, and P. Dash (2012), Selecting a first-guess sea surface temperature field as input to forward radiative transfer models, J. Geophys. Res., 117, C12001, doi:10.1029/2012JC008384.

  3. Ambient Cloud Dependency in MICROS Consistent Warm bias is seen in CRTM-Observation (M-O) BT differences globally a part of which may come from cold bias in “O” Contribution to Cooler Observations “O” could be from Ambient and/or Residual cloud Such Transient state clouds are difficult to detect using simple threshold-based Clear-sky mask

  4. Concept of Number of Clear-Sky Ocean Pixels - NCSOP Center Clear-sky Pixel • Clear sky ocean pixel • Cloudy ocean pixel 100 Km NCSOP around each clear-sky pixel, calculated using sliding window technique, is used as (an inverse) proxy of ambient cloud 100 Km

  5. An exponential fit developed using the Levenberg-Marquardt least-squares minimization to estimate this cloud contamination Ch5 Ch3B Ch4 X = NCSOP, with three following conditions: A0 ≡ Confidently clear-sky (NCSOP  ∞) A0+ A1≡ Cloudy window (NCSOP  0) A1≡ Amplitude ; A2 ≡ Drop-off rate SST Results of this study will also be used to more accurately validate CRTM and its first guess input fields

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