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Revisiting the Application of SIST to AVHRR Snow Scenes

Problem: First guess of cloud temperature and optical depth often lead to a 3.7 reflected portion that exceeds the TOA observed radiance. In these cases, errors are large and the algorithm often iterates in the wrong direction, then quits.

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Revisiting the Application of SIST to AVHRR Snow Scenes

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  1. Problem: First guess of cloud temperature and optical depth often lead to a 3.7 reflected portion that exceeds the TOA observed radiance. In these cases, errors are large and the algorithm often iterates in the wrong direction, then quits. Revisiting the Application of SIST to AVHRR Snow Scenes • Current first guess for Tcloud is halfway between the observed 11 μm BT and 180 K. Change Tcloud minimum to tropopause T instead of 180 K, which is arbitrary • If error is large, force algorithm to continue iterating through all models, even if error increases. This will allow for all models to be tried at least once, even if the first ones are way off • As always, try SIST only if VISST τ is no-retrieval OR is pegged (either 0.05 or 150)

  2. RGB

  3. SIST w/ original settings

  4. SIST w/ new Tcloud first guess

  5. SIST w/ new Tcloud/extended iteration

  6. RGB

  7. VISST

  8. SIST w/ new Tcloud/extended iteration

  9. VISST

  10. SIST w/ new Tcloud/extended iteration

  11. Conclusions • Two adjustments were tested to try and improve 3-channel SIST convergence for daytime/snow • Adjusting minimum bound of Tcloud for the first guess (from 180K to tropopause T) does not seem to significantly affect retrievals • Forcing the algorithm to keep iterating through the models as long as the errors are large seems to be effective in providing convergence for a larger number of retrievals • SIST was applied over snow/ice only in cases where the VISST result was no retrieval or pegged. SIST retrievals were not substituted if they were pegged OR if SIST τ > 6 • Are these results really “better”?? Need more cases to see

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