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Hyperspectral Cloud Boundary Retrievals. Robert E. Holz Steve Ackerman, Paolo Antonelli, Wayne Feltz, Fred Nagle, and Ed Eloranta. Overview. Motivation Cloud level algorithms S-HIS cloud top retrieval AERI cloud base retrieval. Why Investigate Cloud Base-Top Retrievals?.
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Hyperspectral Cloud Boundary Retrievals Robert E. Holz Steve Ackerman, Paolo Antonelli, Wayne Feltz, Fred Nagle, and Ed Eloranta
Overview • Motivation • Cloud level algorithms • S-HIS cloud top retrieval • AERI cloud base retrieval
Why Investigate Cloud Base-Top Retrievals? • Over 70% of the earth is covered with clouds (Wylie) • Knowledge of the cloud level is fundamental for understanding the cloud radiative forcing • Cloudy infrared temperature and water vapor retrievals require knowledge of cloud levels • How do active lidar cloud top-base retrievals compare to hyperspectral infrared retrievals
Cloud Level Determination • MLEV (Minimum Local Emissivity Variance) Strength: Accurate for optically thick clouds • CO2 Slicing Strength: Insensitive to cloud fraction and capable of detecting thin clouds Forward model required to simulate upwelling radiances Problem: Optimal channels are a function of cloud top pressure • CO2 Sorting A new algorithm to pick the optimal CO2 slicing channel pairs. Also has the potential to independently retrieve cloud top pressure
The Sorted Clear Sky Spectrum Sorted Index CO2 Channel Selection Algorithm (CO2 Sorting) The selected clear sky CO2 spectrum is sorted according to brightness temperature
Low Cloud Thinner Cloud Thinner Cloud High and Thick Cloud High and Thick Cloud High and Thick Cloud CO2 Sorting: Sensitivity to Brightness Temperature
Conclusions • MLEV is the consistent performer out of the three cloud top algorithms • The combined CO2 slicing + sorting cloud top retrieval can be an improvement compared to fixed pair CO2 slicing • CO2 sorting improves the up looking AERI cloud base retrieval