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Assessment and Error Analysis of Terra-MODIS and MISR Cloud-top Heights through Comparison with ISS-CATS lidar
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  • Arka Mitra,
  • Larry Di Girolamo,
  • Yulan Hong,
  • Yizhe Zhan,
  • Kevin J Mueller
Arka Mitra
University of Illinois

Corresponding Author:[email protected]

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Larry Di Girolamo
University of Illinois
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Yulan Hong
University of Illinois
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Yizhe Zhan
University of Illinois
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Kevin J Mueller
California Institute of Technology
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Abstract

Cloud-top heights (CTH) from the Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra constitute our longest-running single-platform CTH record from a stable orbit. Here, we provide the first evaluation of the Terra Level 2 CTH record against collocated International Space Station Cloud-Aerosol Transport System (CATS) lidar observations between 50ºN - 50ºS. Bias and precision of Terra CTH relative to CATS is shown to be strongly tied to cloud horizontal and vertical heterogeneity and altitude. For single-layered, unbroken, optically thick clouds observed over all altitudes, the uncertainty in MODIS and MISR CTH are -540±690 m and -280±370 m, respectively. The uncertainties are generally smaller for lower altitude clouds and larger for optically thinner clouds. For multi-layered clouds, errors are summarized herein using both absolute CTH and CATS-layer-altitude proximity to Terra CTH. We show that MISR detects the lower cloud in a two-layered system, provided top-layer optical depth < ~0.3, but MISR low-cloud CTH errors are unaltered by the presence of thin cirrus. Systematic and random errors are propagated to explain inter-sensor disagreements, as well as to provide the first estimate of the MISR stereo-opacity bias. For MISR, altitude-dependent wind-retrieval bias (-90 to -110 m) and stereo-opacity bias (-110 to -150 m) and for MODIS, CO2-slicing bias due to geometrically thick cirrus leads to overall negative CTH bias. MISR’s precision is largely driven by precision in retrieved wind-speed (3.7 m s-1), whereas MODIS precision is driven by forward-modeling uncertainty.