Biogeography of Tropical Montane Cloud Forests. Part I: Remote Sensing of Cloud-Base Heights

Ronald M. Welch Department of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama

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Salvi Asefi Department of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama

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Jian Zeng Department of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama
NOAA/NESDIS Center for Satellite Applications and Research, Camp Springs, Maryland

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Udaysankar S. Nair Department of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama

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Qingyuan Han Department of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama

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Robert O. Lawton Department of Biological Sciences, University of Alabama in Huntsville, Huntsville, Alabama

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Deepak K. Ray Department of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama
Forestry and Natural Resources, Purdue University, West Lafayette, Indiana

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Vani Starry Manoharan Department of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, Alabama

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Abstract

Cloud-base heights over tropical montane cloud forests are determined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products and National Centers for Environmental Prediction global tropospheric final analysis (FNL) fields. Cloud-base heights are computed by subtracting cloud thickness estimates from cloud-top height estimates. Cloud-top pressures determined from the current MODIS retrieval algorithm often have serious cloud-top pressure retrieval errors at pressures > 700 hPa. The problem can be easily remedied by matching cloud-top temperature derived from the 11-μm channel to the dewpoint temperature profile (instead of the temperature profile) obtained from the FNL dataset. The FNL dataset at 1° spatial resolution produced results that were nearly equivalent to those derived from radiosonde measurements. The following three different approaches for estimating cloud thickness are examined: 1) the constant liquid water method, 2) the empirical method, and 3) the adiabatic model method. The retrieval technique is applied first for stratus clouds over U.S. airports for 12 cases, with cloud-base heights compared with ceilometer measurements. Mean square errors on the order of 200 m result. Then, the approach is applied to orographic clouds over Monteverde, Costa Rica, with estimated cloud-base heights compared with those derived from photographs. Mean square errors on the order of 100 m result. Both the empirical and adiabatic model approaches produce superior results when compared with the constant liquid water (CLW) approach. This is due to the fact that CLW is more sensitive to natural variations in cloud optical thickness.

Corresponding author address: Ronald Welch, Dept. of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, AL 35806. Email: welch@nsstc.uah.edu

Abstract

Cloud-base heights over tropical montane cloud forests are determined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products and National Centers for Environmental Prediction global tropospheric final analysis (FNL) fields. Cloud-base heights are computed by subtracting cloud thickness estimates from cloud-top height estimates. Cloud-top pressures determined from the current MODIS retrieval algorithm often have serious cloud-top pressure retrieval errors at pressures > 700 hPa. The problem can be easily remedied by matching cloud-top temperature derived from the 11-μm channel to the dewpoint temperature profile (instead of the temperature profile) obtained from the FNL dataset. The FNL dataset at 1° spatial resolution produced results that were nearly equivalent to those derived from radiosonde measurements. The following three different approaches for estimating cloud thickness are examined: 1) the constant liquid water method, 2) the empirical method, and 3) the adiabatic model method. The retrieval technique is applied first for stratus clouds over U.S. airports for 12 cases, with cloud-base heights compared with ceilometer measurements. Mean square errors on the order of 200 m result. Then, the approach is applied to orographic clouds over Monteverde, Costa Rica, with estimated cloud-base heights compared with those derived from photographs. Mean square errors on the order of 100 m result. Both the empirical and adiabatic model approaches produce superior results when compared with the constant liquid water (CLW) approach. This is due to the fact that CLW is more sensitive to natural variations in cloud optical thickness.

Corresponding author address: Ronald Welch, Dept. of Atmospheric Science, National Space Science and Technology Center, University of Alabama in Huntsville, Huntsville, AL 35806. Email: welch@nsstc.uah.edu

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