Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Melinda Marquis x
  • Journal of Atmospheric and Oceanic Technology x
  • Refine by Access: All Content x
Clear All Modify Search
Theodore M. McHardy, James R. Campbell, David A. Peterson, Simone Lolli, Richard L. Bankert, Anne Garnier, Arunas P. Kuciauskas, Melinda L. Surratt, Jared W. Marquis, Steven D. Miller, Erica K. Dolinar, and Xiquan Dong

Abstract

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

Full access
Theodore M. McHardy, James R. Campbell, David A. Peterson, Simone Lolli, Anne Garnier, Arunas P. Kuciauskas, Melinda L. Surratt, Jared W. Marquis, Steven D. Miller, Erica K. Dolinar, and Xiquan Dong

Abstract

This study develops a new thin cirrus detection algorithm applicable to over-land scenes. The methodology builds from a previously developed over-water algorithm (McHardy et al. 2021), which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378 μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in over-land scenes. Clear sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of < 0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ~1 cm ensures that most low/mid-level clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a pre-determined altitude H removes significant land-surface and low/mid-level cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes non-cirrus pixels such that the remaining sample is comprised of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

Restricted access