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- Author or Editor: Theodore M. McHardy x
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GOES ABI Detection of Thin Cirrus over LandAbstract
This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, 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 overland 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-/midlevel 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 predetermined altitude H removes significant land surface and low-/midlevel 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 noncirrus pixels such that the remaining sample is composed 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.
Abstract
This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, 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 overland 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-/midlevel 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 predetermined altitude H removes significant land surface and low-/midlevel 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 noncirrus pixels such that the remaining sample is composed 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.
Abstract
Cirrus cloud daytime top-of-the-atmosphere radiative forcing (TOA CRF) is estimated for a 2-yr NASA Micro-Pulse Lidar Network (532 nm; MPLNET) dataset collected at Fairbanks, Alaska. Two-year-averaged daytime TOA CRF is estimated to be between −1.08 and 0.78 W·m−2 (from −0.49 to 1.10 W·m−2 in 2017, and from −1.67 to 0.47 W·m−2 in 2018). This subarctic study completes a now trilogy of MPLNET ground-based cloud forcing investigations, following midlatitude and tropical studies by Campbell et al. at Greenbelt, Maryland, and Lolli et al. at Singapore. Campbell et al. hypothesize a global meridional daytime TOA CRF gradient that begins as positive at the equator (2.20–2.59 W·m−2 over land and from −0.46 to 0.42 W·m−2 over ocean at Singapore), becomes neutral in the midlatitudes (0.03–0.27 W·m−2 over land in Maryland), and turns negative moving poleward. This study does not completely confirm Campbell et al., as values are not found as exclusively negative. Evidence in historical reanalysis data suggests that daytime cirrus forcing in and around the subarctic likely once was exclusively negative. Increasing tropopause heights, inducing higher and colder cirrus, have likely increased regional forcing over the last 40 years. We hypothesize that subarctic interannual cloud variability is likely a considerable influence on global cirrus cloud forcing sensitivity, given the irregularity of polar versus midlatitude synoptic weather intrusions. This study and hypothesis lay the basis for an extrapolation of these MPLNET experiments to satellite-based lidar cirrus cloud datasets.
Abstract
Cirrus cloud daytime top-of-the-atmosphere radiative forcing (TOA CRF) is estimated for a 2-yr NASA Micro-Pulse Lidar Network (532 nm; MPLNET) dataset collected at Fairbanks, Alaska. Two-year-averaged daytime TOA CRF is estimated to be between −1.08 and 0.78 W·m−2 (from −0.49 to 1.10 W·m−2 in 2017, and from −1.67 to 0.47 W·m−2 in 2018). This subarctic study completes a now trilogy of MPLNET ground-based cloud forcing investigations, following midlatitude and tropical studies by Campbell et al. at Greenbelt, Maryland, and Lolli et al. at Singapore. Campbell et al. hypothesize a global meridional daytime TOA CRF gradient that begins as positive at the equator (2.20–2.59 W·m−2 over land and from −0.46 to 0.42 W·m−2 over ocean at Singapore), becomes neutral in the midlatitudes (0.03–0.27 W·m−2 over land in Maryland), and turns negative moving poleward. This study does not completely confirm Campbell et al., as values are not found as exclusively negative. Evidence in historical reanalysis data suggests that daytime cirrus forcing in and around the subarctic likely once was exclusively negative. Increasing tropopause heights, inducing higher and colder cirrus, have likely increased regional forcing over the last 40 years. We hypothesize that subarctic interannual cloud variability is likely a considerable influence on global cirrus cloud forcing sensitivity, given the irregularity of polar versus midlatitude synoptic weather intrusions. This study and hypothesis lay the basis for an extrapolation of these MPLNET experiments to satellite-based lidar cirrus cloud datasets.
Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” BandAbstract
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.
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.
