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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 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.

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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.

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James R. Campbell
,
Cui Ge
,
Jun Wang
,
Ellsworth J. Welton
,
Anthony Bucholtz
,
Edward J. Hyer
,
Elizabeth A. Reid
,
Boon Ning Chew
,
Soo-Chin Liew
,
Santo V. Salinas
,
Simone Lolli
,
Kathleen C. Kaku
,
Peng Lynch
,
Mastura Mahmud
,
Maznorizan Mohamad
, and
Brent N. Holben

ABSTRACT

This work describes some of the most extensive ground-based observations of the aerosol profile collected in Southeast Asia to date, highlighting the challenges in simulating these observations with a mesoscale perspective. An 84-h WRF Model coupled with chemistry (WRF-Chem) mesoscale simulation of smoke particle transport at Kuching, Malaysia, in the southern Maritime Continent of Southeast Asia is evaluated relative to a unique collection of continuous ground-based lidar, sun photometer, and 4-h radiosonde profiling. The period was marked by relatively dry conditions, allowing smoke layers transported to the site unperturbed by wet deposition to be common regionally. The model depiction is reasonable overall. Core thermodynamics, including land/sea-breeze structure, are well resolved. Total model smoke extinction and, by proxy, mass concentration are low relative to observation. Smoke emissions source products are likely low because of undersampling of fires in infrared sun-synchronous satellite products, which is exacerbated regionally by endemic low-level cloud cover. Differences are identified between the model mass profile and the lidar profile, particularly during periods of afternoon convective mixing. A static smoke mass injection height parameterized for this study potentially influences this result. The model does not resolve the convective mixing of aerosol particles into the lower free troposphere or the enhancement of near-surface extinction from nighttime cooling and hygroscopic effects.

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David C. Fritts
,
Ronald B. Smith
,
Michael J. Taylor
,
James D. Doyle
,
Stephen D. Eckermann
,
Andreas Dörnbrack
,
Markus Rapp
,
Bifford P. Williams
,
P.-Dominique Pautet
,
Katrina Bossert
,
Neal R. Criddle
,
Carolyn A. Reynolds
,
P. Alex Reinecke
,
Michael Uddstrom
,
Michael J. Revell
,
Richard Turner
,
Bernd Kaifler
,
Johannes S. Wagner
,
Tyler Mixa
,
Christopher G. Kruse
,
Alison D. Nugent
,
Campbell D. Watson
,
Sonja Gisinger
,
Steven M. Smith
,
Ruth S. Lieberman
,
Brian Laughman
,
James J. Moore
,
William O. Brown
,
Julie A. Haggerty
,
Alison Rockwell
,
Gregory J. Stossmeister
,
Steven F. Williams
,
Gonzalo Hernandez
,
Damian J. Murphy
,
Andrew R. Klekociuk
,
Iain M. Reid
, and
Jun Ma

Abstract

The Deep Propagating Gravity Wave Experiment (DEEPWAVE) was designed to quantify gravity wave (GW) dynamics and effects from orographic and other sources to regions of dissipation at high altitudes. The core DEEPWAVE field phase took place from May through July 2014 using a comprehensive suite of airborne and ground-based instruments providing measurements from Earth’s surface to ∼100 km. Austral winter was chosen to observe deep GW propagation to high altitudes. DEEPWAVE was based on South Island, New Zealand, to provide access to the New Zealand and Tasmanian “hotspots” of GW activity and additional GW sources over the Southern Ocean and Tasman Sea. To observe GWs up to ∼100 km, DEEPWAVE utilized three new instruments built specifically for the National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) Gulfstream V (GV): a Rayleigh lidar, a sodium resonance lidar, and an advanced mesosphere temperature mapper. These measurements were supplemented by in situ probes, dropsondes, and a microwave temperature profiler on the GV and by in situ probes and a Doppler lidar aboard the German DLR Falcon. Extensive ground-based instrumentation and radiosondes were deployed on South Island, Tasmania, and Southern Ocean islands. Deep orographic GWs were a primary target but multiple flights also observed deep GWs arising from deep convection, jet streams, and frontal systems. Highlights include the following: 1) strong orographic GW forcing accompanying strong cross-mountain flows, 2) strong high-altitude responses even when orographic forcing was weak, 3) large-scale GWs at high altitudes arising from jet stream sources, and 4) significant flight-level energy fluxes and often very large momentum fluxes at high altitudes.

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