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Steven D. Campbell and Stephen H. Olson

Abstract

This paper describes an artificial intelligence-based approach for automated recognition of wind shear hazards. The design of a prototype system for recognizing low-attitude wind shear events from Doppler radar displays is presented. This system, called WX1, consists of a conventional expert system augmented by a specialized capability for processing radar images. The radar image processing component of the system employs numerical and computer vision techniques to extract features from radar data. The expert system carries out symbolic reasoning on these features using a set of heuristic rules expressing meteorological knowledge about wind shear recognition. Results are provided demonstrating the ability of the system to recognize microburst and gust front wind shear events.

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Young-Joon Kim, William F. Campbell, and Steven D. Swadley

Abstract

This article discusses a practical problem faced in operational atmospheric forecasting and data assimilation, and efforts to improve forecast quality through the choice of quality control parameters. The need to utilize as much data as possible must be carefully balanced against the need to reject observations deemed erroneous because they are far from the background value. Alleviation of forecast bias in the middle atmosphere for a global atmospheric prediction system is attempted via improvement of the quality control and bias correction of the satellite radiance data; in particular, the sensitivity of the analysis to the satellite radiance outlier check parameters for the Naval Research Laboratory’s three-dimensional variational data assimilation system [Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] is investigated. A series of forecast experiments are performed with an extended-top (0.04 hPa or ∼65 km) version of the U.S. Navy’s Operational Global Atmospheric Prediction System (NOGAPS) for the month of January 2007. The experiments vary the prescribed radiance observation error variance for the Advanced Microwave Sounding Unit-A (AMSU-A) and the tolerance factors for the AMSU-A and NAVDAS quality control processes. The biases of geopotential height, temperature, and wind in the middle atmosphere are significantly reduced when the observation error limit for the highest-altitude AMSU-A channel (i.e., 14) is relaxed from 0.95 to 3 K and the tolerance factors for the AMSU-A and NAVDAS quality control processes are relaxed from 3 to 4. The improvement is due to assimilation of more high quality AMSU-A radiance data from the highest-peaking channel.

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John L. Campbell, Lindsey E. Rustad, Sarah Garlick, Noah Newman, John S. Stanovick, Ian Halm, Charles T. Driscoll, Brian L. Barjenbruch, Elizabeth Burakowski, Steven D. Hilberg, Kristopher J. Sanders, Jason C. Shafer, and Nolan J. Doesken

Abstract

Ice storms are important winter weather events that can have substantial environmental, economic, and social impacts. Mapping and assessment of damage after these events could be improved by making ice accretion measurements at a greater number of sites than is currently available. There is a need for low-cost collectors that can be distributed broadly in volunteer observation networks; however, use of low-cost collectors necessitates understanding of how collector characteristics and configurations influence measurements of ice accretion. A study was conducted at the Hubbard Brook Experimental Forest in New Hampshire that involved spraying water over passive ice collectors during freezing conditions to simulate ice storms of different intensity. The collectors consisted of plates composed of four different materials and installed horizontally; two different types of wires strung horizontally; and rods of three different materials, with three different diameters, and installed at three different inclinations. Results showed that planar ice thickness on plates was 2.5–3 times as great as the radial ice thickness on rods or wires, which is consistent with expectations based on theory and empirical evidence from previous studies. Rods mounted on an angle rather than horizontally reduced the formation of icicles and enabled more consistent measurements. Results such as these provide much needed information for comparing ice accretion data. Understanding of relationships among collector configurations could be refined further by collecting data from natural ice storms under a broader range of weather conditions.

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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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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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Peter Bissolli, Catherine Ganter, Tim Li, Ademe Mekonnen, Ahira Sánchez-Lugo, Eric J. Alfaro, Lincoln M. Alves, Jorge A. Amador, B. Andrade, Francisco Argeñalso, P. Asgarzadeh, Julian Baez, Reuben Barakiza, M. Yu. Bardin, Mikhail Bardin, Oliver Bochníček, Brandon Bukunt, Blanca Calderón, Jayaka D. Campbell, Elise Chandler, Ladislaus Chang’a, Vincent Y. S. Cheng, Leonardo A. Clarke, Kris Correa, Catalina Cortés, Felipe Costa, A.P.M.A. Cunha, Mesut Demircan, K. R. Dhurmea, A. Diawara, Sarah Diouf, Dashkhuu Dulamsuren, M. ElKharrim, Jhan-Carlo Espinoza, A. Fazl-Kazem, Chris Fenimore, Steven Fuhrman, Karin Gleason, Charles “Chip” P. Guard, Samson Hagos, Mizuki Hanafusa, H. R. Hasannezhad, Richard R. Heim Jr., Hugo G. Hidalgo, J. A. Ijampy, Gyo Soon Im, Annie C. Joseph, G. Jumaux, K. R. Kabidi, P-H. Kamsu-Tamo, John Kennedy, Valentina Khan, Mai Van Khiem, Philemon King’uza, Natalia N. Korshunova, A. C. Kruger, Hoang Phuc Lam, Mark A. Lander, Waldo Lavado-Casimiro, Tsz-Cheung Lee, Kinson H. Y. Leung, Gregor Macara, Jostein Mamen, José A. Marengo, Charlotte McBride, Noelia Misevicius, Aurel Moise, Jorge Molina-Carpio, Natali Mora, Awatif E. Mostafa, Habiba Mtongori, Charles Mutai, O. Ndiaye, Juan José Nieto, Latifa Nyembo, Patricia Nying’uro, Xiao Pan, Reynaldo Pascual Ramírez, David Phillips, Brad Pugh, Madhavan Rajeevan, M. L. Rakotonirina, Andrea M. Ramos, M. Robjhon, Camino Rodriguez, Guisado Rodriguez, Josyane Ronchail, Benjamin Rösner, Roberto Salinas, Hirotaka Sato, Hitoshi Sato, Amal Sayouri, Joseph Sebaziga, Serhat Sensoy, Sandra Spillane, Katja Trachte, Gerard van der Schrier, F. Sima, Adam Smith, Jacqueline M. Spence, O. P. Sreejith, A. K. Srivastava, José L. Stella, Kimberly A. Stephenson, Tannecia S. Stephenson, S. Supari, Sahar Tajbakhsh-Mosalman, Gerard Tamar, Michael A. Taylor, Asaminew Teshome, Wassila M. Thiaw, Skie Tobin, Adrian R. Trotman, Cedric J. Van Meerbeeck, A. Vazifeh, Shunya Wakamatsu, Wei Wang, Fei Xin, F. Zeng, Peiqun Zhang, and Zhiwei Zhu
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