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Christopher A. Roseman and Brian M. Argrow

the development of sWRM by following the FAA SRM DIAAT process. Section 3 presents a brief demonstration of sWRM and discusses several use cases. Section 4 highlights a number of research areas that must be addressed by the aerospace and meteorological communities before operational sUAS weather risk assessment tools can be deployed. 2. Model framework development A systematic risk assessment procedure for aerospace applications is given by the FAA in the DIAAT process described above. The

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Scott D. Landolt, Roy M. Rasmussen, Alan J. Hills, Warren Underwood, Charles A. Knight, Albert Jachcik, and Andrew Schwartz

Abstract

The National Center for Atmospheric Research (NCAR) developed an artificial snow-generation system designed to operate in a laboratory cold chamber for testing aircraft anti-icing fluids under controlled conditions. Flakes of ice are produced by shaving an ice cylinder with a rotating carbide bit; the resulting artificial snow is dispersed by turbulent airflows and falls approximately 2.5 m to the bottom of the device. The resulting fine ice shavings mimic snow in size, distribution, fall velocity, density, and liquid water equivalent (LWE) snowfall rate. The LWE snowfall rate can be controlled using either a mass balance or a precipitation gauge, which measures the snowfall accumulation over time, from which the computer derives the LWE rate. LWE snowfall rates are calculated every 6 s, and the rate the ice cylinder is fed into the carbide bit is continually adjusted to ensure that the LWE snowfall rate matches a user-selected value. The system has been used to generate LWE snowfall rates ranging from 0 to 10 mm h−1 at temperatures from −2 to −30°C and densities of approximately 0.1–0.5 g cm−3. Comparisons of the snow-machine fluid tests with the outdoor fluid tests have shown that the snow machine can mimic natural outdoor rates under a broad range of conditions.

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Julie A. Haggerty, Allyson Rugg, Rodney Potts, Alain Protat, J. Walter Strapp, Thomas Ratvasky, Kristopher Bedka, and Alice Grandin

1. Introduction Since the mid-1990s, observations of jet-engine power loss in areas with high ice particle concentrations have generated interest from the aviation industry, regulatory agencies, and the meteorological research community. Over 200 ice crystal icing (ICI) events causing power loss or engine rollback (loss of engine control) in commuter and large transport aircraft have been documented as of 2019 ( Lawson et al. 1998 ; Bravin et al. 2015 ; Bravin and Strapp 2019 ). Air data

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Scott D. Landolt, Andrew Gaydos, Daniel Porter, Stephanie DiVito, Darcy Jacobson, Andrew J. Schwartz, Gregory Thompson, and Joshua Lave

1. Introduction The Automated Surface Observing System (ASOS) is one of the primary weather observing systems in the United States, with over 900 stations across the country. These systems are maintained and supported by the National Weather Service (NWS), Federal Aviation Administration (FAA), and Department of Defense (DOD). Most of these systems are located at airports and provide meteorological observations used in aviation routine weather reports (METARs) that are critical for aircraft

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Ron George and John L. Largier

.1 m in total width.c 1996 American Meteorological SocietyDECEMBER 1996 NOTES AND CORRESPONDENCE 1323BASE STATIONRAMDRIFTERMONITORSTATION FIG. 2. General schematic of differential GPS drifter tracking and monitoring system. Signals are received from satellites by drifters andthe base station; the base station determines corrections and transmits them to

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Fernando Carbajo Fuertes, Giacomo Valerio Iungo, and Fernando Porté-Agel

popular for the study of atmospheric flow due to their flexibility regarding transportation, installation, and operation in any type of terrain when compared to traditional meteorological masts. Single- and dual-lidar techniques have already been successfully used to reconstruct two- or three-component wind velocity fields inside a planar or volumetric domain for a diverse range of applications, as in Newsom and Banta (2004) , Newsom et al. (2005) , Drechsel et al. (2010) , Hill et al. (2010

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Yang Yu, Huiping Xu, Changwei Xu, and Rufu Qin

practical performance of the remote control subsystem is specifically described and further supported by the following case study ( Xu et al. 2011 ) on initial analysis of the in situ observations at the Xiaoqushan seafloor observatory. b. Case study Based on the present sediment models, the drag coefficient, shear stress, and sediment transportation rate of the bottom boundary layer for May 2009 in the coastal East China Sea were calculated from the corresponding ADCP and CTD measurements of high

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William L. Rubin

meteorological forecast methods to predict vortex movements and decay in airport air corridors, (b) confirm and update predicted vortex behavior based on observations from an onsite vortex sensor, (c) forecast safe aircraft separations, and (d) provide a waveoff capability when a vortex unpredictably strays into an air corridor. To accomplish their objective, wake vortex avoidance systems require a wake vortex sensor with the following characteristics: (i) adequate vortex detection/measurement sensitivity

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Hideyuki Fuke, Issei Iijima, Naoki Izutsu, Yukihiko Matsuzaka, Yoichi Kato, Yuya Kakehashi, Yasuhiro Shoji, Tetsuya Yoshida, Hideyuki Honda, Shuji Aoki, Yoichi Inai, and Shinji Morimoto

-term changes of greenhouse gas densities. The precise data obtained at various latitudes as well as at various altitudes is very useful to discuss the regional differences of proportion for both major and minor compounds. In this manner, our data have been utilized to elucidate the transportation process and chemical processes in the atmosphere. Precise analyses of the obtained air samples also led us to the discovery of the gravitational separation of atmospheric components in the vertical distributions

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Christopher A. Fiebrich, Cynthia R. Morgan, Alexandria G. McCombs, Peter K. Hall Jr., and Renee A. McPherson

1. Introduction Proper interpretation of meteorological data requires knowledge of its context, including its metadata and any quality assurance procedures applied to the data. Mesoscale data present their own challenges and advantages during the quality assurance process. Unfortunately, a meteorological observation can become inaccurate during many different stages of its life cycle. Although proactive maintenance and sensor recalibration can greatly improve data quality ( Fiebrich et al. 2006

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