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J.-P. Vernier, T. D. Fairlie, J. J. Murray, A. Tupper, C. Trepte, D. Winker, J. Pelon, A. Garnier, J. Jumelet, M. Pavolonis, A. H. Omar, and K. A. Powell

Operational Environmental Satellite or Meteosat) or hyperspectral radiometers from ultraviolet to infrared wavelengths [e.g., Ozone Monitoring Instrument (OMI) on Aura , Atmospheric Infrared Sounder (AIRS) on Aqua , and Infrared Atmospheric Sounding Interferometer (IASI) on the Meteorological Operation-A and -B ( MetOp-A and -B ) satellites; see Clarisse et al. 2012 ; Carn et al. 2009 ; Karagulian et al. 2010 ; Klüser et al. 2013 )]. The Cloud Aerosol–Lidar and Infrared Pathfinder Satellite

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Ronald L. Holle, Nicholas W. S. Demetriades, and Amitabh Nag

-D-15-0032.1 . Holle, R. L. , and Demetriades N. W. S. , 2010 : GLD360 airport lightning warnings. Preprints, Third Int. Lightning Meteorology Conf./21st Int. Lightning Detection Conf. , Orlando, FL, Vaisala. [Available online at,%20Demetriades.pdf .] Holle, R. L. , Cummins K. L. , and Demetriades N. W. S. , 2011 : Monthly distributions of NLDN and GLD360 cloud-to-ground lightning. Proc. Fifth Conf. on

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Susan Frankenstein, Maria Stevens, and Constance Scott


This paper uses simulated SMAP level-3 (L3) soil moisture data to calculate soil strength directly and compares the results against the current Noah Land Information System–based climatology approach. Based on the availability of data, three sites were chosen for the study: Cheorwon, South Korea; Laboue, Lebanon; and Asham, Nigeria. The simulated SMAP satellite data are representative of May conditions. For all three regions, this is best represented by the “average” soil moisture used in the current climatology approach. The cumulative distribution frequency of the two soil moisture sources indicates good agreement at Asham, Nigeria; mixed agreement at Cheorwon, South Korea; and no agreement at Laboue, Lebanon. Soil strengths and resulting vehicle speeds for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097 were calculated based on the Harmonized World Soil Database soil types used by the two soil moisture sources, as well as with a finer-resolution National Geospatial-Intelligence Agency product. Better agreement was found in soil strengths using the finer-resolution soil product. Finally, fairly large differences in soil moisture become muted in the speed calculations even when all factors except soil strength, slope, and vehicle performance are neglected. It is expected that the 0.04 volumetric uncertainty in the final SMAP L3 soil moisture product will have the greatest effect at low vehicle speeds. Field measurements of soil moisture and strength as well as soil type are needed to verify the results.

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


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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Stanley G. Benjamin, Eric P. James, Ming Hu, Curtis R. Alexander, Therese T. Ladwig, John M. Brown, Stephen S. Weygandt, David D. Turner, Patrick Minnis, William L. Smith Jr., and Andrew K. Heidinger

more constrained cloud building using background subgrid-scale cloud fraction gave improved results overall, especially in winter, including a more neutral bias. 5. Conclusions Short-range forecasts of stratiform cloud are extremely important for a wide range of applications, including aviation, ground transportation, and solar energy. The SCH analysis technique described herein represents an effective way to initialize stratiform cloud and hydrometeors in a rapidly updating mesoscale or storm

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Kapil Sheth, Thomas Amis, Sebastian Gutierrez-Nolasco, Banavar Sridhar, and Daniel Mulfinger

modeling enroute pilot convective storm flight deviation behavior. Preprints, 12th Conf. on Aviation, Range, and Aerospace Meteorology, Atlanta, GA, Amer. Meteor. Soc., P12.6. [Available online at .] Dupree, W. J. , Robinson M. , DeLaura R. , and Bieringer P. , 2006 : Echo top forecast generation and evaluation of air traffic flow management needs in the national airspace system. Preprints, 12th Conf. on Aviation, Range, and Aerospace

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Lynette van Schalkwyk and Liesl L. Dyson

synoptic types associated with fog forms part of a number of fog-forecasting processes worldwide. Until recently the Australian Bureau of Meteorology (BOM) used the Generalized Analog Statistics Model (GASM), which employed the current synoptic analysis and NWP projections of future weather conditions for comparison with similar past events, obtained from a climate database ( Miao et al. 2012 ). In response to requests from the Federal Aviation Administration (FAA), Tardif (2004) used National

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Martina Bramberger, Andreas Dörnbrack, Henrike Wilms, Steffen Gemsa, Kevin Raynor, and Robert Sharman

responsible for initiating of the stall-warning event? Are these variations induced by mesoscale processes as propagating mountain waves or are they due to large-scale meteorological processes? Are the observed fluctuations accurately reproduced by high-resolution IFS forecasts and analyses? Which dominating processes can be identified on the basis of higher-resolved mesoscale numerical simulations? Does the GTG predict mountain wave–induced turbulence associated with the forecast fluctuations? Since HALO

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Kathryn L. Verlinden and David R. Bright

:// . Reynolds , D. W. , D. A. Clark , F. W. Wilson , and L. Cook , 2012 : Forecast-based decision support for San Francisco International Airport: A NextGen prototype system that improves operations during summer stratus season . Bull. Amer. Meteor. Soc. , 93 , 1503 – 1518 , doi: 10.1175/BAMS-D-11-00038.1 . 10.1175/BAMS-D-11-00038.1 Stanski , H. , L. J. Wilson , and W. R. Burrows , 1989 : Survey of common verification methods in meteorology. Atmospheric Environmental Service Research Rep. MSRB

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Kelly Soich and Bernhard Rappenglueck

models to quickly calculate atmospheric temperatures and rapidly assimilate sounding data, improving the skill of meteorological predictions ( Ali 2004 ). Computer technology improvements in atmospheric model computations (i.e., processor speed) require continued testing and validation to ensure atmospheric temperature modeling skill is not degraded ( Cheng and Steenburgh 2005 ; Knutti et al. 2010 ). Therefore, atmospheric temperature forecasts require comparison with in situ temperature

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