Pure and Applied Geophysics

, Volume 176, Issue 5, pp 1869–1921 | Cite as

A Review of High Impact Weather for Aviation Meteorology

  • Ismail GultepeEmail author
  • R. Sharman
  • Paul D. Williams
  • Binbin Zhou
  • G. Ellrod
  • P. Minnis
  • S. Trier
  • S. Griffin
  • Seong. S. Yum
  • B. Gharabaghi
  • W. Feltz
  • M. Temimi
  • Zhaoxia Pu
  • L. N. Storer
  • P. Kneringer
  • M. J. Weston
  • Hui-ya Chuang
  • L. Thobois
  • A. P. Dimri
  • S. J. Dietz
  • Gutemberg B. França
  • M. V. Almeida
  • F. L. Albquerque Neto


This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility, aerosol/ash loading, ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can also play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in situ instruments at the surface and in the atmosphere, as well as aircraft and Unmanned Aerial Vehicles mounted sensors, are becoming more common. At smaller time and space scales (e.g., < 1 km), meteorological forecasts from NWP models need to be continuously improved for accurate physical parameterizations. Aviation weather forecasts also need to be developed to provide detailed information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges.


Fog and precipitation visibility aviation meteorology ice microphysics wind shear and gust nowcasting and forecasting 



This review paper is funded by the various institutions representing co-authors given in the title, and received technical and funding support from ECCC and SAR offices in Canada that were related to fog and visibility issues. S. S. Yum is supported by the Research and Development Program for KMA Weather, Climate and Earth System Services (#2016-3100) of National Institute of Meteorological Sciences (NIMS). We also would like to thank for the reviewers for their comments to improve the manuscript, and specifically to one of the reviewers who made specific comments on satellite and radar based platforms to be used for aviation meteorology.


  1. Ahijevych, D., Pinto, J. O., Williams, J. K., & Steiner, M. (2016). Probabilistic forecasts of mesoscale convective system initiation using the random forest data mining technique. Weather Forecasting, 31, 581–599. Scholar
  2. Alapaty, K., Seaman, N. L., Niyogi, D. S., & Hanna, A. F. (2001). Assimilating surface data to improve the accuracy of atmospheric boundary layer simulations. Journal of Applied Meteorology, 40, 2068–2082.Google Scholar
  3. Albers, H. W. (1977). ICAS items. Bulletin American Meteorology Society, 58, 342–343.Google Scholar
  4. Allen, C. T., Haupt, S. E., & Young, G. S. (2007). Source characterization with a genetic algorithm–coupled dispersion–backward model incorporating SCIPUFF. Journal of Applied Meteorology Climatology, 46, 273–287.Google Scholar
  5. Anderson, J. L. (1996). Selection of initial conditions for ensemble forecasts in a simple perfect model framework. Journal of Atmospheric Science, 53, 22–36.Google Scholar
  6. Ansmann, A., Mattis, I., Wandinger, U., Wagner, F., Reichardt, J., & Deshler, T. (1997). Evolution of the pinatubo aerosol: Raman lidar observations of particle optical depth, effective radius, mass, and surface area over Central Europe at 53.4°N. Journal of Atmospheric Science, 54, 2630–2641.Google Scholar
  7. Appleman, H. (1953). The formation of exhaust condensation trails by jet aircraft. Bulletin American Meteorology Society, 34, 14–20.Google Scholar
  8. Argyle, E. M., Gourley, J. J., Flamig, Z. L., Hansen, T., & Manross, K. (2017). Toward a User-Centered Designof a Weather Forecasting Decision-Support Tool. Bulletin of the American Meteorological Society, 98, 373–382. Scholar
  9. Auger, L., Dupont, O., Hagelin, S., Brousseau, P., & Brovelli, P. (2015). AROME–NWC: A new nowcasting tool based on an operational mesoscale forecasting system. Quarterly Journal Royal Meteorological Society, 141, 1603–1611.Google Scholar
  10. Austin, G. L., Dionne, P., & Roch, M. (1987). On the interaction between radar and satellite image nowcasting systems and mesoscale numerical models. In Proceedings mesoscale analysis and forecasting, Vancouver, BC, Canada, European Space Agency, pp. 225–228.Google Scholar
  11. Bailey, M. E., Isaac, G. A., Driedger, N., & Reid, J. (2009). Comparison of nowcasting methods in the context of high-impact weather events for the Canadian Airport Nowcasting Project. In International symposium on nowcasting and very short range forecasting, 30 August–4 September 2009, Whistler, British Columbia.Google Scholar
  12. Bailey, M. E., Isaac, G. A., Gultepe, I., Heckman, I., & Reid, J. (2014). Adaptive blending of model and observations for automated short-range forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games. Journal of Pure and Applied Geophysics, 171, 257–276. Scholar
  13. Baker, W. E., Atlas, R., Cardinali, C., Clement, A., Emmitt, G. D., Gentry, B. M., et al. (2014). Lidar-measured wind profiles: The missing link in the global observing system. Bulletin American Meteorology Society, 95, 543–564.Google Scholar
  14. Bankert, R. L. (1994). Cloud classification of avhrr imagery in maritime regions using a probabilistic neural network. Journal of Applied Meteorology, 33, 909–918.Google Scholar
  15. Bankert, R. L., Hadjimichael, M., Kuciauskas, A. P., Thompson, W. T., & Richardson, K. (2004). Remote cloud ceiling assessment using data-mining methods. Journal of Applied Meteorology, 43, 1929–1946.Google Scholar
  16. Bankert, R. L., Mitrescu, C., Miller, S. D., & Wade, R. H. (2009). Comparison of GOES cloud classification algorithms employing explicit and implicit physics. Journal of Applied Meteorology Climatology, 48, 1411–1421.Google Scholar
  17. Banta, R., Pichugina, Y., Brewer, A., James, E., Olson, J., Benjamin, S., et al. (2017). Evaluating and improving NWP forecast models for the future: How the needs of offshore wind energy can point the way. Bulletin American Meteorology Society. Scholar
  18. Barker, H. W., Jerg, M. P., Wehr, T., Kato, S., Donovan, D. P., & Hogan, R. J. (2011). A 3D cloud construction algorithm for the EarthCARE satellite mission. Quarterly Journal of the Royal Meteorological Society, 137, 1042–1058. Scholar
  19. Bates, T. S., Quinn, P. K., Johnson, J. E., Corless, A., Brechtel, F. J., Stalin, S. E., et al. (2013). Measurements of atmospheric aerosol vertical distributions above Svalbard, Norway, using unmanned aerial systems (UAS). Atmosphere Measure Technology, 6, 2115–2120.Google Scholar
  20. Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., & Greenwald, T. (2010). Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients. Journal of Applied Meteorology Climatology, 49, 181–202.Google Scholar
  21. Bedka, K. M., & Khlopenkov, K. (2016). A probabilistic multispectral pattern recognition method for detection of overshooting cloud tops using passive satellite imager observations. Journal of Applied Meteorology Climatology, 55, 1983–2005. Scholar
  22. Bedka, K. M., Velden, C. S., Petersen, R. A., Feltz, W. F., & Mecikalski, J. R. (2009). Comparisons of satellite-derived atmospheric motion vectors, rawinsondes, and NOAA wind profiler observations. Journal of Applied Meteorology Climatology, 48, 1542–1552.Google Scholar
  23. Behne, D. (2008). NAM-WRF verification of subtropical jet turbulence. Electronics Journal of Operational Meteorology, Paper 2008-EJ3.
  24. Bélair, S., Lacarrère, P., Noilhan, J., Masson, V., & Stein, J. (1998). High-resolution simulation of surface and turbulent fluxes during HAPEX-MOBILHY. Monthly Weather Review, 126, 2234–2253.Google Scholar
  25. Benjamin, S. G., et al. (2009). Rapid Refresh/RUC project technical review. NOAA/ESRL/GSD Internal Review.
  26. Benjamin, S. G., Jamison, B. D., Moninger, W. R., Sahm, S. R., Schwartz, B. E., & Schlatter, T. W. (2010). Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Monthly Weather Review, 138, 1319–1343.Google Scholar
  27. Benjamin, S. G., Moninger, W. R., Smith, T. L., Jamison, B. D., & Schwartz, B. E. (2006a). TAMDAR aircraft impact experiments with the rapid update cycle. In Preprints, 10th Symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface, Atlanta, GA, Amer. Meteor. Soc., 9.8.
  28. Benjamin, S. G., Moninger, W. R., Smith, T. L., Jamison, B. D., & Schwartz, B. E. (2006b) Impact of TAMDAR humidity, temperature, and wind observations in RUC parallel experiments. In Preprints, 12th Conference on aviation, range, and aerospace meteorology, Atlanta, GA, American Meteorology Society, p. 4.5.Google Scholar
  29. Benjamin, S. G., Moninger, W. R., Smith, T. L., Jamison, B. D., Szoke, E. J., & Schlatter, T. W. (2007). 2006 TAMDAR impact experiment results for RUC humidity, temperature, and wind forecasts. In Preprints, 11th symposium on integrated observing and assimilation systems for the atmosphere, oceans, and land surface, San Antonio, TX, Amer. Meteor. Soc., 9.2.
  30. Benjamin, S., Weygandt, S., Brown, J., Hu, M., Alexander, C., Smirnova, T., et al. (2016). A North American hourly assimilation and model forecast cycle: The rapid refresh. Monthly Weather Review, 144(4), 1669–1694. Scholar
  31. Bergot, T., Terradellas, E., Cuxart, J., Mira, A., Liechti, O., Mueller, M., et al. (2007). Intercomparison of single-column numerical models for the prediction of radiation fog. Journal of Applied Meteorology Climatology, 46, 504–521.Google Scholar
  32. Berndt, E., Elmer, N., Schultz, L., & Molthan, A. (2018). A methodology to determine recipe adjustments for multispectral composites derived from next-generation advanced satellite imagers. Journal of Atmosphere Oceanic Technology, 2, 22. Scholar
  33. Bernstein, B. C., McDonough, F., Politovich, M. K., Brown, B. G., Ratvasky, T. P., Miller, D. R., et al. (2005). Current icing potential: Algorithm description and comparison with aircraft observations. Journal of Applied Meteorology, 44, 969–986.Google Scholar
  34. Bernstein, B. C., Wolff, C. A., & Minnis, P. (2006). Practical application of NASA-Langley advanced satellite products to in-flight icing nowcasts. In Proc. 44th AIAA Aerospace Sci. Mtg. & Exhibit, Reno, NV, January 9–12, AIAA-2006-1220, p. 18.Google Scholar
  35. Beswick, K., Baumgardner, D., Gallagher, M., Volz-Thomas, A., Nedelec, P., Wang, K.-Y., et al. (2014). The backscatter cloud probe – a compact low-profile autonomous optical spectrometer. Atmos. Meas. Tech., 7, 1443–1457. Scholar
  36. Bianco, L., Cimini, D., Marzano, F. S., & Ware, R. (2005). Combining microwave radiometer and wind profiler radar measurements for high-resolution atmospheric humidity profiling. Journal of Atmospheric and Oceanic Technology, 22(7), 949–965.Google Scholar
  37. Bilbro, J., Fichtl, G., Fitzjarrald, D., Krause, M., & Lee, R. (1984). Airborne Doppler lidar wind field measurements. Bulletin American Meteorology Society, 65, 348–359.Google Scholar
  38. Bilbro, J. W., & Vaughan, W. W. (1978). Wind field measurement in the nonprecipitous regions surrounding severe storms by an airborne pulsed Doppler lidar system. Bulletin American Meteorology Society, 59, 1095–1100.Google Scholar
  39. Black, A. W., & Mote, T. L. (2015). Characteristics of winter-precipitation-related transportation fatalities in the United States. Weather Climate Society, 7, 133–145.Google Scholar
  40. Bluestein, H. B., French, M. M., PopStefanija, I., Bluth, R. T., & Knorr, J. B. (2010). A mobile, phased-array Doppler radar for the study of severe convective storms: The MWR-05XP. Bulletin American Meteorology Society, 91, 579–600.Google Scholar
  41. Bluestein, H. B., Houser, J. B., French, M. M., Snyder, J. C., Emmitt, G. D., PopStefanija, I., et al. (2014). Observations of the boundary layer near tornadoes and in supercells using a mobile, collocated, pulsed doppler lidar and radar. Journal of Atmosphere Oceanic Technology, 31, 302–325.Google Scholar
  42. Blumstein, D., Tournier, B., Cayla, F. R., Phupin, R. F. T., Bull, C., & Ponce, G. (2007). In-flight performance of the infrared atmospheric sounding interferometer (IASI) on MetOp-A. Atmospheric and environmental remote sensing data processing and utilization III: Readiness for GEOSS. In M. D. Goldberg et al. (Eds.) International Society for Optical Engineering (SPIE Proceedings (vol. 6684, p. 66840).Google Scholar
  43. Boer, G. D., Palo, S., Argrow, B., LoDolce, G., Mack, J., Gao, R.-S., et al. (2017). The pilatus unmanned aircraft system for lower atmospheric research. Atmosphere Measure Technology Discussion, 8, 11987–12023.Google Scholar
  44. Bott, A., Sievers, U., & Zdunkowski, W. (1990). A radiation fog model with a detailed treatment of the interaction between radiative transfer and fog microphysics. Journal of Atmospheric Science, 47, 2153–2166.Google Scholar
  45. Bottyán, Z., Tuba, Z., & Gyöngyösi, A. Z. (2016). Weather Forecasting System for the Unmanned AircraftSystems (UAS) Missions with the Special Regard to Visibility Prediction in Hungary. In L. Nádai & J. Padányi (Eds.), Critical Infrastructure Protection Research (Vol. 12). Cham: Springer. Scholar
  46. Bottyán, Z., Wantuch, F., Gyöngyösi, A. Z., Tuba, Z., Hadobács, K., Kardos, P., et al. (2013). Development of a complex meteorological support system for UAVs. World Academy of Science, Engineering and Technology International Journal of Geology and Environment Engineering, 7(4), 646–651.Google Scholar
  47. Bravin, M., Strapp, J. W., & Mason, J. (2015). An investigation into location and convective lifecycle trends in an ice crystal icing engine database. Tech. rep., SAE Technical Paper 2015-01-2130, SAE International, Warrendale, Pennsylvania, USA.
  48. Bright, D. R., Lack, S. A., & Sparks, J. A. (2016). A summary of turbulence forecasting techniques used by the national weather service. In R. Sharman & T. Lane (Eds.), Aviation turbulence: Processes, detection, prediction (pp. 213–226). Berlin: Springer.Google Scholar
  49. Brooks, G. R., & Oder, A. (2004). Low level turbulence algorithm testing at-or-below 10,000 ft. In Preprints, 11th conference on aviation, range, and aerospace meteorology, Hyannis, MA, Amer. Meteor.Soc., P4.16.
  50. Brown, R. (1973). New indices to locate clear-air turbulence. Meteorological Magazine, 102, 347–361.Google Scholar
  51. Brown, B. G., Thompson, G., Bruintjes, R. T., Bullock, R., & Kane, T. (1997). Intercomparison of in-flight icing algorithms. Part II: Statistical verification results. Weather Forecasting, 12, 890–914.Google Scholar
  52. Browning, K. A. (1980). Local weather forecasting. Proceeding Royal Society of London, A371, 179–211.Google Scholar
  53. Burrows, W. R., Price, C., & Wilson, L. J. (2005). Warm season lightning probability prediction for Canada and the Northern United States. Weather Forecasting, 20, 971–988.Google Scholar
  54. Casadevall, T. J. (1994). The 1989–90 eruption of Redoubt volcano. Alaska: Impacts on aircraft operations. Journal of Volcanology and Geothermal Research, 62, 301–316.Google Scholar
  55. Chachere, C., & Pu, Z. (2018). Numerical simulations of an inversion fog event in the salt lake valley during the MATERHORN-fog field campaign. Pure and Applied Geophysics. Scholar
  56. Chan, P. W. (2014). Performance and aviation applications of minisodars at Hong Kong International Airport. Meteorological Applications, 21, 62–73. Scholar
  57. Chan, P. W. (2016). LIDAR-based turbulence intensity for aviation applications. In R. D. Sharman & T. P. Lane (Eds.), Aviation turbulence: Processes, detection, and prediction (pp. 193–209). Berlin: Springer.Google Scholar
  58. Chan, P. W., & Shao, A. M. (2007). Depiction of complex airflow near Hong Kong International Airport using a Doppler LIDAR with a two-dimensional wind retrieval technique. Meteorologische Zeitschrift, 16, 491–504.Google Scholar
  59. Chan, P. W., Shun, C. M., & Wu, K. C. (2006). Operational LIDARLIDAR-based system for automatic wind shear alerting at the Hong Kong International Airport. In 12th conference on aviation, range, & aerospace meteorology, American Meteorological Society, Atlanta, GA, USA, 29 January–2 February 2006.Google Scholar
  60. Chuang, H.-Y., Mao, Y., & Zhou, B. (2019). R2O Transition of NCAR’s Icingand Turbulence Algorithms into NCEP’s Operations. Pure and Applied Geophysics. Scholar
  61. Chandrasekar, V., Keránen, R., Lim, S., & Moisseev, D. (2013). Recent advances in classification of observations from dual polarization weather radars. Atmospheric Research, 119, 97–111.Google Scholar
  62. Chiodi, A. M., & Harrison, D. E. (2010). Characterizing warm-ENSO variability in the equatorial Pacific: An OLR perspective. Journal of Climate, 23, 2428–2439. Scholar
  63. Chmielecki, R. M., & Raftery, A. E. (2011). Probabilistic visibility forecasting using bayesian model averaging. Monthly Weather Review, 139, 1626–1636.Google Scholar
  64. Cho, J. Y. N. (2015). Enhanced signal processing algorithms for the ASR-9 weather systems processor. Journal of Atmospheric and Oceanic Technology, 32, 1847–1859.Google Scholar
  65. Chun, H., Kim, J., Lee, D., Kim, S., Strahan, M., Pettegrew, B., et al. (2017). Research collaborations for better predictions of aviation weather hazards. Bulletin American Meteorology Society, 98, ES103–ES107. Scholar
  66. Clark, A. J., Gallus, W. A., Jr., Xue, M., & Kong, F. (2009). A comparison of precipitation forecast skill between small convection-permitting and large convection-parameterizing ensembles. Weather and Forecasting, 24, 1121–1140.Google Scholar
  67. Cohen, A. E., Cavallo, S. M., Coniglio, M. C., & Brooks, H. E. (2017). Evaluation of multiple planetary boundary layer parameterization schemes in Southeastern U.S. Cold Season severe weather environments. Weather and Forecasting, 32, 1857–1884.Google Scholar
  68. Cohn, S. A. (1995). Radar measurements of turbulent eddy dissipation rate in the troposphere: A comparison of techniques. Journal of Atmosphere Oceanic Technology, 12, 85–95.Google Scholar
  69. Colson, D., & Panofsky, H. A. (1965). An index of clear-air turbulence. Quarterly Journal of Royal Meteorology Society, 91, 507–513.Google Scholar
  70. Cook, L., Wood, B., Klein, A., Lee, R., & Memarzadeh, B. (2009). Analyzing the share of individual weather factors affecting NAS performance using the weather impacted traffic index. In AIAA 2009-7017. 9th AIAA aviation technology, integration, and operations conference (ATIO), Hilton Head, SC, September 2009.
  71. Cornman, L. B. (2016). Airborne in situ measurements of turbulence. In R. Sharman & T. Lane (Eds.), Aviation turbulence: Processes, detection, prediction (pp. 97–120). Berlin: Springer.Google Scholar
  72. Cornman, L. B., Morse, C. S., & Cunning, J. (1995). Real time estimation of atmospheric turbulence severity from in situ aircraft measurements. Journal of Aircraft, 32, 171–177.Google Scholar
  73. Cooper, W. A., Sand, W. R., Politovich, M. K., & Veal, D. L. (1984). Effects of icing on performance of a research aircraft. Journal of Aircraft, 21, 708–715.Google Scholar
  74. Dehghan, A., Hocking, W. K., & Srinivasan, R. (2014). Comparisons between multiple in situ aircraft measurements and radar in the troposphere. Journal of Atmospheric and Solar-Terrestrial, 118, 64–77.Google Scholar
  75. Del Genio, A. D., Yao, M.-S., & Jonas, J. (2007). Will moist convection be stronger in a warmer environment? Geophysical Research Letters, 34, L16703. Scholar
  76. Deng, M., Mace, G. G., Wang, Z., & Okamoto, H. (2010). Tropical composition, cloud and climate coupling experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar. Journal of Geophysics Research, 115, D00J15. Scholar
  77. DeWekker, S. F. J., Godwin, K. S., Emmitt, G. D., & Greco, S. (2012). Airborne Doppler lidar measurements of valley flows in complex coastal terrain. Journal of Applied Meteorology Climatology, 51, 1558–1574.Google Scholar
  78. Dines, W. H. (1917). Meteorology and aviation. Monthly Weather Review, 45, 401.;2.Google Scholar
  79. Done, J., Davis, C. A., & Weisman, M. L. (2004). The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) model. Atmosphere Science Letter, 5, 110–117.Google Scholar
  80. Donovan, M. F., Williams, E. R., Kessinger, C., Blackburn, G., Herzegh, P. H., Bankert, R. L., et al. (2008). The identification and verification of hazardous convective cells over oceans using visible and infrared satellite observations. Journal of Applied Meteorology Climatology, 47, 164–184.Google Scholar
  81. Dorman, C. E., Mejia, J. F., Korocin, D., & McEvoy, D. J. (2017). Worldwide marine fog occurrence and climatology. In R. Sugan (Ed.), A chapter in the book of marine fog: Challenges and advancements in observations, modeling, and forecasting. Berlin: Springer. Scholar
  82. Doswell, C. A. (1986). Short-range forecasting. In P. Ray (Ed.), Mesoscale meteorology and forecasting (pp. 689–719). Genesco: American Meteor Society.Google Scholar
  83. Doswell, C. A. (1980). Synoptic-scale environments associated with high plains severe thunderstorms. Bulletin of American Meterological Society, 61, 1388–1400.Google Scholar
  84. Dupree, W., Morse, D., Chan, M., Tao, X., Iskenderian, H., Reiche, C., & Wolfson, M., et al. (2009). The 2008 CoSPA Forecast demonstration (collaborative storm prediction for aviation). 89th AMS annual meeting ARAM special symposium on weather—air traffic phoenix, AZ/11-15 January 2009. P1.1, p. 19.Google Scholar
  85. Dutton, J. A., & Panofsky, H. A. (1970). Clear air turbulence: A mystery may be unfolding. Science, 167, 937–944.Google Scholar
  86. Eichinger, W. E., Cooper, D. I., Forman, P. R., Griegos, J., Osborn, M. A., Richter, D., et al. (1999). The development of a scanning raman water vapor lidar for boundary layer and tropospheric observations. Journal of Atmosphere Oceanic Technology, 16, 1753–1766.Google Scholar
  87. Eick, D. (2014) Turbulence related accidents and incidents. Presentation at NCAR Turbulence Impact Mitigation Workshop 2, 3–4 Sep 2014. Accessed 3 Jan 2019
  88. Ellrod, G. P. (1985). Detection of high level turbulence using satellite imagery and upper air data. NOAA Tech. Memo. NESDIS 10, p. 30.Google Scholar
  89. Ellrod, G. P., Connell, B. H., & Hillger, D. W. (2003). Improved detection of airborne volcanic ash using multispectral infrared satellite data. Journal of Geophysical Research, 108, 4356. Scholar
  90. Ellrod, G. P., & Gultepe, I. (2007). Inferring low cloud base heights at night for aviation using satellite infrared and surface temperature data. Journal of Pure and Applied Geophysics, 164, 1193–1205.Google Scholar
  91. Ellrod, G. P., & Knapp, D. I. (1992). An objective clear-air turbulence forecasting technique: Verification and operational use. Weather Forecasting, 7, 150–165.Google Scholar
  92. Ellrod, G. P., & Pryor, K. (2019). Applications of Geostationary Satellite Data to Aviation. Pure Appl Geophys. Scholar
  93. Ellrod, G. P., & Knox, J. A. (2010). Improvements to an operational clear-air turbulence diagnostic index by addition of a divergence trend term. Weather Forecasting, 25, 789–798.Google Scholar
  94. Ellrod, G. P., Lester, P. F., & Ehernberger, J. (2002). Clear air turbulence. In J. R. Holton, et al. (Eds.), Encyclopedia of the atmospheric sciences (pp. 393–403). Oxford: Academic.Google Scholar
  95. Ellrod, G.P., Knox, J.A., Lester, P.F., Ehernberger, L.J., 2015. Aviation: Clear Air Turbulence. In: Gerald R. North (editor-in-chief), J. Pyle and F. Zhang (Eds.), Encyclopedia of Atmospheric Sciences, 2nd edn, Vol 1, (pp. 177–186) Elsevier, Inc.
  96. Elmore, K. L., & Richman, M. B. (2001). Euclidean distance as a similarity metric for principal component analysis. Monthly Weather Review, 129, 540–549.Google Scholar
  97. Endlich, R. M. (1964). The mesoscale structure of some regions of clear-air turbulence. Journal of Applied Meteorology, 3, 261–276.Google Scholar
  98. FAA. (1988). Advisory circular on pilot wind shear guide. AFS-200. AC No: 00-54, p. 56.Google Scholar
  99. FAA. (2017). Continued operational safety (COS) report. Special category light-sport aircraft, July 2004–Sep 2017, p. 50.Google Scholar
  100. FAA-P-8740-40. (2008). Wind shear. HQ 101130, p. 8.Google Scholar
  101. Fahey, T., Wilson, E., O’Loughlin, R., Thomas, M., & Klipfel, S. (2016). A history of weather reporting from aircraft and turbulence forecasting for commercial aviation. In R. Sharman & T. Lane (Eds.), Aviation turbulence: Processes, detection, prediction (pp. 31–58). Berlin: Springer.Google Scholar
  102. Feingold, G., Cotton, W. R., Kreidenweis, S. M., & Davis, J. T. (1999). The impact of giant cloud condensation nuclei on drizzle formation in stratocumulus: Implications for cloud radiative properties. Journal of Atmospheric Science, 56, 4100–4117.Google Scholar
  103. Ferrare, R. A., Melfi, S. H., Whiteman, D. N., Evans, K. D., Schmidlin, F. J., & Starr, D. O. (1995). A comparison of water vapor measurements made by Raman Lidar and radiosondes. Journal of Atmosphere Oceanic Technology, 12, 1177–1195.Google Scholar
  104. Ferrero, E., Mortarini, L., Manfrin, M., Solari, M., & Forza, R. (2014). Physical simulation of atmospheric microbursts. Journal of Geophysics Research Atmosphere, 119, 6292–6305. Scholar
  105. Ferrier, B. S. (1994). A double-moment multiple-phase four-class bulk ice scheme. Part I: Description. Journal of Atmosphere Science, 51, 249–280.Google Scholar
  106. Ferrier, B. S., Tao, W. K., & Simpson, J. (1995). A double-moment multiple-phase four-class bulk ice scheme. Part II: Simulations of convective storms in different large-scale environments and comparisons with other bulk parameterizations. Journal of Atmosphere Science, 52, 1001–1033.Google Scholar
  107. Fischer, C., Montmerle, T., Berre, L., Auger, L., & Stefanescu, S. (2005a). Anover view of the variational assimilation in the ALADIN/France numerical weather-prediction system wave-driven circulation of the mesosphere. Quarterly Journal Royal Meteorological Society, 131, 3477–3492.Google Scholar
  108. Fischer, A. S., Terray, P., Guilyardi, E., Gualdi, S., & Delecluse, P. (2005b). Two independent triggers for the Indian Ocean dipole/zonal mode in a coupled GCM. Journal of Climate, 18, 3428–3449.Google Scholar
  109. Fix, A. (2012). Tunable light sources for lidar applications. In U. Schumann (Ed.), Atmospheric physics—Background, methods, trends, prediction (pp. 509–527). Berlin: Springer.Google Scholar
  110. Folger, K., & Weissmann, M. (2014). Height correction of atmospheric motion vectors using satellite lidar observations from CALIPSO. Journal of Applied Meteorology Climatology, 53, 1809–1819.Google Scholar
  111. Fournier, G. (2006). Development of the Canadian aircraft meteorological data relay (AMDAR) program and plans for the future. In 10th symposium on integrated observing and assimilation systems for the atmosphere, oceans, and land surface, Atlanta, GA, Amer. Meteor. Soc. Annual Meeting. Accessed 3 Jan 2019
  112. França, G. B., Almeida, M. V., Bonnet, S. M., & Albuquerque Neto, F. L. (2018). Nowcasting model of low wind profile based on neural network using SODAR data at Guarulhos Airport, Brazil. International Journal of Remote Sensing, 39(8), 2506–2517. Scholar
  113. Free, M., & Sun, B. (2013). Time-Varying Biases in US total cloud cover data. Journal of Atmosphere Ocean Technology, 30, 2838–2849.Google Scholar
  114. Frehlich, R. G., & Sharman, R. (2010). Climatology of velocity and temperature turbulence statistics determined from rawinsonde and ACARS/AMDAR data. Journal of Applied Meteorology Climatology, 49, 1149–1169. Scholar
  115. Fuertes, F. C., Iungo, G. V., & Porté-Agel, F. (2014). 3D turbulence measurements using three synchronous wind lidars: Validation against sonic anemometry. Journal of Atmosphere Oceanic Technology, 31, 1549–1556.Google Scholar
  116. Gao, F., Zhang, X., Jacobs, N. A., Huang, X.-Y., Zhang, X., & Childs, P. P. (2012). Estimation of TAMDAR observational error and assimilation experiments. Wea. Forecasting, 27, 856–877.Google Scholar
  117. Gerz, T., Holzäpfel, F., Gerling, W., Scharnweber, A., Frech, M., Wiegele, A., et al. (2009). The wake vortex prediction and monitoring system WSVBS—Part II: Performance and ATC integration at Frankfurt airport. Air Traffic Control Quarterly, 17(4), 323–346. Scholar
  118. Ghirardelli, J. E., & Glahn, B. (2010). The meteorological development laboratory’s aviation weather prediction system. Weather Forecasting, 25, 1027–1051.Google Scholar
  119. Gill, P. G. (2012). Objective verification of World Area Forecast Centre clear air turbulence forecasts. Meteorological Applications, 21, 3–11. Scholar
  120. Gill, P. G., & Buchanan, P. (2014). An ensemble based turbulence forecasting system. Meteorological Applications, 21, 12–19. Scholar
  121. Gillette, D. (1978). A wind tunnel simulation of the erosion of soil: Effects of soil texture, sandblasting, wind speed, and soil consolidation on the dust production. Atmospheric Environment, 12, 1735–1743.Google Scholar
  122. Glahn, H. R., & Lowry, D. A. (1972). The use of model output statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology, 11, 1203–1211.Google Scholar
  123. Glahn, B., Schnapp, A. D., Ghirardelli, J. E., & Im, J. (2017). A LAMP–HRRR MELD for improved aviation guidance. Weather Forecasting, 32, 391–405.Google Scholar
  124. Glickman, T. S. (Ed.). (2000). Glossary of meteorology (2nd ed., p. 855). Geneseo: American Meteorological Society.Google Scholar
  125. Golding, B. W. (1998). Nimrod: A system for generating automated very short range forecasts. Meteorological Applications, 5, 1–16.Google Scholar
  126. Goodman, S. J., Blakeslee, R. J., Koshak, W. J., Mach, D., Bailey, J., Buechler, D., et al. (2013). The GOES-R geostationary lightning mapper (GLM). Atmospheric Research, 125–126, 34–49.Google Scholar
  127. Goodman, C., & Griswold, J. (2017). Climate impacts on density altitude and aviation operations. Journal of Applied Meteorology Climatology, 57, 517–523. Scholar
  128. Goodman, S. J., Gurka, J., DeMaria, M., Schmit, T. J., Mostek, A., Jedlovec, G., et al. (2012). The GOES-R proving ground: Accelerating user readiness for the next-generation geostationary environmental satellite system. Bulletin American Meteorology Society, 93, 1029–1040.Google Scholar
  129. Gossard, E. E., Snider, J. B., Clothiaux, E. E., Martner, B., Gibson, J. S., Kropfli, R. A., et al. (1997). The potential of 8-mm radars for remotely sensing cloud drop size distributions. Journal of Atmosphere Oceanic Technology, 14, 76–87.Google Scholar
  130. Gottschall, J., & Peinke, J. (2008). How to improve the estimation of power curves for wind turbines. Environ. Res. Lett., 3, 7. Scholar
  131. Grell, G. A., & Devenyi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysics Research Letters, 29(14), 1693. Scholar
  132. Griffin, S. M., & Velden, C. S. (2018). Hazard avoidance products for convectively-induced turbulence in support of high-altitude global hawk aircraft missions. Pure Applied Geophysics. Scholar
  133. Guedalia, D., & Bergot, T. (1994). Numerical forecasting of radiation fog. Part II: A comparison of model simulation with several observed fog events. Monthly Weather Review, 122, 1231–1246.Google Scholar
  134. Gultepe, I. (2015). Mountain weather: Observations and modeling. Advances in Geophysics, 56, 229–312.Google Scholar
  135. Gultepe, I., Agelin-Chaab, M., Komar, J., Elfstrom, G., Boudala, F., & Zhou, B. (2019). A meteorological supersite for aviation and cold weather applications. Pure Applied Geophysics. (this issue).Google Scholar
  136. Gultepe, I., Fernando, H. J. S., Pardyjak, E. R., Hoch, S. W., Silver, Z., Creegan, E., et al. (2016). An overview of the MATERHORN fog project: Observations and predictability. Pure and Applied Geophysics, 173, 9. Scholar
  137. Gultepe, I., & Heymsfield, A. J. (2016). Ice fog, ice clouds, and remote sensing; Introduction. Pure and Applied Geophysics, 173, 9. Scholar
  138. Gultepe, I., Heymsfield, A. J., Field, P. R., & Axisa, D. (2017a). Ice-phase precipitation. Meteorological Monographs, 58, 6.1–6.36. Scholar
  139. Gultepe, I., Heymsfield, A. J., Gallagher, M., Ickes, L., & Baumgardner, D. (2017b). Ice fog: The current state of knowledge and future challenges. Meteorological Monographs, 58, 4.1–4.24. Scholar
  140. Gultepe, I., & Isaac, G. A. (2004). An analysis of cloud droplet number concentration (Nd) for climate studies: Emphasis on constant Nd. Quarterly Journal of Royal Meterological Society, 130(602), 2377–2390.Google Scholar
  141. Gultepe, I., Isaac, G. A., Joe, P., Kucera, P., Thériault, J., & Fisico, T. (2014a). Roundhouse (RND) mountain top research site: Measurements and uncertainties for winter alpine weather conditions. Journal of Pure and Applied Geophysics. Scholar
  142. Gultepe, I., Kuhn, T., Pavolonis, M., Calvert, C., Gurka, J., Isaac, G. A., et al. (2014b). Ice fog in Arctic during FRAM-IF project: Aviation and nowcasting applications. Bulletin of American Meterological Society, 95, 211–226.Google Scholar
  143. Gultepe, I., Müller, M. D., & Boybeyi, Z. (2006). A new warm fog parameterization scheme for numerical weather prediction models. Journal of Applied Meteorology, 45, 1469–1480.Google Scholar
  144. Gultepe, I., Pawgoski, M., & Reid, J. (2007a). Using surface data to validate a satellite based fog detection scheme. Journal of Weather and Forecasting, 22, 444–456.Google Scholar
  145. Gultepe, I., Tardif, R., Michaelides, S. C., Cermak, J., Bott, A., Bendix, J., et al. (2007b). Fog research: A review of past achievements and future perspectives. Journal of Pure and Applied Geophysics, 164, 1121–1159.Google Scholar
  146. Gultepe, I., Pearson, G., Milbrandt, J. A., Hansen, B., Platnick, S., Taylor, P., et al. (2009). The fog remote sensing and modeling (FRAM) field project. Bulletin of American Meteorological Society, 90, 341–359.Google Scholar
  147. Gultepe, I., & Starr, D. O. C. (1995). Dynamical structure and turbulence in cirrus clouds: Aircraft observations during FIRE. Journal of Atmospheric Science, 52, 4659–4682.Google Scholar
  148. Gultepe, I., Starr, D. O. C., Heymsfield, A. J., Uttal, T., Ackerman, T. P., & Westphal, D. L. (1995). Dynamical characteristics of cirrus clouds from aircraft and radar observations in micro and meso-gamma scales. Journal of Atmospheric Science, 52, 4060–4078.Google Scholar
  149. Gultepe, I., Zhou, B., Milbrandt, J., Bott, A., Li, Y., Heymsfield, A. J., et al. (2015). A review on ice fog measurements and modeling. Atmospheric Research, 151, 2–19.Google Scholar
  150. Guttman, N. B., & Jeck, R. K. (1987). Aircraft icing environment in low ceiling conditions near Washington, D.C. Weather Forecasting, 2, 114–126.Google Scholar
  151. Hadley, D., Hufford, G. L., & Simpson, J. J. (2004). Resuspension of relic volcanic ash and dust from katmai: Still an aviation hazard. Weather Forecasting, 19, 829–840.Google Scholar
  152. Haggerty, J., Defer, E., De Laat, A., Bedka, K., Moisselin, J., Potts, R., et al. (2019). Detecting Clouds Associated with Jet Engine Ice Crystal Icing. Bulletin of the American Meteorological Society, 100, 31–40. Scholar
  153. Haiden, T., Kann, A., & Pistotnik, G. (2014). Nowcasting with INCA during SNOW-V10. Pure and Applied Geophysics, 171–1, 171–172. Scholar
  154. Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., & Gruber, C. (2011). The integrated nowcasting through comprehensive analysis (INCA) system and its validation over the Eastern Alpine region. Weather Forecasting, 26, 166–183.Google Scholar
  155. Hamazu, K., Hashiguchi, H., Wakayama, T., Matsuda, T., Doviak, R. J., & Fukao, S. (2003). A 35-GHz scanning doppler radar for fog observations. Journal of Atmosphere Oceanic Technology, 20, 972–986.Google Scholar
  156. Han, Y., Snider, J. B., Westwater, E. R., Melfi, S. H., & Ferrare, R. A. (1994). Observations of water vapor by ground-based microwave radiometers and Raman lidar. Journal of Geophysics Research, 99(D9), 18695–18702.Google Scholar
  157. Hansen, B. (2007). A fuzzy logic-based analog forecasting system for ceiling and visibility. Weather Forecasting, 22, 1319–1330.Google Scholar
  158. Hansen, B., Gultepe, I., & Ling, A. (2009). Update on WIND-3: An analog forecasting system for ceiling and visibility. In Joint session of the sixth conference on artificial intelligence applications to environmental science and the 13th conference on aviation, range and aerospace meteorology; 88th annual meeting of the american meteorological society, New Orleans, LA, 20–24 January 2008. Oral presentation.Google Scholar
  159. Harrington, J. Y., Sulia, K., & Morrison, H. (2013a). A method for adaptive habit prediction in bulk microphysical models. Part I: Theoretical development. Journal of Atmospheric Science, 70, 349–364.Google Scholar
  160. Harrington, J. Y., Sulia, K., & Morrison, H. (2013b). A method for adaptive habit prediction in bulk microphysical models. Part II: Parcel model corroboration. Journal of Atmospheric Science, 70, 365–376. Scholar
  161. Hart, K. A., Steenburgh, W. J., Onton, D. J., & Siffert, A. J. (2004). An evaluation of mesoscale-model-based model output statistics (MOS) during the 2002 olympic and paralympic winter games. Weather Forecasting, 19, 200–218.Google Scholar
  162. Haupt, S. E., & Delle Monache, L. (2014). Understanding ensemble prediction: How probabilistic wind power prediction can help in optimizing operations. WindTech International.
  163. Heidinger, A. K. (2010). ABI cloud mask algorithm theoretical basis document. NOAA/NESDIS Center for Satellite Applications and Research, p. 67.Google Scholar
  164. Herman, L. (1993). High frequency satellite cloud motion at high latitudes. In Preprints, eighth symp. on meteorological observations and instrumentation, Anaheim, CA, Amer. Meteor. Soc., pp. 465–468.Google Scholar
  165. Herzegh, P., Wiener, G., Bateman, R., Cowie, J., & Black, J. (2015). Data fusion enables better recognition of ceiling and visibility hazards in aviation. Bulletin American Meteorology Society, 96, 526–532.Google Scholar
  166. Herzegh, P. H., Williams, E. R., Lindholm, T. A., Mosher, F. R., Kessinger, C., Sharman, R., Hawkins, J. D., & Johnson, D. B. (2002). Development of automated aviation weather products for oceanic/remote regions: Scientific and practical challenges, research strategies and first steps. In Preprints, 10th aviation, range and aerospace meteorology conference, AMS, Portland, OR, 13–16 May 2002.Google Scholar
  167. Heymsfield, A., Baumgardner, D., DeMott, P., Forster, P., Gierens, K., & Kärcher, B. (2010). Contrail microphysics. Bulletin American Meteorology Society, 91, 465–472.Google Scholar
  168. Heymsfield, A. J., & Sabin, R. M. (1989). Cirrus crystal nucleation by homogeneous freezing of solution drops. Journal of Atmospheric Science, 46, 2252–2264.Google Scholar
  169. Heymsfield, A. J., & Sabin, R. M. (1993). Homogeneous ice nucleation and supercooled liquid water in orographic wave clouds. Journal of Atmospheric Science, 50, 2335–2353.Google Scholar
  170. Heymsfield, A. J., Schmitt, C., Bansemer, A., Twohy, C., Poellot, M., Fridland, A., et al. (2005). Homogeneous ice nucleation in subtropical and tropical convection and its influence on cirrus anvil microphysics. Journal of Atmospheric Science, 62, 41–64.Google Scholar
  171. Heymsfield, A. J., Thompson, G., Morrison, H., Bansemer, A., Rasmussen, R. M., Minnis, P., et al. (2011). Formation and spread of aircraft-induced holes in clouds. Science, AAAS, 333, 77–81.Google Scholar
  172. Hill, M., Calhoun, R., Fernando, H., Wieser, A., Dörnbrack, A., Weissmann, M., et al. (2010). Coplanar Doppler lidar retrieval of rotors from T-REX. Journal of Atmospheric Science, 67(3), 713–729.Google Scholar
  173. Hocking, A., & Hocking, W. K. (2018). Tornado identification and forewarning with very high frequency windprofiler radars. Atmosphere Science Letters, 19, e795. Scholar
  174. Hodges, D., & Pu, Z. (2016). The climatology, frequency, and distribution of cold season fog events in northern Utah. Pure and Applied Geophysics. Scholar
  175. Hodges, D., & Pu, Z. (2018). Characteristics and variations of low level jets in the contrastingprecipitation extremes of 2006 and 2007 over the Southern Great Plains. Theoretical and Applied Climatology. Scholar
  176. Hubbert, J. C. (2017). Differential reflectivity calibration and antenna temperature. Journal of Atmosphere Oceanic Technology, 34, 1885–1906.Google Scholar
  177. Hubbert, J., Bringi, V. N., Carey, L. D., & Bolen, S. (1998). CSU-CHILL Polarimetric Radar Measurementsfrom a Severe Hail Storm in Eastern Colorado. Journal of Applied Meteorology, 37, 749–775.<0749:CCPRMF>2.0.CO;2.Google Scholar
  178. Hubbert, J. C., Wilson, J. W., Weckwerth, T. M., Ellis, S. M., Dixon, M., & Loew, E. (2018). S-Pol’s polarimetric data reveal detailed storm features (and insect behavior). Bulletin American Meteorology Society, 99, 2045–2060.Google Scholar
  179. Hubert, L. F., & Whitney, L. F. (1971). Wind estimation from wind estimation from geostationary –satellite pictures. Monthly Weather Review, 99, 665–672.Google Scholar
  180. Hufford, G. L., Salinas, L. S., Simpson, J. J., Barske, E. G., & Pieri, D. (2000). Operational implications of airborne volcanic ash. Bulletin American Meteorology Society, 81, 745–755.Google Scholar
  181. Hutchison, K. D., Iisager, B. D., Kopp, T. J., & Jackson, J. M. (2008). Distinguishing aerosols from clouds in global, multispectral satellite data with automated cloud classification algorithms. Journal of Atmosphere Oceanic Technology, 25, 501–518.Google Scholar
  182. ICAO. (2005). Manual On Low-Level Wind Shear And Turbulence First Edition—2005. Doc 9817, AN/449. Publisher: International Civil Aviation Organization (ICAO). DOC-09817-001-01-E-P, p. 213.Google Scholar
  183. Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., et al. (2015). The EarthCARE Satellite: The next step forward in global measurements of clouds, aerosols, precipitation, and radiation. Bulletin American Meteorology Society, 96, 1311–1332.Google Scholar
  184. Irvine, E. A., Shine, Keith P., & Stringer, Marc A. (2016). What are the implications of climate change for trans-Atlantic aircraft routing and flight time? Transportation Research Part D, 47, 44–53.Google Scholar
  185. Isaac, G.A., Ayers, J. K., Bailey, M., Bissonnette, L., Bernstein, B. C., Cober, S. G., & Driedger, N., et al. (2005). 43rd AIAA aerospace sciences meeting and exhibit 1013 January 2005, Reno, Nevada, AIAA 2005-252, p. 18.Google Scholar
  186. Isaac, G. A., Bailey, M., Boudala, F., Burrows, W., Cober, S. G., Crawford, R. T., et al. (2014). The Canadian airport nowcasting system (CAN-Now). QJRM Meterological Applications, 21, 30–49.Google Scholar
  187. Isaac, G. A., & Schemenauer, R. S. (1979). Large particles in supercooled regions of northern Canadian cumulus clouds. Journal of Applied Meteorology, 18, 1056–1065.Google Scholar
  188. Ismail, S., & Browell, E. V. (1994). Recent Lidar technology developments and their influence on measurements of tropospheric water vapor. Journal of Atmosphere Oceanic Technology, 11, 76–84.Google Scholar
  189. Jacobs, A. J., & Maat, N. (2005). Numerical guidance methods for decision support in aviation meteorological forecasting. Weather Forecasting, 20, 82–100.Google Scholar
  190. Jewell, R., & Brimelow, J. (2009). Evaluation of Alberta Hail growth model using severe hail proximity soundings from the United States. Weather Forecasting, 24, 1592–1609. Scholar
  191. Jonassen, M. O., Ólafsson, H., Ágústsson, H., Rögnvaldsson, Ó., & Reuder, J. (2012). Improving high-resolution numerical weather simulations by assimilating data from an unmanned aerial system. Monthly Weather Review, 140, 3734–3756.Google Scholar
  192. Jones, R. H. (1965). Optimal estimation of initial conditions for numerical prediction. Journal of Atmospheric Science, 22, 658–663.Google Scholar
  193. Jones, T. A., Knopfmeier, K., Wheatley, D., Creager, G., Minnis, P., & Palikonda, R. (2016). Storm-scale data assimilation and ensemble forecasting with the NSSL Experimental Warn-on Forecast. Part 2: Combined radar and satellite data experiments. Weather Forecasting, 31, 297–327. Scholar
  194. Kain, J. S., et al. (2010). Assessing advances in the assimilation of radar data within a collaborative forecasting-research environment. Weather Forecasting, 25, 1510–1521.Google Scholar
  195. Kalnay, E. (2003). Atmospheric modeling, data assimilation and predictability (p. 341). Cambridge: Cambridge University Press.Google Scholar
  196. Kalnay, E., et al. (1996). The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77, 437–471.Google Scholar
  197. Kann, A., Schellander-Gorgas, T., & Wittmann, C. (2015). Enhanced short-range forecasting of sub-inversion cloudiness in complex terrain. Atmospheric Science Letters, 16, 1–9.Google Scholar
  198. Kaplan, M. L., Huffman, A. W., Lux, K. M., Cetola, J. D., Charney, J. J., Riordan, A. J., et al. (2005a). Characterizing the severe turbulence environments associated with commercial aviation accidents. Part 1: A 44-case study synoptic observational analyses. Meteorology and Atmospheric Physics, 88, 129–152.Google Scholar
  199. Kaplan, M. L., Huffman, A. W., Lux, K. M., Cetola, J. D., Charney, J. J., Riordan, A. J., et al. (2005b). Characterizing the severe turbulence environments associated with commercial aviation accidents. Part 2: Hydrostatic mesoscale numerical simulations of super gradient wind flow and streamwise ageostrophic frontogenesis. Meteorology and Atmospheric Physics, 88, 153–173.Google Scholar
  200. Kara, A. B., Wallcraft, A. J., Barron, C. N., Hurlburt, H. E., & Bourassa, M. A. (2008). Accuracy of 10 m winds from satellites and NWP products near land-sea boundaries. Journal of Geophysics Research, 113, C10020. Scholar
  201. Kara, A. B., Wallcraft, A. J., & Hurlburt, H. E. (2007). A correction for land contamination of atmospheric variables near land-sea boundaries. Journal of Physical Oceanography, 37, 803–818.Google Scholar
  202. Karstens, C. D., Correia, J., Jr., LaDue, D. S., Wolfe, J., Meyer, T. C., Harrison, D. R., et al. (2018). Development of a human-machine mix for forecasting severe convective events. Weather and Forecasting, 33, 715–737.Google Scholar
  203. Karstens, C. D., et al. (2015). Evaluation of a probabilistic forecasting methodology for severe convective weather in the 2014 hazardous weather testbed. Weather Forecasting, 30, 1551–1570.Google Scholar
  204. Kelly, D. S., & Ghirardelli, J. E. (1998). A general overview of methodology and applications of the Local AWIPS MOS Program (LAMP), a short-range forecast guidance product. In Preprints, 16th conf. on weather analysis and forecasting, Phoenix, AZ, Amer. Meteor. Soc., pp. 437–439.Google Scholar
  205. Kelsch, M., & Wharton, L. (1996). Comparing PIREPs with NAWAU turbulence and icing forecasts: issues and results. Weather Forecasting, 11, 385–390.Google Scholar
  206. Kessinger, C., et al. (2006a). The FAA AWRP Oceanic weather program development team. In Preprints-CD, 12th aviation, range and aerospace meteorology conference, AMS, Atlanta, GA, 30 Jan–2 Feb 2006.Google Scholar
  207. Kessinger, C., Herzegh, P., Blackburn, G., Sharman, R., Wiener, G., Hendrickson, B., & Levesque, K., et al. (2006b). The FAA AWRP oceanic weather program development team. In Preprints, AMS 12th conf. on aviation range and aerospace. Poster 3.9/P1.19.Google Scholar
  208. Key, J. R., Santek, D., Velden, C. S., Bormann, N., Riishojgaard, J.-N. L. P., Zhu, Y., et al. (2003). Cloud drift and water vapor winds in the polar regions from MODIS. IEEE Transactions on Geoscience and Remote Sensing, 41, 482–492.Google Scholar
  209. Khairoutdinov, M., & Kogan, Y. (2000). A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Monthly Weather Review, 128, 229–243.Google Scholar
  210. Khairoutdinov, M., & Randall, D. (2006). High-resolution simulation of shallow-to-deep convection transition over land. Journal of Atmospheric Science, 63, 3421–3436.Google Scholar
  211. Kilpinen, J. (1994). Computer-aided weather forecasting system set to enter operation in Scandinavia. ICAO Journal, 49(8), 17–18.Google Scholar
  212. Kim, J. H., Chan, W. N., Banavar, S., & Sharman, R. D. (2015). Combined winds and turbulence prediction system for automated air-traffic management applications. Journal of Applied Meteorology Climatology, 54, 766–784. Scholar
  213. Kim, J. H., Chan, W. N., Sridhar, B., Sharman, R. D., Williams, P. D., & Strahan, M. (2016). Impact of the North Atlantic Oscillation on transatlantic flight routes and clear-air turbulence. Journal of Applied Meteorology Climatology, 55, 763–771. Scholar
  214. Kim, S.-H., Chun, H.-Y., & Chan, P. W. (2017). Comparison of turbulence indicators obtained from in situ flight data. Journal of Applied Meteorology Climatology, 56, 1609–1623. Scholar
  215. Kim, J.-H., Chun, H.-Y., Sharman, R. D., & Keller, T. L. (2011). Evaluations of upper-level turbulence diagnostics performance using the Graphical Turbulence Guidance (GTG) system and pilot reports (PIREPs) over East Asia. Journal of Applied Meteorology Climatology, 50, 1936–1951. (Corrigendum, 50, 2193).Google Scholar
  216. Knox, J. A. (1997). Possible mechanisms of clear-air turbulence in strongly anticyclonic flow. Monthly Weather Review, 125, 1251–1259.Google Scholar
  217. Knox, J. A., McCann, D. W., & Williams, P. D. (2008). Application of the lighthill-ford theory of spontaneous imbalance to clear-air turbulence forecasting. Journal of Atmospheric Science, 65, 3292–3304. Scholar
  218. Knox, J. A., et al. (2016). Automated turbulence forecasting strategies. In R. Sharman & T. Lane (Eds.), Aviation turbulence: Processes, detection, prediction (pp. 243–260). Berlin: Springer.Google Scholar
  219. Knüppfer, K. (1997). Automation of aviation forecasts. The projects AUTOTAF and AUTOGAFOR. In Preprints, seventh conf. on aviation, range, and aerospace meteorology, Long Beach, CA, Amer. Meteor. Soc., pp. 444–449.Google Scholar
  220. Kober, K., Craig, G. C., Keil, C., & Dörnbrack, A. (2012). Blending a probabilistic nowcasting method with a high-resolution numerical weather prediction ensemble for convective precipitation forecasts. Quarterly Journal Royal Meteorological Society, 138, 755–768.Google Scholar
  221. Koch, S. E., & Caracena, F. (2002) Predicting clear-air turbulence from diagnosis of unbalanced flow. In Preprints, 10th conf. on aviation, range, and aerospace meteorology, Portland, OR, Amer. Meteor. Soc., 10.4.
  222. Kopeć, J. M., Kwiatkowski, K., de Haan, S., & Malinowski, S. P. (2016). Retrieving atmospheric turbulence information from regular commercial aircraft using Mode-S and ADS-B. Atmosphere Measure Technology, 9, 2253–2265. Scholar
  223. Kücken, M., Hauffe, D., & Österle, H. (2012). A high-resolution simulation of the year 2003 for Germany using the regional model COSMO. Journal of Applied Meteorology Climatology, 51, 1889–1903.Google Scholar
  224. Kumjian, M. R. (2012). Freezing of raindrops in deep convective updrafts: A microphysical and polari-metric model. Journal of Atmospheric Science, 69, 3471–3490.Google Scholar
  225. Kumjian, M. R. (2013a). Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables. Journal of Operational Meteorology, 1, 226–242.Google Scholar
  226. Kumjian, M. R. (2013b). Principles and applications of dual-polarization weather radar. Part II: Warm- and cold-season applications. Journal of Operational Meteorology, 1, 243–264.Google Scholar
  227. Lakshmanan, V., & Smith, T. (2009). Data mining storm attributes from spatial grids. Journal of Atmosphere Oceanic Technology, 26, 2353–2365.Google Scholar
  228. Landolt, S., Politovich, M., Rasmussen, R., & Gaydos, A. (2010). A comparison of an automated freezing drizzle algorithm to human observations. AMS annual meeting, Atlanta, Georgia, on 16–21 Jan 2010. In 14th conference on aviation, range, and aerospace meteorology, p. 4.Google Scholar
  229. Langland, R. H., et al. (1999). The North Pacific Experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bulletin American Meteorology Society, 80, 1363–1384.Google Scholar
  230. Langmuir, I., Schaefer, V.J., Vonnegut, B., Maynard, K., Smith-Johannsen, R., Blanchard, D., & Falconer, R. E. (1948). Final reports, project cirrus, RL140. General Electric Res. Lab. Req 21190 with the Depart. Of the Army Project: 3-99-07-022, p. 119.Google Scholar
  231. Lawrence, D. A., & Balsley, B. B. (2013). High-resolution atmospheric sensing of multiple atmospheric variables using the DataHawk small airborne measurement system. Journal of Atmosphere Oceanic Technology, 30, 2352–2366. Scholar
  232. Lawson, R. P., Angus, L. J., & Heymsfield, A. J. (1998). Cloud 5 particle measurements in thunderstorm anvils and possible weather threat to aviation. Journal of Aircraft, 35, 113–121.Google Scholar
  233. Lee, D. R., Stull, R. S., & Irvine, W. S. (1984). Clear air turbulence forecasting techniques. Air Weather Service Tech. Note AFGWC/TN-79/001 (REV), Air Force Global Weather Central, Offutt AFB, NE, p. 16.Google Scholar
  234. Leroy, D., Fontaine, E., Schwarzenboeck, A., & Strapp, J. W. (2016). Ice crystal sizes in high ice water content clouds. Part I: On the computation of median mass diameter from in situ measurements. Journal of Atmosphere Oceanic Technology, 33, 2461–2476.Google Scholar
  235. Leroy, D., Fontaine, E., Schwarzenboeck, A., Strapp, J. W., Korolev, A., McFarquhar, G., et al. (2017). Ice crystal sizes in high ice water content clouds. Part II: statistics of mass diameter percentiles in tropical convection observed during the HAIC/HIWC project. Journal of Atmosphere Oceanic Technology, 34, 117–136.Google Scholar
  236. Lewis, W. (1947). A flight investigation of the meteorological conditions conducive to the formation of ice on airplanes. NACA TN 1393.Google Scholar
  237. Lin, Y. L., Farley, R. D., & Orville, H. D. (1983). Bulk parameterization of the snow field in a cloud model. Journal of Climatology Applied Meteorology, 22, 1065–1092.Google Scholar
  238. Lin, C.-Y., Zhang, Z., Pu, Z., & Wang, F. (2017). Numerical simulations of an advection fog event over the Shanghai Pudong Airport with the WRF model. Journal of Meteorological Research, 31, 874–889.Google Scholar
  239. Linden, P. F., & Simpson, J. E. (1985). Microbursts: A hazard to aviation. Nature, 317(17), 601–602.Google Scholar
  240. Liu, Y., Xia, J., Shi, C. X., & Hong, Y. (2009). An improved cloud classification algorithm for China’s FY-2C multi-channel images using artificial neural network. Sensor, 9, 5558–5579.Google Scholar
  241. Löffler-Mang, M., Kunz, M., & Schmid, W. (1999). On the performance of a low-cost K-band doppler radar for quantitative rain measurements. Journal of Atmosphere Oceanic Technology, 16, 379–387.Google Scholar
  242. Luce, H., Nakamura, T., Yamamoto, M. K., Yamamoto, M., & Fukao, S. (2010). MU Radar and Lidar Observations of Clear-Air Turbulence underneath Cirrus. Monthly Weather Review, 138, 438–452.Google Scholar
  243. Ludlam, F. H. (1956). Fall-streak holes. Weather, 11, 89–90.Google Scholar
  244. Lynn, K. J. (1997) International survey of TAF automation systems. Forecasting systems, Met Office Internal Rep., p. 20.Google Scholar
  245. Mancuso, R. L., & Endlich, R. M. (1966). Clear air turbulence frequency as a function of wind shear and deformation. Monthly Weather Review, 94, 581–585.Google Scholar
  246. Mandel, E. (1975). An early look at the development of an unmanned automated surface aviation weather observation system. Bulletin American Meteorology Society, 56, 979–982.Google Scholar
  247. Markowicz, K. M., & Witek, M. L. (2011). Simulations of contrail optical properties and radiative forcing for various crystal shapes. Journal of Applied Meteorology Climatology, 50, 1740–1755.Google Scholar
  248. Mason, J. G., Strapp, J. W., & Chow, P. (2006). The ice particle threat to engines in flight. In: 44th AIAA aerospace sciences meeting, Reno, Nevada, abstract number AIAA 2006-206, 9–12 January.Google Scholar
  249. Matrosov, S. Y. (2005). Attenuation-based estimates of rainfall rates aloft with vertically pointing Ka-band radars. Journal of Atmosphere Oceanic Technology, 22, 43–54.Google Scholar
  250. Matthes, S., Schumann, U., Grewe, V., Frömming, C., Dahlmann, K., Koch, A., et al. (2012). Climate optimized air transport. In U. Schumann (Ed.), Atmospheric physics—Background, methods, trends, prediction (pp. 727–746). Berlin: Springer.Google Scholar
  251. McCann, D. W. (2001). Gravity waves, unbalanced flow, and clear air turbulence. National Weather Digest, 25, 3–14.Google Scholar
  252. McCann, D. W., Know, J. A., & Williams, P. D. (2012). An improvement in clear-air turbulence forecasting based on spontaneous imbalance theory: the ULTURB algorithm. Meteorological Applications, 19, 71–78.Google Scholar
  253. McCarthy, J., & Serafin, R. J. (1984). The microburst hazard to aircraft. Weatherwise, 37, 120–127.Google Scholar
  254. McCarthy, J., Wilson, J. W., & Fujita, T. T. (1982). The joint airport weather studies (JAWS) project. Bulletin American Meteorology Society, 63, 15–22.Google Scholar
  255. McCarty, J., Banta, R. M., Olson, J. B., Carley, J. R., Marquis, M. C., Brewer, W. A., et al. (2017). Assessment of NWP forecast models in simulating offshore winds through the lower boundary layer by measurements from a ship-based scanning Doppler lidar. Monthly Weather Review, 22, 22. Scholar
  256. McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D., Lagerquist, R., et al. (2017). Using artificial intelligence to improve real-time decision-making for high-impact weather. Bulletin American Meteorology Society, 98, 2073–2090.Google Scholar
  257. Mead, J. B., Mcintosh, R. E., Vandemark, D., & Swift, C. T. (1989). Remote sensing of clouds and fog with a 1.4-mm radar. Journal of Atmosphere Oceanic Technology, 6, 1090–1097.Google Scholar
  258. Meckalski, J. R., et al. (2002). NASA advanced satellite aviation-weather products (ASAP) study report. NASA Tech. Rep., p. 65.Google Scholar
  259. Meckalski, J. R., Feltz, W. F., Murray, J. J., Johnson, D. B., Bedka, K. M., Bedka, S. T., et al. (2007). Aviation applications for satellite-based observations of cloud properties, convection initiation, in-flight icing, turbulence, and volcanic ash. Bulletin American Meteorology Society, 88, 1589–1607.Google Scholar
  260. Megenhardt, D., Mueller, C. K., Rehak, N., & Cunning, G. (2000). Evaluation of the national convective weather forecast product. In Preprints. 9th conf on aviation, range, and aerospace meteorology, AMS, Orlando, FL, pp. 171–176.Google Scholar
  261. Meischner, P., Baumann, R., Höller, H., & Jank, T. (2001). Eddy dissipation rates in thunderstorms estimated by Doppler Radar in relation to aircraft in situ measurements. Journal of Atmosphere Oceanic Technology, 18, 1609–1627.Google Scholar
  262. Menzel, W. P., Schmit, T. J., Zhang, P., & Li, J. (2018). Satellite-based atmospheric infrared sounder development and applications. Bulletin American Meteorology Society, 99, 583–603. Scholar
  263. Merritt, L. P. (1969). Comparison of airborne and ground based weather radars. Journal of Applied Meteorology, 8, 963–974.Google Scholar
  264. Michelson, M., Shrader, W. W., & Wieler, J. G. (1990). Terminal doppler weather radar. Microwave Journal, 33, 139–148.Google Scholar
  265. Miller, T. P., & Casadevall, T. J. (1999). Volcanic ash hazards to aviation. In H. Sigurdsson, et al. (Eds.), Encyclopedia of volcanoes (pp. 925–930). Oxford: Academic.Google Scholar
  266. Miller, Steven D., et al. (2014). Estimating three-dimensional cloud structure via statistically blended satellite observations. Journal of Applied Meteorology Climatology, 53, 437–455.Google Scholar
  267. Minnis, P., Bedka, S. T., Duda, D. P., Bedka, K. M., Chee, T. L., Ayers, J. K., et al. (2013). Linear contrails and contrail cirrus properties determined from satellite data. Geophysical Research Letters, 40, 3220–3226. Scholar
  268. Minnis, P., Bedka, K., Trepte, Q., Yost, C. R., Bedka, S. T., Scarino, B., & Khlopenkov, K., et al. (2016). A consistent long-term cloud and clear-sky radiation property dataset from the Advanced Very High Resolution Radiometer (AVHRR). Climate Algorithm Theoretical Basis Document (C-ATBD), CDRP-ATBD-0826 Rev 1 AVHRR Cloud Properties—NASA, NOAA CDR Program, 19 September, p. 159.Google Scholar
  269. Minnis, P., Nguyen, L., Palikonda, R., Heck, P. W., Spangenberg, D. A., Doelling, D. R., & Ayers, J. K., et al. (2008) Near-real time cloud retrievals from operational and research meteorological satellites. In Proc. SPIE 7108, Remote Sens. Clouds Atmos. XIII, Cardiff, Wales, UK, 15–18 September, p. 8.
  270. Minnis, P., Schumann, U., Doelling, D. R., Gierens, K. M., & Fahey, D. W. (1999). Global distribution of contrail radiative forcing. Geophysical Research Letters, 26, 1853–1856.Google Scholar
  271. Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., et al. (2011). CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data, Part I: Algorithms. IEEE Transactions on Geoscience and Remote Sensing, 49(11), 4374–4400. Scholar
  272. Minnis, P., Young, D. F., Nguyen, L., Garber, D. P., Smith, W. L., Jr., & Palikonda, R. (1998). Transformation of contrails into cirrus during SUCCESS. Geophysical Research Letters, 25, 1157–1160.Google Scholar
  273. Mittaz, J., & Harris, A. (2011). A Physical method for the calibration of the AVHRR/3 thermal IR channels. Part II: An in-orbit comparison of the AVHRR longwave thermal IR channels on board MetOp-A with IASI. Journal of Atmosphere Oceanic Technology, 28, 1072–1087. Scholar
  274. Moninger, W. R., Benjamin, S. G., Jamison, B. D., Schlatter, T. W., Smith, T. L., & Szoke, E. J. (2010). Evaluation of regional aircraft observations using TAMDAR. Weather Forecasting, 25, 627–645.Google Scholar
  275. Moninger, W. R., Mamrosh, R. D., & Pauley, P. M. (2003). Automated meteorological reports from commercial aircraft. Bulletin American Meteorology Society, 84, 203–216.Google Scholar
  276. Moosakhanian, A., Schmidt, S., Dash, E. R., Daniels, T.vS., & Stough, P. (2006). FAA–NASA collaboration on automated aircraft weather observations—culminating in TAMDAR. In 12th conference on aviation range and aerospace meteorology, Atlanta, GA, Amer. Meteor. Soc. Annual Meeting.
  277. Morrison, H., & Milbrandt, J. (2015). Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. Journal of Atmospheric Science, 72, 287–311.Google Scholar
  278. Motta, M., Barthelmie, R. J., & Vølund, P. (2005). The influence of non-logarithmic wind speed profiles on potential power output at danish offshore sites. Wind Energy, 8, 219–236. Scholar
  279. Mueller, C., Saxen, T., Roberts, R., Wilson, J., Betancourt, T., Dettling, S., et al. (2003). NCAR auto-nowcast system. Weather Forecasting, 18, 545–561.Google Scholar
  280. Mueller, C. K., Wilson, J. W., & Crook, N. A. (1993). The utility of sounding and mesonet data to nowcast thunderstorm initiation. Weather Forecasting, 8, 132–146.Google Scholar
  281. Myhre, G., & Stordal, F. (2001). Global sensitivity experiments of the radiative forcing due to mineral aerosols. Journal of Geophysics Research, 106, 18193–18204.Google Scholar
  282. Nair, U. S., Weger, R. C., Kuo, K. S., & Welch, R. M. (1998). Clustering, randomness, and regularity in-cloud fields. The nature of regular cumulus cloud fields. Journal of Geophysics Research, 103, 11363–11380.Google Scholar
  283. Nance, L. B., & Durran, D. R. (1997). A modeling study of nonstationary trapped mountain lee waves. Part I: Mean-flow variability. Journal of Atmospheric Science, 54, 2275–2291.Google Scholar
  284. Neely, R. R., & Thayer, J. P. (2011). Raman lidar profiling of tropospheric water vapor over Kangerlussuaq, Greenland. Journal of Atmosphere Oceanic Technology, 28, 1141–1148.Google Scholar
  285. Newton, D. W. (1978). An integrated approach to the problem of aircraft icing. Journal of Aircraft, 15, 374–381.Google Scholar
  286. Nicholls, M., Pielke, R., & Meroney, R. (1993). Large eddy simulation of microburst winds flowing around a building. Journal of Wind Engineering and Industrial Aerodynamics, 46–47, 229–237.Google Scholar
  287. NOAA, 2012: NWSChat Live User Manual, p. 12.Google Scholar
  288. NOAA NWS. (2016). Terminal aerodrome forecasts. Instruction 10-813.
  289. Noh, Y., Seaman, C. J., Vonder Haar, T. H., & Liu, G. (2013). In situ aircraft measurements of the vertical distribution of liquid and ice water content in midlatitude mixed-phase clouds. Journal of Applied Meteorology Climatology, 52, 269–279.Google Scholar
  290. NTSB. (1996). Aircraft accident report. Vol. 1. National Transportation Safety Board NTSB/AAR–96/01–PB96–910401, 322 pp. [Available from NTSB, 490 L’Enfant Plaza, S.W., Washington, DC 20594.].Google Scholar
  291. NTSB (2010). NASDAC Review of National Transportation Safety Board (NTSB) weather-related accidents (20032007). http://www.asias.faagov/.
  292. NWPSD (2004). Operations and services, aviation weather services, NWSPD, 10-8, 2004, NWS Instruction 10-813,Terminal Aerodrome Forecasts, Feb. 1, 2004, p. 51.Google Scholar
  293. Ødegaard, V. (1997). Ice phase parameterization in a numerical weather prediction model. Weather Forecasting, 12, 127–139.Google Scholar
  294. Orf, L. G., & Anderson, J. R. (1999). A numerical study of traveling microbursts. Monthly Weather Review, 127, 1244–1257.Google Scholar
  295. Orf, L. G., Anderson, J. R., & Straka, J. M. (1996). A three-dimensional numerical analysis of colliding microburst outflow dynamics. Journal of Atmospheric Science, 53, 2490–2511.Google Scholar
  296. Orville, R. E. (2008). Development of the national lightning detection network. Bulletin American Meteorology Society, 89, 180–190.Google Scholar
  297. Oude Nijhuis, A., Thobois, L., Barbaresco, F., De Haan, S., Dolfi-Bouteyre, A., Kovalev, D., et al. (2018). Wind hazard and turbulence monitoring at airports with lidar, radar and Mode-S downlinks: The UFO Project. Bulletin American Meteorology Society. Scholar
  298. Pasini, A., & Marzban, C. (2008). Artificial intelligence methods in the environmental sciences (p. 424). Berlin: Springer.Google Scholar
  299. Pavolonis, M. J. (2010a). Advances in extracting cloud composition information from space borne infrared radiances: A robust alternative to brightness temperatures. Part I: Theory. Journal of Applied Meteorology Climatology, 49, 1992–2012.Google Scholar
  300. Pavolonis, M. J. (2010b). ABI cloud type/phase algorithm theoretical basis document. NOAA/NESDIS/Center for Satellite Applications and Research (STAR), p. 60.Google Scholar
  301. Pavolonis, M. J., Feltz, W. F., Heidinger, A. K., & Gallina, G. M. (2006). A daytime complement to the reverse absorption technique for improved automated detection of volcanic ash. Atmosphere Oceanic Technology, 23, 1422–1444.Google Scholar
  302. Pavolonis, M. J., & Heidinger, A. K. (2004). Daytime cloud overlap detection from AVHRR and VIIRS. Journal of Applied Meteorology, 43, 762–778.Google Scholar
  303. Pavolonis, M. J., Heidinger, A. K., & Uttal, T. (2005). Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons. Journal of Atmosphere Oceanic Technology, 44, 804–826.Google Scholar
  304. Pavolonis, M. J., Sieglaff, J., & Cintineo, J. (2018). Automated detection of explosive volcanic eruptions using satellite-derived cloud vertical growth rates. Earth and Space Science, 5, 903–928. Scholar
  305. Pearson, J. M., & Sharman, R. D. (2017). Prediction of energy dissipation rates for aviation turbulence. Part II: Nowcasting convective and nonconvective turbulence. Journal of Applied Meteorology Climatology, 56, 339–351. Scholar
  306. Perrie, W., Zhang, W., Bourassa, M., Shen, H., & Vachon, P. W. (2008). Impact of satellite winds on marine wind simulations. Weather Forecasting, 23, 290–303.Google Scholar
  307. Peters, G. (1990). Temperature and wind profiles from radar wind profilers equipped with acoustic sources. Meteorological Rundsch, 42, 152–154.Google Scholar
  308. Peters, G., Hasselmann, D., & Pang, S. (1988). Radio acoustic sounding using an FM CW radar. Radio Science, 23, 640–646.Google Scholar
  309. Pinto, J. O., Grim, J. A., & Steiner, M. (2015). Assessment of the high-resolution rapid refresh model’s ability to predict mesoscale convective systems using object-based evaluation. Weather Forecasting, 30, 892–913.Google Scholar
  310. Pithani, P., Ghude, S., Naidu, C. V., Kulkarni, R. G., Steeneveld, G., Sharma, A., et al. (2018). WRF model Prediction of a dense fog event occurred during WInter Fog EXperiment (WIFEX). Pure and Applied Geophysics. Scholar
  311. Pleim, J. E., & Xiu, A. (1995). Development and testing of a surface flux and planetary boundary layer model for application in mesoscale models. Journal of Applied Meteorology, 34, 16–31.Google Scholar
  312. Politovich, M. K. (1989). Aircraft icing caused by large supercooled droplets. Journal of Applied Meteorology, 28, 856–868.Google Scholar
  313. Politovich, M. K. (1996). Response of a research aircraft to icing and evaluation of severity indices. Journal of Aircraft, 33, 291–297.Google Scholar
  314. Prata, A. J. (1989). Observations of volcanic ash clouds in the 10-12 micrometer window using AVHRR/2 data. International Journal of Remote Sensing, 10, 751–761.Google Scholar
  315. Proctor, F. H. (1988). Numerical simulations of an isolated microburst. Part I: Dynamics and structure. Journal of Atmospheric Science, 45, 3137–3160.Google Scholar
  316. Proctor, F. H. (1989). Numerical simulations of an isolated microburst. Part II: Sensitivity experiments. Journal of Atmospheric Science, 46, 2143–2165.Google Scholar
  317. Protat, A., Rauniyar, S., Kumar, V. V., & Strapp, J. W. (2014). Optimizing the probability of flying in high ice water content conditions in the tropics using a regional-scale climatology of convective cell properties. Journal of Applied Meteorology Climatology, 53, 2438–2456.Google Scholar
  318. Pu, Z. (2017). Surface data assimilation and near-surface weather prediction over complex terrain. Book Chapter. In S. K. Park & L. Xu (Eds.), Data assimilation for atmospheric, oceanic and hydrologic applications (Vol. III, pp. 219–240). Berlin: Springer. Google Scholar
  319. Pu, Z., Chachere, C., Hoch, S., Pardyjak, E., & Gultepe, I. (2016). Numerical prediction of cold season fog events over complex terrain: The performance of the WRF model during MATERHORN-fog and early evaluation. Pure and Applied Geophysics, 22, 22. Scholar
  320. Pu, Z., Lin, C., Dong, X., & Krueger, S. (2018). Sensitivity of numerical simulations of a mesoscale convective system to ice hydrometeors in bulk microphysical parameterization. Pure and Applied Geophysics. Scholar
  321. Puempel, H., & Williams, P. D. (2016). The impacts of climate change on aviation: Scientific challenges and adaptation pathways. ICAO Environmental Report, pp. 205–207. Accessed 3 Jan 2019
  322. Ralph, F. M., Intrieri, J., Andra, D., Atlas, R., Boukabara, S., Bright, D., et al. (2013). The emergence of weather-related test beds linking research and forecasting operations. Bulletin American Meteorology Society, 94, 1187–1211.Google Scholar
  323. Ramsay, A. C. (1999). A multi-sensor freezing drizzle algorithm for the automated surface observing system. In Preprints, 15th Int. conf. on interactive information and processing systems for meteorology, oceanography, and hydrology, Dallas, TX, Amer. Meteor. Soc., pp. 193–196.Google Scholar
  324. Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., et al. (2012). How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bulletin American Meteorology Society, 93, 811–829.Google Scholar
  325. Rasmussen, R., Dixon, M., Hage, F., Cole, J., Wade, C., Tuttle, J., et al. (2001). Weather support to deicing decision making (WSDDM): A winter weather nowcasting system. Bulletin American Meteorology Society, 82, 579–596.Google Scholar
  326. Rasmussen, R., Dixon, M., Vasiloff, S., Hage, F., Knight, S., Vivekanandan, J., et al. (2003). Snow nowcasting using a real-time correlation of radar reflectivity with snow gauge accumulation. Journal of Applied Meteorology, 42, 20–36.;2.Google Scholar
  327. Rasmussen, R., Politovich, M., Marwitz, J., Sand, W., McGinley, J., Smart, J., et al. (1992). Winter icing and storms project (WISP). Bulletin American Meteorology Society, 73, 951–974.Google Scholar
  328. Raytheon. (2016). AWIPS CAVE-D2D user’s manual: AWIPS II operational build 13.4.1. Raytheon Doc. AWP.MAN.UM.A2-OB13.4.1, p. 609.
  329. Reehorst, A. L., Brinker, D. J., Ratvasky, T. P., Ryerson, C. C., & Koenig, G. G. (2005). The NASA icing remote sensing system. NASA/TM—2005-213591. Paper # 80776, p. 11.Google Scholar
  330. Reineman, B. D., Lenain, L., & Melville, W. K. (2016). The use of ship-launched fixed-wing UAVs for measuring the marine atmospheric boundary layer and ocean surface processes. Journal of Atmosphere Oceanic Technology, 33, 2029–2052.Google Scholar
  331. Reineman, B. D., Lenain, L., Statom, N. M., & Melville, W. K. (2013). Development and testing of instrumentation for UAV-based flux measurements within terrestrial and marine atmospheric boundary layers. Journal of Atmosphere Oceanic Technology, 30, 1295–1319.Google Scholar
  332. Reitebuch, O. (2012). Wind lidar for atmospheric research. Atmospheric physics. In U. Schumann (Ed.), Background, methods, trends, prediction (pp. 487–507). Berlin: Springer.Google Scholar
  333. Roberts, R. D., & Rutledge, S. (2003). Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Weather Forecasting, 18, 562–584.Google Scholar
  334. Roberts, R. D., Saxen, T., Mueller, C., Wilson, J., Crook, A., Sun, J., & Henry, S. (1999). Operational application and use of NCAR’s thunderstorm nowcasting system. In Preprints, Int. conf. on interactive information and processing systems, Dallas, TX, Amer. Meteor. Soc., pp. 158–161.Google Scholar
  335. Roquelaure, S., & Bergot, T. (2007). Seasonal sensitivity on COBEL-ISBA local forecast system for fog and low clouds. Pure and Applied Geophysics, 164, 1283–1301.Google Scholar
  336. Roquelaure, S., & Bergot, T. (2008). A local ensemble prediction system for fog and low clouds: Construction, bayesian model averaging calibration, and validation. Journal of Applied Meteorology Climatology, 47, 3072–3088.Google Scholar
  337. Roquelaure, S., & Bergot, T. (2009). Contributions from a local ensemble prediction system (LEPS) for improving fog and low cloud forecasts at airports. Weather Forecasting, 24, 39–52.Google Scholar
  338. Rose, S. F., Hobbs, P. V., Locatelli, J. D., & Stoelinga, M. T. (2004). A 10-yr climatology relating the locations of reported tornadoes to the quadrants of upper-level jet streaks. Weather Forecasting, 19, 301–309.Google Scholar
  339. Rudack, D. E., & Ghirardelli, J. E. (2010). A comparative verification of localized aviation model output statistics program (LAMP) and numerical weather prediction (NWP) model forecasts of ceiling height and visibility. Weather Forecasting, 25, 1161–1178.Google Scholar
  340. Rudra, R., Dickinson, W. T., Ahmed, S. I., Patel, P., Zhou, J., & Gharabaghi, B. (2015). Changes in rainfall extremes in Ontario. International Journal of Environment Research, 9(4), 1117–1372.Google Scholar
  341. Ryerson, W. R., & Hacker, J. P. (2014). The potential for mesoscale visibility predictions with a multimodel ensemble. Weather Forecasting, 29, 543–562.Google Scholar
  342. Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., & Zhang, P. (2013). Polarimetric radar characteristics of melting hail. Part II: Practical implications. Journal of Applied Meteorology Climatology, 52, 2871–2886.Google Scholar
  343. Ryzhkov, A. V., Zrnic, D. S., Hubbert, J. C., Bringi, V. N., Vivekanandan, J., & Brandes, E. A. (2002). Polarimetric radar observations and interpretation of co-cross-polar correlation coefficients. Journal of Atmosphere Oceanic Technology, 19, 340–354.Google Scholar
  344. Sand, W. R., Cooper, W. A., Politovich, M. K., & Veal, D. L. (1984). Icing Conditions Encountered by a Research Aircraft. Journal of Climate and Applied Meteorology., 23, 1427–1440. Scholar
  345. Santel, D. (2010). The impact of satellite-derived polar winds on lower-latitude forecasts. Monthly Weather Review, 138, 123–139.Google Scholar
  346. Sathe, A., Mann, J., Gottschall, J., & Courtney, M. S. (2011). Can wind lidars measure turbulence? Journal of Atmosphere Oceanic Technology, 28, 853–868.Google Scholar
  347. Schmit, T. J., Griffith, P., Gunshor, M. M., Daniels, J. M., Goodman, S. J., & Lebair, W. J. (2017). A closer look at the ABI on the GOES-R series. Bulletin American Meteorology Society, 98, 681–698.Google Scholar
  348. Schmit, T. J., Gunshor, M. M., Menzel, W. P., Li, J., Bachmeier, S., & Gurka, J. J. (2005). Introducing the next-generation advanced baseline imager on GOES-R. Bulletin American Meteorology Society, 86, 1079–1096.Google Scholar
  349. Schreiner, A. J., Unger, D. A., Menzel, W. P., Ellrod, G. P., Strabala, K. I., & Pellett, J. L. (1993). A comparison of ground and satellite observations of cloud cover. Bulletin American Meteorology Society, 74, 1851–1861.Google Scholar
  350. Schultz, P., & Politovich, M. K. (1992). Toward the improvement of aircraft-icing forecasts for the continental United States. Weather Forecasting, 7, 491–500.Google Scholar
  351. Schumann, U., Graf, K., Mannstein, H., & Mayer, B. (2012a). Contrails: Visible aviation induced climate impact. In U. Schumann (Ed.), Atmospheric physics—Background, methods, trends, prediction (pp. 239–257). Berlin: Springer.Google Scholar
  352. Schumann, U., Mayer, B., Graf, K., & Mannstein, H. (2012b). A parametric radiative forcing model for contrail cirrus. Journal of Applied Meteorology Climatology, 51, 1391–1406.Google Scholar
  353. Schumann, U., & Heymsfield, A. J. (2017). On the life cycle of individual contrails and contrail cirrus. Meteorological Monographs, 58, 3.1–3.24.Google Scholar
  354. Schumann, U., & Mayer, B. (2017). Sensitivity of surface temperature to radiative forcing by contrail cirrus in a radiative-mixing model. Atmospheric Chemistry Physics, 17, 13833–13848.Google Scholar
  355. Schuur, T. J., Park, H., Ryzhkov, A. V., & Reeves, H. D. (2012). Classification of precipitation types during transitional winter weather using the RUC model and polarimetric radar retrievals. Journal of Applied Meteorology Climatology, 51, 763–779.Google Scholar
  356. Schwartz, B. (1996). The quantitative use of PIREPs in developing aviation weather guidance products. Weather Forecasting, 11, 372–384.,;2.Google Scholar
  357. Schwartz, B. E., & Benjamin, S. G. (1995). A comparison of temperature and wind measurements from ACARS equipped aircraft and rawinsondes. Weather Forecasting, 10, 528–544.Google Scholar
  358. Seity, Y., Brousseau, P., Malardel, S., Hello, G., Benard, P., Bouttier, F., et al. (2011). The AROME–France convective-scale operational model. Monthly Weather Review, 139, 976–991.Google Scholar
  359. Selz, T., & Craig, G. C. (2015). Upscale Error growth in a high-resolution simulation of a summertime weather event over Europe. Monthly Weather Review, 143, 813–827.Google Scholar
  360. Serke, D., Hall, E., Bogna, J., Jordan, A., Abdo, S., Baker, K., et al. (2014). Supercooled liquid water content profiling case studies with a new vibrating wire sonde compared to a ground-based microwave radiometer. Atmospheric Research, 149, 77–87.Google Scholar
  361. Serke, D., Politovich, M., Reehorst, A., & Gaydos, A. (2008). The use of X-band radar to support the detection of in-flight icing hazards by the NASA Icing Remote Sensing System During AIRS-II. In Proc. SPIE 7088, #18, 2008.Google Scholar
  362. Sharman, R. (2016). Nature of aviation turbulence. In R. Sharman & T. Lane (Eds.), Aviation turbulence: Processes, detection, prediction (pp. 3–30). Berlin: Springer.Google Scholar
  363. Sharman, R., Cornman, L. B., Meymaris, G., Pearson, J., & Farrar, T. (2014). Description and derived climatologies of automated in situ eddy dissipation rate reports of atmospheric turbulence. Journal of Applied Meteorology Climatology, 53, 1416–1432. Scholar
  364. Sharman, R., & Lane, T. (2016). Aviation turbulence: Processes, detection, prediction (p. 523). Berlin: Springer.Google Scholar
  365. Sharman, R., & Pearson, J. M. (2017). Prediction of energy dissipation rates for aviation turbulence. Part I: Forecasting non-convective turbulence. Journal of Applied Meteorology Climatology, 56, 317–337.Google Scholar
  366. Sharman, R., Tebaldi, C., Wiener, G., & Wolff, J. (2006). An integrated approach to mid- and upper-level turbulence forecasting. Weather Forecasting, 21, 268–287.Google Scholar
  367. Sharman, R. D., & Trier, S. B. (2018). Influences of gravity waves on convectively induced turbulence (CIT): A review. Pure and Applied Geophysics. Scholar
  368. Sharman, R., Trier, S. B., Lane, T. P., & Doyle, J. D. (2012). Sources and dynamics of turbulence in the upper troposphere and lower stratosphere: A review. Geophysical Research Letters, 39, L12803. Scholar
  369. Sieglaff, J. M., Cronce, L. M., Feltz, W. F., Bedka, K. M., Pavolonis, M. J., & Heidinger, A. K. (2011). Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. Journal of Applied Meteorology Climatology, 50, 110–126.Google Scholar
  370. Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. V., & Bronaugh, D. (2013a). Climate extremes indices in the CMIP5 multimodel ensemble. Part 1: Model evaluation in the present climate. Journal of Geophysics Research Atmosphere, 118, 1716–1733.Google Scholar
  371. Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X., & Bronaugh, D. (2013b). Climate extreme indices in the CMIP5 multi-model ensemble. Part 2: Future projections. Journal of Geophysics Researc, 22, 22. Scholar
  372. Silva, W. L., Albuquerque Neto, F. A., França, G. B., & Matschinske, M. (2016). Conceptual model for runway change procedure in guarulhos international airport based on SODAR data. The Aeronautical Journal, 120(1227), 725–734. Scholar
  373. Simpson, J. J., Hufford, G. L., Pieri, D. C., & Berg, J. (2000). Failures in detecting volcanic ash from satellite-based technique. Remote Sensing of Environment, 72, 191–217.Google Scholar
  374. Simpson, J. J., Hufford, G. L., Servranckx, R., Berg, J., & Pieri, D. (2003). Airborne Asian dust: Case study of long-range transport and implications for the detection of volcanic ash. Weather Forecasting, 18, 121–141.Google Scholar
  375. Sims, D. L., Fidalgo, C. B., & Carty, T. C. (2000). Integrated Icing Diagnostic Algorithm assessment at regional airlines. In 9th conference on aviation, range, and aerospace meteorology. AMS 2000 Annual Meeting. Preprints, #4.16.Google Scholar
  376. Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., et al. (2017). The global precipitation measurement (GPM) mission for science and society. Bulletin American Meteorology Society, 98, 1679–1695.Google Scholar
  377. Smith, W. L. (2014). 4-D cloud properties from passive satellite data and applications to resolve the flight icing threat to aircraft. PhD Dissertation, University of Wisconsin-Madison, July 22, p. 15.Google Scholar
  378. Smith, T. M., Lakshmanan, V., Stumpf, G. J., Ortega, K. L., Hondl, K., Cooper, K., et al. (2016). Multi-radar multi-sensor (MRMS) severe weather and aviation products: initial operating capabilities. Bulletin American Meteorology Society, 97, 1617–1630.Google Scholar
  379. Smith, W. L., Minnis, P., Fleeger, C., Spangenberg, D., Palikonda, R., & Nguyen, L. (2012). Determining the flight icing threat to aircraft with single-layer cloud parameters derived from operational satellite data. Journal of Applied Meteorology Climatology, 51, 1794–1810.Google Scholar
  380. Soden, B. J., Ackerman, S. A., Starr, D. O. C., Melfi, S. H., & Ferrare, R. A. (1994). Comparison of upper tropospheric water vapor from GOES, Raman lidar, and cross-chain loran atmospheric sounding system measurements. Journal of Geophysics Research, 99(D10), 21005–21016.Google Scholar
  381. Sokol, Z., & Zacharov, P. (2012). Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity. Quarterly Journal of Royal Meteorological Society, 138, 1072–1082.Google Scholar
  382. Solheim, F., Godwin, J., & Ware, R. (1998). Passive ground-based remote sensing of atmospheric temperature, water vapor, and cloud liquid water profiles by a frequency synthesized microwave radiometer. Meterologische Zeitschrift, 7, 370–376.Google Scholar
  383. Sorenson, J. E. (1964). Synoptic patterns for clear air turbulence. UAL Meteorology Circular 56, 64 pp. Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO 80523.Google Scholar
  384. Spangenberg, D. A., Minnis, P., Bedka, S. T., Palikonda, R., Duda, D. P., & Rose, F. G. (2013). Contrail radiative forcing over the Northern Hemisphere from 2006 Aqua MODIS data. Geophysics Research Letters, 40, 595–600. Scholar
  385. Stano, G. T., Fuell, K. K., & Jedlovec, G. J. (2010). NASA SPoRT GOES-R Proving Ground activities. In Preprints, Sixth annual symp. on future national operational environmental satellite systems: NPOESS and GOES-R, Atlanta, GA, Amer. Meteor. Soc., 8.2. Accessed 3 Jan 2019
  386. Stickland, J. J. (1998). An assessment of two algorithms for automatic measurement and reporting of turbulence from commercial public transport aircraft (p. 42). Melbourne, Australia: Rep. to the ICAO METLINK Study Group, Bureau of Meteorology.Google Scholar
  387. Stobie, J., Moosakhanian, A., Jackson, P., & Brown, W. N. (2008). Evolution of FAA’s weather and radar processor (WARP) into the next generation air transportation system (NextGen). In 13th conference on aviation, range and aerospace meteorology, 21–24 January 2008, New Orleans, Amer. Met. Soc.Google Scholar
  388. Stoelinga, M. T., & Warner, T. T. (1999). Nonhydrostatic, meso-beta scale model simulations of cloud ceiling and visibility for an east coast winter precipitation event. Journal of Applied Meteorology, 38, 385–404.Google Scholar
  389. Storer, L. N., Williams, P. D., & Joshi, M. M. (2017). Global response of clear-air turbulence to climate change. Geophysical Research Letters, 44, 9976–9984. Scholar
  390. Strapp, J. W., Korolev, A., Ratvasky, R., Potts, A., Protat, P., May, A., & Ackerman, A., et al. (2016). The high ice water content study of deep convective clouds: Report on science and technical plan. Final Report. DOT/FAA/TC-14/31, p. 92. Accessed 3 Jan 2019
  391. Sumner, J., & Mason, C. (2006). A turbulence-based model for resolving velocity and temperature profiles in the atmospheric surface layer. Wind Energy, 30, 317–340. Scholar
  392. Sun, J., Xue, M., Wilson, J. W., Zawadzki, I., Ballard, S. P., Onvlee-Hooimeyer, J., et al. (2014). Use of NWP for nowcasting convective precipitation: recent progress and challenges. Bulletin American Meteorology Society, 95, 409–426.Google Scholar
  393. Sun-Mack, S., Minnis, P., Smith, W. L., Hong, G., & Chen, Y. (2017). Detection of single and multilayer clouds in an artificial neural network approach. In Proc. SPIE 10424, remote sensing of clouds and the atmosphere XXII, 1042408 (13 October 2017);
  394. Tafferner, A., Hauf, T., Leifeld, C., Hafner, T., Leykauf, H., & Voigt, U. (2003). ADWICE: advanced diagnosis and warning system for aircraft icing environments. Weather Forecasting, 18, 184–203.Google Scholar
  395. Tag, P. M., Bankert, R. L., & Brody, L. R. (2000). An AVHRR multiple cloud-type classification package. Journal of Applied Meteorological, 39, 125–134.Google Scholar
  396. Tan, I., & Storelvmo, T. (2016). Sensitivity study on the influence of cloud microphysical parameters on mixed-phase cloud thermodynamic phase partitioning in CAM5. Journal of Atmospheric Science, 73, 709–728.Google Scholar
  397. Tardif, R., & Rasmussen, R. M. (2007). Event-based climatology and typology of fog in the New York City region. Journal of Applied Meteorology Climatology, 46, 1141–1168.Google Scholar
  398. TC. (2004). Small and large aircraft; Aircraft critical surface contamination training for aircrew and ground crew. TP 10643E, 12/2004. Ottawa, Ont., Canada, p. 138.Google Scholar
  399. Teixeira, J., et al. (2008). Parameterization of the atmospheric boundary layer: A view from just above the inversion. Bulletin American Meteorology Society, 89, 453–458.Google Scholar
  400. Thobois, L, Cariou, J. P., & Gultepe, I. (2018). Review of lidar based applications for aviation weather. Pure and Applied Geophysics.
  401. Thompson, G., Bruintjes, R. T., Brown, B. G., & Hage, F. (1997). Intercomparison of in-flight icing algorithms. Part I: WISP94 real-time icing prediction and evaluation program. Weather Forecasting, 12, 878–889.Google Scholar
  402. Thompson, G., Politovich, M. K., & Rasmussen, R. M. (2017). A numerical weather model’s ability to predict characteristics of aircraft icing environments. Weather Forecasting, 32, 207–221.Google Scholar
  403. Tomita, H. (2008). New microphysical schemes with five and six categories by diagnostic generation of cloud ice. Journal of the Meteorological Society of Japan. In: Ser. II, Special Issue: The International Workshop on High-Resolution and Cloud Modeling, 2006, 86A, pp. 121–142.Google Scholar
  404. Trier, S. B., Sharman, R. D., & Lane, T. P. (2012). Influences of moist convection on a cold-season outbreak of clear-air turbulence (CAT). Monthly Weather Review, 140, 2477–2496.Google Scholar
  405. Tucker, S. C., Senff, C. J., Weickmann, A. M., Brewer, W. A., Banta, R. M., Sandberg, S. P., et al. (2009). Doppler Lidar estimation of mixing height using turbulence, shear, and aerosol profiles. Journal of Atmosphere Oceanic Technology, 26, 673–688.Google Scholar
  406. Turcotte, M.-F., & Verret, R. (1999). In-flight icing and turbulence forecasts for aviation. In Proc. Sixth Workshop on Operational Meteorology, Halifax, NS, Canada, Canadian Meteorological and Oceanographic Society, pp. 53–56.Google Scholar
  407. Turner, D. D., Clough, S. A., Liljegren, J. C., Clothiaux, E. E., Cady-Pereira, K., & Gaustad, K. L. (2007). Retrieving liquid water path and precipitable water vapor from Atmospheric Radiation Measurement (ARM) microwave radiometers. IEEE Transactions on Geoscience and Remote Sensing, 45, 3680–3690. Scholar
  408. Turner, J., & Warren, D. E. (1989). Cloud track winds in the polar regions from sequences of AVHRR images. International Journal of Remote Sensing, 10, 695–703.Google Scholar
  409. Turp, D., & Gill, P. (2008). Developments in numerical clear air turbulence forecasting at the U.K. Met Office. In Preprints, 13th conf. on aviation, range and aerospace meteorology, New Orleans, LA, Amer. Meteor. Soc., P3.10A. Accessed 3 Jan 2019
  410. Uccellini, L. W. (1980). On the role of upper tropospheric jet streaks and leeside cyclogenesis in the development of low-level jets in the great plains. Monthly Weather Review, 108, 1689–1696.Google Scholar
  411. Uccellini, L. W., & Johnson, D. R. (1979). The coupling of upper and lower tropospheric jet streaks and implications for the development of severe convective storms. Monthly Weather Review, 107, 682–703.Google Scholar
  412. Van den Berg, G. P. (2008). Wind turbine power and sound in relation to atmospheric stability. Wind Energy, 11(2), 151–169. Scholar
  413. Van Den Broeke, M. S. (2016). Polarimetric variability of classic supercell storms as a function of environment. Journal of Applied Meteorology Climatology, 55, 1907–1925.Google Scholar
  414. Van Den Broeke, M. S., Tobin, D. M., & Kumjian, M. R. (2016). Polarimetric radar observations of precipitation type and rate from the 2–3 March 2014 winter storm in Oklahoma and Arkansas. Weather and Forecasting, 31, 1179–1196.Google Scholar
  415. Velden, C., Daniels, J., Stettner, D., Santek, D., Key, J., Dunion, J., et al. (2005). Recent innovations in deriving tropospheric winds from meteorological satellites. Bulletin American Meteorology Society, 86, 205–224.Google Scholar
  416. Velden, C. S., Hayden, C. M., Nieman, S., Menzel, W. P., Wanzong, S., & Goerss, J. (1997). Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bulletin American Meteorology Society, 78, 173–195.Google Scholar
  417. Velden, C. S., Olander, T. L., & Wanzong, S. (1998). The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset methodology, description, and case analysis. Monthly Weather Review, 126, 1202–1218.Google Scholar
  418. Verlinden, K. L., & Bright, D. R. (2017). Using the second-generation GEFS reforecasts to predict ceiling, visibility, and aviation flight category. Weather Forecasting, 32, 1765–1780.Google Scholar
  419. Vislocky, R. L., & Fritsch, J. M. (1995). Generalized additive models versus linear regression in generating probabilistic mos forecasts of aviation weather parameters. Weather Forecasting, 10, 669–680.Google Scholar
  420. Vislocky, R. L., & Fritsch, J. M. (1997). An automated, observations-based system for short-term prediction of ceiling and visibility. Weather Forecasting, 12, 31–43.Google Scholar
  421. Vivekanandan, J., Zhang, G., & Politovich, M. K. (2001). An assessment of droplet size and liquid water content derived from dual-wavelength radar measurements to the application of aircraft icing detection. Journal of Atmosphere Oceanic Technology, 18, 1787–1798.Google Scholar
  422. Vrancken, P. S. (2016). Airborne remote detection of turbulence with forward-pointing LIDAR. In R. D. Sharman & T. P. Lane (Eds.), Aviation turbulence: Processes, detection, and prediction (pp. 443–464). Berlin: Springer.Google Scholar
  423. Wade, C. G. (2003). A Multisensor Approach to Detecting Drizzle on ASOS. Journal of Atmospheric and Oceanic Technology, 20, 820–832.<0820:AMATDD>2.0.CO;2.Google Scholar
  424. Walker, J. R., MacKenzie, W. M., Mecikalski, J. R., & Jewett, C. P. (2012). An enhanced geostationary satellite-based convective initiation algorithm for 0–2-h nowcasting with object tracking. Journal of Applied Meteorology and Climatology, 51, 1931–1949.Google Scholar
  425. Wang, W., & Seaman, N. L. (1997). A Comparison Study of Convective Parameterization Schemes in aMesoscale Model. Monthly Weather Review, 125, 252–278.<0252:ACSOCP>2.0.CO;2.Google Scholar
  426. Wang, L., & Cao, C. (2008). On-orbit calibration assessment of AVHRR longwave channels on MetOp-A using IASI. IEEE Transactions on Geoscience and Remote Sensing, 46, 4005–4013.Google Scholar
  427. Wang, L., Han, Y., Tremblay, D., Weng, F., & Goldberg, M. (2012). Intercomparison of NPP/CrIS radiances with VIIRS, AIRS, and IASI: A post-launch calibration assessment. Earth Observing Missions and Sensors: Development, Implementation, and Characterization II. In H. Shimoda et al. Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 8528), 85280 J,
  428. Ware, R., Cimini, D., Campos, E., Giuliani, G., Albers, S., Nelson, M., et al. (2013). Thermodynamic and liquid profiling during the 2010 Winter Olympics. Atmosphere Research., 132–133, 278–290.Google Scholar
  429. Warner, T. T. (2011). Numerical Weather and Climate Prediction. Cambridge University Press, Cambridge, p. 526.Google Scholar
  430. Weber, M. E., & Stone, M. L. (1995). Low altitude wind shear detection using airport surveillance radars. IEEE Aerospace and Electronic Systems Magazine, 10, 3–9. Scholar
  431. Weisman, M. L., Davis, C., Wang, W., Manning, K. W., & Klemp, J. B. (2008). Experiences with 0–36-h explicit convective forecasts with the WRF-ARW model. Weather Forecasting, 23, 407–437.Google Scholar
  432. Wen, Y., Kirstetter, P., Gourley, J. J., Hong, Y., Behrangi, A., & Flamig, Z. (2017). Evaluation of MRMS Snowfall Products over the Western United States. Journal of Hydrometeorology, 18, 1707–1713.Google Scholar
  433. Westwater, E. (1978). The accuracy of water vapor and cloud liquiddeter minations by dual-frequency ground-based microwave radiometry. Radio Sci., 13, 677–685.Google Scholar
  434. Weygandt, S., Smirnova, T., Benjamin, S., Brundage, K., Sahm, S., Alexander, C., & Schwartz, B. (2009). The High Resolution Rapid Refresh (HRRR): An hourly updated convection resolving model utilizing radar reflectivity assimilation from the RUC/RR. In 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 15A.6.Google Scholar
  435. Whiteman, D. N., Melfi, S. H., & Ferrare, R. A. (1992). Raman lidar system for the measurement of water vapor and aerosols in the earth’s atmosphere. Applied Optics, 31, 3068–3082.Google Scholar
  436. Wick, G., Hock, T., Neiman, P., Vömel, H., Black, M., & Spackman, J. (2018). The NCAR/NOAA Global Hawk Dropsonde System. Journal of Atmosphere Oceanic Technology, 22, 222. Scholar
  437. Williams, J. K. (2014). Using random forests to diagnose aviation turbulence. Machine Learning, 95, 51–70. Scholar
  438. Williams, P. D. (2016). Transatlantic flight times and climate change. Environmental Research Letters., 11(2), 024008. Scholar
  439. Williams, P. D. (2017). Increased light, moderate, and severe clear-air turbulence in response to climate change. Advances in Atmospheric Sciences, 34, 576–586. Scholar
  440. Williams, P. D., & Joshi, M. M. (2013). Intensification of winter transatlantic aviation turbulence in response to climate change. Nature. Climate Change., 3(7), 644–648. Google Scholar
  441. Williams, J. K., & Meymaris, G. (2016). Remote turbulence detection using ground-based Doppler radar, 2016: Aviation turbulence forecast verification. In R. Sharman & T. Lane (Eds.), Aviation turbulence: Processes, detection, prediction (pp. 149–177). Berlin: Springer.Google Scholar
  442. Wilson, J. W. (1966). Movement and predictability of radar echoes. National Severe Storms Laboratory Tech. Memo. ERTM-NSSL-28, p. 30.Google Scholar
  443. Wilson, J. W., Crook, N. A., Mueller, C. K., Sun, J., & Dixon, M. (1998). Nowcasting thunderstorms: A status report. Bulletin American Meteorology Society, 79, 2079–2099.Google Scholar
  444. Wilson, J. W., Ebert, E., Saxen, T., Roberts, R., Mueller, C., Sleigh, M., et al. (2004). Sydney 2000 forecast demonstration project: Convective storm nowcasting. Weather Forecasting, 19, 131–150.Google Scholar
  445. Wilson, F. W., & Gramzow, R. H. (1991). The redesigned low level wind shear alert system. In Preprints, 4th Int. conf. on aviation weather systems, Paris, France, pp. 370–375.Google Scholar
  446. Wilson, J. W., & Roberts, R. D. (2006). Summary of convective storm initiation and evolution during IHOP: Observational and modeling perspective. Monthly Weather Review, 134, 23–47.Google Scholar
  447. Wilson, J. W., Roberts, R. D., Kessinger, C., & McCarthy, J. (1984). Microburst structure and evaluation of Doppler radar for airport wind shear detection. Journal of Climate and Applications Meteorology, 23, 898–915.Google Scholar
  448. Wilson, J. W., & Wakimoto, R. M. (1982). The discovery of the downburst: T. T. Fijita’s contribution. Bulletin American Meteorology Society, 82(1), 49–62.Google Scholar
  449. Wirth, M. (2012). Measuring water vapor with differential absorption lidar. In U. Schumann (Ed.), Atmospheric physics—Background, methods, trends, prediction (pp. 465–476). Berlin: Springer.Google Scholar
  450. Wolfson, M. M., & Clark, D. A. (2006). Advanced aviation weather forecasts. The Lincoln Laboratory Journal, 16(1), 31–58.Google Scholar
  451. Wolfson, M. M., Delanoy, R. L., Forman, B. E., Hallowell, R. G., Pawlak, M. L., & Smith, P. D. (1994). Automated microburst wind-shear prediction. The Lincoln Laboratory Journal, 7(2), 399–426.Google Scholar
  452. Wolfson, M. M., Dupree, W. J., Rasmussen, R., Steiner, M., Benjamin, S., & Weygandt S. (2008). Consolidated storm prediction for aviation (CoSPA). In AMS 13th conference on aviation, range, and aerospace meteorology, New Orleans, LA, 2008.Google Scholar
  453. Wong, M., Skamarock, W. C., Lauritzen, P. H., & Stull, R. B. (2013). A cell-integrated semi-lagrangian semi-implicit shallow-water model (CSLAM-SW) with conservative and consistent transport. Monthly Weather Review, 141, 2545–2560.Google Scholar
  454. Woodley, W. L., Henderson, T. J., Vonnegut, B., Gordon, G., Breidenthal, R., & Holle, S. M. (1991). Aircraft-produced ice particles (APIPs) in supercooled clouds and the probable mechanism for their production. Journal of Applied Meteorology, 30, 1469–1489.Google Scholar
  455. Wulfmeyer, V. (1998). Ground-based differential absorption lidar for water-vapor and temperature profiling: Development and specifications of a high-performance laser transmitter. Applied Optics, 37, 3804–3824.Google Scholar
  456. Wulfmeyer, V. (1999). Investigation of turbulent processes in the lower troposphere with water vapor DIAL and radar–RASS. Journal of Atmospheric Science, 56, 1055–1076.Google Scholar
  457. Wulfmeyer, V., & Bosenberg, J. (1996). Single-mode operation of an injection seeded alexandrite ring laser for application in water-vapor and temperature differential absorption lidar. Optics Letters, 21, 1150–1152.Google Scholar
  458. Wulfmeyer, V., & Bosenberg, J. (1998). Ground-based differential absorption lidar for water-vapor profiling: Assessment of accuracy, resolution, and meteorological applications. Applied Optics, 37, 3825–3844.Google Scholar
  459. Wulfmeyer, V., Lehmann, S., Senff, C., & Schmitz, S. (1995). Injection seeded alexandrite ring laser: Performance and application in a water-vapor differential absorption lidar. Optics Letters, 20, 638–640.Google Scholar
  460. Wurman, J., Dowell, D., Richardson, Y., Markowski, P., Rasmussen, E., Burgess, D., et al. (2012). The second verification of the origins of rotation in tornadoes experiment: VORTEX2. Bulletin American Meteorology Society, 93, 1147–1170.Google Scholar
  461. Yang, B., Qian, Y., Lin, G., Leung, R., & Zhang, Y. (2012). Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model. Atmosphere Chemistry Physics, 12, 2409–2427. Scholar
  462. Yang, J., Zhang, Z., Wei, C., Lu, F., & Guo, Q. (2017). Introducing the new generation of chinese geostationary weather satellites, fengyun-4. Bulletin American Meteorology Society, 98, 1637–1658. Scholar
  463. Yano, J., Ziemiański, M. Z., Cullen, M., Termonia, P., Onvlee, J., Bengtsson, L., et al. (2018). Scientific challenges of convective-scale numerical weather prediction. Bulletin American Meteorology Society, 99, 699–710. Scholar
  464. Yost, C. R., Bedka, K. M., Minnis, P., Nguyen, L., Strapp, J. W., Palikonda, R., et al. (2017). A prototype method for diagnosing high ice water content probability using satellite imager data. Journal of Atmosphere Measure Technology. Scholar
  465. Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., et al. (2016). Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bulletin American Meteorology Society, 97, 621–637. Scholar
  466. Zhang, J., Howard, K., Langston, C., Vasiloff, S., Kaney, B., Arthur, A., et al. (2011). National mosaic and multi-sensor QPE (NMQ) system: Description, results, and future plans. Bulletin American Meteorology Society, 92, 1321–1338. Scholar
  467. Zhou, B., & Du, J. (2010). Fog prediction from a multimodel mesoscale ensemble prediction system. Weather Forecasting, 25, 303–322.Google Scholar
  468. Zhou, B., Du, J., McQueen, J., & Dimego, G. (2009). Ensemble forecast of ceiling, visibility, and fog with NCEP Short-Range Ensemble Forecast system (SREF). In Preprints. Aviation, range, and aerospace meteorology special symp. on weatherair traffic management integration, Phoenix, AZ, Amer. Meteor.Soc., 4.5.
  469. Zhou, B., Du, J., McQueen, J., Dimego, G., Manikin, G., Ferrier, B., & Toth, Z., et al. (2004). An introduction to NCEP SREF aviation project. In 11th conference on aviation range and aerospace, Oct 4–8, Hyannis, MA, Amer. Meteor. Soc. Presentation, p. 10.Google Scholar

Copyright information

© Crown 2019

Authors and Affiliations

  • Ismail Gultepe
    • 1
    • 2
    Email author
  • R. Sharman
    • 3
  • Paul D. Williams
    • 4
  • Binbin Zhou
    • 5
  • G. Ellrod
    • 6
  • P. Minnis
    • 7
  • S. Trier
    • 3
  • S. Griffin
    • 8
  • Seong. S. Yum
    • 9
  • B. Gharabaghi
    • 10
  • W. Feltz
    • 8
  • M. Temimi
    • 11
  • Zhaoxia Pu
    • 12
  • L. N. Storer
    • 4
  • P. Kneringer
    • 13
  • M. J. Weston
    • 14
  • Hui-ya Chuang
    • 15
  • L. Thobois
    • 16
  • A. P. Dimri
    • 17
  • S. J. Dietz
    • 18
  • Gutemberg B. França
    • 19
  • M. V. Almeida
    • 19
  • F. L. Albquerque Neto
    • 19
  1. 1.MRD, ECCCTorontoCanada
  2. 2.Faculty of Engineering and Applied ScienceOntario Technical UniversityOshawaCanada
  3. 3.National Center for Atmospheric Research, Research Applications LaboratoryBoulderUSA
  4. 4.Department of MeteorologyUniversity of ReadingReadingUK
  5. 5.IMSG and EMC/NCEP/NOAACollege ParkUSA
  6. 6.EWxC, LLCGranbyUSA
  7. 7.Science Systems and Applications, Inc.HamptonUSA
  8. 8.Space Science and Engineering Center, Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-MadisonMadisonUSA
  9. 9.Department of Atmospheric SciencesYonsei UniveristySeoulKorea
  10. 10.Water Resources Engineering, School of EngineeringUniversity of GuelphGuelphCanada
  11. 11.Water and Environment Engineering Program, Masdar InstituteKhalifa University of Science and TechnologyAbu DhabiUAE
  12. 12.Department of Atmospheric SciencesUniversity of UtahSalt Lake CityUSA
  13. 13.University InnsbruckInnsbruckAustria
  14. 14.Department of Civil Infrastructure and Environmental EngineeringKhalifa UniversityAbu DhabiUAE
  15. 15.NCEP Environmental Modeling CenterCollege ParkUSA
  16. 16.Leosphere Inc.OrsayFrance
  17. 17.School of Environmental SciencesJawaharlal Nehru UniversityNew DelhiIndia
  18. 18.University of InnsbruckInnsbruckAustria
  19. 19.Department of MeteorologyFederal University of Rio de JaneiroRio de JaneiroBrazil

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