Advertisement

Applications of Geostationary Satellite Data to Aviation

  • Gary P. Ellrod
  • Kenneth Pryor
Article
  • 116 Downloads

Abstract

Weather is by far the most important factor in air traffic delays in the United States’ National Airspace System (NAS) according to the Federal Aviation Administration (FAA). Geostationary satellites have been an effective tool for the monitoring of meteorological conditions that affect aviation operations since the launch of the first Synchronous Meteorological Satellite (SMS) in the United States in 1974. This paper will review the global use of geostationary satellites in support of aviation weather since their inception, with an emphasis on the latest generation of satellites, such as Geostationary Operational Environmental Satellite (GOES)-R (16) with its Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM). Specific applications discussed in this paper include monitoring of convective storms and their associated hazards, fog and low stratus, turbulence, volcanic hazards, and aircraft icing.

Keywords

Aviation weather geostationary satellites GOES-R Himiwari Meteosat Fengyun ABI AHI SEVIRI thunderstorms convective initiation fog low stratus volcanic ash volcanic SO2 aircraft icing microbursts geostationary lightning mapper 

Notes

Acknowledgements

The authors would like to thank Dr. Michael Pavolonis (NOAA/CIMSS) for his contributions to the section on volcanic ash detection. Many of the images in this paper were obtained from the University of Wisconsin CIMSS blog pages on the use of improved satellite image data from GOES-R (16) and Himiwari. We also acknowledge the comments of two anonymous reviewers that greatly improved the quality of the paper.

References

  1. Ackerman, S., Schreiner, A., Schmit, T., Woolf, H., Li, J., & Pavolonis, M. (2008). Using the GOES Sounder to monitor upper level SO2 from volcanic eruptions. Journal of Geophysical Research: Atmospheres, 113.  https://doi.org/10.1029/2007jd009622.
  2. Adler, R. F., & Fenn, D. D. (1979). Thunderstorm intensity as determined from satellite data. Journal of Applied Meteorology, 18, 502–517.CrossRefGoogle Scholar
  3. Atkins, N. T., & Wakimoto, R. M. (1991). Wet microburst activity over the southeastern United States: Implications for forecasting. Weather and Forecasting, 6, 470–482.CrossRefGoogle Scholar
  4. Bachmeier, S. (2017a). Tornadoes and Large Hail in Minnesota and Wisconsin. CIMSS Satellite Blog, Cooperative Institute for Meteorological Satellite Studies, 16 May 2017. http://cimss.ssec.wisc.edu/goes/blog/archives/category/severe-convection/page/6.
  5. Bachmeier, S. (2017b). Eruption of Kambalny Volcano in Kamchatka, Russia. CIMSS Satellite Blog, Cooperative Institute for Meteorological Satellite Studies, 25 March 2017. http://cimss.ssec.wisc.edu/goes/blog/archives/category/volcanic-activity/page/2.
  6. Bass, R. (2017). The FAA’s convective weather research program. In AMS 18th Conf. on Aviation, Range and Aerospace Meteorology (ARAM), 23 January 2017, Seattle, Washington. https://ams.confex.com/ams/97Annual/webprogram/Paper313429.html.
  7. Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., & Greenwals, T. (2010). Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients. Journal of Applied Meteorology and Climatology, 49, 181–202.CrossRefGoogle Scholar
  8. Bessho, K., et al. (2016). An introduction to Himiwari-8/9—Japan’s new-generation geostationary meteorological satellites. Journal of the Meteorological Society of Japan, 94, 151–183.CrossRefGoogle Scholar
  9. Bézy, J.-L., Aminou, D., Bensi, P., Stuhlman, R., Tjemkes, S., & Rodriguez, A. (2005). Meteosat Third Generation. European Space Agency Bulletin, 123, 28–32.Google Scholar
  10. Byers, H. R., & Braham, R. R. (1949). The thunderstorm (p. 247). Washington, DC: U. S. Government Printing Office.Google Scholar
  11. Calvert, C., & Pavolonis, M. (2011). GOES-R Advanced Baseline Imager (ABI) Theoretical Basis Document for Low Cloud and Fog, Version 2.0. NOAA/NESDIS Center for Satellite Applications and Research, p. 72.Google Scholar
  12. Caracena, F., & Flueck, J. A. (1988). Classifying and forecasting microburst activity in the Denver area. Journal of Aircraft, 25, 525–530.CrossRefGoogle Scholar
  13. Casadevall, T. (1994). The 1989–1990 eruption of Redoubt Volcano, Alaska: Impacts on aircraft operations. Journal of Volcanology and Geothermal Research, 62, 301–316.CrossRefGoogle Scholar
  14. Chai, T., Crawford, A., Stunder, B., Pavolonis, M., Draxler, R., & Stein, A. (2017). Improving volcanic ash predictions with the HYSPLIT dispersions model by assimilating MODIS satellite retrievals. Atmospheric Chemistry and Physics, 17, 2865–2879.CrossRefGoogle Scholar
  15. Clarisse, L., Prata, F., Lacour, J.-L., Hurtmans, D., Clerbaux, C., & Coheur, P.-F. (2010). A correlation method for volcanic ash detection using hyperspectral infrared measurements. Geophysical Research Letters, 37, L198065. (p. 5).CrossRefGoogle Scholar
  16. Conover, J. H. (1964). The identification and significance of orographically induced clouds observed by TIROS satellites. Journal of Applied Meteorology, 3, 226–234.CrossRefGoogle Scholar
  17. DeMaria, M., DeMaria, R. T., Knaff, J., & Molenar, D. (2012). Tropical cyclone lightning and rapid intensity change. Monthly Weather Review, 140, 1828–1842.CrossRefGoogle Scholar
  18. Donovan, M., Williams, E., Kessinger, C., Blackburn, G., Herzegh, P., Bankert, R., et al. (2008). The identification and verification of hazardous convective cells over oceans using visible and infrared satellite observations. Journal of Applied Meteorology and Climatology, 47, 164–184.CrossRefGoogle Scholar
  19. Dworak, R., Bedka, K., Brunner, J., & Feltz, W. (2012). Comparison between GOES-12 overshooting-top detections, WSR-88D radar reflectivity and severe storm reports. Weather and Forecasting, 27, 684–699.CrossRefGoogle Scholar
  20. Eckert, M. (2017). GOES-16 enhancements to IDSS for San Francisco International Airport (SFO) on 03/03/17. NWA Specialized Operations CommitteeIDSS Success Stories. http://nwas.org/wp-content/uploads/2016/08/DSS-Success-Story-SFO_GOES-16-1.pdf.
  21. Ellrod, G. P. (1985). Indicators of high altitude, non-convective turbulence observed in satellite images. In Proc. of the 2nd Intl. Conf. on the Aviation Weather System (pp. 277–284). Boston, MA: Amer. Meteor. Soc.Google Scholar
  22. Ellrod, G. P. (1987). Identifying high altitude mountain wave turbulence and strong chinook wind events with satellite imagery. In Proc. Of the AIAA 25th Aerospace Sciences Meeting, Jan. 12–15, Reno (p. 7). Washington, DC: Amer. Inst. For Aeronautics and Astronautics.Google Scholar
  23. Ellrod, G. P. (1989). Environmental conditions associated with the Dallas microburst storm determined from satellite soundings. Weather and Forecasting, 4, 469–484.CrossRefGoogle Scholar
  24. Ellrod, G. P. (1990). A water vapor feature related to severe thunderstorms. National Weather Digest, 15, 19–29.Google Scholar
  25. Ellrod, G. P. (1995). Advances in the detection of fog at night using GOES multispectral infrared imagery. Weather and Forecasting, 10, 606–619.CrossRefGoogle Scholar
  26. Ellrod, G. P. (1996). The use of GOES-8 multi-spectral imagery for the detection of aircraft icing regions. In 8th Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., (pp. 168–171).Google Scholar
  27. Ellrod, G. P. (2002). Estimation of low cloud base heights at night from satellite infrared and surface temperature data. National Weather Digest, 26, 39–44.Google Scholar
  28. Ellrod, G. P. (2005). Remote sensing of volcanic ash. Tutorial developed for National Weather Association Remote Sensing Committee. http://www.ellrodweather.com/volcano/ash.htm.
  29. Ellrod, G. P., & Bailey, A. (2007). Assessment of aircraft icing potential and maximum icing altitude from geostationary meteorological satellite data. Weather and Forecasting, 22, 160–174.CrossRefGoogle Scholar
  30. Ellrod, G. P., Connell, B., & Hillger, D. (2003). Improved detection of airborne volcanic ash using multi-spectral infrared satellite data. Journal of Geophysical Research, 108(D12), 4356.CrossRefGoogle Scholar
  31. Ellrod, G. P., & Gultepe, I. (2007). Inferring low cloud base heights at night for aviation using satellite infrared and surface temperature data. Pure and Applied Geophysics, 164, 1193–1205.CrossRefGoogle Scholar
  32. Ellrod, G. P., Maturi, E., & Steger, J. (1989). Detection of fog at night using dual channel GOES-VAS imagery. In Proc. 12th Conf. on Wea. Analysis and Forecasting, Monterey, California, Amer. Meteor. Soc. (pp. 515–520).Google Scholar
  33. Ellrod, G. P., & Schreiner, A. (2004). A first look at volcanic ash detection in the GOES-12 era. In 11th Conf. on Aviation, Range, and Aerospace Meteorology, Hyannis, MA, 4–8 October 2004, Paper #8.13. Boston, MA: Amer. Meteor. Soc.Google Scholar
  34. Eyre, J. R., Brownscombe, J. L., & Allam, R. J. (1984). Detection of fog at night using Advanced Very High Resolution Radiometer (AVHRR) imagery. Meteorological Magazine, 114, 187–201.Google Scholar
  35. Federal Aviation Administration (FAA). (2017). NextGen Weather FAQ: Weather Delays. https://www.faa.gov/nextgen/programs/weather/faq/.
  36. Fritz, S. (1965). The significance of mountain lee waves as seen from satellite pictures. Journal of Applied Meteorology, 4, 31–37.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. Gravelle, C., Mecikalski, J., Line, W., Bedka, K., Petersen, R., Sieglaff, J., et al. (2016). Satellite convective toolkit to “bridge the gap” between severe weather watches and warnings: An example from the 20 May 2013 Moore, Oklahome tornado outbreak. Bulletin of the American Meteorological Society, 97(1), 69–84.CrossRefGoogle Scholar
  39. Gultepe, I., Pagowski, M., & Reid, J. (2007). Using surface data to validate a satellite based fog detection scheme. Weather and Forecasting, 22, 444–456.CrossRefGoogle Scholar
  40. Gurka, J. J. (1978). The use of enhanced visible imagery for predicting the time of fog dissipation. In Proc. Conf. on Weather Forecasting and Analysis and Aviation Meteorology, Silver Spring, MD, Amer. Meteor. Soc., (pp. 343–346).Google Scholar
  41. Han, H., Lee, S., Im, J., Kim, M., Lee, M.-I., Ahn, M., et al. (2015). Detection of convective initiation using Meteorological Imager onboard Communications, Ocean, and Meteorological Satellite based on machine learning approaches. Remote Sensing, 7, 9184–9204.CrossRefGoogle Scholar
  42. Hartung, D. C., Sieglaff, J. M., Cronce, L. M., & Feltz, W. F. (2013). An intercomparison of UW cloud-top cooling rates with WSR-88D radar data. Weather and Forecasting, 28, 463–480.CrossRefGoogle Scholar
  43. Heymsfield, G. M., & Blackmer, R. H. (1988). Satellite-observed characteristics of Midwest severe thunderstorm anvils. Monthly Weather Review, 116, 2200–2224.CrossRefGoogle Scholar
  44. Hillger, D., Kopp, T., Lee, T., Lindsey, D., Seaman, C., Miller, S., et al. (2013). First-light imagery from Suomi NPP VIIRS. Bulletin of the American Meteorological Society, 94, 1019–1029.CrossRefGoogle Scholar
  45. Hufford, G., Salinas, L., Simpson, J., Barske, E., & Pieri, D. (2000). Operational implications of airborne volcanic ash. Bulletin of the American Meteorological Society, 81(4), 745–755.CrossRefGoogle Scholar
  46. Kessinger, C., Megenhardt, D., Blackburn, G., Olivo, J., Lin, L., Hoang, V., Nayote, M., Sievers, K., Ritter, A., Wolf, D., Matz, O., Scheinhartz, R., & Cahall, J. (2017). Displaying convective weather products on an electronic flight bag. Journal of Air Traffic Control, Fall Issue, 52–61.Google Scholar
  47. Kim, D., & Ahn, M. (2014). Introduction of the in-orbit test and its performance for the first meteorological imager of the Communications, Ocean and Meteorological Satellite. Atmospheric Measurement Techniques, 7, 2471–2485.CrossRefGoogle Scholar
  48. Knox, J., Bachmeier, S., Carter, W., Tarantino, J., Paulik, L., Wilson, E., Bechdol, G., & Mays, M. (2010). Transverse cirrus bands in weather systems: a grand tour of an enduring enigma. Weather Magazine, 65(2), 35–41.  https://doi.org/10.1002/wea.417.CrossRefGoogle Scholar
  49. Lay, E. H., Holzworth, R. H., Rodger, C. J., Thomas, J. N., Pinto, O., & Dowden, R. L. (2004). WWLL global lightning detection system: Regional validation study in Brazil. Geophysical Research Letters, 31, L03102.  https://doi.org/10.1029/2003GL018882.Google Scholar
  50. Lee, Y.-K., Li, Z., Li, J., & Schmit, T. (2014). Evaluation of the GOES-R ABI LAP retrieval algorithm using the GOES-13 sounder. Journal of Atmospheric and Oceanic Technology, 31(1), 3–19.CrossRefGoogle Scholar
  51. Lee, T. F., Turk, F. J., & Richardson, K. (1997). Stratus and fog products using GOES-8-9 3.9 µm data. Weather and Forecasting, 12, 664–677.CrossRefGoogle Scholar
  52. Li, Z., Li, J., Menzel, W. P., Schmit, T. J., Nelson, J. P., III, Daniels, J., et al. (2008). GOES sounding improvement and applications to severe storm nowcasting. Geophysical Research Letters, 35, L03806.  https://doi.org/10.1029/2007gl032797.Google Scholar
  53. Lilly, D. K. (1978). A severe downslope windstorm and aircraft turbulence event induced by a mountain wave. Journal of the Atmospheric Sciences, 34, 59–77.CrossRefGoogle Scholar
  54. Lindstrom, S. (2017). Harvey and GLM Lightning. CIMSS Satellite Blog, Cooperative Institute for Meteorological Satellite Studies, 25 August 2017. http://cimss.ssec.wisc.edu/goes/blog/archives/date/2017/08/page/2.
  55. Liu, C., & Heckman, S. (2011). The application of total lightning detection and cell tracking for severe weather prediction. In 91st American Meteorological Society Annual Meeting, Seattle, 1–10.Google Scholar
  56. Martin, D. W., Kohrs, R. A., Mosher, F. R., Medaglia, C. M., & Adamo, C. (2008). Over-ocean validation of the Global Convective Diagnostic. Journal of Applied Meteorology and Climatology, 47, 525–543.CrossRefGoogle Scholar
  57. McCann, D. W. (1983). The enhanced-V, a satellite observable severe storm signature. Monthly Weather Review, 111, 887–894.CrossRefGoogle Scholar
  58. Mecikalski, J., & Bedka, K. (2006). Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Monthly Weather Review, 134, 49–77.CrossRefGoogle Scholar
  59. Mecikalski, J., Williams, J. K., Jewett, C. P., Ahijevych, D., LeRoy, A., & Walker, J. R. (2015). Probabilistic 0–1-h convective initiation nowcasts that combine geostationary satellite observations and numerical weather prediction model data. Journal of Applied Meteorology and Climatology, 54, 1039–1059.  https://doi.org/10.1175/JAMC-D-14-0129.1.CrossRefGoogle Scholar
  60. Menzel, W. P., Holt, F. C., Schmit, T. J., Aune, R., Schreiner, A. J., Wade, G. S., et al. (1998). Application of GOES-8/9 soundings to weather forecasting and nowcasting. Bulletin of the American Meteorological Society, 10, 2059–2077.CrossRefGoogle Scholar
  61. Menzel, W. P., & Purdom, J. F. W. (1994). Introducing GOES-I: The first of a new generation of geostationary operational environmental satellites. Bulletin of the American Meteorological Society, 75, 757–780.CrossRefGoogle Scholar
  62. Merk, D., & Zinner, T. (2013). Detection of convective initiation using Meteosat SEVIRI implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM. Atmospheric Measurement Techniques, 6, 1903–1918.CrossRefGoogle Scholar
  63. Miller, T., & Casadevall, T. (2000). Volcanic ash hazards to aviation: Encyclopedia of volcanoes. In H. Sigurdsson (Ed.), (pp. 915–930) San Diego, California: Academic.Google Scholar
  64. Miller, S. D., Schmit, T., Seaman, C., Lindsey, D., Gunshor, M., Kohrs, R., et al. (2016). A sight for sore eyes: The return of true color to geostationary satellites. Bulletin of the American Meteorological Society, 97, 1803–1816.CrossRefGoogle Scholar
  65. Minnis, P., et al. (2004). Real-time cloud, radiation, and aircraft icing parameters from GOES over the USA. In 13th Conf. on Satellite Oceanography and Meteorolology, Norfolk, Virginia, Amer. Meteor. Soc., P7.1. http://ams.confex.com/ams/pdfpapers/79179.pdf.
  66. Smith, W. L. Jr, Minnis, P., Bernstein, B. C., McDonough, F., & Khaiyer, M. M. (2003). Comparison of super-cooled liquid water cloud properties derived from satellite and aircraft measurements. In Proc. in-flight icing/de-icing int. conf., Chicago, IL, Federal Aviation Administration 2003-01-2156.Google Scholar
  67. Smith, W. L. Jr, Minnis, P., & Young, D. F. (2000). An icing product derived from operational satellite data. In Proc. AMS 9th conference on aviation, range and aerospace meteorology, 11–15 September 2000 (pp. 256–259). Orlando, FL: Amer. Meteor. Soc.Google Scholar
  68. Mohr, T. (2014). Preparing for the use of new generation geostationary meteorological satellites. WMO Bulletin, 63, 42–44.Google Scholar
  69. Morel, P., Desbois, M., & Szewach, G. (1978). A new insight into the troposphere with the water vapor channel of Meteosat. Bulletin of the American Meteorological Society, 59, 711–714.Google Scholar
  70. Mosher, F. R. (2001). A satellite diagnostic of global convection. In 11th Conf. on Satellite Meteorology and Oceanography (pp. 416–419). Madison, Wisconsin: Amer. Meteor. Soc.Google Scholar
  71. Mosher, F. R. (2002). Detection of deep convection around the globe. In 10th Conf. on Aviation, Range, and Aerospace Meteorology (pp. 289–292). Portland, Oregon: Amer. Meteor. Soc.Google Scholar
  72. Negri, A. J., & Adler, R. F. (1981). Relation of satellite-based thunderstorm intensity to radar-estimated rainfall. Journal of Applied Meteorology, 20, 288–300.CrossRefGoogle Scholar
  73. Pavolonis, M. J. (2010). Advances in extracting cloud composition information from spaceborne infrared radiances—A robust alternative to brightness temperatures. Part I: Theory. Journal of Applied Meteorology and Climatology, 49(9), 1992–2012.CrossRefGoogle Scholar
  74. Pavolonis, M. J. (2018). Personal communication.Google Scholar
  75. Pavolonis, M. J., Feltz, W. F., Heidinger, A. K., & Gallina, G. M. (2005). A daytime complement to the reverse absorption technique for improved automated detection of volcanic ash. Journal of Atmospheric and Oceanic Technology, 23, 1422–1444.CrossRefGoogle Scholar
  76. Pavolonis, M. J., Heidinger, A., & Sieglaff, J. (2013). Automated retrievals of volcanic ash and dust cloud properties from upwelling infrared measurements. Journal of Geophysical Research-Atmospheres, 118(3), 1436–1458.CrossRefGoogle Scholar
  77. Pavolonis, M. J., Sieglaff, J. M., & Cintineo, J. L. (2015a). Spectrally enhanced cloud objects (SECO): A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements, Part I: Multispectral analysis. Journal of Geophysical Research: Atmospheres, 120, 7813–7841.Google Scholar
  78. Pavolonis, M. J., Sieglaff, J. M., & Cintineo, J. L. (2015b). Spectrally enhanced cloud objects (SECO): A generalized framework for automated detection of volcanic ash and dust clouds using passive satellite measurements, part II: Cloud object analysis and global application. Journal of Geophysical Research: Atmospheres, 120, 7842–7870.Google Scholar
  79. Prata, F. (1989). Observations of volcanic ash clouds in the 10–12 µm window using AVHRR/2 data. International Journal of Remote Sensing, 10, 751–761.CrossRefGoogle Scholar
  80. Prata, F., Schreiner, A., Schmit, T. J., & Ellrod, G. P. (2004). First measurements of volcanic sulfur dioxide from the GOES Sounder: Implications for improved aviation safety. In Proceedings, 2nd Intl. Conf. on Volcanic Ash and Aviation Safety, 21–24 June 2004, Alexandria, Virginia, Paper number 3.7.Google Scholar
  81. Pryor K. (2010). Recent developments in microburst nowcasting using GOES. In 17th conf. on satellite meteorology and oceanography (p. 9.7). Annapolis, Maryland: Amer. Meteor. Soc.Google Scholar
  82. Pryor K. (2012). Microburst nowcasting applications of GOES. In 18th conf. on satellite meteorology, oceanography and climatology (p. 471). New Orleans, Louisiana: Amer. Meteor. Soc.Google Scholar
  83. Pryor, K. (2014). Downburst prediction applications of meteorological geostationary satellites. In Proc. SPIE conf. on remote sensing of the atmosphere, clouds, and precipitation V, Beijing, China.  https://doi.org/10.1117/12.2069283.
  84. Pryor, K. (2015). Progress and developments of downburst prediction applications of GOES. Weather and Forecasting, 30, 1182–1200.CrossRefGoogle Scholar
  85. Pryor, K. (2017). Advances in downburst monitoring and prediction with GOES-16. In 17th conf. on mesoscale processes, San Diego, CA, Amer. Meteor. Soc., Paper No. 10.6.Google Scholar
  86. Pryor, K., & Ellrod, G. P. (2004a). Recent improvements to the GOES microburst products. Weather and Forecasting, 19, 582–594.CrossRefGoogle Scholar
  87. Pryor, K., & Ellrod, G. P. (2004b). WMSI—A new index for forecasting wet microburst severity. Electronic Journal Of Operational Meteorology, 5(3), 1–25.Google Scholar
  88. Purdom, J. F. W. (1973). Meso-highs and satellite imagery. Monthly Weather Review, 101, 180–181.CrossRefGoogle Scholar
  89. Purdom, J. F. W. (1976). Some uses of high-resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Monthly Weather Review, 104, 1474–1483.CrossRefGoogle Scholar
  90. Rauber, R., & Tokay, A. (1991). An explanation for the existence of supercooled water at the top of cold clouds. Journal of Atmospheric Science, 48, 1005–1023.CrossRefGoogle Scholar
  91. Rose, W. I., Kostinski, A. B., & Kelley, L. (1995). Real time C band radar observations of 1992 eruption clouds from Crater Peak/Spurr Volcano, Alaska. U. S. Geological Survey Bulletin 2139, p. 19.Google Scholar
  92. Rose, W. I., & Mayberry, G. C. (2000). Use of GOES thermal infrared imagery for eruption scale measurements, Soufriere Hills, Montserrat. Geophysical Research Letters, 27, 3097–3100.CrossRefGoogle Scholar
  93. Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., et al. (2002). An introduction to Meteosat Second Generation (MSG). Bulletin of the American Meteorological Society, 83, 977–992.CrossRefGoogle Scholar
  94. Schmetz, J., Tjemkes, S. A., Gube, M., & van de Berg, L. (1997). Monitoring deep convection and convective overshooting with METEOSAT. Advances in Space Research, 19, 433–441.CrossRefGoogle Scholar
  95. Schmit, T., Goodman, S., Gunshor, M., Sieglaff, J., Heidinger, A., Bachmeier, S., et al. (2015). Rapid refresh information of significant events: Preparing users for the next generation of geostationary operational satellites. Bulletin of the American Meteorological Society, 96, 561–575.CrossRefGoogle Scholar
  96. Schmit, T., Griffith, P., Gunshor, M., Daniels, J., Goodman, S., & Lebair, W. (2017). A closer look at the ABI on the GOES-R series. Bulletin of the American Meteorological Society, 98(4), 681–698.CrossRefGoogle Scholar
  97. Schmit, T., Gunshor, M., Menzel, W. P., Gurka, J., Li, J., & Bachmeier, S. (2005). Introducing the next-generation Advanced Baseline Imager on GOES-R. Bulletin of the American Meteorological Society, 86, 1079–1096.CrossRefGoogle Scholar
  98. Schmit, T., Li, J., Gurka, J. J., Goldberg, M. D., Schrab, K. J., Li, J., et al. (2008). The GOES-R advanced baseline imager and the continuation of current sounder products. Journal of Applied Meteorology and Climatology, 47, 2696–2711.CrossRefGoogle Scholar
  99. Schultz, C. J., Petersen, W. A., & Carey, L. D. (2009). Preliminary development and evaluation of lightning jump algorithms for the real-time detection of severe weather. Journal of Applied Meteorology and Climatology, 48, 2543–2563.CrossRefGoogle Scholar
  100. Seftor, C. J., Hsu, N., Herman, J., Bhartia, P., Torres, O., Rose, W., et al. (1997). Detection of volcanic ash clouds from Nimbus 7/Total Ozone Mapping Spectrometer. Journal of Geophysical Research, 102(D14), 16749–16760.CrossRefGoogle Scholar
  101. Sikdar, D. N., Suomi, V. E., & Anderson, C. E. (1970). Convective transport of mass and energy in severe storms over the United States—An estimate from a geostationary altitude. Tellus, 22, 521–532.CrossRefGoogle Scholar
  102. Smith, W. L., Jr., 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 and Climatology, 51, 1794–1810.CrossRefGoogle Scholar
  103. Smith, W. L., Jr., Suomi, V., Menzel, W., Woolf, H., Sromovsky, L., Revercomb, H., et al. (1981). First sounding results from VAS-D. Bulletin of the American Meteorological Society, 62, 232–236.Google Scholar
  104. Srivastava, R. C. (1987). A model of intense downdrafts driven by the melting and evaporation of precipitation. Journal of Atmospheric Science, 44, 1752–1773.CrossRefGoogle Scholar
  105. Suomi, V. E., & Krauss, R. J. (1978). The spin scan camera system: Geostationary meteorological satellite workhorse for a decade. Optical Engineering, 17, 6–13.CrossRefGoogle Scholar
  106. Tag, P., Bankert, R., & Brody, L. (2000). An AVHRR multiple cloud-type classification package. Journal of Applied Meteorology, 39, 125–134.CrossRefGoogle Scholar
  107. Thompson, G., Bullock, R., & Lee, T. F. (1997). Using satellite data to reduce spatial extent of diagnosed icing. Weather and Forecasting, 12, 185–190.CrossRefGoogle Scholar
  108. University of Wisconsin CIMSS. (2017). GOES-R Fog Product Examples, 17 October 2017. http://fusedfog.ssec.wisc.edu/?cat=18.
  109. Wakimoto, R. M. (1985). Forecasting dry microburst activity over the High Plains. Monthly Weather Review, 113, 1131–1143.CrossRefGoogle Scholar
  110. Walker, J. R., MacKenzie, W. M., Jr., 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.  https://doi.org/10.1175/JAMC-D-11-0246.1.CrossRefGoogle Scholar
  111. Wimmers, A. J., & Feltz, W. (2010). Tropopause folding turbulence product. GOES-R Algorithm Theoretical Basis Document (ATBD). NOAA Center for Satellite Applications and Research. https://www.goes-r.gov/products/ATBDs/option2/Aviation_Turbulence_v1.0_no_color.pdf.
  112. Wimmers, A. J., Griffin, S., Gerth, J., Bachmeier, S., & Lindstrom, S. (2018). Observations of gravity waves with high-pass filtering in the new generation of geostationary imagers and their relation to aircraft turbulence. Weather and Forecasting, 33, 139–144.CrossRefGoogle Scholar
  113. Wimmers, A. J., & Moody, J. L. (2001). A fixed-layer estimation of upper tropospheric specific humidity from the GOES water vapor channel: Parameterization and validation of the altered brightness temperature product. Journal of Geophysical Research, 106(D15), 17115–17132.CrossRefGoogle Scholar
  114. Wimmers, A. J., & Moody, J. L. (2004a). Tropopause folding at satellite-observed spatial gradients: 1. Verification of an empirical relationship. Journal of Geophysical Research, 109, D19306.  https://doi.org/10.1029/2003JD004145.CrossRefGoogle Scholar
  115. Wimmers, A. J., & Moody, J. L. (2004b). Tropopause folding at satellite-observed spatial gradients: 2. Development of an empirical model. Journal of Geophysical Research, 109, D19307.  https://doi.org/10.1029/2003JD004146.CrossRefGoogle Scholar
  116. Yang, J., Zhang, Z., Wei, C., Lu, F., & Guo, Q. (2017). Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bulletin of the American Meteorological Society, 98, 1637–1659.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.EWxC, LLCGranbyUSA
  2. 2.NOAA/NESDIS Center for Satellite Applications and ResearchCollege ParkUSA

Personalised recommendations