Articles | Volume 16, issue 5
https://doi.org/10.5194/amt-16-1391-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-1391-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Stephen W. Nesbitt
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Robert J. Trapp
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Larry Di Girolamo
Department of Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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Cited articles
Ackerman, S. A.: Global satellite observations of negative brightness
temperature differences between 11 and 6.7 µm, J. Atmos. Sci., 53,
2803–2812, https://doi.org/10.1175/1520-0469(1996)053<2803:GSOONB>2.0.CO;2, 1996.
Adams, D. K. and Comrie, A. C.: The North American Monsoon, B. Am.
Meteorol. Soc., 78, 2197–2213, https://doi.org/10.1175/1520-0477(1997)078<2197:TNAM>2.0.CO;2, 1997.
Alcala, C. M. and Dessler, A. E.: Observations of deep convection in the
tropics using the Tropical Rainfall Measuring Mission (TRMM) precipitation
radar, J. Geophys. Res.-Atmos., 107, 4792, https://doi.org/10.1029/2002JD002457, 2002.
Astin, I., Di Girolamo, L., and Van De Poll, H. M.: Bayesian confidence
intervals for true fractional coverage from finite transect measurements:
Implications for cloud studies from space, J. Geophys. Res.-Atmos.,
106, 17303–17310, https://doi.org/10.1029/2001JD900168, 2001.
Barnes, W. L., Pagano, T. S., and Salomonson, V. V.: Prelaunch
characteristics of the moderate resolution, IEEE Trans. Geosci. Remote
Sens., 36, 1088–1100, 1998.
Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., and Greenwald, T.:
Objective satellite-based detection of overshooting tops using infrared
window channel brightness temperature gradients, J. Appl. Meteorol.
Climatol., 49, 181–202, https://doi.org/10.1175/2009JAMC2286.1, 2010.
Bedka, K. M.: Overshooting cloud top detections using MSG SEVIRI Infrared
brightness temperatures and their relationship to severe weather over
Europe, Atmos. Res., 99, 175–189, https://doi.org/10.1016/j.atmosres.2010.10.001,
2011.
Bedka, K. M. and Khlopenkov, K.: A probabilistic multispectral pattern
recognition method for detection of overshooting cloud tops using passive
satellite imager observations, J. Appl. Meteorol. Climatol., 55,
1983–2005, https://doi.org/10.1175/JAMC-D-15-0249.1, 2016.
Bedka, K. M., Allen, J. T., Punge, H. J., Kunz, M., and Simanovic, D.: A
long-term overshooting convective cloud-top detection database over
Australia derived from MTSAT Japanese Advanced Meteorological Imager
Observations, J. Appl. Meteorol. Climatol., 57, 937–951,
https://doi.org/10.1175/JAMC-D-17-0056.1, 2018.
Bosilovich, M. G., Lucchesi, R., and Suarez, M.: MERRA-2: File Specification,
Earth, 9, 73, http://gmao.gsfc.nasa.gov/pubs/office_notes (last access: 12 May 2022), 2016.
Chung, E. S., Sohn, B. J., Schmetz, J., and Koenig, M.: Diurnal variation of upper tropospheric humidity and its relations to convective activities over tropical Africa, Atmos. Chem. Phys., 7, 2489–2502, https://doi.org/10.5194/acp-7-2489-2007, 2007.
Chung, E. S., Sohn, B. J., and Schmetz, J.: CloudSat shedding new light on
high-reaching tropical deep convection observed with Meteosat, Geophys. Res.
Lett., 35, 1–5, https://doi.org/10.1029/2007GL032516, 2008.
Cloudsat: CloudSat DPC, CloudSat [data set], https://www.cloudsat.cira.colostate.edu/ (last access: 26 November 2021), 2023.
Dworak, R., Bedka, K., Brunner, J., and Feltz, W.: Comparison between GOES-12
overshooting-top detections, WSR-88D radar reflectivity, and severe storm
reports, Weather Forecast., 27, 684–699, https://doi.org/10.1175/WAF-D-11-00070.1,
2012.
EarthData: Your Source for Level-1 and Atmospheric Data, EarthData [data set], https://ladsweb.modaps.eosdis.nasa.gov/ (last access: 8 February 2022), 2023.
Funk, C., Verdin, A., Michaelsen, J., Peterson, P., Pedreros, D., and Husak, G.: A global satellite-assisted precipitation climatology, Earth Syst. Sci. Data, 7, 275–287, https://doi.org/10.5194/essd-7-275-2015, 2015.
Geerts, B., Giangrande, S. E., McFarquhar, G. M., Xue, L., Abel, S. J.,
Comstock, J. M., Crewell, S., DeMott, P. J., Ebell, K., Field, P., Hill, T.
C. J., Hunzinger, A., Jensen, M. P., Johnson, K. L., Juliano, T. W.,
Kollias, P., Kosovic, B., Lackner, C., Luke, E., Lüpkes, C., Matthews,
A. A., Neggers, R., Ovchinnikov, M., Powers, H., Shupe, M. D., Spengler, T.,
Swanson, B. E., Tjernström, M., Theisen, A. K., Wales, N. A., Wang, Y.,
Wendisch, M., and Wu, P.: The COMBLE Campaign: A Study of Marine Boundary
Layer Clouds in Arctic Cold-Air Outbreaks, B. Am. Meteorol. Soc., 103,
E1371–E1389, https://doi.org/10.1175/bams-d-21-0044.1, 2022.
Gettelman, A., Salby, M. L., and Sassi, F.: Distribution and influence of
convection in the tropical tropopause region, J. Geophys. Res.-Atmos.,
107, 9–10, https://doi.org/10.1029/2001jd001048, 2002.
Gettelman, A., Forster, P. M. de F., Fujiwara, M., Fu, Q., Voömel, H., Gohar, L. K., Johanson, C., and Ammerman, M.:
Radiation balance of the tropical tropopause layer, J. Geophys. Res.,
109, D07103, https://doi.org/10.1029/2003JD004190, 2004.
Griffin, S. M.: Climatology of tropical overshooting tops in North Atlantic
tropical cyclones, J. Appl. Meteorol. Climatol., 56, 1783–1796,
https://doi.org/10.1175/JAMC-D-16-0413.1, 2017.
Griffin, S. M., Bedka, K. M., and Velden, C. S.: A method for calculating the
height of overshooting convective cloud tops using satellite-based IR imager
and CloudSat cloud profiling radar observations, J. Appl. Meteorol.
Climatol., 55, 479–491, https://doi.org/10.1175/JAMC-D-15-0170.1, 2016.
Grise, K. M., Thompson, D. W. J., and Birner, T.: A global survey of static
stability in the stratosphere and upper troposphere, J. Climate, 23,
2275–2292, https://doi.org/10.1175/2009JCLI3369.1, 2010.
Groenemeijer, P., Púcik, T., Holzer, A. M., Antonescu, B.,
Riemann-Campe, K., Schultz, D. M., Kühne, T., Feuerstein, B., Brooks, H.
E., Doswell, C. A., Koppert, H. J., and Sausen, R.: Severe convective storms
in Europe: Ten years of research and education at the European Severe Storms
Laboratory, B. Am. Meteorol. Soc., 98, 2641–2651,
https://doi.org/10.1175/BAMS-D-16-0067.1, 2017.
Heymsfield, G. M., Tian, L., Heymsfield, A. J., Li, L., and Guimond, S.:
Characteristics of deep tropical and subtropical convection from
nadir-viewing high-altitude airborne doppler radar, J. Atmos. Sci., 67,
285–308, https://doi.org/10.1175/2009JAS3132.1, 2010.
Hong, G., Heygster, G., Miao, J., and Kunzi, K.: Detection of tropical deep
convective clouds from AMSU-B water vapor channels measurements, J. Geophys.
Res.-Atmos., 110, 1–15, https://doi.org/10.1029/2004JD004949, 2005.
Hong, G., Heygster, G., Notholt, J., and Buehler, S. A.: Interannual to
diurnal variations in tropical and subtropical deep convective clouds and
convective overshooting from seven years of AMSU-B measurements, J. Climate,
21, 4168–4189, https://doi.org/10.1175/2008JCLI1911.1, 2008.
Hong, Y. and Liu, G.: The characteristics of ice cloud prop- erties derived from CloudSat and CALIPSO measurements, J. Climate, 28, 3880–3901, https://doi.org/10.1175/JCLI-D-14-00666.1, 2015.
Hong, Y. and Di Girolamo, L.: Cloud phase characteristics over Southeast Asia from A-Train satellite observations, Atmos. Chem. Phys., 20, 8267–8291, https://doi.org/10.5194/acp-20-8267-2020, 2020.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D.,
Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The global precipitation
measurement mission, B. Am. Meteorol. Soc., 95, 701–722,
https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Hourngir, D., Panegrossi, G., Casella, D., Sanò, P., D'adderio, L. P.,
and Liu, C.: A 4-year climatological analysis based on gpm observations of
deep convective events in the mediterranean region, Remote Sens., 13,
1–21, https://doi.org/10.3390/rs13091685, 2021.
Iguchi, T. and Meneghini, R.: GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), NASA [data set], https://doi.org/10.5067/GPM/DPR/GPM/2A/07, 2021.
Janiga, M. A. and Thorncroft, C. D.: Convection over tropical Africa and the
East Atlantic during the West African monsoon: Regional and diurnal
variability, J. Climate, 27, 4189–4208, https://doi.org/10.1175/JCLI-D-13-00449.1,
2014.
Jeyaratnam, J., Luo, Z. J., Giangrande, S. E., Wang, D., and Masunaga, H.: A
Satellite-Based Estimate of Convective Vertical Velocity and Convective Mass
Flux: Global Survey and Comparison With Radar Wind Profiler Observations,
Geophys. Res. Lett., 48, 1–11, https://doi.org/10.1029/2020GL090675, 2021.
Johnson, R. H.: Diurnal cycle of monsoon convection, The Global Monsoon System: Research and Forecast, edited by: Chang, C.-P., World Scientific Series on Asia-Pacific Weather and Climate, Vol. 5, World Scientific Publishing Company, 257–276, https://doi.org/10.1142/9789814343411_0015, 2011.
Khlopenkov, K. V., Bedka, K. M., Cooney, J. W., and Itterly, K.: Recent
Advances in Detection of Overshooting Cloud Tops From Longwave Infrared
Satellite Imagery, J. Geophys. Res.-Atmos., 126, 1–25,
https://doi.org/10.1029/2020jd034359, 2021.
King, M. D., Kaufman, Y. J., Menzel, W. P., and Tanré, D.: Remote sensing
of cloud, aerosol, and water vapor properties from the Moderate Resolution
Imaging Spectrometer (MODIS), IEEE Trans. Geosci. Remote Sens., 30, 2–27,
https://doi.org/10.1109/36.124212, 1992.
Li, H., Wei, X., Min, M., Li, B., Nong, Z., and Chen, L.: A Dataset of
Overshooting Cloud Top from 12-Year CloudSat/CALIOP Joint Observations,
Remote Sens., 14, 2417, https://doi.org/10.3390/rs14102417, 2022.
Liu, C. and Zipser, E. J.: Global distribution of convection penetrating the
tropical tropopause, J. Geophys. Res.-Atmos., 110, 1–12,
https://doi.org/10.1029/2005JD006063, 2005.
Liu, N. and Liu, C.: Global distribution of deep convection reaching
tropopause in 1 year GPM observations, J. Geophys. Res.-Atmos.,
121, 3924–3842, https://doi.org/10.1002/ 2015JD024430, 2016.
Liu, N., Liu, C., and Hayden, L.: Climatology and Detection of Overshooting
Convection From 4 Years of GPM Precipitation Radar and Passive Microwave
Observations, J. Geophys. Res.-Atmos., 125, 1–14,
https://doi.org/10.1029/2019JD032003, 2020.
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
detection using Cloudsat - An earth-orbiting 94-GHz cloud radar, J. Atmos.
Ocean. Technol., 25, 519–533, https://doi.org/10.1175/2007JTECHA1006.1, 2008.
Marion, G. R., Trapp, R. J., and Nesbitt, S. W.: Using overshooting top area
to discriminate potential for large, intense tornadoes, Geophys. Res. Lett.,
46, 12520–12526, https://doi.org/10.1029/2019GL084099, 2019.
Monette, S. A., Velden, C. S., Griffin, K. S., and Rozoff, C. M.: Examining
trends in satellite-detected tropical overshooting tops as a potential
predictor of tropical cyclone rapid intensification, J. Appl. Meteorol.
Climatol., 51, 1917–1930, https://doi.org/10.1175/JAMC-D-11-0230.1, 2012.
Murphy, A. M., Rauber, R. M., McFarquhar, G. M., Finlon, J. A., Plummer, D.
M., Rosenow, A. A., and Jewett, B. F.: A microphysical analysis of elevated
convection in the comma head region of continental winter cyclones, J.
Atmos. Sci., 74, 69–91, https://doi.org/10.1175/JAS-D-16-0204.1, 2017.
NASA: goldsmr4.gesdisc.eosdis.nasa.gov, NASA [data set], https://goldsmr4.gesdisc.eosdis.nasa.gov/data/MERRA2/M2I1NXASM.5.12.4/ (last access: 12 May 2022), 2023.
Nesbitt, S. W. and Zipser, E. J.: The diurnal cycle of rainfall and
convective intensity according to three years of TRMM measurements, J.
Climate, 16, 1456–1475, https://doi.org/10.1175/1520-0442(2003)016<1456:TDCORA>2.0.CO;2, 2003.
Papritz, L., Rouges, E., Aemisegger, F., and Wernli, H.: On the Thermodynamic
Preconditioning of Arctic Air Masses and the Role of Tropopause Polar
Vortices for Cold Air Outbreaks From Fram Strait, J. Geophys. Res.-Atmos.,
124, 11033–11050, https://doi.org/10.1029/2019JD030570, 2019.
Partain, P.: Cloudsat ECMWF-AUX auxiliary data process description and
interface control document, Coop. Inst. Res. Atmos. Color. State Univ.,
http://129.82.109.192/ICD/ECMWF-AUX/ECMWF-AUX_PDICD_3.0.pdf (last access: 26 November 2021), 2007.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A.,
Riédi, J. C., and Frey, R. A.: The MODIS cloud products: Algorithms and
examples from Terra, IEEE Trans. Geosci. Remote Sens., 41, 459–473,
https://doi.org/10.1109/TGRS.2002.808301, 2003.
Proud, S. R.: Analysis of overshooting top detections by Meteosat Second
Generation: A 5-year dataset, Q. J. Roy. Meteorol. Soc., 141, 909–915,
https://doi.org/10.1002/qj.2410, 2015.
Proud, S. R. and Bachmeier, S.: Record-low cloud temperatures associated with a tropical deep convective event, Geophys. Res. Lett., 48, e2020GL092261, https://doi.org/10.1029/2020GL092261, 2021.
Rauber, R. M., Wegman, J., Plummer, D. M., Rosenow, A. A., Peterson, M.,
McFarquhar, G. M., Jewett, B. F., Leon, D., Market, P. S., Knupp, K. R.,
Keeler, J. M., and Battaglia, S. M.: Stability and charging characteristics
of the comma head region of continental winter cyclones, J. Atmos. Sci.,
71, 1559–1582, https://doi.org/10.1175/JAS-D-13-0253.1, 2014.
Rauber, R. M., Plummer, D. M., Macomber, M. K., Rosenow, A. A., McFarquhar,
G. M., Jewett, B. F., Leon, D., Owens, N., and Keeler, J. M.: The role of
cloud-top generating cells and boundary layer circulations in the finescale
radar structure of a winter cyclone over the great lakes, Mon. Weather Rev.,
143, 2291–2318, https://doi.org/10.1175/MWR-D-14-00350.1, 2015.
Rysman, J. F., Claud, C., and Delanoe, J.: Monitoring Deep Convection and
Convective Overshooting from 60∘ S to 60∘ N Using MHS: A
Cloudsat/CALIPSO-Based Assessment, IEEE Geosci. Remote Sens. Lett., 14,
159–163, https://doi.org/10.1109/LGRS.2016.2631725, 2017.
Schmetz, J., Tjemkes, S. A., Gube, M., and Van De Berg, L.: Monitoring deep
convection and convective overshooting with METEOSAT, Adv. Sp. Res., 19,
433–441, https://doi.org/10.1016/S0273-1177(97)00051-3, 1997.
Setvák, M., Rabin, R. M., and Wang, P. K.: Contribution of the MODIS
instrument to observations of deep convective storms and stratospheric
moisture detection in GOES and MSG imagery, Atmos. Res., 83,
505–518, https://doi.org/10.1016/j.atmosres.2005.09.015, 2007.
Setvák, M., Bedka, K., Lindsey, D. T., Sokol, A., Charvát, Z.,
Šťástka, J., and Wang, P. K.: A-Train observations of deep
convective storm tops, Atmos. Res., 123, 229–248,
https://doi.org/10.1016/j.atmosres.2012.06.020, 2013.
Shikhov, A., Chernokulsky, A., Kalinin, N., Bykov, A., and Pischalnikova, E.:
Climatology and Formation Environments of Severe Convective Windstorms and
Tornadoes in the Perm Region (Russia) in 1984–2020, Atmosphere (Basel),
(12), 1407, https://doi.org/10.3390/atmos12111407, 2021.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang,
Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L.,
Miller, S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: The cloudsat
mission and the A-Train: A new dimension of space-based observations of
clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790,
https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M.,
Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J.,
Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang,
Z., and Marchand, R.: CloudSat mission: Performance and early science after
the first year of operation, J. Geophys. Res., 113, D00A18,
https://doi.org/10.1029/2008JD009982, 2008.
Stewart, R. E., Szeto, K. K., Reinking, R. F., Clough, S. A., and Ballard, S.
P.: Midlatitude cyclonic cloud systems and their features affecting large
scales and climate, Rev. Geophys., 36, 245–273, https://doi.org/10.1029/97RG03573,
1998.
Sun, L. X., Zhuge, X. Y., and Wang, Y.: A Contour-Based Algorithm for
Automated Detection of Overshooting Tops Using Satellite Infrared Imagery,
IEEE Trans. Geosci. Remote Sens., 57, 497–508,
https://doi.org/10.1109/TGRS.2018.2857486, 2019.
Tao, C. and Jiang, H.: Global distribution of hot towers in tropical
cyclones based on 11-Yr TRMM data, J. Climate, 26, 1371–1386,
https://doi.org/10.1175/JCLI-D-12-00291.1, 2013.
Terpstra, A., Renfrew, I. A., and Sergeev, D. E.: Characteristics of cold-air
outbreak events and associated polar mesoscale cyclogenesis over the north
Atlantic region, J. Climate, 34, 4567–4584, https://doi.org/10.1175/JCLI-D-20-0595.1,
2021.
Tian, B., Soden, B. J., and Wu, X.: Diurnal cycle of convection, clouds, and
water vapor in the tropical upper troposphere: Satellites versus a general
circulation model, J. Geophys. Res.-Atmos., 109, 1–16,
https://doi.org/10.1029/2003JD004117, 2004.
Tian, B., Held, I. M., Lau, N. C., and Soden, B. J.: Diurnal cycle of
summertime deep convection over North America: A satellite perspective, J.
Geophys. Res.-Atmos., 110, 1–10, https://doi.org/10.1029/2004JD005275, 2005.
Trapp, R. J., Marion, G. R., and Nesbitt, S. W.: The regulation of tornado
intensity by updraft width, J. Atmos. Sci., 74, 4199–4211,
https://doi.org/10.1175/JAS-D-16-0331.1, 2017.
Vergados, P., Luo, Z. J., Emanuel, K., and Mannucci, A. J.: Observational
tests of hurricane intensity estimations using GPS radio occultations, J.
Geophys. Res.-Atmos., 119, 1936–1948, https://doi.org/10.1002/2013JD020934, 2014.
Wang, C., Luo, Z. J., and Huang, X.: Parallax correction in collocating
CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS)
observations: Method and application to convection study, J. Geophys. Res.-Atmos., 116, 1–9, https://doi.org/10.1029/2011JD016097, 2011.
Wang, Z., Vane, D., and Staphens, G.: Level 2 Combined Radar and Lidar Cloud
Scenario Classification Product Process Description and Interface Control
document, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Level+2+Combined+Radar+and+Lidar+Cloud+Scenario+Classification+Product+Process+Description+and+Interface+Control+Document#1 (last access: 26 November 2021),
2012.
Wilcox, L. J., Hoskins, B. J., and Shine, K. P.: A global blended tropopause
based on ERA data. Part I: Climatology, Q. J. Roy. Meteorol. Soc., 138,
561–575, https://doi.org/10.1002/qj.951, 2012.
Winker, D. M., Pelon, J. R., and McCormick, M. P.: The CALIPSO mission:
Spaceborne lidar for observation of aerosols and clouds, Lidar Remote Sens.
Ind. Environ. Monit. III, 4893, 1–11, https://doi.org/10.1117/12.466539, 2003.
Xiong, X., Sun, J., Wu, A., Chiang, K.-F., Esposito, J., and Barnes, W.:
Terra and Aqua MODIS calibration algorithms and uncertainty analysis,
Sensors, Syst. Next-Generation Satell. IX, 5978, 59780V,
https://doi.org/10.1117/12.627631, 2005.
Xiong, X., Angal, A., Barnes, W. L., Chen, H., Chiang, V., Geng, X., Li, Y.,
Twedt, K., Wang, Z., Wilson, T., and Wu, A.: Updates of Moderate Resolution
Imaging Spectroradiometer on-orbit calibration uncertainty assessments, J.
Appl. Remote Sens., 12, 1, https://doi.org/10.1117/1.jrs.12.034001, 2018.
Yuter, S. E. and Houze, R. A.: Three-dimensional kinematic and microphysical
evolution of Florida cumulonimbus. Part II: Frequency distributions of
vertical velocity, reflectivity, and differential reflectivity, Mon. Weather
Rev., 123, https://doi.org/10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2, 1995.
Zhuge, X. Y., Ming, J., and Wang, Y.: Reassessing the use of inner-core hot
towers to predict tropical cyclone rapid intensification, Weather Forecast.,
30, 1265–1279, https://doi.org/10.1175/WAF-D-15-0024.1, 2015.
Zipser, E. J., Cecil, D. J., Liu, C., Nesbitt, S. W., and Yorty, D. P.: Where
are the most intense thunderstorms on Earth?, B. Am. Meteorol. Soc.,
87, 1057–1071, https://doi.org/10.1175/BAMS-87-8-1057, 2006.
Short summary
Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Deep convective updrafts form overshooting tops (OTs) when they extend into the upper...