Articles | Volume 23, issue 9
https://doi.org/10.5194/acp-23-5297-2023
© 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/acp-23-5297-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 May 2023
Research article |
| 11 May 2023
| 11 May 2023
Seasonal controls on isolated convective storm drafts, precipitation intensity, and life cycle as observed during GoAmazon2014/5
Environmental and Climate Sciences Department, Brookhaven National
Laboratory, Upton, NY, USA
Thiago S. Biscaro
Meteorological Satellites and Sensors Division, National Institute for
Space Research, Cachoeira Paulista, São Paulo, Brazil
John M. Peters
Department of Meteorology and Atmospheric Science, The Pennsylvania
State University, University Park, PA, USA
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Christopher R. Williams, Karen L. Johnson, Scott E. Giangrande, Joseph C. Hardin, Ruşen Öktem, and David M. Romps
Atmos. Meas. Tech., 14, 4425–4444, https://doi.org/10.5194/amt-14-4425-2021, https://doi.org/10.5194/amt-14-4425-2021, 2021
Short summary
Short summary
In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.
Thiago S. Biscaro, Luiz A. T. Machado, Scott E. Giangrande, and Michael P. Jensen
Atmos. Chem. Phys., 21, 6735–6754, https://doi.org/10.5194/acp-21-6735-2021, https://doi.org/10.5194/acp-21-6735-2021, 2021
Short summary
Short summary
This study suggests that there are two distinct modes driving diurnal precipitating convective clouds over the central Amazon. In the wet season, local factors such as turbulence and nighttime cloud coverage are the main controls of daily precipitation, while dry-season daily precipitation is modulated primarily by the mesoscale convective pattern. The results imply that models and parameterizations must consider different formulations based on the seasonal cycle to correctly resolve convection.
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69, https://doi.org/10.5194/amt-14-53-2021, https://doi.org/10.5194/amt-14-53-2021, 2021
Short summary
Short summary
About 4 years of 2D video disdrometer data in Darwin are used to develop and validate rainfall retrievals for tropical convection in C- and X-band radars in Darwin. Using blended techniques previously used for Colorado and Manus and Gan islands, with modified coefficients in each estimator, provided the most optimal results. Using multiple radar observables to develop a rainfall retrieval provided a greater advantage than using a single observable, including using specific attenuation.
Cited articles
Ackerman, T. P. and Stokes, G. M.: The Atmospheric Radiation Measurement
Program, Phys. Today, 56, 38–44, https://doi.org/10.1063/1.1554135, 2003.
Adams, D. K., Gutman, S., Holub, K., and Pereira, D.: GNSS Observations of
Deep Convective timescales in the Amazon, Geophys. Res. Lett., 40,
2818–2823, https://doi.org/10.1002/grl.50573, 2013.
Adams, D. K., Barbosa, H. M. J., and Gaitán De Los Ríos, K. P.: A
Spatiotemporal Water Vapor–Deep Convection Correlation Metric Derived from
the Amazon Dense GNSS Meteorological Network, Mon. Weather Rev., 145,
279–288, https://doi.org/10.1175/MWR-D-16-0140.1, 2017.
Anagnostou, E. N.: A convective/stratiform precipitation classification
algorithm for volume scanning weather radar observations, Meteorol. Appl., 11, 291–300, https://doi.org/10.1017/S1350482704001409, 2004.
Anderson, N. F., Grainger, C. A., and Stith, J. L.:. Characteristics
of Strong Updrafts in Precipitation Systems over the Central Tropical
Pacific Ocean and in the Amazon, J. Appl. Meteorol., 44,
731–738, 2005.
Atmospheric Radiation Measurement (ARM): Climate Research Facility:
Balloon-Borne Sounding System (SONDE), 3.21297∘ S 60.5981∘ W: ARM Mobile
Facility (MAO) Manacapuru, Amazonas, Brazil; AMF1 (M1), in: Atmospheric Radiation
Measurement (ARM) Climate Research Facility Data Archive, edited by:
Holdridge, D., Kyrouac, J., and Coulter, R., Oak Ridge,
Tennessee, USA,
https://doi.org/10.5439/1025284, 1993.
Biscaro, T. S., Machado, L. A. T., Giangrande, S. E., and Jensen, M. P.: What drives daily precipitation over the central Amazon? Differences observed between wet and dry seasons, Atmos. Chem. Phys., 21, 6735–6754, https://doi.org/10.5194/acp-21-6735-2021, 2021.
Borque, P., Kollias, P., and Giangrande, S.: First Observations of
Tracking Clouds Using Scanning ARM Cloud Radars, J. Appl. Meteorol. Clim., 53, 2732–2746, 2014.
Cifelli, R., Petersen, W. A., Carey, L. D., Rutledge, S. A., and da Silva Dias, M. A. F.: Radar observations of the kinematic, microphysical, and
precipitation characteristics of two MCSs in TRMM LBA, J. Geophys. Res.,
107, 8077, https://doi.org/10.1029/2000JD000264, 2002.
Coulter, R., Muradyan, P., and Martin, T.: Radar Wind Profiler (1290RWPPRECIPMOM),
Atmospheric Radiation Measurement (ARM) User Facility, mao1290precipmomM1.a0, [data set], https://doi.org/10.5439/1256461 (last access: 10 August 2022), 2015.
Dixon, M. and Wiener, G.: 1 TITAN: Thunderstorm Identification,
Tracking, Analysis, and Nowcasting – A radar-based methodology, J. Atmos.
Ocean. Technol., 10, 785–797, 1993.
Feng Z., Dong, X., Xi, B., Xi, B., McFarlane, S. A., Kennedy, A., and Lin, B.: Life
Cycle of Midlatitude Deep Convective Systems in a Lagrangian Framework, J. Geophys. Res.-Atmos., 117, doi:10.1029/2012JD018362, 2012.
Feng, Z., Leung, L. R., Hagos, S., Houze, R. A., Burleyson, C. D., and Balaguru, K.: More frequent intense and long-lived storms dominate
the springtime trend in central US rainfall, Nat. Commun., 7, 13429, https://doi.org/10.1038/ncomms13429, 2016.
Feng, Z., Houze, R. A., Leung, L. R., Song, F., Hardin, J. C., Wang,
J., Gustafson, W. I., and Homeyer, C. R.: Spatiotemporal
Characteristics and Large-Scale Environments of Mesoscale Convective Systems
East of the Rocky Mountains, J. Climate, 32, 7303–7328, 2019.
Foote, G. B. and Du Toit, P. S.: Terminal Velocity of Raindrops
Aloft, J. Appl. Meteorol. Clim., 8, 249–253, 1969.
Fridlind, A. M., van Lier-Walqui, M., Collis, S., Giangrande, S. E., Jackson, R. C., Li, X., Matsui, T., Orville, R., Picel, M. H., Rosenfeld, D., Ryzhkov, A., Weitz, R., and Zhang, P.: Use of polarimetric radar measurements to constrain simulated convective cell evolution: a pilot study with Lagrangian tracking, Atmos. Meas. Tech., 12, 2979–3000, https://doi.org/10.5194/amt-12-2979-2019, 2019.
Ghate, V. P. and Kollias, P.: On the Controls of Daytime
Precipitation in the Amazonian Dry Season, J. Hydrometeorol.,
17, 3079–3097, 2016.
Giangrande, S. E., Collis, S., Straka, J., Protat, A., Williams, C., and
Krueger, S.: A Summary of Convective-Core Vertical Velocity Properties Using
ARM UHF Wind Profilers in Oklahoma, J. Appl. Meteorol. Clim., 52, 2278–2295,
2013.
Giangrande, S. E., Johnson, K., Clothiaux, E., and Kollias, P.: W-band Cloud Radar Active Remote
Sensing of Cloud (ARSCLWACRBND1KOLLIAS), Atmospheric Radiation Measurement (ARM) User
Facility, maoarsclwacrbnd1kolliasM1.c1, [data set], https://doi.org/10.5439/1097548, 2015.
Giangrande, S. E., Toto, T., Jensen, M. P., Bartholomew, M. J., Feng, Z., Protat, A., Williams, C.
R., Schumacher, C., and Machado, L.: Convective cloud vertical velocity and mass-flux
characteristics from radar wind profiler observations during GoAmazon2014/5, J. Geophys. Res.-Atmos., 121, 12891–12913, doi:10.1002/2016JD025303, 2016.
Giangrande, S. E., Feng, Z., Jensen, M. P., Comstock, J. M., Johnson, K. L., Toto, T., Wang, M., Burleyson, C., Bharadwaj, N., Mei, F., Machado, L. A. T., Manzi, A. O., Xie, S., Tang, S., Silva Dias, M. A. F., de Souza, R. A. F., Schumacher, C., and Martin, S. T.: Cloud characteristics, thermodynamic controls and radiative impacts during the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment, Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, 2017.
Giangrande, S. E., Wang, D., and Mechem, D. B.: Cloud regimes over the Amazon Basin: perspectives from the GoAmazon2014/5 campaign, Atmos. Chem. Phys., 20, 7489–7507, https://doi.org/10.5194/acp-20-7489-2020, 2020.
Göke, S., Ochs, H. T., and Rauber, R. M.: Radar analysis of
precipitation initiation in maritime versus continental clouds near the
Florida coast: Inferences concerning the role of CCN and giant nuclei, J.
Atmos. Sci., 64, 3695–3707, https://doi.org/10.1175/JAS3961.1, 2007.
Holdridge, D., Ritsche, M., Coulter, R., Kyrouac, J., and Keeler, E.: Atmospheric Radiation
Measurement (ARM) user facility, updated hourly, Balloon-Borne Sounding System
(SONDEWNPN), ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), ARM
Data Center, maosondewnpnM1.b1, [data set], https://doi.org/10.5439/1595321, 2015.
Hu, J., Rosenfeld, D., Ryzhkov, A., Zrnic, D., Williams, E., Zhang, P., Snyder, J. C., Zhang, R., and Weitz, R.: Polarimetric radar convective cell tracking reveals large
sensitivity of cloud precipitation and electrification properties to CCN,
J. Geophys. Res.-Atmos., 124, 12194–12205,
https://doi.org/10.1029/2019JD030857, 2019.
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, e2020GL090675,
https://doi.org/10.1029/2020GL090675, 2021.
Jorgensen, D. P., Zipser, E. J., and LeMone, M. A.: Vertical Motions
in Intense Hurricanes, J. Atmos. Sci., 42, 839–856, 1985.
Kumar, V. V., Jakob, C., Protat, A., Williams, C. R., and May, P. T.: Mass-Flux Characteristics of Tropical Cumulus Clouds from Wind
Profiler Observations at Darwin, Australia, J. Atmos. Sci., 72, 1837–1855, 2015.
Kumar, V. V., Protat, A., Jakob, C., Williams, C. R., Rauniyar, S., Stephens, G. L., and May, P. T.: The estimation of convective mass flux from radar reflectivities, J. Appl. Meteorol. Clim., 55, 1239–1257, https://doi.org/10.1175/JAMC-D-15-0193., 2016.
Kyrouac, J. and Shi, Y.: Surface Meteorological Instrumentation (MET), Atmospheric Radiation
Measurement (ARM) User Facility, maometM1.b1, [data set], https://doi.org/10.5439/1786358, 2015.
Limpert, G., Houston, A., and Lock, N.: The advanced algorithm for tracking
objects (AALTO), Meteor. Apps., 22, 694–704,
https://doi.org/10.1002/met.1501, 2015.
Machado, L. A., Rossow, W. B., Guedes, R. L., and Walker, A. W.: Life
cycle variations of mesoscale convective systems over the Americas, Mon.
Weather Rev., 126, 1630–1654, 1998.
Machado, L. A. T., Laurent, H., Dessay, N., and Miranda, I.: Seasonal
and diurnal variability of convection over the Amazonia: A comparison of
different vegetation types and large scale forcing, Theor. Appl.
Climatol., 78, 61–77, https://doi.org/10.1007/s00704-004-0044-9, 2004.
Machado, L. A. T., Silva Dias, M. A. F., Morales, C., Fisch, G., Vila, D.,
Albrecht, R., Goodman, S. J., Calheiros, A. J. P., Biscaro, T., Kummerow,
C., Cohen, J., Fitzjarrald, D., Nascimento, E. L., Sakamoto, M. S.,
Cunningham, C., Chaboureau, J.-P., Petersen, W. A., Adams, D. K., Baldini,
L., Angelis, C. F., Sapucci, L. F., Salio, P., Barbosa, H. M. J., Landulfo,
E., Souza, R. A. F., Blakeslee, R. J., Bailey, J., Freitas, S., Lima, W. F.
A., and Tokay, A.: The CHUVA Project: How Does Convection Vary across
Brazil?, B. Am. Meteorol. Soc., 95, 1365–1380,
https://doi.org/10.1175/BAMS-D-13-00084.1, 2014.
Machado, L. A. T., Calheiros, A. J. P., Biscaro, T., Giangrande, S., Silva Dias, M. A. F., Cecchini, M. A., Albrecht, R., Andreae, M. O., Araujo, W. F., Artaxo, P., Borrmann, S., Braga, R., Burleyson, C., Eichholz, C. W., Fan, J., Feng, Z., Fisch, G. F., Jensen, M. P., Martin, S. T., Pöschl, U., Pöhlker, C., Pöhlker, M. L., Ribaud, J.-F., Rosenfeld, D., Saraiva, J. M. B., Schumacher, C., Thalman, R., Walter, D., and Wendisch, M.: Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA, Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp-18-6461-2018, 2018.
Maddox, R. A.: Mesoscale convective complexes, B. Am. Meteorol. Soc., 61, 1374–1387, 1980.
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A.,
Schumacher, C., Wang, J., Biscaro, T., Brito, J., Calheiros, A., Jardine,
K., Medeiros, A., Portela, B., de Sá, S. S., Adachi, K., Aiken, A. C.,
Albrecht, R., Alexander, L., Andreae, M. O., Barbosa, H. M., Buseck, P.,
Chand, D., Comstock, J. M., Day, D. A., Dubey, M., Fan, J., Fast, J., Fisch,
G., Fortner, E., Giangrande, S., Gilles, M., Goldstein, A. H., Guenther, A.,
Hubbe, J., Jensen, M., Jimenez, J. L., Keutsch, F. N., Kim, S., Kuang, C.,
Laskin, A., McKinney, K., Mei, F., Miller, M., Nascimento, R., Pauliquevis,
T., Pekour, M., Peres, J., Petäjä, T., Pöhlker, C., Pöschl,
U., Rizzo, L., Schmid, B., Shilling, J. E., Dias, M. A., Smith, J. N.,
Tomlinson, J. M., Tóta, J., and Wendisch, M.: The Green Ocean Amazon
Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols,
Clouds, and Rainfall over the Rain Forest, B. Am. Meteorol. Soc., 98,
981–997, https://doi.org/10.1175/BAMS-D-15-00221.1, 2017.
Mather, J. H. and Voyles, J. W.: The ARM Climate Research Facility: A Review
of Structure and Capabilities, B. Am. Meteorol. Soc., 94, 377–392, https://doi.org/10.1175/BAMS-D-11-00218.11, 2013.
Morris, V., Zhang, D., and Ermold, B.: Ceilometer (CEIL), Atmospheric Radiation Measurement
(ARM) User Facility, maoceilM1.b1, [data set], https://doi.org/10.5439/1181954, 2015.
Morrison, H. and Peters, J. M.: Theoretical Expressions for the
Ascent Rate of Moist Deep Convective Thermals, J. Atmos. Sci., 75, 1699–1719, 2018.
Nobre, P., Malagutti, M., Urbano, D. F., De Almeida, R. A. F., and Giarolla,
E.: Amazon deforestation and climate change in a coupled model simulation,
J. Climate, 22, 5686–5697, 2009.
Peters, J. M., Mulholland, J. P., and Chavas, D. R.: Generalized
Lapse Rate Formulas for Use in Entraining CAPE Calculations, J. Atmos. Sci., 79, 815–836, 2022.
Petersen, W. A., Nesbitt, S. W., Blakeslee, R. J., Cifelli, R., Hein, P.,
and Rutledge, S. A.: TRMM Observations of Intraseasonal Variability
in Convective Regimes over the Amazon, J. Climate, 15,
1278–1294, 2002.
Prein, A. F., Liu, C. K. I., Trier, S. B., Rasmussen, R., M., Holland, G. J., and Clark, M. P.: Increased rainfall volume from future convective storms
in the US, Nat. Clim. Change, 7, 880–884, 2017.
Protat, A. and Williams, C. R.: The Accuracy of Radar Estimates of
Ice Terminal Fall Speed from Vertically Pointing Doppler Radar Measurements,
J. Appl. Meteorol. Clim., 50, 2120–2138, 2011.
Rosenfeld, D.: Objective method for analysis and tracking of
convective cells as seen by radar, J. Atmos. Ocean. Technol., 4, 422–434, 1987.
Saraiva, I., Silva Dias, M. A. F., Morales, C. A. R., and Saraiva, J. M. B.:
Regional Variability of Rain Clouds in the Amazon Basin as Seen by a Network
of Weather Radars, J. Appl. Meteorol. Clim., 55,
2657–2675, https://doi.org/10.1175/JAMC-D-15-0183.1, 2016.
Schiro, K. A., Ahmed, F., Giangrande, S. E., and Neelin, J. D.: GoAmazon2014/5
campaign points to deep-inflow approach to deep convection across scales,
P. Natl. Acad. Sci. USA, 115,
4577–4582, https://doi.org/10.1073/pnas.1719842115, 2018.
Stein, T. H. M., Hogan, R. J., Clark, P. A., Halliwell, C. E., Hanley, K.
E., Lean, H. W., Nicol, J. C., and Plant, R. S.: The DYMECS project: A
statistical approach for the evaluation of convective storms in
high-resolution NWP models, B. Am. Meteorol. Soc., 96, 939–951,
https://doi.org/10.1175/BAMS-D-13-00279.1, 2015.
Steiner, M., Houze, R. A., and Yuter, S. E.: Climatological
Characterization of Three-Dimensional Storm Structure from Operational Radar
and Rain Gauge Data, J. Appl. Meteorol. Clim., 34,
1978–2007, 1995.
Sušelj, K., Teixeira, J., and Chung, D.: A Unified Model for
Moist Convective Boundary Layers Based on a Stochastic
Eddy-Diffusivity/Mass-Flux Parameterization, J. Atmos.
Sci., 70, 1929–1953, 2013.
Sušelj, K., Kurowski, M. J., and Teixeira, J.: On the Factors
Controlling the Development of Shallow Convection in
Eddy-Diffusivity/Mass-Flux Models, J. Atmos. Sci.,
76, 433–456, 2019.
Tang, S., Xie, S., Zhang, Y., Zhang, M., Schumacher, C., Upton, H., Jensen, M. P., Johnson, K. L., Wang, M., Ahlgrimm, M., Feng, Z., Minnis, P., and Thieman, M.: Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment, Atmos. Chem. Phys., 16, 14249–14264, https://doi.org/10.5194/acp-16-14249-2016, 2016.
Tian, Y., Zhang, Y., Klein, S. A., and Schumacher, C.: Interpreting
the diurnal cycle of clouds and precipitation in the ARM GoAmazon
observations: Shallow to deep convection transition, J. Geophys. Res.-Atmos., 126, e2020JD033766,
https://doi.org/10.1029/2020JD033766, 2021.
Tian, Y., Zhang, Y., and Klein, S. A.: What determines the number
and the timing of pulses in afternoon precipitation in the Green Ocean
Amazon (GoAmazon) observations?, Geophys. Res. Lett., 49,
e2021GL096075, https://doi.org/10.1029/2021GL096075, 2022.
Vila, D. A., Machado, L. A. T., Laurent, H., and Velasco, I.: Forecast and
Tracking the Evolution of Cloud Clusters (ForTraCC) Using Satellite Infrared
Imagery: Methodology and Validation, Weather Forecast., 23, 233–245,
https://doi.org/10.1175/2007WAF2006121.1, 2008.
Wang, J., Krejci, R., Giangrande, S., Kuang, C., Barbosa, H. M., Brito, J., Carbone, S.,
Chi, X., Comstock, J., Ditas, F., Lavric, J., Manninen, H. E., Mei, F., Moran-Zuloaga, D., Pöhlker, C.,
Pöhlker, M. L., Saturno, J., Schmid, B., Souza, R. A., Springston, S. R., Tomlinson, J. M., Toto, T., Walter, D.,
Wimmer, D., Smith, J. N., Kulmala, M., Machado, L. A., Artaxo, P., Andreae, M. O., Petäjä, T., and Martin, S. T.:
Amazon boundary layer aerosol concentration sustained by vertical transport Nature, 539, 416–419, https://doi.org/10.1038/nature19819, 2016.
Wang, D., Giangrande, S. E., Bartholomew, M. J., Hardin, J., Feng, Z., Thalman, R., and Machado, L. A. T.: The Green Ocean: precipitation insights from the GoAmazon2014/5 experiment, Atmos. Chem. Phys., 18, 9121–9145, https://doi.org/10.5194/acp-18-9121-2018, 2018.
Wang, D., Giangrande, S. E., Schiro, K., Jensen, M. P., and Houze, R. A.:
The characteristics of tropical and midlatitude mesoscale convective systems
as revealed by radar wind profilers, J. Geophys. Res.-Atmos., 124,
4601–4619, https://doi.org/10.1029/2018JD030087, 2019.
Wang, D., Giangrande, S. E., Feng, Z., Hardin, J. C., and Prein, A. F.:
Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind
Profilers: MCS Observations and Idealized Model Comparisons, J. Geophys.
Res.-Atmos., 125, e2019JD031774, https://doi.org/10.1029/2019JD031774, 2020.
Williams, M. and Houze, R. A.: Satellite-observed characteristics of
winter monsoon cloud clusters, Mon. Weather Rev., 115, 505–519, 1987.
Williams, E., Rosenfeld, D., Madden, N., Gerlach, J., Gears, N., Atkinson, L., Dunnemann, N., Frostrom, G., Antonio, M., Biazon, B., Camargo, R., Franca, H., Gomes, A., Lima, M., Machado, R.,
Manhaes, S., Nachtigall, L., Piva, H., Quintiliano, W., Machado, L., Artaxo, P., Roberts, G., Renno, N.,
Blakeslee, R., Bailey, J., Boccippio, D., Betts, A., Wolff, D., Roy, B., Halverson, J., Rickenbach, T.,
Fuentes, J., and Avelino, E.: Contrasting convective regimes over the
Amazon: Implications for cloud electrification, J. Geophys. Res., 107, 8082,
https://doi.org/10.1029/2001JD000380, 2002.
Yin, L., Fu, R., Shevliakova, E., and Dickinson, R.: How well can CMIP5
simulate precipitation and its controlling processes over tropical South
America?, Clim. Dynam., 41, 3127–3143,
https://doi.org/10.1007/s00382-012-1582-y, 2013.
Yuter, S. E. and Houze, R. A.: Three-dimensional kinematic and
microphysical evolution of Florida cumulonimbus, Part II: Frequency
distribution of vertical velocity, reflectivity, and differential
reflectivity, Mon. Weather Rev., 123, 1941–1963, 1995.
Short summary
Our study tracks thunderstorms observed during the wet and dry seasons of the Amazon Basin using weather radar. We couple this precipitation tracking with opportunistic overpasses of a wind profiler and other ground observations to add unique insights into the upwards and downwards air motions within these clouds at various stages in the storm life cycle. The results of a simple updraft model are provided to give physical explanations for observed seasonal differences.
Our study tracks thunderstorms observed during the wet and dry seasons of the Amazon Basin using...
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