Articles | Volume 24, issue 15
https://doi.org/10.5194/acp-24-8529-2024
© Author(s) 2024. 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-24-8529-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Environmental controls on isolated convection during the Amazonian wet season
Leandro Alex Moreira Viscardi
CORRESPONDING AUTHOR
Institute of Physics, University of São Paulo, São Paulo, SP, Brazil
Department of Atmospheric Sciences, University of Hawai′i at Mānoa, Honolulu, HI, USA
Giuseppe Torri
Department of Atmospheric Sciences, University of Hawai′i at Mānoa, Honolulu, HI, USA
David K. Adams
El Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Mexico City, Mexico
Henrique de Melo Jorge Barbosa
CORRESPONDING AUTHOR
Institute of Physics, University of São Paulo, São Paulo, SP, Brazil
Physics Department, University of Maryland, Baltimore County, Baltimore, MD, USA
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Marie Brunel, Stephen Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
EGUsphere, https://doi.org/10.5194/egusphere-2025-922, https://doi.org/10.5194/egusphere-2025-922, 2025
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Farmers often use fire to clear dead pasture biomass, impacting vegetation and soil nutrients. This study integrates fire management into a DGVM to assess its effects, focusing on Brazil. The results show that combining grazing and fire management reduces vegetation carbon and soil nitrogen over time. The research highlights the need to include these practices in models to improve pasture management assessments and calls for better data on fire usage and its long-term effects.
Brent A. McBride, J. Vanderlei Martins, J. Dominik Cieslak, Roberto Fernandez-Borda, Anin Puthukkudy, Xiaoguang Xu, Noah Sienkiewicz, Brian Cairns, and Henrique M. J. Barbosa
Atmos. Meas. Tech., 17, 5709–5729, https://doi.org/10.5194/amt-17-5709-2024, https://doi.org/10.5194/amt-17-5709-2024, 2024
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The Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) is a new Earth-observing instrument that provides highly accurate measurements of the atmosphere and surface. Using a physics-based calibration technique, we show that AirHARP achieves high measurement accuracy in laboratory and field environments and exceeds a benchmark accuracy requirement for modern aerosol and cloud climate observations. Therefore, the HARP design is highly attractive for upcoming NASA climate missions.
Juan Vicente Pallotta, Silvânia Alves de Carvalho, Fabio Juliano da Silva Lopes, Alexandre Cacheffo, Eduardo Landulfo, and Henrique Melo Jorge Barbosa
Geosci. Instrum. Method. Data Syst., 12, 171–185, https://doi.org/10.5194/gi-12-171-2023, https://doi.org/10.5194/gi-12-171-2023, 2023
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Lidar networks coordinate efforts of different groups, providing guidelines to homogenize retrievals from different instruments. We describe an ongoing effort to develop the Lidar Processing Pipeline (LPP) collaboratively, a collection of tools developed in C/C++ to handle all the steps of a typical lidar analysis. Analysis of simulations and real lidar data showcases the LPP’s features. From this exercise, we draw a roadmap to guide future development, accommodating the needs of our community.
Elion Daniel Hack, Theotonio Pauliquevis, Henrique Melo Jorge Barbosa, Marcia Akemi Yamasoe, Dimitri Klebe, and Alexandre Lima Correia
Atmos. Meas. Tech., 16, 1263–1278, https://doi.org/10.5194/amt-16-1263-2023, https://doi.org/10.5194/amt-16-1263-2023, 2023
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Water vapor is a key factor when seeking to understand fast-changing processes when clouds and storms form and develop. We show here how images from a calibrated infrared camera can be used to derive how much water vapor there is in the atmosphere at a given time. Comparing our results to an established technique, for a case of stable atmospheric conditions, we found an agreement within 2.8 %. Water vapor sky maps can be retrieved every few minutes, day or night, under partly cloudy skies.
Marco A. Franco, Florian Ditas, Leslie A. Kremper, Luiz A. T. Machado, Meinrat O. Andreae, Alessandro Araújo, Henrique M. J. Barbosa, Joel F. de Brito, Samara Carbone, Bruna A. Holanda, Fernando G. Morais, Janaína P. Nascimento, Mira L. Pöhlker, Luciana V. Rizzo, Marta Sá, Jorge Saturno, David Walter, Stefan Wolff, Ulrich Pöschl, Paulo Artaxo, and Christopher Pöhlker
Atmos. Chem. Phys., 22, 3469–3492, https://doi.org/10.5194/acp-22-3469-2022, https://doi.org/10.5194/acp-22-3469-2022, 2022
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In Central Amazonia, new particle formation in the planetary boundary layer is rare. Instead, there is the appearance of sub-50 nm aerosols with diameters larger than about 20 nm that eventually grow to cloud condensation nuclei size range. Here, 254 growth events were characterized which have higher predominance in the wet season. About 70 % of them showed direct relation to convective downdrafts, while 30 % occurred partly under clear-sky conditions, evidencing still unknown particle sources.
Janaína P. Nascimento, Megan M. Bela, Bruno B. Meller, Alessandro L. Banducci, Luciana V. Rizzo, Angel Liduvino Vara-Vela, Henrique M. J. Barbosa, Helber Gomes, Sameh A. A. Rafee, Marco A. Franco, Samara Carbone, Glauber G. Cirino, Rodrigo A. F. Souza, Stuart A. McKeen, and Paulo Artaxo
Atmos. Chem. Phys., 21, 6755–6779, https://doi.org/10.5194/acp-21-6755-2021, https://doi.org/10.5194/acp-21-6755-2021, 2021
Anin Puthukkudy, J. Vanderlei Martins, Lorraine A. Remer, Xiaoguang Xu, Oleg Dubovik, Pavel Litvinov, Brent McBride, Sharon Burton, and Henrique M. J. Barbosa
Atmos. Meas. Tech., 13, 5207–5236, https://doi.org/10.5194/amt-13-5207-2020, https://doi.org/10.5194/amt-13-5207-2020, 2020
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In this work, we report the demonstration and validation of the aerosol properties retrieved using AirHARP and GRASP for data from the NASA ACEPOL campaign 2017. These results serve as a proxy for the scale and detail of aerosol retrievals that are anticipated from future space mission data, as HARP CubeSat (mission begins 2020) and HARP2 (aboard the NASA PACE mission with the launch in 2023) are near duplicates of AirHARP and are expected to provide the same level of aerosol characterization.
Kirk Knobelspiesse, Henrique M. J. Barbosa, Christine Bradley, Carol Bruegge, Brian Cairns, Gao Chen, Jacek Chowdhary, Anthony Cook, Antonio Di Noia, Bastiaan van Diedenhoven, David J. Diner, Richard Ferrare, Guangliang Fu, Meng Gao, Michael Garay, Johnathan Hair, David Harper, Gerard van Harten, Otto Hasekamp, Mark Helmlinger, Chris Hostetler, Olga Kalashnikova, Andrew Kupchock, Karla Longo De Freitas, Hal Maring, J. Vanderlei Martins, Brent McBride, Matthew McGill, Ken Norlin, Anin Puthukkudy, Brian Rheingans, Jeroen Rietjens, Felix C. Seidel, Arlindo da Silva, Martijn Smit, Snorre Stamnes, Qian Tan, Sebastian Val, Andrzej Wasilewski, Feng Xu, Xiaoguang Xu, and John Yorks
Earth Syst. Sci. Data, 12, 2183–2208, https://doi.org/10.5194/essd-12-2183-2020, https://doi.org/10.5194/essd-12-2183-2020, 2020
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The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign is a resource for the next generation of spaceborne multi-angle polarimeter (MAP) and lidar missions. Conducted in the fall of 2017 from the Armstrong Flight Research Center in Palmdale, California, four MAP instruments and two lidars were flown on the high-altitude ER-2 aircraft over a variety of scene types and ground assets. Data are freely available to the public and useful for algorithm development and testing.
Cited articles
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. a, b
Adams, D. K., Fernandes, R. M. S., Holub, K. L., Gutman, S. I., Barbosa, H. M. J., Machado, L. A. T., Calhlheiros, A. J. P., Bennett, R. A., Kursinski, E. R., Sapucci, L. F., DeMets, C., Chagas, G. F. B., Arellano, A., Filizola, N., Rocha, A. A. A., Silva, R. A., Assuncao, L. M. F., Cirino, G. G., Pauliquevis, T., Portela, B. T. T., Sa, A., De Sousa, J. M., and Tanaka, L. M. S.: THE AMAZON DENSE GNSS METEOROLOGICAL NETWORK A New Approach for Examining Water Vapor and Deep Convection Interactions in the Tropics, B. Am. Meteorol. Soc., 96, 2151–2165, https://doi.org/10.1175/BAMS-D-13-00171.1, 2015. a
Anselmo, E. M., Schumacher, C., and Machado, L. A. T.: The Amazonian Low-Level Jet and Its Connection to Convective Cloud Propagation and Evolution, Mon. Weather Rev., 148, 4083–4099, https://doi.org/10.1175/MWR-D-19-0414.1, 2020. a
ARM (Atmospheric Radiation Measurement): Meteorological Measurements associated with the Aerosol Observing System (AOSMET). 2013-12-12 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, MAOS (S1), https://doi.org/10.5439/1984920, 2013. a, b, c
ARM, (Atmospheric Radiation Measurement): ARM Best Estimate Data Products (ARMBEATM). 2014-01-01 to 2015-12-31, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), https://doi.org/10.5439/1333748, 2014a. a, b
ARM (Atmospheric Radiation Measurement): Eddy Correlation Flux Measurement System (30ECOR). 2014-04-03 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), https://doi.org/10.5439/1025039, 2014b. a
ARM (Atmospheric Radiation Measurement): Laser Disdrometer (LD). 2014-09-24 to 2015-08-13, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, Supplemental Site (S10), https://doi.org/10.5439/1973058, 2014c. a, b
ARM (Atmospheric Radiation Measurement): Atmospheric Radiation Measurement (ARM) user facility. 2014. Microwave Radiometer (MWRLOS). 2014-01-06 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), https://doi.org/10.5439/1046211, 2014d. a, b
ARM (Atmospheric Radiation Measurement): Planetary Boundary Layer Height (PBLHTSONDE1MCFARL). 2014-01-01 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), https://doi.org/10.5439/1150253, 2014e. a, b
ARM (Atmospheric Radiation Measurement): Balloon-Borne Sounding System (SONDEWNPN). 2014-01-01 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), https://doi.org/10.5439/1595321, 2014f. a, b
ARM (Atmospheric Radiation Measurement): Rain Gauge (RAINTB). 2014-10-14 to 2015-07-03, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, Supplemental Site (S10), https://doi.org/10.5439/1224827, 2014g. a, b
Barber, K. A., Burleyson, C. D., Feng, Z., and Hagos, S. M.: The Influence of Shallow Cloud Populations on Transitions to Deep Convection in the Amazon, J. Atmos. Sci., 79, 723–743, https://doi.org/10.1175/jas-d-21-0141.1, 2022. a, b, c
Bechtold, P., Chaboureau, J.-P., Beljaars, A., Betts, A. K., Köhler, M., Miller, M., and Redelsperger, J.-L.: The simulation of the diurnal cycle of convective precipitation over land in a global model, Q. J. Roy. Meteor. Soc., 130, 3119–3137, https://doi.org/10.1256/qj.03.103, 2004. a
Betts, A. K.: Evaluation of the diurnal cycle of precipitation, surface thermodynamics, and surface fluxes in the ECMWF model using LBA data, J. Geophys. Res., 107, LBA 12-1–LBA 12-8, https://doi.org/10.1029/2001jd000427, 2002. a, b
Betts, A. K. and Jakob, C.: Study of diurnal cycle of convective precipitation over Amazonia using a single column model, J. Geophys. Res.-Atmos., 107, ACL 25-1–ACL 25-13, https://doi.org/10.1029/2002jd002264, 2002. a, b
Bretherton, C. S., Peters, M. E., and Back, L. E.: Relationships between Water Vapor Path and Precipitation over the Tropical Oceans, J. Climate, 17, 1517–1528, https://doi.org/10.1175/1520-0442(2004)017<1517:RBWVPA>2.0.CO;2, 2004. a
Cecchini, M. A., de Bruine, M., Vilà-Guerau de Arellano, J., and Artaxo, P.: Quantifying vertical wind shear effects in shallow cumulus clouds over Amazonia, Atmos. Chem. Phys., 22, 11867–11888, https://doi.org/10.5194/acp-22-11867-2022, 2022. a
Chakraborty, S., Schiro, K. A., Fu, R., and Neelin, J. D.: On the role of aerosols, humidity, and vertical wind shear in the transition of shallow-to-deep convection at the Green Ocean Amazon 2014/5 site, Atmos. Chem. Phys., 18, 11135–11148, https://doi.org/10.5194/acp-18-11135-2018, 2018. a, b, c, d
Feng, Z. and Giangrande, S.: Merged RWP-WACR-ARSCL Cloud Mask and Cloud Type, ARM [data set], https://doi.org/10.5439/1462693, 2018. a, b
Freitas, S. R., Putman, W. M., Arnold, N. P., Adams, D. K., and Grell, G. A.: Cascading Toward a Kilometer-Scale GCM: Impacts of a Scale-Aware Convection Parameterization in the Goddard Earth Observing System GCM, Geophys. Res. Lett., 47, e2020GL087682, https://doi.org/10.1029/2020gl087682, 2020. a
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., 121, 12891–12913, 2016. a
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. a, b, c, d
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. a, b
Grabowski, W. W., Bechtold, P., Cheng, A., Forbes, R., Halliwell, C., Khairoutdinov, M., Lang, S., Nasuno, T., Petch, J., Tao, W.-K., Wong, R., Wu, X., and Xu, K.-M.: Daytime convective development over land: A model intercomparison based on LBA observations, Q. J. Roy. Meteorol. Soc., 132, 317–344, https://doi.org/10.1256/qj.04.147, 2006. a, b
Greco, S., Swap, R., Garstang, M., Ulanski, S., Shipham, M., Harriss, R., Talbot, R., Andreae, M., and Artaxo, P.: Rainfall and surface kinematic conditions over central Amazonia during ABLE 2B, J. Geophys. Res.-Atmos., 95, 17001–17014, https://doi.org/10.1029/JD095iD10p17001, 1990. a
Gupta, A. K., Deshmukh, A., Waman, D., Patade, S., Jadav, A., Phillips, V. T. J., Bansemer, A., Martins, J. A., and Gonçalves, F. L. T.: The microphysics of the warm-rain and ice crystal processes of precipitation in simulated continental convective storms, Commun. Earth Environ., 4, 226, https://doi.org/10.1038/s43247-023-00884-5, 2023. a
Holloway, C. E. and Neelin, J. D.: Moisture Vertical Structure, Column Water Vapor, and Tropical Deep Convection, J. Atmos. Sci., 66, 1665–1683, https://doi.org/10.1175/2008JAS2806.1, 2009. a
Itterly, K. F., Taylor, P. C., and Dodson, J. B.: Sensitivity of the Amazonian Convective Diurnal Cycle to Its Environment in Observations and Reanalysis, J. Geophys. Res.-Atmos., 123, 12621–12646, https://doi.org/10.1029/2018jd029251, 2018. a, b, c
Jewtoukoff, V., Plougonven, R., and Hertzog, A.: Gravity waves generated by deep tropical convection: Estimates from balloon observations and mesoscale simulations, J. Geophys. Res.-Atmos., 118, 9690–9707, https://doi.org/10.1002/jgrd.50781, 2013. a
Khairoutdinov, M. and Randall, D.: High-Resolution Simulation of Shallow-to-Deep Convection Transition over Land, J. Atmos. Sci., 63, 3421–3436, https://doi.org/10.1175/jas3810.1, 2006. a
Kuang, Z. and Bretherton, C. S.: A Mass-Flux Scheme View of a High-Resolution Simulation of a Transition from Shallow to Deep Cumulus Convection, J. Atmos. Sci., 63, 1895–1909, https://doi.org/10.1175/jas3723.1, 2006. a
Machado, L., 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. a
Machado, L. A. T.: Diurnal march of the convection observed during TRMM-WETAMC/LBA, J. Geophys. Res., 107, LBA 31-1–LBA 31-15, https://doi.org/10.1029/2001jd000338, 2002. a, b
Maher, P., Vallis, G. K., Sherwood, S. C., Webb, M. J., and Sansom, P. G.: The Impact of Parameterized Convection on Climatological Precipitation in Atmospheric Global Climate Models, Geophys. Res. Lett., 45, 3728–3736, https://doi.org/10.1002/2017gl076826, 2018. a
Mapes, B. and Neale, R.: Parameterizing Convective Organization to Escape the Entrainment Dilemma, J. Adv. Model. Earth Sy., 3, M06004, https://doi.org/10.1029/2011MS000042, 2011. a
Mapes, B., Tulich, S., Lin, J., and Zuidema, P.: The mesoscale convection life cycle: Building block or prototype for large-scale tropical waves?, Dynam. Atmos. Oceans, 42, 3–29, https://doi.org/10.1016/j.dynatmoce.2006.03.003, 2006. a
Marengo, J. A., Alves, L. M., Soares, W. R., Rodriguez, D. A., Camargo, H., Riveros, M. P., and Pabló, A. D.: Two Contrasting Severe Seasonal Extremes in Tropical South America in 2012: Flood in Amazonia and Drought in Northeast Brazil, J. Climate, 26, 9137–9154, https://doi.org/10.1175/jcli-d-12-00642.1, 2013. a
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, 2016. a, b, c
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A. F., 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. J., 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. S., 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. a, b
Morrison, H., Peters, J. M., Chandrakar, K. K., and Sherwood, S. C.: Influences of Environmental Relative Humidity and Horizontal Scale of Subcloud Ascent on Deep Convective Initiation, J. Atmos. Sci., 79, 337–359, https://doi.org/10.1175/jas-d-21-0056.1, 2022. a
Schiro, K. A., Neelin, J. D., Adams, D. K., and Lintner, B. R.: Deep Convection and Column Water Vapor over Tropical Land versus Tropical Ocean: A Comparison between the Amazon and the Tropical Western Pacific, J. Atmos. Sci., 73, 4043–4063, https://doi.org/10.1175/jas-d-16-0119.1, 2016. a, b, c
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. a, b, c, d
Schlemmer, L. and Hohenegger, C.: Modifications of the atmospheric moisture field as a result of cold-pool dynamics, Q. J. Roy. Meteor. Soc., 142, 30–42, https://doi.org/10.1002/qj.2625, 2015. a
Schumacher, C. and Funk, A.: GoAmazon2014/5 Three-dimensional Gridded S-band Reflectivity and Radial Velocity from the SIPAM Manaus S-band Radar, ARM-IOP, https://doi.org/10.5439/1459573, 2018a. a, b, c
Schumacher, C. and Funk, A.: GoAmazon2014/5 Rain Rates from the SIPAM Manaus S-band Radar, ARM-IOP, https://doi.org/10.5439/1459578, 2018b. a, b, c
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42, https://doi.org/10.1038/nature12829, 2014. a
Silva Dias, M. A. F.: Cloud and rain processes in a biosphere-atmosphere interaction context in the Amazon Region, J. Geophys. Res., 107, LBA 39-1–LBA 39-18, https://doi.org/10.1029/2001jd000335, 2002. a
Stevens, B. and Bony, S.: What Are Climate Models Missing?, Science, 340, 1053–1054, https://doi.org/10.1126/science.1237554, 2013. a
Stull, R.: Practical Meteorology: An Algebra-Based Survey of Atmospheric Science – version 1.02b, Univ. of British Columbia, 940 pp., ISBN 978-0-88865-283-6, 2017. a
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 (data available at: http://iop.archive.arm.gov/arm-iop/0eval-data/xie/scm-forcing/iop_at_mao/, last access: 19 November 2023). a, b, c
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. a, b, c, d, e, f, g, h, i, j, k
Tota, J., Fisch, G., Oliveira, P., Fuentes, J., and Siegler, J.: Análise da variabilidade diária da precipitação em área de pastagem para a época chuvosa de 1999 – projeto TRMM/LBA, Acta Amazon., 30, 629–629, https://doi.org/10.1590/1809-43922000304639, 2000. a, b
Waite, M. L. and Khouider, B.: The Deepening of Tropical Convection by Congestus Preconditioning, J. Atmos. Sci., 67, 2601–2615, https://doi.org/10.1175/2010jas3357.1, 2010. a
Wu, C.-M., Stevens, B., and Arakawa, A.: What Controls the Transition from Shallow to Deep Convection?, J. Atmos. Sci., 66, 1793–1806, https://doi.org/10.1175/2008jas2945.1, 2009. a
Zhuang, Y., Fu, R., Marengo, J. A., and Wang, H.: Seasonal variation of shallow-to-deep convection transition and its link to the environmental conditions over the Central Amazon, J. Geophys. Res.-Atmos., 122, 2649–2666, https://doi.org/10.1002/2016jd025993, 2017. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Short summary
We evaluate the environmental conditions that control how clouds grow from fair weather cumulus into severe thunderstorms during the Amazonian wet season. Days with rain clouds begin with more moisture in the air and have strong convergence in the afternoon, while precipitation intensity increases with large-scale vertical velocity, moisture, and low-level wind. These results contribute to understanding how clouds form over the rainforest.
We evaluate the environmental conditions that control how clouds grow from fair weather cumulus...
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