Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13207-2021
https://doi.org/10.5194/acp-21-13207-2021
Research article
 | 
06 Sep 2021
Research article |  | 06 Sep 2021

Morning boundary layer conditions for shallow to deep convective cloud evolution during the dry season in the central Amazon

Alice Henkes, Gilberto Fisch, Luiz A. T. Machado, and Jean-Pierre Chaboureau

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Cited articles

ARM (Atmospheric Radiation Measurement user facility): Boundary-layer height data with CEIL (CEILPBLHT), updated hourly, 2014-01-01 to 2015-11-30, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), compiled by: Morris, V. and Shi, Y., ARM Data Center [data set], https://doi.org/10.5439/1095593, last access: 24 July 2020, 2014a. a
ARM (Atmospheric Radiation Measurement user facility): Eddy Correlation Flux Measurement System (30ECOR), updated hourly, 2014-04-03 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), compiled by: Sullivan, R., Cook, D., and Keeler, E., ARM Data Center [data set], https://doi.org/10.5439/1025039, last access: 24 July 2020, 2014b. a
ARM (Atmospheric Radiation Measurement user facility): Radiative Flux Analysis (RADFLUX1LONG), updated hourly, 2013-12-23 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil; AMF1 (M1), compiled by: Long, C., Gaustad, K., and Riihimaki, L., ARM Data Center [data set], last access: 4 April 2019, https://doi.org/10.5439/1157585, 2014c. a
ARM (Atmospheric Radiation Measurement): Surface Energy Balance System (SEBS), 2014-01-01 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil; AMF1 (M1), compiled by: Sullivan, R., Cook, D., and Keeler, E., ARM Data Center [data set], last access: 14 November 2020, https://doi.org/10.5439/1025274, 2014d. a
ARM (Atmospheric Radiation Measurement user facility): Mini Sound Detection and Ranging (SODAR), updated hourly, 2014-02-18 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil; MAOS (S1), compiled by: Coulter, R., Muradyan, P., and Martin, T., ARM Data Center [data set], last access: 20 October 2020, https://doi.org/10.5439/1150265, 2014e. a
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Short summary
The Amazonian boundary layer is investigated during the dry season in order to better understand the processes that occur between night and day until the stage where shallow cumulus clouds become deep. Observations show that shallow to deep clouds are characterized by a shorter morning transition stage (e.g., the time needed to eliminate the stable boundary layer inversion), while higher humidity above the boundary layer favors the evolution from shallow to deep cumulus clouds.
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