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Title: Cloud regimes over the Amazon Basin: perspectives from the GoAmazon2014/5 campaign

Journal Article · · Atmospheric Chemistry and Physics (Online)
ORCiD logo [1]; ORCiD logo [1];  [2]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. Univ. of Kansas, Lawrence, KS (United States)
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Radiosonde observations collected during the GoAmazon2014/5campaign are analyzed to identify the primary thermodynamic regimes accompanying different modes of convection over the Amazon. This analysis identifies five thermodynamic regimes that are consistent with traditional Amazon calendar definitions of seasonal shifts, which include one wet, one transitional, and three dry season regimes based on a k-means cluster analysis. A multisensor ground-based approach is used to project associated bulk cloud and precipitation properties onto these regimes. This is done to assess the propensity for each regime to be associated with different characteristic cloud frequency, cloud types, and precipitation properties.Additional emphasis is given to those regimes that promote deep convective precipitation and organized convective systems. Overall, we find reduced cloud cover and precipitation rates to be associated with the three dry regimes and those with the highest convective inhibition. While approximately 15% of the data set is designated as organized convection,these events are predominantly contained within the transitional regime.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0012704; SC0016522
OSTI ID:
1635302
Alternate ID(s):
OSTI ID: 1637475
Report Number(s):
BNL-216105-2020-JAAM
Journal Information:
Atmospheric Chemistry and Physics (Online), Vol. 20, Issue 12; ISSN 1680-7324
Publisher:
European Geosciences UnionCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 13 works
Citation information provided by
Web of Science

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dataset January 2018
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dataset January 2018

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