Detecting and Characterizing Convective Downdrafts during GoAmazon2014/5
Imparte Ph.D Candidate Sophie Mayne
Department of Atmospheric Sciences, Texas A&M
Convectively generated cold pools are instrumental to boundary layer structure due to their roles in energy transfer, convective initiation and organization, and the thermodynamic modulation of the sub-cloud layer. Despite their importance, analyses of cold pools in the tropics are constrained by a lack of observational data; insight into the phenomena therefore relies heavily on numerical models. The two year DOE GoAmazon2014/5 field campaign centered on Manacapuru, Brazil (T3) in the central Amazon provides a unique opportunity to characterize tropical cold pools and allows for the comparison of observational data with theoretical results from cold pool simulations and model parameterizations. Cold pools were identified using a novel detection method that utilizes surface meteorological observations and the Brazilian military (SIPAM) operational S-band radar in Manaus. Approximately 400 cold pools were observed at T3 during 2014. They had an average temperature decrease of 2.5 K, accompanied by an increase in relative humidity of 16 % and an increase in wind speed of 1.2 m/s. These values are similar to studies carried out in other tropical continental regions. Minimal differences were observed between the wet and dry seasons. Another unique aspect of this research was the use of the Atmospheric Emitted Radiance Interferometer (AERI) instrument to further investigate 20 cold pool events. AERI retrievals of potential temperature and specific humidity capture the passage of cold pools, and show promising similarities with theoretical results calculated using the cold pool parameterization discussed in Del Genio et al. (2015); however, results are very sensitive to both the mass of air injected into the cold pool after its formation, and the thermodynamic characteristics of the downdraft. It will be shown that the downdraft mass flux, which is difficult to quantify, can be estimated from this analysis.