Local and large-scale controls of moisture variability in the shallow-to-deep transition during GOAmazon

Poster PDF

Description

In order to improve our understanding of land-atmosphere processes and their broader impacts, the poorly represented shallow-to-deep transition needs to be better understood. Observations collected during the GOAmazon/CHUVA field campaign provide a means to improve our understanding of this transition and its controlling factors. Through coincident measurements of clouds and precipitation from the ARM deployment in the Amazon and high temporally and spatially resolved GPS-Met precipitable water vapor data from the CHUVA GNSS network, the complex link between the convective transition and moisture is explored. Previous studies have described local shallow-to-deep convection in this region for days with no MCS development using moisture data available from radiosondes. Our study builds on that work by describing the shallow-to-deep transition with new cloud-type products derived from ASR PIs in the context of the GPS-Met stations for scenarios with MCSs developing locally within the domain, MCSs moving into the domain, and days with and without MCSs in the region the day prior. MCSs are defined within a domain provided by the operational S-band (SIPAM) radar in Manaus, Brazil. Within this framework, we describe diurnal, intraseasonal, and seasonal characteristics of the shallow-to-deep transition both as composites with respect to local and large-scale environmental factors, and through detailed case studies that allow us to fully utilize the high temporal resolution of the GPS-Met stations and better understand casual relationships. Both local and large-scale impacts on the moisture variability will be described, including the observed role of active Kelvin waves in increasing moisture above the typical moist anomalies during the wet season and promoting deep convective development more frequently and earlier than during suppressed and neutral phases of the waves. Future work involves comparing these observed relationships to those produced in regional and global simulations for the GOAmazon region.