Observing Shallow-to-Deep Convective Transitions Using ARM GoAmazon2014/5 and Geostationary Satellite Observations

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Description

The transition between cumulus congestus and cumulonimbus cloud populations is an important component of the convective lifecycle, but is often only crudely captured in GCMs. In this study we use the GoAmazon2014/5 S-band scanning radar (SIPAM) dataset (12 min, 2 km resolution) in combination with geostationary satellite measurements of IR brightness temperatures (30 min, 4 km resolution) to statistically characterize the typical spatial and temporal scales and, to the extent possible, environmental controls on transitions between cloud populations dominated by cumulus congestus to those dominated by isolated deep convection or deep convection organized on the mesoscale. The GoAmazon2014/5 SIPAM radar provides a more direct measurement of the evolution of the vertical structures of convective cloud populations over an area of approximately 200 km x 200 km, while the geostationary satellite dataset provides a proxy of cloud-top heights over much larger areas and longer periods of time. We use the SIPAM radar dataset to develop a new methodology to quantify congestus to deep convective cloud population transitions using the satellite data. We explore the following research questions: What is the probability that a region filled with cumulus congestus eventually transitions to a population containing some deep convection? How do these probabilities vary by location, time of day, season, and under varying synoptic scale environmental conditions? How long do these transitions typically take? This work is done as part of a larger effort to understand the full convective lifecycle and processes controlling the evolution of convective clouds in the central Amazon (see Feng et al. abstract).