Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle
- a Brookhaven National Laboratory, Upton, New York
- a Brookhaven National Laboratory, Upton, New York, b Stony Brook University, State University of New York, Stony Brook, New York
- c McGill University, Montreal, Quebec, Canada
- b Stony Brook University, State University of New York, Stony Brook, New York
- d Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
Abstract
Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- NONE; SC0021160; SC0012704
- OSTI ID:
- 2217634
- Alternate ID(s):
- OSTI ID: 2007450
OSTI ID: 2204133
- Journal Information:
- Journal of Atmospheric and Oceanic Technology, Journal Name: Journal of Atmospheric and Oceanic Technology Journal Issue: 11 Vol. 40; ISSN 0739-0572
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English