Optimizing radar scan strategies for convective cell observations
Oue, Mariko — Stony Brook University
Area of research
This study quantifies uncertainties in radar observations for convective cells and provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments, through the use of high spatiotemporal cloud-resolving model simulations coupled with a radar simulator and a cell-tracking algorithm.
The quantified uncertainties in this study can be a reference for future analysis using the radar data for convective cells. This study sheds light on the challenge of designing remote-sensing observations strategies in pre-field-campaign periods.
Optimizing radar observation strategies is one of the most important considerations in pre-field-campaign periods. This study investigates uncertainties in radar observations of the convective cell evolution in clean and polluted environments, using high spatiotemporal cloud-resolving model simulations coupled with a radar simulator and a cell-tracking algorithm. Our analysis includes the following outcomes. First, a 5-7 m s-1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations. This demonstrates the importance of obtaining accurate updraft estimates from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells using every-minute vertical cross-section scans captures the evolution of precipitation particle size and number concentration and middle- and upper-level updraft better than the operational radar observations. Third, we propose an optimized strategy composed of cell tracking by quick (1-2 min) vertical cross-section scans from more than one radar in addition to the operational volume scans. Finally, sample size of more than 10 deep cells better represents the maximum updraft evolution (error < 3 m s-1).