Optimizing Radar Scan Strategies for Observing Deep Convection Using Observing System Simulation Experiments

Abstract. Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments and aims to optimize the radar observation strategy for deep convection through the use of cloud-resolving model simulations coupled with a radar simulator and a cell tracking algorithm. Our analysis results include the following four outcomes. First, a 5–7 m s-1 median difference in maximum updrafts of tracked cells was shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute captures the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves the multi-Doppler radar updraft retrievals above 5 km AGL for regions with updraft velocities greater than 10 m s-1. Third, we propose an optimized strategy which is 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. We also propose the use of a single range-height indicator updraft retrieval technique for cells close to the radars, where the multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s-1.