Monday, 28 August 2023: 10:45 AM
Great Lakes A (Hyatt Regency Minneapolis)
Handout (19.7 MB)
Ice crystal formation and growth processes in mixed-phase clouds (MPCs) are not sufficiently understood leading to uncertainties of atmospheric models in representing MPCs. One of these processes is riming, which occurs when liquid water droplets freeze onto ice crystals. Riming plays a key role in precipitation formation in MPCs by efficiently converting liquid cloud water into ice. However, riming is challenging to observe directly and there are only few studies quantifying riming in Arctic MPCs.
In this study, we derive the normalized rime mass M to quantify riming. We use airborne data collected during the (AC)3 field campaign HALO-(AC)3 performed in 2022. For this campaign, two aircraft were flying in formation collecting closely spatially collocated and almost simultaneous in situ and remote sensing observations.
We present an Optimal Estimation algorithm to retrieve M from radar reflectivities measured by the 94 GHz FMCW radar MiRAC-A. We find M by matching measured with simulated radar reflectivities obtained from observed in situ particle number concentrations. As forward operators, we use the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA) and parameterizations of particle mass-size and backscattering cross-section depending on M ("riming-dependent parameterization"). The riming-dependent parameterization is derived via aggregation and riming model calculations.
We compare to M derived from in situ measured particle shape. For this purpose, we calculate the complexity of in situ measured particles, which relates particle perimeter to area. We then derive M from empirical relationships that were again obtained from synthetic particles. We evaluate the occurrence of riming in terms of meteorological conditions and macrophysical cloud properties to understand external drivers of riming. Further, we compute power spectra to study the spatial variability of riming in arctic MPC.
This will lead to a better understanding of riming and thereby helps to improve modelling of this important arctic MPC process.
In this study, we derive the normalized rime mass M to quantify riming. We use airborne data collected during the (AC)3 field campaign HALO-(AC)3 performed in 2022. For this campaign, two aircraft were flying in formation collecting closely spatially collocated and almost simultaneous in situ and remote sensing observations.
We present an Optimal Estimation algorithm to retrieve M from radar reflectivities measured by the 94 GHz FMCW radar MiRAC-A. We find M by matching measured with simulated radar reflectivities obtained from observed in situ particle number concentrations. As forward operators, we use the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA) and parameterizations of particle mass-size and backscattering cross-section depending on M ("riming-dependent parameterization"). The riming-dependent parameterization is derived via aggregation and riming model calculations.
We compare to M derived from in situ measured particle shape. For this purpose, we calculate the complexity of in situ measured particles, which relates particle perimeter to area. We then derive M from empirical relationships that were again obtained from synthetic particles. We evaluate the occurrence of riming in terms of meteorological conditions and macrophysical cloud properties to understand external drivers of riming. Further, we compute power spectra to study the spatial variability of riming in arctic MPC.
This will lead to a better understanding of riming and thereby helps to improve modelling of this important arctic MPC process.
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