A Microwave Scattering Database of Oriented Ice and Snow Particles: Supporting Habit-Dependent Growth Models and Radar Applications
Abstract
The optical properties of atmospheric hydrometeors are a key component of forward operators used in data assimilation and model evaluation. Recent advances in microphysical modelling, such as Lagrangian super-particle models with habit prediction, enable the continuous evolution of ice particle properties and therefore require scattering databases that cover a wide range of particle morphologies.The discrete dipole approximation (DDA) provides highly accurate scattering calculations for arbitrarily shaped ice particles but is computationally expensive, which has limited the diversity of particle shapes or environmental conditions represented in existing databases. This restricts their applicability for models with complex and evolving microphysics.We present a new DDA-based database of ice particle optical properties at 5.6, 9.6, 35.6, and 94 GHz, designed to support habit-evolving microphysical schemes. The database follows two complementary designs. The first includes approximately 2500 ice crystals and 450 aggregates with scattering properties computed for multiple orientations, allowing a flexible treatment of particle canting. The second design sacrifices orientation diversity to maximize particle variability, comprising scattering properties for about 10 million aggregates and 1 million ice crystals calculated for a single orientation.This newly developed database allows to forward simulate the output of the Lagrangian Monte-Carlo particle model McSnow, in which a habit prediction for ice crystals and aggregates has recently been implemented. The simulations reproduce characteristic radar signatures in the ice phase and thus allow to study the ice microphysical processes responsible for these radar signatures.
Fields of Study & Subjects
Atmospheric aerosols and clouds
Electromagnetic Scattering and Analysis
Cryospheric studies and observations
Publication details
- Journal / source
- EGU General Assembly Conference Abstracts
- Publication date
- 14 March 2026
- Language
- EN