Improving Polarimetric Radar Simulations in AROME: Evaluation of Microphysics and Forward Operator Assumptions

Accurate simulation of dual-polarization radar variables is essential for their assimilation into convective-scale NWP models. This requires both realistic microphysical parameterization and a reliable representation of hydrometeor scattering characteristics through a polarimetric radar forward operator.This study evaluates the performance of two microphysics schemes in the AROME model: ICE3 (1-moment) and LIMA (2-moment), using the polarimetric radar forward operator developed by Augros et al. (2016). Simulations were compared with C-band dual-polarization radar observations over 34 convective days in 2022. An object-based analysis of convective cells focused on hydrometeor structure, vertical profiles, and polarimetric signatures such as differential reflectivity (ZDR) columns. LIMA more accurately reproduced low-level polarimetric variables, notably by simulating larger raindrops and stronger ZDR and specific differential phase (KDP) values within convective cell cores. It also generated a realistic number of ZDR columns, along with accurate lifetimes and areal extents. However, both microphysics schemes showed deficiencies within and above the melting layer, highlighting limitations in the radar forward simulations in these regions.To address these limitations, ongoing work focuses on refining the assumptions used in the radar forward operator. A two-step methodology is applied. First, a theoretical sensitivity study explores how variations in hydrometeor mass–size relations, shape, particle size distributions, and melting state affect simulated radar variables. Initial results, in particular, show the strong influence of density–size assumptions for dry snow. Second, the refined assumptions are evaluated in AROME simulations of convective and stratiform events to quantify their impact on radar outputs. This work aims to reduce systematic biases and improve consistency with observed polarimetric radar fields.References: Augros, C. et al. (2016), QJRMS, 142(S1), 347–362. https://doi.org/10.1002/qj.2572David, C. et al. (2025), EGUsphere. https://doi.org/10.5194/egusphere-2025-685