Aerosol–Cloud–Precipitation Interactions

Aerosol particles act as cloud condensation and ice nuclei, so changes in aerosol loading alter cloud droplets, ice and, in the end, precipitation — sometimes raising it, sometimes lowering it. Using WRF coupled with bin microphysics schemes, I study these aerosol–cloud–precipitation interactions in idealized orographic clouds, in real cases, and with colleagues in deep convective systems.

Figure. Deep orographic convection averaged over t = 4–6 h in clean (CLN), control (CNT) and polluted (PLT) cases (Seo & Baik 2026, JMSJ).

Mountain geometry and aerosol effects

Using WRF with bin microphysics over idealized mountains, I showed that the response of orographic precipitation to aerosol loading depends on mountain geometry. In warm, shallow clouds, aerosols change how long cloud droplets take to grow into raindrops, while the mountain shape sets how long they have before being carried over the crest (Seo et al. 2020). In deep mixed-phase convection, interactions between vigorous lower-layer clouds and upstream-extending upper-layer clouds enhance surface precipitation through melting and direct sedimentation of graupel and hail; over a symmetric mountain more aerosol enhances precipitation, while over asymmetric mountains the response changes with upslope steepness (Seo and Baik 2026).

Figure. Schematic for the autoconversion timescale τ* = (x* − x_LCL)/U and the sedimentation timescale τ_T = (x_T − x*)/U over a mountain; r* = 40 μm marks the boundary between cloud droplets and raindrops (Seo et al. 2020, JMSJ).

Aerosols and deep convective systems

With colleagues, I examined a deep convective precipitation event over north central Mongolia, a high-altitude inland region, with bin microphysics and aerosol number concentrations of 150–4800 cm⁻³. Surface precipitation slightly decreases from 150 to 600 cm⁻³ but increases by 22% from 600 to 4800 cm⁻³, as more small drops freeze and feed snow growth through deposition and riming (Lkhamjav et al. 2018, APJAS). We also evaluated an improved quasi-stochastic collection model in precipitation prediction there (Lkhamjav et al. 2017, JGR).

Figure. Accumulated surface precipitation from 00 UTC 21 to 00 UTC 22 August 2014 for aerosol number concentrations N₀ = 150, 300, 600, 1200, 2400 and 4800 cm⁻³ (Lkhamjav et al. 2018).