Comparer des méthodes

	Analyse des noyaux de condensation nuageuse	Théorie de Köhler	Microphysique spectrale par classes
Domaine	Météorologie	Météorologie	Météorologie
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1959	1936	1999
Auteur d'origine	Twomey, Woodard	Hilding Kohler	Khain, Ovtchinnikov
Type	Cloud microphysical measurement	Thermodynamic equilibrium framework	Explicit particle size distribution model
Source fondatrice	Dusek, U., Frank, G. P., Hildebrandt, L., et al. (2006). Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science, 312(5778), 1375-1378.	Köhler, H. (1936). The nucleus in and the growth of hygroscopic droplets. Transactions of the Faraday Society, 32, 1152-1161.	Khain, A. P., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., & Krugliak, H. (2000). Notes on the state-of-the-art numerical modeling of cloud microphysics. Atmospheric Research, 55(3–4), 159-224.
Alias	CCN analysis, Cloud condensation nuclei, CCN measurement	Kohler theory, Kohler equilibrium, Cloud droplet nucleation	Bin microphysics, Spectral microphysics, Explicit microphysics
Apparentées	3	3	3
Résumé	Cloud condensation nuclei (CCN) analysis examines the number and properties of aerosol particles capable of nucleating cloud droplets at various supersaturation levels. This field involves measuring CCN concentrations, characterizing their chemical composition and size, and relating aerosol properties to cloud microphysical processes.	Köhler theory is a foundational framework in cloud microphysics that predicts the equilibrium supersaturation required for an aerosol particle of given size and composition to grow into a cloud droplet. Published in 1936 by Hilding Köhler, it combines the Kelvin effect (vapor pressure enhancement over curved surfaces) with the Raoult effect (vapor pressure depression from dissolved solute) to explain cloud droplet formation.	Spectral bin microphysics is a detailed cloud microphysical modeling approach that explicitly represents the particle size distribution (PSD) by dividing particles into discrete size bins. Rather than assuming a fixed shape for the PSD, bin models track the number and mass of particles in each size category, allowing detailed simulation of cloud and precipitation processes.