השוואת שיטות

	תאוריית קֶהלֶר	ניתוח גרעיני עיבוי עננים	מיקרופיזיקה ספקטרלית מבוססת תאים (Bin Microphysics)
תחום	מטאורולוגיה	מטאורולוגיה	מטאורולוגיה
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	1936	1959	1999
הוגה השיטה	Hilding Kohler	Twomey, Woodard	Khain, Ovtchinnikov
סוג	Thermodynamic equilibrium framework	Cloud microphysical measurement	Explicit particle size distribution model
מקור מכונן	Köhler, H. (1936). The nucleus in and the growth of hygroscopic droplets. Transactions of the Faraday Society, 32, 1152-1161.	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.	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.
כינויים	Kohler theory, Kohler equilibrium, Cloud droplet nucleation	CCN analysis, Cloud condensation nuclei, CCN measurement	Bin microphysics, Spectral microphysics, Explicit microphysics
קשורות	3	3	3
תקציר	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.	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.	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.