We explore the physics of clouds and precipitation, ocean-atmosphere interactions, and nonlinear dynamics, all within the context of climate.
Our research approaches include theoretical exploration of nonlinear differential equations, numerical simulations of clouds and cloud fields, satellite data analysis, field measurements, and theoretical work on remote sensing and radiative transfer.
Our group develops new approaches in fluid dynamics, complex systems, electromagnetic radiation, cloud microphysics, computer vision, and statistics.
Gray P. C., Boss E., Bourdin G., Linkowski T., Pesant S., Sanchez S., Troublé R., Laxenaire R., Wincker P., Moll M., Guidi L., Schramm J., Lombard F., Mayeux E., Petit E., Vardi A., Trainic M., Koren I., Lang-Yona N. & Lehahn Y.
(2025)
Nature Communications.
16,
1808.
While a rich history of patchiness research has explored spatial structure in the ocean, there is no consensus over the controls on biological patchiness and how physical-ecological-biogeochemical processes and patchiness relate. The prevailing thought is that physics structures biology, but this has not been tested at basin scale with consistent in situ measurements. Here we use the slope of the relationship between variance vs spatial scale to quantify patchiness and ~650,000 nearly continuous (dx ~ 200 m) measurements - representing the Atlantic, Pacific, and Southern Oceans - and find that patchiness of biological parameters and physical parameters are uncorrelated. We show variance slope is an emergent property with unique patterns in biogeochemical properties distinct from physical tracers, yet correlated with other biological tracers. These results provide context for decades of observations with different interpretations, suggest the use of spatial tests of biogeochemical model parameterizations, and open the way for studies into processes regulating the observed patterns.
Arieli Y., Khain A., Gavze E., Altaratz O., Eytan E. & Koren I.
(2025)
Journals of the Atmospheric Sciences.
82,
1,
p. 107-118
This study employs a high-resolution (10 m) System for Atmospheric Modeling (SAM) coupled with the spectral bin microphysical (SBM) scheme to thoroughly investigate the processes governing the evolution of aerosol properties within and outside a shallow cumulus cloud. The model encompasses the complete life cycle of cloud droplets, starting from their formation through their evolution until their complete evaporation or sedimentation to the ground. Additionally, the model tracks the aerosols' evolution both within the droplets and in the air. Aerosols are transported within the droplets, grow by droplet coalescence, and are released into the atmosphere after droplet evaporation (regeneration process). The aerosol concentration increases by droplet evaporation and decreases along with falling drops. So, the effects of clouds on the surrounding aerosols depend on the microphysical and dynamic processes, which in turn depend on the amount of background aerosols; here, we compare clean and polluted conditions. It is shown that the regeneration process is highly important and that shallow trade cumulus clouds significantly impact the vertical profile of aerosol concentration in the lower troposphere, as well as their size distribution, and can serve as a source of large cloud condensation nuclei. Furthermore, it is shown that both precipitating and nonprecipitating boundary layer clouds contribute to a substantial increase in aerosol concentration within the inversion layer due to intense evaporation.
Koren I., Kostinski A. & Wollner U.
(2024)
Geophysical Research Letters.
51,
24,
e2024GL110.
The glory, a striking optical phenomenon seen from space in unpolarized satellite images can be mapped onto the cloud's droplet sizes with a characteristic scale of 10 (Formula presented.). Such a mapping allows us to infer the mean and variance of the cloud droplets' radius, an important property that has remained elusive and inaccessible to passive unpolarized satellite sensing. Here, we propose a simple and robust polarization-like differential approach to map the glory's spectral properties to the desired moments of the droplet size distribution. By taking the differences between two spectrally close channels, we reduce multiple scattering contributions and amplify the single-scattering signal, thus allowing for a simple and rapidly converging map from glory to droplet size distribution. Moreover, the droplet information reflects the upper part of the cloud, adding another sample to the traditional multiple scattering-based retrievals that reflect droplet properties deeper in the cloud.