Michal Segal Rozenhaimer

Affiliation: NASA Ames Research Center (ARC)/ Bay Area Environmental Research Institute (BAERI)

Branch: Atmospheric Science Branch (SGG)

Email: michal.segalrozenhaimer@nasa.gov / segalrozenhaimer@baeri.org

Phone: 650-604-4392

Professional Biography

Dr. Segal-Rozenhaimer is a Senior Research Scientist employed through the Bay Area Environmental Research Institute in support of a cooperative agreement between BAERI and ARC (ARC-CREST). Her work is focused on the utilization of atmospheric measurements (from satellite, airborne and ground-based platforms), big-data and machine learning techniques to better understand atmospheric process mechanisms, phenomenon, and their improved implementation in global climate models. She has more than a decade of experience with NASA airborne missions, among them ARISE, KORUS-AQ and ORACLES, as an instrument scientist and instrument PI, leading aerosol, cloud and gas measurements from remote-sensing and in-situ instruments. Among her research projects, she was leading airborne observation-based Arctic research focusing on improving radiative flux and cloud predictions in climate models through her NASA New Investigator grant. She was a core team member of the NASA NeMO-Net Project that developed a convolutional neural-network to detect coral reef habitats from remote sensing measurements. She is currently leading a NASA Atmospheric composition and modeling project for the development of machine learning based algorithms for detecting low-level cloud types and understanding their diurnal cycle under different conditions over the South-East Atlantic Ocean.

Education

Ph.D. Civil and Environmental Engineering, Technion – Israel Institute of Technology

M. Sc. (Cum Laude) Agricultural Engineering, Technion – Israel Institute of Technology

B. Sc. (Cum Laude) Chemical Engineering, Technion – Israel Institute of Technology

Research Interest

Machine learning applications in Earth Science, Aerosol, Clouds and Radiative budget of the Earth, Remote Sensing Retrieval algorithms.

Awards/Honors

NASA Group Achievement Award, NeMO-Net (2021)

NASA Group Achievement Award, ORACLES (2020)

NASA Group Achievement Award, KORUS-AQ (2018)

NASA Group Achievement Award, ARISE (2016)

NASA Postdoctoral Program Fellow (2011)

Select Publications

Segal Rozenhaimer Michal, Alan Li, Kamalika Das and Ved Chirayath, (2020), Cloud Detection Algorithm for Multi-Modal Satellite Imagery using Convolutional Neural-Networks (CNN), Remote Sensing of Environment, Remote Sensing of Environment 237 (2020) 111446

Samuel E. LeBlanc, Jens Redemann, Connor Flynn, Kristina Pistone, Meloë Kacenelenbogen, Michal Segal-Rosenheimer, Yohei Shinozuka, Stephen Dunagan, Robert P. Dahlgren, Kerry Meyer, James Podolske, Steven G. Howell, Steffen Freitag, Jennifer Small-Griswold, Brent Holben, Michael Diamond, Paola Formenti, Stuart Piketh, Gillian Maggs-Kölling, Monja Gerber, and Andreas Namwoonde, (2020), Above Cloud Aerosol Optical Depth from airborne observations in the South-East Atlantic, Atmos. Chem. Phys., 20, 1565–1590, 2020 https://doi.org/10.5194/acp-20-1565-2020

Daniel J. Miller, Michal Segal-Rozenhaimer, Kirk Knobelspiesse, Jens Redemann, Brian Cairns, Mikhail Alexandrov, Bastiaan van Diedenhoven, and Andrzej Wasilewski, (2020),
Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm, Atmos. Meas. Tech., 13, 3447–3470, 2020, https://doi.org/10.5194/amt-13-3447-2020

Chen, H., Schmidt, S., King, M. D., Wind, G., Bucholtz, A., Reid, E. A., Segal-Rozenhaimer, M., Smith, W. L., Taylor, P. C., Kato, S., and Pilewskie, P.: Shortwave Radiative Effect of Arctic Low-Level Clouds: Evaluation of Imagery-Derived Irradiance with Aircraft Observations, Atmos. Meas. Tech., 14, 2673–2697, 2021

Li, Alan, Ved Chirayath, Michal Segal-Rozenhaimer, Juan L. Torres-Perez, (2020), NASA NeMO-Net’s Convolutional Neural Network: Mapping Marine Habitats through Spectrally Heterogeneous Remote Sensing Imagery, IEEE-Journal of Selected Topics in Applied Earth Observations and Remote Sensing, JSTARS-2020-00665

Yongjoo Choi, Young Sung Ghim; Michal Segal Rozenhaimer; Jens Redemann; Samuel E LeBlanc; Connor J Flynn; Roy J Johnson; Yonghwan Lee; Taehyoung Lee; Taehyun Park; Joshua P Schwarz; Kara D Lamb; Anne E Perring, (2021), Temporal and spatial variations of aerosol optical properties over the Korean peninsula during KORUS-AQ, Atmospheric Environment

van den Bergh J, Chirayath V, Li A, Torres-Pérez JL and Segal-Rozenhaimer M (2021) NeMO-Net – Gamifying 3D Labeling of Multi-Modal Reference Datasets to Support Automated Marine Habitat Mapping. Front. Mar. Sci. 8:645408. doi: 10.3389/fmars.2021.645408

Dang Caroline, Michal Segal-Rozenhaimer, Haochi Che, Lu Zhang, Paola Formenti, Jonathan Taylor, Amie Dobracki, Sara Purdue, Pui-Shan Wong, Athanios Nenes, Arthur Sedlacek, Hugh Coe, Jens Redemann, Paquita Zuidema, and James Haywood, Biomass burning and marine aerosol processing over the southeast Atlantic Ocean: A TEM single particle analysis, Atmos. Chem. Phys., 22, 9389–9412, 2022, https://doi.org/10.5194/acp-22-9389-2022, (2022)

Lu Zhang, Michal Segal-Rozenhaimer, Haochi Che, Caroline Dang, Arthur J. Sedlacek III, Ernie R. Lewis, Amie Dobracki, Jenny P.S. Wong, Paola Formenti, Steven G. Howell, and Athanasios Nenes,  Light Absorption by Brown Carbon over the South-East Atlantic Ocean, Atmos. Chem. Phys., 22, 9199–9213, 2022, https://doi.org/10.5194/acp-22-9199-2022, (2022)

Che, H., Segal-Rozenhaimer, M., Zhang, L., Dang, C., Zuidema, P., Sedlacek III, A.J., Zhang, X. and Flynn, C., 2022. Seasonal variations in fire conditions are important drivers to the trend of aerosol optical properties over the south-eastern Atlantic, Atmos. Chem. Phys., 22, 8767–8785, 2022, https://doi.org/10.5194/acp-22-8767-2022, (2022)

Che, H., Segal-Rozenhaimer, M., Zhang, L., Dang, C., Paquita Zuidema , Amie Dobracki, Arthur Sedlacek , Hugh Coe , Huihui Wu , Jonathan Taylor , Jens Redemann , Jim Haywood, Cloud processing and weeklong ageing affect biomass burning aerosol properties over the south-eastern Atlantic. Commun Earth Environ 3, 182 (2022), https://doi.org/10.1038/s43247-022-00517-3

Pešek, O.; Segal-Rozenhaimer, M.; Karnieli, A. Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types. Remote Sens. 2022, 14, 5210. https://doi.org/10.3390/rs14205210

LeBlanc, S. E., M. Segal-Rozenhaimer, Redemann, J., Flynn, C. J., Johnson, R. R., Dunagan, S. E., Dahlgren, R., Kim, J., Choi, M., da Silva, A. M., Castellanos, P., Tan, Q., Ziemba, L., Thornhill, K. L., and Kacenelenbogen, M. S.: Airborne observation during KORUS-AQ show aerosol optical depth are more spatially self-consistent than aerosol intensive properties, Atmos. Chem. Phys., 22, 11275–11304, 2022, https://doi.org/10.5194/acp-22-11275-2022

Michal Segal Rozenhaimer, David Nukrai, Haochi Che, Robert Wood and Zhibo Zhang, Cloud Mesoscale Cellular Classification and Diurnal Cycle Using a Convolutional Neural Network (CNN), Remote Sens. 2023, 15(6), 1607; https://doi.org/10.3390/rs15061607