Sciences and Exploration Directorate

Daniel J Miller



Org Code: 613

Mail Code: 613
Greenbelt, MD 20771


Brief Bio

Dan obtained a bachelors in Physics from Michigan Technological University (MTU) in 2011. A rewarding and unique undergraduate research experience in Raymond Shaw’s experimental cloud physics lab, coupled with just the tiniest offhand comment expressing skepticism about the accuracy of satellite cloud droplet size estimates sent him down a long road pursuing the improved cloud remote sensing observations required to study physical processes and climate change. In 2017, he obtained his PhD in Atmospheric Physics from the University of Maryland, Baltimore County (UMBC) and was a founding member of Zhibo Zhang’s Aerosol, Cloud, Radiation-Observation, and Simulation (ACROS) group as his first PhD student.

Joining Goddard as a NASA Postdoctoral Fellow, Dan worked on polarized remote sensing of clouds and aerosols with Kirk Knobelspiesse and Kerry Meyer during the ORACLES sub-orbital field campaign and the early stages of development NASA Plankton, Aerosols, Clouds, and Ecosystems (PACE) missions. This eventually led Dan to his current position is in the Climate and Radiation Lab (Code 613) working in the Cloud imager Retrieval Group improving and evaluating NASA's cloud remote sensing data products.

Dan’s research interests range from studying the fundamental physics of clouds and light scattering to the development of novel satellite observing missions and retrieval techniques. On the fundamental physics side of things Dan's interests include study of the influence of the entrained mixing of dry air at cloud tops - which has the potential to impact precipitation processes depending on how turbulently it is mixed. As well as developing theory and models for describing the depth of penetration that polarized light is scattered from clouds before it becomes depolarized by multiple scattering - which sets limits on where our information comes from in the cloud and what cloud processes we can observe from polarimetry. On the satellite mission development side, Dan's work focuses on the simulation of shortwave passive remote sensing clouds and the impact that realistic spatial and microphysical inhomogeneity has on retrievals of cloud properties. These modeled cloud simulators help prepare NASA for future missions and advance our remote sensing retrieval methods in the process. Currently Dan is both developing a polarimetric satellite simulator as part of the PACE Science Team, and serving as the polarimetric cloud microphysics Algorithm Science Lead for the forthcoming Atmosphere Observing System decadal science mission (AOS)


Associate Research Scientist

UMBC GESTAR2 - NASA Goddard Spaceflight Center

December 2019 - Present

NASA Postdoctoral Program Fellow

Universities Space Research Association - NASA Goddard Spaceflight Center

November 2017 - December 2019

Graduate Research Assistant

Joint Center for Earth Systems Technology - UMBC, Physics Department

August 2013 - November 2017

Undergraduate Research Assistant

Physics Department - Michigan Technological University

August 2008 - May 2011


Ph.D., Atmospheric Physics, UMBC, Baltimore, MD. Dissertation title: “Satellite simulator studies of the impact of cloud inhomogeneity on passive cloud remote sensing retrievals,” 2017.

B.S., Physics, Michigan Technological University, Houghton, MI, 2011.


Scientific Leadership - Climate & Radiation Lab Peer Award



Alexandrov, M. D., D. J. Miller, C. Rajapakshe, et al. A. Fridlind, B. van Diedenhoven, B. Cairns, A. S. Ackerman, and Z. Zhang. 2020. Vertical profiles of droplet size distributions derived from cloud-side observations by the research scanning polarimeter: Tests on simulated data Atmospheric Research 239 104924 [10.1016/j.atmosres.2020.104924]

Miller, D. J., M. Segal-Rozenhaimer, K. Knobelspiesse, et al. J. Redemann, B. Cairns, M. Alexandrov, B. van Diedenhoven, and A. Wasilewski. 2020. Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm Atmospheric Measurement Techniques 13 3447-3470 [10.5194/amt-13-3447-2020]

Segal-Rozenhaimer, M., D. J. Miller, K. Knobelspiesse, et al. J. Redemann, B. Cairns, and M. D. Alexandrov. 2018. Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations Journal of Quantitative Spectroscopy and Radiative Transfer 220 39-51 [10.1016/j.jqsrt.2018.08.030]

Werner, F., Z. Zhang, G. Wind, et al. D. J. Miller, S. Platnick, and L. Di Girolamo. 2018. Improving cloud optical property retrievals for partly cloudy pixels using coincident higher-resolution single band measurements: A feasibility study using ASTER observations Journal of Geophysical Research: Atmospheres [10.1029/2018jd028902]

Miller, D. J., Z. Zhang, S. Platnick, et al. A. S. Ackerman, F. Werner, C. Cornet, and K. Knobelspiesse. 2018. Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator Atmospheric Measurement Techniques 11 (6): 3689-3715 [10.5194/amt-11-3689-2018]

Werner, F., Z. Zhang, G. Wind, D. J. Miller, and S. Platnick. 2018. Quantifying the Impacts of Subpixel Reflectance Variability on Cloud Optical Thickness and Effective Radius Retrievals Based On High-Resolution ASTER Observations Journal of Geophysical Research: Atmospheres 123 (8): 4239-4258 [10.1002/2017jd027916]

Miller, D. J., Z. Zhang, A. S. Ackerman, S. Platnick, and B. A. Baum. 2016. The impact of cloud vertical profile on liquid water path retrieval based on the bispectral method: A theoretical study based on large-eddy simulations of shallow marine boundary layer clouds Journal of Geophysical Research: Atmospheres 121 (8): 4122-4141 [10.1002/2015jd024322]

Talks, Presentations and Posters


Public research talks and posters can be found on:

3, 2023