Machine learning and big data analytics are ideally suited for the classification and detection of various properties of physical systems (e.g., atmosphere, ocean, and the solid earth), with extraordinary precision in the presence of an enormous amount of disparate data. They are also highly effective tools for developing models and making predictions where data are sparse, and uncertainty is high. For example, machine-learning techniques are currently being developed to detect rare but extremely dangerous events such as tsunamis, submarine earthquakes, and volcanoes. The pairing of machine learning with Bayesian statistics combines multiple prediction tools that can provide more reliable forecasts and a detailed understanding of the uncertainties inherent in forecasting in general. As such, these tools can help solve the problem of estimating current states in physical systems where it is difficult to conduct in situ observations, such as in the deep ocean and polar ice caps, and assisting in accurate weather, climate, and solid earth predictions.

Ben Kirtman, PhD

William R Middelthon III Endowed Chair of Earth Sciences
Professor, Atmospheric Sciences, Rosenstiel School of Marine, Atmospheric, and Earth Science
Deputy Director, Frost Institute for Data Science and Computing
Director, IDSC Earth Systems Science
Director, Cooperative Institute for Marine & Atmospheric Studies (CIMAS)

In addition to his roles at IDSC, Ben Kirtman is a Professor in the Department of Atmospheric Sciences and the Director of NOAA’s Cooperative Institute for Marine and Atmospheric Studies. Dr. Kirtman received his BS in Applied Mathematics from the University of California-San Diego in 1987, and his MS and PhD in 1992 from the University of Maryland–College Park. From 1993-2002, Dr. Kirtman was a research scientist with the Center for Ocean-Land-Atmosphere Studies, and in 2002, joined the faculty of George Mason University as a tenured Associate Professor. In 2007, Dr. Kirtman moved to the University of Miami’s Rosenstiel School for Marine and Atmospheric Science as a full professor of meteorology and physical oceanography and Program Director for Climate & Environmental Hazards at the Center for Computational Science (now IDSC). He currently serves as the Deputy Director of the Institute for Data Science and Computing, and as Director for IDSC Earth Systems Science program. Dr. Kirtman is also a 2017-18 recipient of the UM Provost’s Award for Scholarly Activity and was awarded the Department of Atmospheric Sciences undergraduate teaching award in 2016, 2017, and 2018. In 2018, Dr. Kirtman was elected as a Fellow in the American Meteorological Society. In January 2023, Dr. Kirtman was named the inaugural William R. Middelthon III Endowed Chair in Earth Sciences.

In 2011, he was appointed Associate Dean of Research for the Rosenstiel School. In 2008, Professor Kirtman received the Distinguished Alumnus Award from the Department of Atmospheric and Oceanic Science at the University of Maryland. Dr. Kirtman was also awarded the Stony Brook University School of Marine and Atmospheric Science (SOMAS) Robert D. Cess Distinguished Lecture in Recognition of Outstanding Contributions to Atmospheric Sciences in 2018.

Internationally, Dr. Kirtman has enjoyed a leadership role in the World Climate Research Program (WCRP) seasonal-to-interannual prediction activities. In particular, he has chaired the international CLIVAR Working Group on Seasonal to Interannual Prediction (WGSIP), and the WCRP Task Force for Seasonal Prediction (TFSP). Dr. Kirtman was a coordinating lead author for the Intergovernmental Panel on Climate Change (IPCC) working group one—the Scientific Basis.

Professor Kirtman was an Executive Editor of Climate Dynamics, and is an Associate Editor of the American Geophysical Union  Journal of Geophysical Research and has received numerous research grants from the National Science Foundation (NSF), Department of Energy (DOE), NOAA, NASA, and the Office of Naval Research, and he leads the North American Multi-Model Ensemble Prediction (NMME) Experiment. Professor Kirtman is the author and/or co-author of over 130 peer reviewed papers focused on understanding and predicting climate variability on time scales from days to decades.

[Header Photo Source: NEWS@TheU]

Earth Systems Experts

Sam Goldberg, PhD Dr. Sam Goldberg is an assistant professor in the Department of Marine Geosciences at the Rosenstiel School of Marine, Atmospheric, and Earth Science with a joint appointment at the Institute for Data Science and Computing. He is a geologist whose research focuses on the evolution of Earth’s landscapes and surface environments, including their response to changing climates and their interactions with human systems. He received his Ph.D. in geology from the Massachusetts Institute of Technology, and was an NSF postdoctoral fellow at the University of Miami before joining the faculty. IDSC Magazine Profile:  Geologist Combines AI, Big Data, Computing Power, and Curiosity

Earth Systems Team

Recent Publications The Effect of the Deepwater Horizon Oil Spill on two Ecosystem Services in the Northern Gulf of Mexico Predicting the Impact of Future Oil-Spill Closures on Fishery-Dependent Communities—A Spatially Explicit Approach A Study of Mesoscale Air–Sea Interaction in the Southern Ocean with a Regional Coupled Model Understanding Intermodel Diversity When Simulating the Time of Emergence in CMIP5 Climate Models Current and Emerging Developments in Subseasonal to Decadal Prediction Aligning Climate Models with Stakeholder Needs: Advances in Communicating Future Rainfall . . . How MJO Teleconnections and ENSO Interferencce Impacts U.S. Precipitation Evolution of the North American Multi-Model Ensemble (NMME) Dynamic and Thermodynamic Modulators of European Atmospheric Rivers Invisible Oil Beyond the Deepwater Horizon Satellite Footprint Predictability of Midsummer Great Plains Low-Level Jet and Associated Precipitation Deep Sea Oil Spills-Investigating Droplet Size Distributions and Oil Fate in Experiments and Modeling Evaluating the Roles of Tropical North Atlantic SSTs and Local and Remote Dust Impacts on North American Dought Understanding the Signal-to-Noise Paradox with a Simple Markov Model Extreme Environmental Forcing on the Container Ship SS El Faro Assessment of Uncertainty in Multi-Model Means of Downscaled South Florida Precipitation for Projected (2019-2099) Climate The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment Cloud Radiative Feedbacks and El Niño—Southern Oscillation Comparative Environmental Sensitivity of Offshore Gulf of Mexico Waters Potentially Impacted by Ultra-Deep Oil Well Blowouts Comparison of the Spatial Extent, Impacts to Shorelines, and Ecosystem, and Four-Dimensional Characteristics of Simulated Oil Spills