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]

IDSC Earth Systems Core Faculty Members
	Milan Curcic, PhD Curcic earned his PhD in Meteorology and Physical Oceanography from the University of Miami and in January was given a joint appointment as an Assistant Professor at the Rosenstiel School of Marine, Atmospheric, and Earth Science and as a core faculty member at the Frost Institute for Data Science and Computing (IDSC). Along the way, Curcic co-led a large National Science Foundation project to measure air-sea exchange in hurricane conditions, co-founded several private companies, wrote a book about Fortran programming, and pioneered attempts to combine numerical models that predict the behavior of the atmosphere, the ocean, and the waves formed in between. Read more:  The ‘Fluid Dynamics’ That Charted Milan Curcic’s Career
	Michael Scott Fischer, PhD Michael Fischer was too young to remember Hurricane Andrew tearing apart his family’s home south of Miami in 1992, but he remembers the destruction the Category 5 behemoth left in its wake. He remembers the chainsaws and hammers pounding away as the region recovered from the historic storm. And he remembers wondering how wind and rain could cause so much damage. That curiosity quickly morphed into Fischer’s passion, leading him on a lifelong quest to better understand hurricanes, why they form, and how they intensify so quickly. n his younger years, that meant eagerly waiting until 50 minutes past the hour to watch the tropical forecast and charting incoming storms on a tracking map taped to the door of the family laundry room. Now, Fischer is armed with a Ph.D. in atmospheric science and has joined the University of Miami as a joint appointment between the Frost Institute for Data Science and Computing (IDSC) and the Rosenstiel School of Marine, Atmospheric, and Earth Science. Read more:  Welcome new IDSC Core Faculty Member Professor Michael Scott Fischer
	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. Read more:  Geologist Combines AI, Big Data, Computing Power, and Curiosity
	Gabriel Reygondeau, PhD Dr. Reygondeau is a joint appointment between the Frost Institute for Data Science and Computing (IDSC) and the Rosenstiel School of Marine, Atmospheric, and Earth Science. The core of his research is a series of computational models he’s developed to attempt the unimaginable: to estimate the location of every marine species in the world’s oceans and predict where they are moving as climate change alters their environment. Read more:  Gabriel Reygondeau, Next-Gen AquaMaps, and the Launch of AquaX Laboratory
	Juan Carlos Villaseñor-Derbez, PhD Dr. Villaseñor-Derbez joins IDSC as a Core Faculty Member in the Earth Systems Science program area. He is also an Assistant Professor at the Marine Technology Life Sciences & Seawater Complex at the University’s Rosenstiel School of Marine, Atmospheric, and Earth Science. Moving from the University of California-Santa Barbara to UM, Villaseñor-Derbez is hoping to track far more vessels around the world and extract even more insights based on their movement patterns, a task that will require IDSC’s supercomputers and experts in artificial intelligence and machine learning. He wants to predict how much fish a vessel is harvesting based on their movement patterns and location. And he wants to track fishing vessels that aren’t required to send out pings by using indirect methods, including satellite observations of the bright lights they use at night or the reflections from their metallic surfaces. Read more:  Bridging Quantitative Data About Environmental Change and Human Behavior

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