climate prediction – Frost Institute for Data Science & Computing

Meet A Data Scientist-Ben Kirtman

If you have ever wondered what data scientists do, the University of Miami Institute for Data Science and Computing (IDSC) is giving you an opportunity to learn about this important, multifaceted profession. On November 18, IDSC is hosting the second in the series of “Meet a Data Scientist” talks with Ben Kirtman, PhD, a Professor of Atmospheric Sciences at the University of Miami Rosenstiel School of Marine and Atmospheric Science. Dr. Kirtman uses atmosphere-ocean general circulation models to study the predictability and variability of the Earth’s climate system.
Read more “Meet A Data Scientist-Ben Kirtman” →

Grants

IDSC Partners with NSF Cyberinfrastructure Network FABRIC

The University of Miami’s Institute for Data Science and Computing (IDSC) has joined FABRIC, the advanced U.S. cyberinfrastructure network funded by the National Science Foundation (NSF), as a scientific partner. A three-year research award to UM was included in a $3 million NSF grant that will expand FABRIC to four leading scientific institutions in Asia and Europe, and support international research that benefits from real-time sharing of large-scale datasets. Read more “IDSC Partners with NSF Cyberinfrastructure Network FABRIC” →

Publications

Evolution of the North American Multi-Model Ensemble (NMME)

Predicting the climate more than a few weeks in advance requires forecasters to bet on more than one computer model, similar to the method of diversifying an investment portfolio. Many earlier studies have shown that the combination of predictions generated by different computer models, known as a multi‐model ensemble, almost always achieves better forecast skill than using a single model alone. The North American Multi‐Model Ensemble (NMME), a sub-seasonal and seasonal prediction system combining individual North American state‐of‐the‐art climate prediction models, has become an integral part of sub-seasonal and seasonal research and applications. Read more “Evolution of the North American Multi-Model Ensemble (NMME)” →

Publications

Predictability of Midsummer Great Plains Low-Level Jet and Associated…

Abstract Warm-season precipitation in the U.S. “Corn Belt,” the Great Plains, and the Midwest greatly influences agricultural production and is subject to high interannual and intraseasonal variability. Unfortunately, current seasonal and subseasonal forecasts for summer precipitation have relatively low skill. Read more “Predictability of Midsummer Great Plains Low-Level Jet and Associated Precipitation” →

Grants

UM and NOAA Launch One-of-a-Kind Climate Collaboration

Residents of Nicaragua were still being pummeled by Hurricane Iota’s powerful winds and life-threatening storm surge on Tuesday when a group of University of Miami scientists and administrators more than a thousand miles away explained to a virtual audience how the work of a newly funded cooperative research institute and the infrastructure that supports it will help improve our understanding of tropical cyclones and other climate events. Read more “UM and NOAA Launch One-of-a-Kind Climate Collaboration” →

Publications

Understanding the Signal-to-Noise Paradox with a Simple Markov Model

There is a growing list of examples for the existence of the signal‐to‐noise paradox, where in the ensemble‐based climate prediction, the model ensemble mean forecast generally shows higher correlations with observations than with individual ensemble members. Read more “Understanding the Signal-to-Noise Paradox with a Simple Markov Model” →

n>climate prediction

Meet A Data Scientist-Ben Kirtman

IDSC Partners with NSF Cyberinfrastructure Network FABRIC

Evolution of the North American Multi-Model Ensemble (NMME)

Predictability of Midsummer Great Plains Low-Level Jet and Associated…

UM and NOAA Launch One-of-a-Kind Climate Collaboration

Understanding the Signal-to-Noise Paradox with a Simple Markov Model