WHAT WE DO

The Cloud Computing stream focuses on the role of clouds in weather and climate through cutting edge computational analysis of very large datasets of cloud properties collected by national research institutions like NOAA and NASA.

In 2020 students in the stream characterized the spatial and temporal distributions of cloud cover and polar storms identified by satellite and ground photography. They also used artificial intelligence and machine learning algorithms, both supervised and unsupervised techniques to segment the images, classify cloud and storm types, and cluster images based on similar features. The ultimate goal is to learn something physical about the system revealed through these cutting edge techniques to analyse large and complex datasets.

WHY IT MATTERS

Weather and climate impact nearly every aspect of society including transportation, food supply, renewable energy, tourism, infrastructure standards, natural resource availability, and national security. Clouds play an incredibly important role in weather and climate, redistributing heat and moisture throughout the atmosphere, impacting sensible temperature felt by people on the ground and the water cycle.

The fields of atmospheric and computer science came into their own around the same time and have remained tightly linked, with advances in one pushing forward advances in the other. The cutting edge of atmospheric computational science utilizes principles of artificial intelligence, big data analysis, and high performance computing to harness the information contained in the ever growing collection of model output and observations. Developing experience in these computing and analysis skills will prepare you for careers in the ever growing field of data science, regardless of the subject matter.

WHAT YOU LEARN

Students in this stream will learn basic principles of atmospheric science including atmospheric structure, cloud formation , how measurements of clouds are made from both the ground and space and how to identify cloud types by their appearance. They will also learn the role of different types of observations and models in understanding and predicting weather and climate. In addition to this subject matter knowledge students will gain technical knowledge and experience in working with large datasets broadly applicable beyond the realm of atmospheric science. This includes basic Python, Python packages used by data scientists to clean, organize, and analyze large datasets, visualizing geospatial datasets, and applying existing artificial intelligence tools to our stream's datasets.

  • Dr. Alexandra Jones

    Research Educator

  • Dr. Tim Canty

    Faculty Advisor

University of Maryland

Office of the Senior Vice President and Provost

The First-Year Innovation & Research Experience

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Dr. Patrick Killion

Director of Discovery-Based Learning

Email: pkillion@umd.edu

Tel: 301-405-0057