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This image shows Hurricane Milton’s storm path between October 5-10, 2024 using Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Level 1B Brightness Temperature and International Best Track Archive for Climate Stewardship database from NOAA’s National Centers for Environmental Information (NCEI) datasets.
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This image shows Hurricane Milton’s storm path between October 5-10, 2024, using Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Level 1B Brightness Temperature and the International Best Track Archive for Climate Stewardship database from NOAA’s National Centers for Environmental Information (NCEI)​ datasets. Image Credit: NASA GES DISC

In early October 2024, a tropical low-pressure system formed in the Gulf of Mexico, just west of the Yucatan Peninsula, subsequently intensifying at an unprecedented rate into a Category 5 hurricane. This fierce system, Milton, was immediately recognized as a serious threat to the state of Florida. Evacuations and landfall just south of the entrance to Tampa Bay ultimately reduced Milton’s impact, but the storm still brought record rainfall totals and destructive winds and triggered dozens of tornadoes across the state.

This webinar will utilize several datasets available at NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC), including data from the cutting-edge Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Mission along with Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), Global Precipitation Measurement (GPM) mission and Land Data Assimilation System (LDAS) datasets to investigate the factors that made Milton a remarkable, record-setting hurricane. 

We also explore the storm's aftermath and its significant impact on the Sunshine State. Finally, a Python Jupyter Notebook will demonstrate data access and map creation using the earthaccess Python library and the Open-source Project for a Network Data Access Protocol (OPeNDAP) in the Cloud subsetting service.
 

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