Using station data and downscaled reanalysis to assess the occurrence of extreme weather

Submission information

Submission Number: 25

Submission ID: 42

Submission UUID: 5162671c-015f-46ab-baaa-8162faf0e070

Submission URI: /form/project

Created: Tue, 09/03/2019 - 13:58

Completed: Tue, 09/03/2019 - 14:00

Changed: Thu, 04/28/2022 - 13:22

Remote IP address: 130.215.55.243

Submitted by: Sean Birkel

Language: English

Is draft: No

Webform: Project

Project Title Using station data and downscaled reanalysis to assess the occurrence of extreme weather

Program Northeast

Project Image nam_prcp-tcld_9-26-2017_0 (1).png

Tags data-management (260)

Status Complete

Project Leader

Project Leader Sean Birkel

Email birkel@maine.edu

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Project Personnel

Mentor(s) Chris Wilson, Larry Whitsel

Student-facilitator(s) Daniel Paradis

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Project Information

Project Description Meteorological observations across North America and Europe suggest a significant increase in the frequency and intensity of extreme weather (heat waves, cold waves, precipitation events) coincident with satellite-measured major decline of Arctic sea ice over the past decade. This project will assess the occurrence and impact of extreme weather events across Northern New England using both station data and climate reanalysis models. Weather and climate are critically important across Northeast New England, owing to the heavy reliance of natural resources for its economy. In particular this project involves mining multi-terabyte databases and model outputs to visualize data in a variety of formats.

Project Information Subsection

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Student Research Computing Facilitator Profile An undergraduate student with a knowledge of programming and data handling.

Mentee Research Computing Profile {Empty}

Student Facilitator Programming Skill Level Practical applications

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Project Institution University of Maine

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Anchor Institution NE-University of Maine

Preferred Start Date 11/10/2017

Start as soon as possible. No

Project Urgency Already behind3Start date is flexible

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What will the student learn? Student will learn how to deal with the particular data format that climate data typically uses, how to parse and create graphics on a web page from such data and how to port web pages from the existing physical server to a virtual machine.

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HPC resources needed to complete this project? Virtual machine resources from the ACG at UMaine

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Final Report

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Is there an impact on society beyond science and technology? This work helped launch the climate reanalyzer (https://climatereanalyzer.org/) to help make weather and climate more accessible.

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Overall results The site has been operational for a number of years and attracts hundreds of hits per day.