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The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Iceland East (ICE) SuperDARN HF radar binned onto an equal-area 24 km grid. The ICE radar is located in Þykkvibær, Iceland (63.77°N (North), 20.54°W (West)) and is operated by Dartmouth College (Principal Investigator: Simon G. Shepherd, simon.g.shepherd@dartmouth.edu) with funding support from the National Science Foundation. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Iceland West (ICW) SuperDARN HF radar binned onto an equal-area 24 km grid. The ICW radar is located in Þykkvibær, Iceland (63.77°N, 20.54°W) and is operated by Dartmouth College (Principal Investigator: Simon G. Shepherd, simon.g.shepherd@dartmouth.edu) with funding support from the National Science Foundation. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Clyde River (CLY) SuperDARN HF radar binned onto an equal-area 24 km grid. The CLY radar is located in Clyde River, Nunavut (70.49°N, 68.50°W) and is operated by the University of Saskatchewan (Principal Investigator: Kathryn A. McWilliams, kathryn.mcwilliams@usask.ca) with funding support from the Canada Foundation for Innovation, the Province of Saskatchewan, and the Canadian Space Agency. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Rankin Inlet (RKN) SuperDARN HF radar binned onto an equal-area 24 km grid. The RKN radar is located in Rankin Inlet, Nunavut (62.83°N (North), 92.11°W (West)) and is operated by the University of Saskatchewan (Principal Investigator: Kathryn A. McWilliams, kathryn.mcwilliams@usask.ca) with funding support from the Canada Foundation for Innovation, the Province of Saskatchewan, and the Canadian Space Agency. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Longyearbyen (LYR) SuperDARN HF radar binned onto an equal-area 24 km grid. The LYR radar is located in Svalbard, Norway (78.15°N, 16.07°E) and is operated by the University Centre in Svalbard (Principal Investigator: Dag A. Lorentzen, dagl@unis.no) with funding support from the University Centre in Svalbard (UNIS) and the ConocoPhillips and Lundin Arctic Approach Research Project. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Goose Bay (GBR) SuperDARN HF radar binned onto an equal-area 24 km grid. The GBR radar is located in Labrador, Canada (53.32°N, 60.46°W) and is operated by Virginia Tech (Principal Investigator: J. Michael Ruohoniemi, mikeruo@vt.edu) with funding support from the National Science Foundation. 

The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Kapuskasing (KAP) SuperDARN HF radar binned onto an equal-area 24 km grid. The KAP radar is located in Ontario, Canada (49.39°N, 82.32°W) and is operated by Virginia Tech (Principal Investigator: J. Michael Ruohoniemi, mikeruo@vt.edu) with funding support from the National Science Foundation. 

Global climate change and associated environmental extremes present a pressing need to understand and predict social–environmental impacts while identifying opportunities for mitigation and adaptation. In support of informing a more resilient future, emerging data analytics technologies can leverage the growing availability of Earth observations from diverse data sources ranging from satellites to sensors to social media. Yet, there remains a need to transition from research for knowledge gain to sustained operational deployment. In this paper, we present a research-to-commercialization (R2C) model and conduct a case study using it to address the wicked wildfire problem through an industry–university partnership. We systematically evaluated 39 different user stories across eight user personas and identified information gaps in public perception and dynamic risk. We discuss utility and challenges in deploying such a model as well as the relevance of the findings from this use case. We find that research-to-commercialization is non-trivial and that academic–industry partnerships can facilitate this process provided there is a clear delineation of (i) intellectual property rights; (ii) technical deliverables that help overcome cultural differences in working styles and reward systems; and (iii) a method to both satisfy open science and protect proprietary information and strategy. The R2C model presented provides a basis for directing solutions-oriented science in support of value-added analytics that can inform a more resilient future.