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Home Astronomy

New Dataset Maps Earth’s Entire Atmosphere

by Muhammad Tuhin
January 10, 2025
New Dataset Maps Earth’s Entire Atmosphere

This infographic shows the multiple layers of our atmosphere, extending from the ground up into space, and how the new dataset compares in coverage to those currently available. Credit: D.Koshin, K. Sato, S. Watanabe and K. Miyazaki, 2025/ Progress in Earth and Planetary Science (PEPS)

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The University of Tokyo has recently made a significant breakthrough in atmospheric research, publishing a comprehensive dataset that covers the entire Earth’s atmosphere, providing insight into previously inaccessible regions. This new dataset, known as JAWARA, was developed by a team of researchers using a novel data-assimilation system called JAGUAR-DAS, and the findings were published in Progress in Earth and Planetary Science.

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The JAWARA dataset is a nearly two-decade-long record that spans from September 2004 to December 2023. It combines observational data with a sophisticated numerical modeling technique to deliver a global, vertical view of the atmosphere, reaching from the Earth’s surface all the way to the lower edges of space—roughly 110 kilometers above Earth’s surface. Such comprehensive data offers opportunities for conducting in-depth studies of atmospheric layers in ways that were not possible before, and the research promises to bring tangible benefits in a variety of scientific fields.

A particularly remarkable feature of this new dataset is the opportunity it provides to explore the atmospheric regions around 50 to 110 kilometers above Earth’s surface. This area, known in scientific circles as the “ignorosphere,” is notoriously difficult to study. It is too high to be effectively probed by weather balloons and too low for typical satellite observation, which has left it under-explored. This gap in research has had a significant impact on atmospheric science, as this zone is critically important. It is characterized by major global atmospheric tides and small-scale gravity waves that influence wind patterns and temperatures in this region, impacting overall atmospheric dynamics. Additionally, this zone plays a critical role in modulating the effects of space weather on Earth, including phenomena such as solar winds and geomagnetic storms.

Professor Kaoru Sato, a key researcher at the University of Tokyo, highlighted the significance of JAWARA’s ability to reveal previously opaque layers of the atmosphere. Specifically, the dataset allows scientists to quantitatively study the dynamics of atmospheric general circulation, as well as the complex structures of waves and vortices that are found within the mesosphere (50–90 kilometers above the surface) and the lower thermosphere (90–110 kilometers above Earth). “Understanding the processes in these layers has the potential to significantly enhance our ability to predict and respond to climate change, improve seasonal weather forecasts, and deepen our understanding of space weather phenomena,” said Professor Sato.

JAGUAR-DAS, the data-assimilation system behind the JAWARA dataset, integrates real-time observational data into a mathematical model that simulates atmospheric conditions. Through this process, it produces a more refined and accurate atmospheric model, one that can be used to study both large-scale global circulation patterns as well as smaller-scale atmospheric phenomena. This new system enhances the quality and precision of weather forecasts, particularly as atmospheric interactions between various layers become better understood.

One of the unique aspects of JAWARA is that it extends traditional atmospheric models well beyond their usual range. Atmospheric general circulation models, which describe how different layers of the atmosphere move and interact, have previously been restricted in their vertical limits. The uppermost layers, which include the mesosphere and thermosphere, were not sufficiently represented in these models due to a lack of observational data. However, with the JAWARA dataset, these models have been enhanced to cover the entirety of the Earth’s atmosphere up to the lower edge of space, opening new opportunities for research in areas previously unknown. Professor Sato noted that very few research institutions worldwide, including the University of Tokyo, have the capacity to build such comprehensive atmospheric models.

Recent findings indicate that extreme weather phenomena, especially those in the stratosphere, can sometimes originate in the upper layers of the mesosphere. As a result, a deeper understanding of the mesosphere and lower thermosphere is increasingly crucial to improving forecasts of long-term climate patterns and extreme weather events. The JAWARA dataset provides an invaluable tool for scientists to better understand these previously understudied regions, which could be pivotal in improving our response to climatic shifts and enhancing long-range weather predictions.

With this dataset now publicly available, the research team plans to use it for ongoing studies into the large-scale circulation dynamics of the atmosphere, and to further explore vertical and interhemispheric couplings. Interhemispheric couplings, or interactions between the Northern and Southern Hemispheres, have significant implications for climate variability, as these interactions can influence weather patterns on a global scale. Additionally, the dataset opens the door to collaboration between atmospheric scientists and space scientists. By studying interactions between the atmosphere and space, particularly in the mesosphere and thermosphere, scientists hope to better understand how space weather phenomena—such as solar flares and cosmic radiation—impact life on Earth, affecting both communication systems and the general climate.

The potential applications of this research are vast. With the world grappling with the effects of climate change and the increasing unpredictability of weather events, the need for accurate models that can forecast climate and weather trends has never been greater. The insights provided by JAWARA, in conjunction with improved understanding of the interconnectedness of atmospheric and space phenomena, have the potential to improve the quality and accuracy of climate models significantly.

This innovation could also spur greater interdisciplinary collaboration, connecting researchers from a wide range of fields, from atmospheric science and meteorology to space physics and environmental science. By fostering deeper understanding and stronger collaboration between disciplines, the dataset may lead to new approaches in understanding the fundamental workings of Earth’s atmospheric system and its relationship with space weather.

Moreover, with space weather continuing to have more direct effects on our technology-driven society, such as disrupting communication systems and satellite functions, understanding atmospheric dynamics at the edge of space becomes even more critical. Solar activity, including solar storms and the resulting space weather, has the potential to wreak havoc on satellite networks, power grids, and GPS systems. By building a more complete understanding of how these events interact with Earth’s atmosphere, scientists can better predict and mitigate these impacts.

The creation of the JAWARA dataset and the associated advances in modeling provide exciting new opportunities for atmospheric scientists and space researchers alike. With new tools for observing and understanding the atmospheric layers that bridge Earth and space, we stand on the cusp of a new era of climate and space weather research. The JAWARA dataset exemplifies how cutting-edge technology and scientific innovation can bring clarity to previously opaque scientific phenomena, offering not only the promise of better weather forecasting but also the potential for long-term advancements in how we approach and understand the Earth’s atmospheric system and its relation to space.

Reference: The JAGUAR=DAS Whole neutral Atmosphere Reanalysis: JAWARA, Progress in Earth and Planetary Science (2025). DOI: 10.1186/s40645-024-00674-3

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