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| Main Authors: | , , , |
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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17830262 |
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Table of Contents:
- <p>Arctic PASSION (“Pan‑Arctic observing System of Systems: Implementing Observations for<br>societal Needs”) is an EU‑funded Horizon 2020 initiative and its mission is to co-create and<br>implement a coherent, integrated pan‑Arctic Observing System of Systems (pan‑AOSS).<br>Work package 3 of the project is supporting an intelligent AOSS through model-based<br>impact assessment providing evidence about the greatest forecasting and safety benefits,<br>cost-efficient investment strategies, and societal and economic impacts of improved<br>observations.<br>The atmosphere over high latitudes is primarily observed through satellite-based remote<br>sensing. In fact, these satellite observations are often the only source of information<br>available for monitoring atmospheric and environmental changes in remote and sparsely<br>populated regions. Beyond simply observing these areas, accurately predicting future<br>atmospheric changes is also essential—and largely unfeasible without satellite data.<br>Weather prediction in these regions is therefore initialized using satellite observations,<br>often combined with prior forecasts, to provide vital information for people living in the<br>Arctic. However, such predictions rely on numerous assumptions and typically utilize only a<br>portion of the available satellite data. This is due to necessary trade-offs between the<br>timeliness of forecasts and the accuracy of the delivered information.<br>To effectively use satellite observations for weather prediction, an appropriate<br>representation of surface characteristics is also required. Like the observations themselves,<br>this surface modeling carries uncertainties and is based on simplifying assumptions.<br>To maximize the impact of satellite data—both for improving current weather predictions<br>and for informing the design of future satellite missions—we developed an experimental<br>framework that simulates components of the weather prediction system. Within this<br>framework, we focus specifically on the role of enhanced surface characterization in<br>satellite measurements and its effect on Arctic weather forecasting.<br>Assuming the validity of our framework, we find that reducing the uncertainty in a<br>particular surface component by 20% can lead to improvements of 3–5% in temperature<br>predictions and 2–3% in humidity forecasts. These findings may help guide investment in<br>weather prediction research and support the design of future satellite missions aimed at<br>improving forecast quality for Arctic populations.</p>