Deliverable 3.4: Impact on forecast quality and value assessment of selected atmospheric observing system evolution scenarios (T3.2)

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