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ARPA-E to award $10M to explore developing aviation contrail predictive system; PRE-TRAILS

The US Department of Energy Advanced Research Projects Agency - Energy (ARPA-E) announced up to $10 million in funding to develop new technologies and tools to reduce the environmental impact of aviation. The funding, part of the ARPA-E broad Exploratory Topics FOA, is for the Predictive Real-time Emissions Technologies Reducing Aircraft Induced Lines in the Sky (PRE-TRAILS) topic. (DE-FOA-0002784)

Aircraft consume fuels and emit a range of emissions, including carbon dioxide and water vapor in the form of condensation trails. Those condensation trails—contrails—occur when aircraft exhaust water mixes with cold, ambient humid air. Most contrails dissipate in under ten minutes and are of no concern.

However, when nucleation sites and specific atmospheric conditions exist (such as ice super-saturated regions (ISSR)), engine exhaust can cause the formation of persistent contrails, which can in turn produce persistent cirrus clouds known as aircraft-induced cirrus (AIC). These upper atmospheric clouds can last for hours and may grow to span several hundreds of kilometers.

Recent studies have indicated that contrails likely contribute to global radiative forcing at a level that is roughly equivalent to that of the CO2 emissions from the entire aviation sector, which is estimated to be about 2% of total global CO2 emissions.

Unfortunately, at present, pilots, air traffic controllers, and aerospace system designers have little to no information on whether a specific flight may result in persistent cirrus clouds.

ARPA-E envisions the development of a system to predict aviation contrails (an “Aviation Contrail Predictive System”, ACPS) that would be capable of informing pilots and ground controllers in real-time whether an airplane is likely to produce persistent AIC. This new system could foster the development of:

  1. avoidance strategies—allowing re-direction of airplanes by ground control to more favorable (non-AIC) flight trajectories; and/or

  2. on-board mitigation technologies.


An envisioned use of a near real-time AIC predictive model. Flight data and other environmental data sources are assimilated into a best-guess AIC predictive model during flight planning. Further in-situ data from the current flight, in-situ data from previous or following flights, and observational data from satellite or ground-based sources would constrain and improve the model output, resulting in improved predictions and better in-flight decision support either via simple monitoring and reporting to the pilot/flight operator or via continuously optimized tactical flight routing. The program outcome is the AIC predictive model and data or sensors needed to make an accurate AIC prediction validated using observations.

The development of an ACPS will be particularly challenging—in part because AIC can form several hours after the passage an aircraft. These predictive models will need to consider both dynamic atmospheric conditions and engine emissions. This may require, for example, the assimilation of in-situ data from onboard sensor systems as well as off-aircraft observational data from ground- and/or satellite-based sources and previous flight reports.

Technical Areas of Interest. The aim of PRE-TRAILS is to support the development of a predictive capability that in “real-time” and with high confidence could inform a pilot or flight operator whether an aircraft is likely to produce persistent aircraft induced cirrus clouds (AIC), even hours before they are fully developed. Each project proposal for the funding must address the following three technology areas to develop an Aviation Contrail Predictive System:

  • Aircraft, Environmental Data, and Sensor Development: New sensors or environmental data sources may be needed to provide sufficient training and validation data for the envisioned predictive capabilities. Contrail forming conditions are identified by the Schmidt-Appleman criterion: where water vapor content reaches liquid saturation under specific temperature and saturation conditions in the presence of nucleation sites. Especially important are persistent contrails formed when airplanes travel through atmospheric ISSR, leading to AIC. As the persistent contrail formation regime is a combination of Schmidt-Appleman and ISSR criteria, sensors capable of identifying these parameters accurately in real-time are of particular interest, e.g. sensor systems capable of measuring upper atmospheric humidity at or below 10 ppm.

  • Predictive Modeling: Advanced machine learning computational methods developed in the past decade allow the exploration of larger sets of input data and explore complex multivariate correlations to solve more complex problems than ever before. ARPA-E is interested in project teams that explore whether such methods can be leveraged to develop a real-time predictive system for AIC development. To inform avoidance and mitigation strategies, it is important that any predictive model gives reasonably accurate results, minimizing false positive (type I) and false negative (type II) errors. For the purposes of this Exploratory Topic, this can be captured in the balanced F-score (F1-score) which is the harmonic mean of precision and recall. It is important that sufficient confidence in the model exists to inform avoidance and mitigation solutions, while minimizing unnecessary and burdensome rerouting.

  • Observer Data: A predictive model needs to be trained and validated. For an Aircraft Contrail Predictive System, this will likely require observers and additional sensors. It is anticipated that teams will need to obtain sufficient relevant flight and observer data from available sources or dedicated flight tests to provide true AIC observations and validation, rather than theoretical studies alone. Additionally, ARPA-E envisions a contrail reporting and observational data aggregation mechanism that mimics current tools for turbulence reporting and could further serve to continuously refine and improve AIC predictive modeling capabilities going forward.


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