Predictive Real-time Emissions Technologies Reducing Aircraft Induced Lines in the Sky (PRE-TRAILS)
Aviation is an important part of our domestic and international transportation networks. Fuel consuming aircraft emit a range of emissions. From a climate-forcing standpoint, the most significant are carbon dioxide and water vapor. The Schmidt-Appleman criterion describes specific temperature, pressure and humidity conditions where the mixing of aircraft exhaust water with colder ambient humid air can result in the formation of condensation trails (contrails).
Fortunately, most contrails dissipate in under 10 minutes and are of no concern. However, when nucleation sites and specific atmospheric conditions exist (such as Ice SuperSaturated 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 (hereinafter referred to as an “Aviation Contrail Predictive System”) 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 a) avoidance strategies – allowing re-direction of airplanes by ground control to more favorable (non-AIC) flight trajectories – and/or b) on-board mitigation technologies.
Program Director(s):
Dr. Peter de Bock
Projects Selected Within This Exploratory Topic
GE Research – Niskayuna, NY
Engine-informed Prediction of Aviation Induced Cirrus Trails-EPIC-Trails
Principal Investigator: Dr. Saikat Ray Majumder
GE Research is developing a real-time, in-flight prediction system for aircraft-induced cirrus formed from contrails for commercial aircraft operators, who typically have little to no information on which flights cause long-lived cirrus clouds. In partnership with Southwest Airlines, GE’s system would combine detailed engine operational data, a hybrid physics and machine learning model, on-airplane data, and real-time satellite observations to predict aviation-induced cirrus that last more than 5 hours.
RTX Technologies Research Center – East Hartford, CT
CONFIRMMS: CONtrail Forecasting through In-situ Reliable Multisourced Modeling and Sensing
Principal Investigator: Dr. Miad Yazdani
RTX Technologies Research Center will develop a platform for a physics-informed forecast of aircraft induced cirrus potential 100 kilometers ahead of the aircraft (up to 10 minutes ahead of time). The platform would include a novel on-board lidar sensor for water vapor that would be installed on a small fraction of a fleet’s aircraft to furnish data and predictions for the entire fleet.
The Boeing Company – Everett, WA
Contrail INformation for Collaborative Operations (CINCO)
Principal Investigator: Ann Guthrie
The Boeing Company will develop a comprehensive approach for mitigating aircraft induced cirrus that would leverage satellite observations, deep learning, new developments in onboard humidity sensors, and a numerical weather prediction model. Useful for flight planning, Boeing’s approach could improve observational datasets, forward scientific understanding of humidity in the upper troposphere, and advance weather forecasting capabilities for the general public.
Universities Space Research Association – Mountain View, CA
Physics & Machine Learning Based Aviation Contrails Prediction and Observation System
Principal Investigator: Dr. David Bell
Universities Space Research Association is developing a real-time, cloud-based aviation contrail prediction and observation system that would improve airspace operations through new atmospheric data services and ensemble modeling approaches. The system would advance an existing cutting-edge contrail computer model with a novel machine learning approach to produce forecasts of persistent contrail-forming regions.
Northrop Grumman Systems Corporation – Redondo Beach, CA
Contrail Avoidance System
Principal Investigator: Dr. William Deal
Northrop Grumman is developing a contrail prediction and avoidance system to scout optimal altitudes for flight crew that would feature a predictive algorithm and new airborne instrumentation. Northrop Grumman’s radiometric temperature and humidity sensor would measure the environmental conditions above, below, and in front of an aircraft to enable flight crew to proactively respond to regions conducive to long-lived cirrus formation minutes before entering the area.