Scott Geraedts
Scott joined the Climate & Energy research team in 2018. He's a generalist willing to learn about whatever it takes to help Climate & Energy projects succeed. His research on the team as focused on predicting where aircraft contrails will form, evaluating wildfire satellite detection designs, and building tools to analyse plasma experiments.
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Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Vincent Rudolf Meijer
Rémi Chevallier
Allie Duncan
Kyle McConnaughay
Atmospheric Measurement Techniques, 18 (2025), pp. 3495-3532
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Condensation trail (contrail) cirrus clouds cause a substantial fraction of aviation's climate impact. One proposed method for the mitigation of this impact involves modifying flight paths to avoid particular regions of the atmosphere that are conducive to the formation of persistent contrails, which can transform into contrail cirrus. Determining the success of such avoidance maneuvers can be achieved by ascertaining which flight formed each nearby contrail observed in satellite imagery. The same process can be used to assess the skill of contrail forecast models. The problem of contrail-to-flight attribution is complicated by several factors, such as the time required for a contrail to become visible in satellite imagery, high air traffic densities, and errors in wind data. Recent work has introduced automated algorithms for solving the attribution problem, but it lacks an evaluation against ground-truth data. In this work, we present a method for producing synthetic contrail detections with predetermined contrail-to-flight attributions that can be used to evaluate – or “benchmark” – and improve such attribution algorithms. The resulting performance metrics can be employed to understand the implications of using these observational data in downstream tasks, such as forecast model evaluation and the analysis of contrail avoidance trials, although the metrics do not directly quantify real-world performance. We also introduce a novel, highly scalable contrail-to-flight attribution algorithm that leverages the characteristic compounding of error induced by simulating contrail advection using numerical weather models. The benchmark shows an improvement of approximately 25 % in precision versus previous contrail-to-flight attribution algorithms, without compromising recall.
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The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
Marc Shapiro
Zebediah Engberg
Marc E.J. Stettler
Roger Teoh
Ulrich Schumann
Susanne Rohs
Erica Brand
Environmental Research Communications, 6 (2024), pp. 095015
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Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain. In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight segments with high contrail energy forcing. We find that skill is greater than climatological predictions alone, even accounting for uncertainty in weather fields and model parameters. We estimate the uncertainty due to humidity by using the ensemble ERA5 weather reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity measurements taken at cruising altitude. We take CoCiP energy forcing estimates calculated using one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying segments with large positive proxy energy forcing. We further estimate the uncertainty due to model parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from uncertainty distributions consistent with the literature. When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost and fuel impact of contrail avoidance.
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A scalable system to measure contrail formation on a per-flight basis
Erica Brand
Sebastian Eastham
Carl Elkin
Thomas Dean
Zebediah Engberg
Ulrike Hager
Ian Langmore
Joe Ng
Dinesh Sanekommu
Marc Shapiro
Environmental Research Communications (2024)
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In this work we describe a scalable, automated system to determine from satellite data whether a given flight has made a persistent contrail.
The system works by comparing flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a `flight matching' algorithm and use it to label each flight segment as a `match' or `non-match'. We perform this analysis on 1.6 million flight segments and compare these labels to existing contrail prediction methods based on weather forecast data. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We find that current contrail prediction models fail to correctly predict whether we will match a contrail in many cases.
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Feasibility test of per-flight contrail avoidance in commercial aviation
Dinesh Sanekommu
Zebediah Engberg
Ulrike Hager
John P Dudley
Aarón Sonabend
Joe Ng
Carl Elkin
Sixing Chen
Noman Ali
Marc Shapiro
Frank Opel
Rachel Soh
Erica Brand
Ole Schütt
Marco Jany
Thomas Dean
Nature Communications Engineering (2024) (to appear)
Preview abstract
Contrails, formed by aircraft engines, are a major source of anthropogenic climate change. Contrail avoidance, a promising climate change mitigation strategy, has been shown to be feasible in simulations but not yet in practice. We conducted a feasibility randomized controlled trial of contrail avoidance in commercial aviation at the per-flight level. Predictions for regions prone to contrail formation came from a physics-based simulation model and a machine learning model. Participating pilots made flight-altitude adjustments based on contrail formation predictions for flights assigned to the treatment arm. We manually verified results using satellite-based imagery and found a statistically significant reduction in contrails in the treatment group (p = 0.0316), with 63.6% fewer contrails observed than in the control group. This study demonstrates that per-flight contrail avoidance is feasible in commercial aviation and suggests it could lead to a significant reduction in the climate impact of aviation.
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Contrail Detection on GOES-16 ABI with the OpenContrails Dataset
Joe Ng
Jian Cui
Vincent Rudolf Meijer
Erica Brand
IEEE Transactions on Geoscience and Remote Sensing (2023)
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Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are a substantial contributor to aviation-induced climate change. Contrail avoidance is potentially an inexpensive way to significantly reduce the climate impact of aviation. An automated contrail detection system is an essential tool to develop and evaluate contrail avoidance systems. In this article, we present a human-labeled dataset named OpenContrails to train and evaluate contrail detection models based on GOES-16 Advanced Baseline Imager (ABI) data. We propose and evaluate a contrail detection model that incorporates temporal context for improved detection accuracy. The human labeled dataset and the contrail detection outputs are publicly available on Google Cloud Storage at gs://goes_contrails_dataset .
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Estimates of broadband upwelling irradiance from GOES-16 ABI
Sixing Chen
Vincent Rudolf Meijer
Joe Ng
Geoff Davis
Carl Elkin
Remote Sensing of Environment, 285 (2023)
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Satellite-derived estimates of the Earth’s radiation budget are crucial for understanding and predicting the weather and climate. However, existing satellite products measuring broadband outgoing longwave radiation (OLR) and reflected shortwave radiation (RSR) have spatio-temporal resolutions that are too coarse to evaluate important radiative forcers like aircraft condensation trails. We present a neural network which estimates OLR and RSR based on narrowband radiances, using collocated Cloud and Earth’s Radiant Energy System (CERES) and GOES-16 Advanced Baseline Imager (ABI) data. The resulting estimates feature strong agreement with the CERES data products (R^2 = 0.977 for OLR and 0.974 for RSR on CERES Level 2 footprints), and we provide open access to the collocated satellite data and model outputs on all available GOES-16 ABI data for the 4 years from 2018–2021.
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Preview abstract
Hamiltonian Monte Carlo is discussed in the context of a fusion plasma reconstruction. Ill conditioned covariance and multi-modality are discussed in depth.
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A human-labeled Landsat contrails dataset
Vincent Rudolf Meijer
Erica Wickstrom Brand
Carl Elkin
ICML workshop on Climate Change 2021 (2021)
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Contrails (condensation trails) are the ice clouds that trail behind aircraft as they fly through cold and moist regions of the atmosphere. Avoiding these regions could potentially be an inexpensive way to reduce over half of aviation's impact on global warming. Development and evaluation of these avoidance strategies greatly benefits from the ability to detect contrails on satellite imagery. Since little to no public data is available to develop such contrail detectors, we construct and release a dataset of several thousand Landsat-8 scenes with pixel-level annotations of contrails. The dataset will continue to grow, but currently contains 3431 scenes (of which 47\% have at least one contrail) representing 800+ person-hours of labeling time.
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Multi-instrument Bayesian reconstruction of plasma shape evolution in C-2W experiment
Erik Trask
Hiroshi Gota
Ian Langmore
Jesus Romero
Rob von Behren
Tom Madams
Physics of Plasmas (2021)
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We determined the time-dependent geometry including high-frequency oscillations of the plasma density in TAE’s C2W experiment. This was done as a joint Bayesian reconstruction from a 14-chord FIR interferometer in the midplane, 32 Mirnov probes at the periphery, and 8 shine-through detectors at the targets of the neutral beams. For each point in time we recovered, with credibility intervals: the radial density profile of the plasma; bulk plasma displacement; amplitudes, frequencies and phases of the azimuthal modes n=1 to n=4. Also reconstructed were the radial profiles of the deformations associated with each of the azimuthal modes. Bayesian posterior sampling was done via Hamiltonian
Monte Carlo with custom preconditioning. This gave us a comprehensive uncertainty quantification of the reconstructed values, including correlations and some understanding of multimodal posteriors. This method was applied to thousands of experimental shots on C-2W, producing a rich data set for analysis of plasma performance.
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OVERVIEW OF C-2W: HIGH TEMPERATURE, STEADY-STATE BEAM-DRIVEN FIELD-REVERSED CONFIGURATION PLASMAS
Ian Langmore
Rob von Behren
TAE
Tom Madams
William D Heavlin
Nuclear Fusion (2021)
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TAE Technologies, Inc. (TAE) is pursuing an alternative approach to magnetically confined fusion, which relies on field-reversed configuration (FRC) plasmas composed of mostly energetic and well-confined particles by means of a state-of-the-art tunable energy neutral-beam (NB) injector system. TAE’s current experimental device, C-2W (also called “Norman”), is the world’s largest compact-toroid device and has made significant progress in FRC performance, producing record breaking, high temperature (electron temperature, Te >500 eV; total electron and ion temperature, Ttot >3 keV) advanced beam-driven FRC plasmas, dominated by injected fast particles and sustained in steady-state for up to 30 ms, which is limited by NB pulse duration. C-2W produces significantly better FRC performance than the preceding C-2U experiment, in part due to Google’s machine-learning framework for experimental optimization, which has contributed to the discovery of a new operational regime where novel settings for the formation sections yield consistently reproducible, hot, and stable plasmas. Active plasma control system has been developed and utilized in C-2W to produce consistent FRC performance as well as for reliable machine operations using magnets, electrodes, gas injection, and tunable NBs. The active control system has demonstrated a stabilization of FRC axial instability. Overall FRC performance is well correlated with NBs and edge-biasing system, where higher total plasma energy is obtained with increasing both NB injection power and applied-voltage on biasing electrodes. C-2W divertors have demonstrated a good electron heat confinement on open-field-lines using strong magnetic mirror fields as well as expanding the magnetic field in the divertors (expansion ratio >30); the electron energy lost per ion, ~6–8, is achieved, which is close to the ideal theoretical minimum.
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