Contrail forecasting

  • The system uses two models: a machine learning model to predict contrail formation probability and a physics-based model (CoCiP) to predict contrail energy forcing.

  • The machine learning model uses various weather and flight-related features to predict Contrail Likely Zones (CLZs).

  • The CoCiP model simulates contrail evolution and calculates energy forcing based on atmospheric conditions, aircraft type, and flight path.

  • The final energy forcing is an average of the CoCiP ensemble members and a climatological estimate, ensuring an impact estimate is always available.

  • The expected effective energy forcing is calculated by combining the contrail formation probability and the contrail energy forcing estimate.

The Contrails API uses two different models to forecast contrails: a machine learning(ML)-based model that predicts contrail-likely zones and a physics-based model, Contrail Cirrus Prediction (CoCiP), that predicts warming.

ML-based model

The ML-based Contrail Likely Zone (CLZ) forecasting model predicts the probability of contrail formation. The model is a deep neural network that takes weather features as inputs and predicts CLZs based on satellite contrail detections (Geraedts et al. 2023).

Its inputs consist primarily of HRES weather features: specific humidity, temperature, u component of wind, v component of wind, vertical velocity, relative vorticity, fraction of cloud cover, specific cloud ice water content, specific snow water content, and divergence. Relative humidity is calculated using specific humidity and temperature. Inputs to the model also include local solar time, day of year, latitude, and altitude of flight waypoints.

For some geographic areas (e.g., United States), the model is fine-tuned to use latitude, longitude, and altitude as input features, whereas the model's global fallback uses only latitude and altitude.

The model achieves state-of-the-art performance when evaluated against observational contrail data.

CoCiP model

The Contrail Cirrus Prediction model predicts the energy forcing of the contrail, which is a measure of the climate impact of the contrail.

Energy forcing is defined as:

\[ EF [J] = \int_{0}^{t} RF'(t) \times L(t) \times W(t)dt \]

meaning the instantaneous radiative forcing of the contrail integrated over its lifetime (Teoh et al. 2020). Energy forcing is normalized by flight distance, leading to its units of (J/m).

CoCiP is a physics-based model that simulates contrail formation, evolution, and impact using atmospheric conditions, aircraft type, flight path, and other features (Schumann 2012; Schumann et al. 2012). The model uses 10 ensemble members from ECMWF's high-resolution forecast ensembles (HRES ENS) as inputs for advecting the flight waypoints where contrails have formed forward in time (Hersbach et al. 2020). The CoCiP model also uses cloud microphysics theory to determine which contrails persist, accounting for initial downdraft, fall, and sublimation. Given the simulated evolution of the contrail, CoCiP calculates energy forcing based on the contrail properties and the surrounding weather conditions.

In addition to CoCiP's estimate of energy forcing, a climatological estimate of energy forcing is computed by averaging a year of CoCiP outputs, binned by time of day, season, and latitude.

The final energy forcing quantity is an average of the energy forcing from the CoCiP ensemble members with nonzero EF and the climatological average, which is always nonzero. Including climatology in the average ensures an estimate of contrail impact, even when CoCiP does not predict the formation of a contrail using any of the weather ensemble members.

Expected effective energy forcing

Combining these two forecasts using a product:

expected effective energy forcing $=$ (probability of forming a contrail, ML model) $\times$ (energy forcing of the contrail, CoCiP and climatology) $\times$ (RF -> ERF conversion factor, 0.42)

Contrail forcing index values

The computed expected effective energy forcing values can then be mapped to a scale of 0 to 4 to produce Contrail forcing index values. Contrail forcing index values are inspired by turbulence forecast values.

The Contrails API translates expected_effective_energy_forcing to the contrails severity index through clipping and a linear scale:

ds["contrails"] = ds["expected_effective_energy_forcing"].clip(min=2e7, max=2e8)
ds["contrails"] = ((ds["contrails"] - 2e7) / (2e8 - 2e7)) * 4

For more background information on this mapping, see Energy Forcing Interpretation.

Additionally, you can read the Contrails API release notes and subscribe to announcements to be notified about changes to this scale.

What's next

References

  • Geraedts, Scott, Erica Brand, Thomas R. Dean, Sebastian Eastham, Carl Elkin, Zebediah Engberg, Ulrike Hager, et al. 2023. "A Scalable System to Measure Contrail Formation on a per-Flight Basis." arXiv [physics.ao-Ph]. arXiv. http://arxiv.org/abs/2308.02707.
  • Hersbach, Hans, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, et al. 2020. "The ERA5 Global Reanalysis." Quarterly Journal of the Royal Meteorological Society 146 (730): 1999-2049.
  • Schumann, U. 2012. "A Contrail Cirrus Prediction Model." Geoscientific Model Development 5 (3): 543-80.
  • Schumann, U., B. Mayer, K. Graf, and H. Mannstein. 2012. "A Parametric Radiative Forcing Model for Contrail Cirrus." Journal of Applied Meteorology and Climatology 51 (7): 1391-1406.
  • Shapiro, Marc, Zeb Engberg, Roger Teoh, Marc Stettler, and Tom Dean. 2023. Pycontrails: Python Library for Modeling Aviation Climate Impacts. https://doi.org/10.5281/zenodo.825291
  • Teoh, Roger, Ulrich Schumann, Arnab Majumdar, and Marc E. J. Stettler. 2020. "Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption." Environmental Science & Technology 54 (5): 2941-50.