Pre-Tactical Contrail Avoidance

• Pre-tactical contrail avoidance is a dispatch-time flight-planning strategy that uses weather forecasts to identify and reroute around persistent contrail formation zones.
• It employs optimization frameworks that balance fuel efficiency, forecast stability, and contrail radiative forcing reduction, achieving significant climate impact benefits.
• Despite promising results from modeling and operational trials, challenges such as forecast uncertainty, execution fidelity, and network coordination remain.

Searching arXiv for papers on pre-tactical contrail avoidance and related validation/forecast stability. arxiv_search(query="pre-tactical contrail avoidance contrail forecast stability airline-led contrail avoidance", max_results=10) Pre-tactical contrail avoidance is a dispatch-time flight-planning approach that uses weather-based forecasts, available before departure, to identify atmospheric regions where persistent contrails are likely and to adjust the planned trajectory so as to reduce exposure to those regions. In the recent literature, it is explicitly distinguished from strategic avoidance based on climatology and tactical avoidance based on in-flight intervention, and it is usually framed around lead times in the 6–24+ hour range, with some analyses extending to 48 hours (Dean et al., 17 Jul 2025). The topic sits at the intersection of upper-troposphere and lower-stratosphere meteorology, contrail microphysics, trajectory optimization, and air traffic operations. Current research does not present a single consensus operational recipe: some studies report large climate-impact reductions with small fuel penalties when optimization is designed to tolerate spatial forecast error, whereas others emphasize forecast uncertainty, airspace-capacity effects, and execution constraints as central limits on feasibility (Dean et al., 17 Jul 2025).

1. Definition, scope, and operational framing

Pre-tactical contrail avoidance in the recent operational literature means generating contrail-aware flight plans at dispatch or in day-of-operations planning, using forecast products rather than climatological averages or controller-led real-time intervention. In this framing, airlines or dispatchers select or review candidate trajectories before departure, typically hours ahead of execution, while air navigation service providers, area control centers, network managers, and regulators define the operational envelope within which those plans can be accepted, coordinated, and monitored (Sun et al., 9 Apr 2025).

This planning layer is narrower than strategic airspace design and broader than tactical climb or descent requests. A dispatcher-led implementation integrated into standard flight planning tools has been demonstrated on eastbound transatlantic operations, where contrail risk was embedded in the same decision environment used for ordinary dispatch and contrasted with non-avoidance plans (Sankar et al., 6 Mar 2026). By contrast, a Europe-scale feasibility analysis treated pre-operational routing as a network-management problem in which route choice, fuel allowances, altitude windows, and post hoc capacity consequences must be considered jointly (Sun et al., 9 Apr 2025).

A recurrent distinction in the literature is between physical feasibility and operational scalability. Physical feasibility concerns whether the planned trajectory can avoid forecast persistent-contrail regions with acceptable fuel and time costs. Operational scalability concerns whether the same approach survives dispatch workload, organized track preferences, turbulence concerns, capacity balancing, and regulatory accountability. This distinction helps explain why large model-based reductions in contrail climate forcing and more cautious ATM-oriented feasibility assessments can coexist without contradiction (Dean et al., 17 Jul 2025).

2. Physical basis and impact metrics

The physical target of pre-tactical avoidance is the overlap between air that is ice-supersaturated and air that is cold enough for exhaust mixing to satisfy the Schmidt–Appleman criterion. Across the studies considered here, persistence is represented by ice-supersaturated regions, operationalized as $RHi > 100\%$, while formation is governed by SAC, although several papers explicitly note that they do not publish a closed-form SAC expression in their implementation (Dean et al., 17 Jul 2025). In practice, this means that pre-tactical methods do not avoid all cold air or all humid air; they target forecast volumes where formation and persistence are jointly plausible.

The literature uses several impact metrics, and the choice of metric materially affects both optimization and interpretation. One line of work optimizes on contrail energy forcing, defined as the lifetime-integrated contrail radiative forcing per flight segment or volume. In that setting, “high-EF regions” are defined where contrail EF per unit flight distance exceeds $10^7\ \mathrm{J\ m^{-1}}$, a threshold described as capturing essentially all contrails that contribute an appreciable amount of warming (Dean et al., 17 Jul 2025). Another line of work uses “persistent contrail distance,” namely the segment length or time inside four-dimensional cells that satisfy SAC and ISSR conditions, as an operational proxy for climate impact rather than a full radiative metric (Sun et al., 9 Apr 2025). A third line of work evaluates success against observed contrail distance inferred from satellite imagery and expresses the outcome as an observed contrail rate per unit flight distance: $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$ This observational endpoint is central to randomized operational validation because it avoids relying solely on the same forecast model used for planning (Sankar et al., 6 Mar 2026).

These metric choices imply different optimization logics. EF-based planning targets warming intensity directly. Persistent-contrail-distance proxies prioritize avoidance of forecast persistent segments without modeling optical depth or full radiative evolution. Observation-based endpoints measure realized contrail formation rather than forecasted susceptibility. A plausible implication is that apparent disagreements across studies often reflect differences in target variable, not merely differences in forecast quality or optimizer design.

3. Forecast stability, forecast error, and the meaning of skill

Forecast stability has emerged as a core concept because pre-tactical decisions are made before departure and may rely on forecasts issued 8–24 hours in advance, with some use cases extending to 48 hours. One recent analysis defines stability operationally as consistency of contrail-forming regions across ECMWF IFS HRES forecast cycles at different lead times and quantifies it by comparing HRES contrail forecasts with contrail hindcasts based on ECMWF ERA5 reanalysis using both pointwise skill metrics and spatial proximity metrics (Dean et al., 17 Jul 2025).

The pointwise metric used in that analysis is the Equitable Threat Score,

$$ETS = \frac{a - r}{a + b + c - r}, \qquad r = \frac{(a + b)(a + c)}{a + b + c + d},$$

evaluated at flight waypoints for ISSRs and high-EF regions. Reported ISSR ETS values are around 0.4 at short lead times and decrease with increasing lead time by about 0.1 per day; high-EF ETS is only slightly higher, decreasing from around 0.45 at short lead times to around 0.25 at 48 hour lead times (Dean et al., 17 Jul 2025). On their face, such values indicate low pointwise consistency. However, the same study reports that spatial proximity remains high: for ERA5 ISSRs versus HRES forecasts, the fraction of waypoints in ERA5 ISSRs within 1 hour flight time of an HRES ISSR exceeds 95% at short lead times and is slightly above 90% at 48 hours; for high-EF regions it is slightly above 90% at short lead times and around 85% at 48 hours. ERA5 versus IAGOS proximity distributions are described as similarly tight (Dean et al., 17 Jul 2025).

This distinction between pointwise mismatch and small spatial displacement is central. The literature shows that low waypoint-by-waypoint agreement does not necessarily mean that forecast-based routing is operationally useless. Instead, forecast error may often take the form of modest location shifts of contrail-forming regions. That interpretation is reinforced by observational benchmarking work using automated detection and matching on GOES-16 ABI imagery, which finds that weather-based models have poor precision and recall against observed persistent contrails and that upper-tropospheric humidity error is the principal source of mismatch (Geraedts et al., 2023). In that study, a baseline HRES configuration at $RHi>95\%$ yields precision $0.15$ and recall $0.33$, and the resulting cost/benefit penalty factor is

$$CBPF = \frac{1}{\mathrm{precision}\times \mathrm{recall}},$$

with values reported near 20 for that case and roughly 16 after threshold tuning across models (Geraedts et al., 2023).

A common misconception is that low forecast skill scores automatically preclude useful pre-tactical planning. The forecast-stability results suggest a narrower conclusion: low pointwise skill is real, but if spatial displacement errors are small, an optimizer that avoids broad envelopes rather than chasing fine-grained maxima can still perform well (Dean et al., 17 Jul 2025). Conversely, the ADM benchmarking results show that imperfect humidity prediction imposes a substantial cost-effectiveness penalty relative to idealized perfect-prediction analyses, so forecast imperfection remains a first-order operational concern (Geraedts et al., 2023).

4. Optimization architectures and planning workflows

The pre-tactical literature spans several optimization paradigms. One trajectory-optimization framework takes an existing horizontal trajectory as input and optimizes only the vertical profile, using a two-dimensional grid along the route with approximately 1-minute spacing horizontally and standard flight levels in 1000 ft increments vertically. Search is performed with a breadth-first Dykstra-like method to minimize a composite objective,

$$\mathrm{Total\,Cost} = \mathrm{Fuel\,burn}\,[\mathrm{kg}] + \mathrm{Cost\,Index}\,[\mathrm{kg/min}] \cdot \mathrm{Flight\,time}\,[\mathrm{min}] + \mathrm{Contrail\,Cost\,Index}\,[\mathrm{kg/J}] \cdot \mathrm{Contrail\,EF}\,[\mathrm{J}],$$

with Cost Index fixed at $70\ \mathrm{kg/min}$ and a default contrail-optimal Contrail Cost Index of $10^7\ \mathrm{J\ m^{-1}}$0 (Dean et al., 17 Jul 2025). This framework uses gridded CoCiP outputs for EF, Poll–Schumann aircraft-performance and envelope constraints, and a minimum segment length between altitude changes.

The minimum segment length is not a mere operational convenience. It is introduced specifically to exploit the empirical observation that forecast and reanalysis contrail regions are close but not coincident. A nominal 90-minute level-flight minimum creates broader vertical avoidance envelopes, reduces sensitivity to noisy small-scale EF variations, and is described as mitigating the double-penalty problem (Dean et al., 17 Jul 2025). This is an important design principle: robustness is built not only from better forecasts, but also from optimization constraints that regularize the response to forecast error.

A separate ATM-oriented line of work uses the TOP tool with OpenAP performance and a $10^7\ \mathrm{J\ m^{-1}}$1 four-dimensional cost grid of persistent contrail risk created from ERA5 or ARPEGE fields and smoothed with a 3D Gaussian filter with $10^7\ \mathrm{J\ m^{-1}}$2 grid points (Sun et al., 9 Apr 2025). In that formulation, optimization is performed over feasible airspace paths under tight altitude constraints of $10^7\ \mathrm{J\ m^{-1}}$3 ft around the initial or maximum cruise altitude, and five trajectories are computed per flight: fuel-optimal, contrail-optimal using ERA5 with $10^7\ \mathrm{J\ m^{-1}}$4 and $10^7\ \mathrm{J\ m^{-1}}$5 extra fuel, and contrail-optimal using ARPEGE with the same two fuel caps. The general multi-objective form is written as

$10^7\ \mathrm{J\ m^{-1}}$6

although the experiments primarily impose fuel upper bounds rather than tuning $10^7\ \mathrm{J\ m^{-1}}$7 directly (Sun et al., 9 Apr 2025).

In airline operations, a third workflow has been demonstrated at scale by integrating an ML contrail forecast into Flightkeys for normal dispatch use. In that trial, forecast inputs came from ECMWF, the native $10^7\ \mathrm{J\ m^{-1}}$8 contrail field was coarse-grained to $10^7\ \mathrm{J\ m^{-1}}$9, and the forecast was updated hourly with a 24-hour horizon. To focus on net-warming contrails, formation probability was multiplied by a climatological EF map binned by local time, latitude, and season. Flights whose planned-path impact was below $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$0 tonnes $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$1 were not considered for avoidance; above that threshold, treatment-group dispatchers saw a contrail-optimized plan alongside non-avoidance plans, with a contrail term weighted by a marginal cost index of \$50/$$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$2 in a Dijkstra/A* path-finding framework (Sankar et al., 6 Mar 2026).

5. Reported performance in modeling studies and operational trials

Quantitative results differ by study design, constraints, and evaluation metric. In the HRES-versus-ERA5 forecast-stability analysis, forecast-based vertical-profile optimization with a 90-minute minimum segment length yields fleet-aggregated EF reductions above 80% at 8–24 hour lead times and near 70% at 48 hours when evaluated with ERA5. Because cooling segments are not penalized in optimization but are counted in evaluation, some short-lead-time reductions exceed 100%; a warming-only recalculation gives 80–90% avoided at short lead times. The same study reports that gridded CoCiP and trajectory CoCiP agree to within a few percent across all lead times, and that main-experiment fuel penalties are under 0.4% (Dean et al., 17 Jul 2025). Appendix results in that work show that, with vertical-only deviations and the default contrail cost index, an approximately 86% EF reduction is achieved with approximately 0.33% additional fuel burn, while lowering the contrail cost index by 1000 times yields greater than 33% EF reduction at less than 0.01% fuel penalty (Dean et al., 17 Jul 2025).

Sensitivity analyses in that same framework indicate that the robustness constraint matters. At 42–48 hour lead times, reducing minimum segment length from 90 to 10 minutes lowers EF reduction from above approximately 70% to below approximately 60%; at 8–24 hours, shorter segments reduce EF reduction by about 10 percentage points (Dean et al., 17 Jul 2025). At one hour flight time from a forecast high-EF region, the share of true ERA5 high-EF waypoints included in avoidance maneuvers is approximately 70% with a 90-minute minimum segment length versus approximately 30% with a 10-minute minimum segment length (Dean et al., 17 Jul 2025).

The Europe-scale feasibility study produces a more cautious picture under different assumptions. On 20 February 2022, using approximately 15,000 flights from OpenSky over Europe, with approximately 4,000 flights identified with persistent contrails, the authors find that allowing $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$3 extra fuel under tight altitude bounds often fails to reduce contrail distance compared to fuel-optimal trajectories. Allowing $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$4 extra fuel yields visible reductions for some flights, but boxplots show non-substantial reductions overall. Forecast-based optimization using ARPEGE degrades relative to ERA5-based optimization when evaluated on ERA5, and ACC-level analyses show that contrail-optimal trajectories can shift workload substantially, with peaks up to approximately 15 additional simultaneous aircraft in some ACCs in 10-minute bins (Sun et al., 9 Apr 2025). These results do not contradict the large EF-reduction study; they reflect a different objective proxy, a different optimizer, tighter vertical bounds, network-level capacity considerations, and explicit caution about pre-operational deployment.

Direct operational validation has now moved beyond simulation. A randomized controlled trial on eastbound transatlantic flights during a 17-week period from January 15 to May 13, 2025 reported that, relative to control, the intent-to-treat group had an 11.6% reduction in observed contrail formation rate for $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$5 flights, with $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$6. The per-protocol group of flights that released and flew contrail-optimized plans had a 62.0% lower contrail formation rate for $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$7, with $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$8, and no statistically significant fuel-usage difference was observed for the per-protocol groups (Sankar et al., 6 Mar 2026). Counterfactual analysis against minimum-cost non-avoidance plans gave a 25.0% reduction for released flights and a 49.2% reduction for released-and-flown flights, both with $$\lambda_C^{(k)} = \frac{\sum_{i \in k} C_i}{\sum_{i \in k} L_i}.$$9 (Sankar et al., 6 Mar 2026). Those results establish that scalable dispatcher-led pre-tactical avoidance can reduce observed contrail formation in routine operations, while also showing that realized fleet-wide benefit depends strongly on dispatcher engagement and plan adherence.

Study	Operational setting	Reported outcome
(Dean et al., 17 Jul 2025)	HRES forecast optimization, ERA5 evaluation, vertical-only	$$ETS = \frac{a - r}{a + b + c - r}, \qquad r = \frac{(a + b)(a + c)}{a + b + c + d},$$0 EF reduction at 8–24 h; near $$ETS = \frac{a - r}{a + b + c - r}, \qquad r = \frac{(a + b)(a + c)}{a + b + c + d},$$1 at 48 h; fuel penalties under $$ETS = \frac{a - r}{a + b + c - r}, \qquad r = \frac{(a + b)(a + c)}{a + b + c + d},$$2
(Sun et al., 9 Apr 2025)	Europe-wide pre-operational routing with TOP/OpenAP	$$ETS = \frac{a - r}{a + b + c - r}, \qquad r = \frac{(a + b)(a + c)}{a + b + c + d},$$3 fuel often insufficient; $$ETS = \frac{a - r}{a + b + c - r}, \qquad r = \frac{(a + b)(a + c)}{a + b + c + d},$$4 mixed benefits; peaks up to $$ETS = \frac{a - r}{a + b + c - r}, \qquad r = \frac{(a + b)(a + c)}{a + b + c + d},$$5 extra aircraft in some ACCs
(Sankar et al., 6 Mar 2026)	Airline-led RCT in standard dispatch workflow	11.6% intent-to-treat reduction; 62.0% per-protocol reduction; no statistically significant per-protocol fuel difference

6. Limitations, controversies, and research directions

The principal limitation repeatedly identified across the literature is meteorological uncertainty, especially in upper-tropospheric humidity. Reanalysis is not reality, and experiments that compare forecast optimization to ERA5 evaluation isolate forecast lead-time error growth rather than absolute truth error (Dean et al., 17 Jul 2025). Observational benchmarking shows that both simple SAC-plus-RHi models and more elaborate CoCiP-based visibility proxies are dominated by humidity error and vertical-resolution limits rather than by a lack of microphysical sophistication alone (Geraedts et al., 2023). This suggests that improvements in humidity analysis, vertical resolution, and observational fusion remain the main route to better pre-tactical targeting.

A second limitation is operational execution. In the airline-led trial, take rates were 15.4% for released contrail-optimized plans relative to the eligible treatment population and 7.8% for flights that released and flew the avoidance plan as planned. Dispatchers reported that contrail-optimized plans sometimes included mid-flight descents or ascents that were safe but dispreferred, that preferred tools for adhering to the North Atlantic Organized Track System were unavailable for contrail flights during the trial, and that turbulence concerns, operational busyness, and limited vertical-profile displays reduced engagement (Sankar et al., 6 Mar 2026). This indicates that operational bottlenecks may reside less in the existence of avoidable contrail risk than in user interfaces, workflow alignment, and coordination rules.

A third area of controversy concerns network externalities and governance. The ATM-oriented feasibility study argues that contrail-optimal routing should be approached with caution because weather-forecast uncertainty, impacts on airspace capacity, and questions of responsibility complicate implementation (Sun et al., 9 Apr 2025). That caution is not simply institutional conservatism: the study shows that re-routing can redistribute traffic burdens across neighboring ACCs and may plausibly require regulations and delays when performed widely without central coordination (Sun et al., 9 Apr 2025). A plausible implication is that airline-level optimization and network-level feasibility cannot be evaluated independently in dense airspace.

Related modeling developments point toward more physically complete future planning systems. A computational study of early contrail formation argues that equilibrium SAC-type models can overpredict condensed mass and onset location because non-equilibrium nucleation and freezing kinetics, nozzle geometry, and exhaust composition materially affect near-field initiation pathways (Tegethoff et al., 29 Apr 2025). A separate Eulerian framework for long-term contrail evolution introduces spatiotemporal wind variability, nonlinear diffusion with diffusion-blocking, bulk settling, and ice-crystal habit dynamics, and proposes a separable computational structure for large-scale plume simulations over hours (Jafarimoghaddam et al., 31 Aug 2025). These studies are not operational pre-tactical planners, but they indicate where future forecast products and cost functions may become more discriminating: pathway-dependent initiation, habit-dependent persistence, and more explicit treatment of long-lived radiative impact.

Within the operational literature itself, several research directions recur. Ensemble or probabilistic forecasts and explicit uncertainty modeling are proposed as a way to formalize buffering instead of relying on hand-tuned robustness constraints such as minimum segment length (Dean et al., 17 Jul 2025). Improved observability through satellite products and automated attribution systems is needed both for validation and for bias correction (Geraedts et al., 2023). Integrated routing that includes lateral options and capacity constraints is repeatedly identified as a missing link between promising single-flight avoidance results and system-wide deployment (Sun et al., 9 Apr 2025). The accumulated evidence therefore supports neither an unqualified endorsement nor a rejection of pre-tactical contrail avoidance. Rather, it supports a conditional conclusion: with planning-relevant lead times, appropriately designed optimizers, and operational integration, substantial reductions in contrail impact are achievable, but their realized value depends on forecast uncertainty, execution fidelity, and network coordination (Dean et al., 17 Jul 2025).