# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 5 Future Roadmap and Implementation
### Phase 1: Pilot project and initial deployment  
The first phase of the implementation strategy would involve a small-scale pilot project focused on a high-priority region, such as the North Atlantic Corridor. This pilot project would involve the deployment of 9 CubeSats equipped with the necessary sensors and communication systems to monitor contrail formation in real time. The primary objectives of this phase would be to validate the technology, identify any technical challenges, and refine the system design before scaling up. During this phase, the CubeSats would collect data on atmospheric conditions, which would be processed and analyzed using machine learning algorithms to predict contrail formation. The data would also be used to validate and refine the predictive models, ensuring their accuracy and reliability. The results of the pilot project would be used to inform the design of the full-scale deployment, identifying areas where improvements can be made. More specifically, the proposed system integrates KalmanNet [58], a hybrid model-based (MB) and data-driven (DD) framework, to process and combine multi-source data for contrail prediction and mitigation. KalmanNet is particularly suited for handling partially known or non-linear dynamics, making it ideal for atmospheric state estimation from noisy, diverse data sources, such as CubeSats, airborne sensors, and ground-based observations. The proposed contrail mitigation system, illustrated in Figure 10, leverages KalmanNet, to enhance atmospheric state estimation by learning the Kalman gain (KG) directly from data. As shown in Figure 10, the system integrates data from three primary sources: space  
Figure 9: Phased implementation strategy for the CubeSat-based contrail monitoring system. -based atmospheric variables (e.g., temperature and humidity) derived from CubeSats, real-time flight data (e.g., aircraft emissions, speed, aerosol, ice particles, and pressure), and ground-based meteorological observations and climate models. These inputs feed into the Kalman Filter Module, which performs two critical steps: a prediction step to estimate a priori state variables (e.g., supersaturation levels) using partially known system dynamics, and an update step to refine these estimates by incorporating real-time observational data through the neural network-aided Kalman gain. The refined estimates are then passed to the Neural Network Module, which is responsible for predicting contrail formation probability and generating actionable strategies for flight path adjustments, such as altitude or route changes. The outputs of this system, as depicted in Figure 10, include a contrail risk map and flight adjustment recommendations, providing airlines with real-time information to minimize contrail formation. KalmanNet's hybrid design ensures data efficiency by combining domain knowledge with labeled datasets, while its robustness allows it to handle uncertainties in system dynamics and sensor noise. Furthermore, its low computational complexity makes it feasible for real-time operation, enabling continuous dynamic adaptation [58; 59]. While this paper outlines the conceptual framework and application of the system, further algorithm development, including training and validation methodologies, will be detailed in a dedicated future publication. **Phase 2: Expanded deployment and system integration**  
The second phase would involve the expansion of the CubeSat constellation to cover additional high-priority regions, such as the North Pacific Corridor and key flight routes in Europe and Asia. This phase would also involve the integration of the CubeSat system with existing monitoring networks, including larger satellites, ground-based sensors, and airborne platforms. The goal would be to create a comprehensive, multi-layered monitoring network capable of providing real-time data on atmospheric conditions across the globe.