Validation of Satellite Lifetime Predictions at Leonid Space

TL;DR

This paper validates satellite lifetime prediction methods using extensive backtesting with historical data, demonstrating significant accuracy improvements over existing tools and establishing a performance baseline for operational use.

Contribution

It introduces a comprehensive validation approach for satellite lifetime prediction, combining probabilistic orbit propagation with space weather forecasts, and demonstrates substantial accuracy and speed improvements.

Findings

Achieved median 1-year prediction accuracy of 6 days under perfect knowledge.

Demonstrated 4x accuracy improvement over ESA's standard tools.

Provided a fast semianalytic propagator enabling large-scale Monte Carlo analysis.

Abstract

We validate Leonid Space's satellite lifetime prediction pipeline through comprehensive backtesting against 934 non-maneuvering satellites that deorbited from LEO between 1961 and 2024. This represents the first large-scale validation of lifetime prediction tooling using forecasted space weather conditions rather than historical hindsight. Our toolchain combines ballistic coefficient estimation from on-orbit data with probabilistic orbit propagation under varying environmental conditions. Using TLE data and space weather records spanning six solar cycles, our three-stage validation approach progressively removes hindsight bias to arrive at fully predictive operational conditions. We achieve 1-year prediction accuracy (median continuously ranked probability score) of 6.0 days (1.6%) under perfect knowledge conditions, 18.6 days (5.1%) with estimated ballistic coefficients and known space weather, and 45.5 days (12.4%) under fully predictive conditions. Comparison against ESA's standard DRAMA & DISCOS toolchain demonstrates a 4x improvement in state-of-the-art accuracy for well-characterized satellites, and an 8x improvement over NASA's DAS software. A custom semianalytic propagator provides a 340x speedup over Orekit and 55x speedup over DRAMA, enabling rapid Monte Carlo analysis across large satellite populations. Our analysis reveals that solar cycle forecasting dominates error budgets after ballistic coefficient estimation, with higher-fidelity propagators and atmosphere models providing marginal benefit. These results establish a validated performance baseline for operational lifetime prediction services supporting LEO mission planning and regulatory compliance.