Using a Multiobjective Genetic Algorithm to Design Satellite Constellations for Recovering Earth System Mass Change

The Gravity Recovery and Climate Experiment (GRACE) GRACE Follow-On (GRACE-FO) provided twenty years of data on Earth’s time-varying gravity field. Due to their design, GRACE-FO are inherently limited in spatiotemporal coverage, limiting resolution a few hundred kilometers temporally roughly monthly solutions. To increase the global allow for determination submonthly field signals, constellation GRACE-type satellite pairs is possible path forward. Advances small form factor instrumentation satellites have become progressively inexpensive, reliable, higher quality. This leads us consider that could be part future gravimetric missions. In this work, we investigate viability limitations genetic-algorithm-based optimization its objective function generate constellations recover daily Earth system mass changes. developed approach used create optimally designed variations sufficient at range temporal frequencies (i.e., monthly). We analyze constellation’s performance using combination observability space, accounting directionality, homogeneity time. allows navigate through vast search space relatively short period time estimate relative each other. Using evolutionary theory, converge towards set selected orbits. characteristics been validated high-fidelity numerical simulations. summarize these results discuss implications GRACE-like pairs. resulting an improved performance, which reduces aliasing errors characterization mass-change effects.