# Data Assimilation **Domain:** Atmospheric Science, Meteorology, Data Science **Doc Type:** Concept Node **Classification:** Infrastructure Concept **Maturity:** Foundational **Related:** [[Data Infrastructure]], [[Computational Process]], [[Sensing]], [[Control Theory]] --- ## Definition A **computational technique in which observational data from sensors and measurements are combined with mathematical models to produce best-estimate descriptions of system state**. Assimilation reconciles model predictions with empirical measurements to reduce uncertainty. --- ## General Context Data assimilation emerged in meteorology where observations are incomplete, scattered across space, and taken at different times, but models require complete state descriptions. Techniques (Kalman filtering, variational methods) optimally weight observations and models based on their respective uncertainties. --- ## Planetary Sensing Context Environmental monitoring via satellite and ground sensors requires constant data assimilation to produce state estimates. The choice of which observations to weight heavily versus lightly affects downstream governance decisions. --- ## Governance Context Climate models used for policy rely on data assimilation. Decisions about observation weighting and uncertainty treatment are scientific but have governance implications. --- ## Computational Governance Context Data assimilation is computationally intensive. Only institutions with significant computational capacity can produce state estimates from raw observations. --- ## Key Insight Data assimilation is not objective merging of information; algorithmic choices about weighting observations versus models, treating uncertainty, and interpolating across space embed methodological assumptions that affect outcomes. --- ## See Also [[Data Platforms]], [[Data Stack]], [[Continuous Adaptive Management]]