Data Assimilation

Data Assimilation

Data assimilation integrates observed data, which may be incomplete, noisy, or sparsely distributed, with a numerical model that represents the underlying dynamics of the system being studied. By combining the available observations and the model predictions, data assimilation aims to reduce uncertainties and improve the estimation of the true state of the system.

This approach is used in various fields, particularly in meteorology, oceanography, and environmental sciences, where real-world weather measurements from satellites, buoys, weather stations, or other sources are used to adjust the initial conditions of the model to make it consistent with the observed data. The assimilation process can reduce error, increase resolution, and reduce uncertainty in the predicted state of the system. The image below shows an example of data assimilation where current forecast models are combined with direct measurements to predict object drift probabilities in the open ocean.

 

Assimilation of measured and forecast current data to predict object drift.

Combining measurements with modeling requires knowledge of physical processes, along with the challenges and shortcomings of modeling and measuring these processes. At GDA we have significant research experience in atmospheric science, computational physics, and engineering. We have successfully leveraged our data assimilation expertise to develop localized wind predictions, ocean current and wave height measurements, and agricultural spray drift solutions. These efforts have been developed in support of multiple research programs at DARPA, other DoD branches, and numerous federal agencies.