Atmospheric Constituent Data Assimilation in NASA's Goddard Earth Observing System

Abstract

Many of today’s most significant and urgent scientific questions, including those about the impact of human activity on air quality and radiative forcing, require the synthesis of observations of atmospheric constituents and scientific theory. The past three decades have each seen a step-change in the coverage, accuracy, and precision of constituent observations, and hence the questions they can address, especially from the growing constellation of Earth-observing satellites. This paper describes the design and implementation of NASA’s Constituent Data Assimilation System (CoDAS), an open-source software package for assimilating constituent observations into the Goddard Earth Observing System (GEOS), a collection of integrated models of the Earth’s atmosphere, ocean, and land surface. Data assimilation provides a means of monitoring constituent changes, intercomparing heterogeneous types of observations, and reconciling data and models. Assimilation produces an optimal synthesis of the ingested data and model, taking advantage of the fact that the statistics of model-data differences are simpler than those of the observations themselves. The use of a model enables the propagation of information from all previous observations across space and time, which can improve estimates even where and when data are unavailable.