Scientists have developed research systems to test new ideas in data assimilation, but these often lack the efficiency and robustness needed for operational use. We addressed this gap with key innovations: a flexible observation database, first guess at the appropriate time, and modular, parallelised software enabling the assimilation of millions of observations.

Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for forecasting applications.

Accurate sea ice conditions are crucial for quality sea ice projections, which have been connected to rapid warming over the Arctic. Knowing which observations to assimilate into models will help produce more accurate sea ice conditions. We found that not assimilating sea ice concentration led to more accurate sea ice states. The methods typically used to assimilate observations in our models apply assumptions to variables that are not well suited for sea ice because they are bounded variables.

We assimilate different MOPITT CO products to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately.

Observations of winds in the planetary boundary layer remain sparse, making it challenging to simulate and predict the atmospheric conditions that are most important for describing and predicting urban air quality. Here we investigate the application of data assimilation of NO₂ columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of wind fields in the boundary layer.