We commonly refer to state estimation theory in geosciences as data assimilation (DA). This
term encompasses the entire sequence of operations that, starting from the observations of a …

The standard formulation of 4D‐Var assumes random zero‐mean errors for all sources of
information used in the analysis. This assumption is usually not well verified in real‐world …

This contribution addresses the characterization of the model-error covariance matrix from
the new theoretical perspective provided by the parametric Kalman filter method which …

This contribution explores a new approach to forecasting multivariate covariances for
atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF …

Forecasting uncertainties among meteorological fields have long been recognized as the
main limitation on the accuracy and predictability of air quality forecasts. However, the …

Forecasts of biogenic trace gases in the planetary boundary layer (PBL) are highly affected
by simulated emission and transport processes. The Po region during the PEGASOS …

In this work, we present the development of a 4D-Ensemble-Variational (4DEnVar) data
assimilation technique to estimate NO x top-down emissions using the regional chemical …

Observations of winds in the planetary boundary layer remain sparse making it challenging
to simulate and predict atmospheric conditions that are most important for describing and …

This report presents a review of data assimilation methods applicable to air quality. In the
introduction, we first describe a brief history of data assimilation method development in the …

Atmospheric chemical forecasts heavily rely on various model parameters, which are often
insufficiently known, such as emission rates and deposition velocities. However, a reliable …