Representation, representativity, representativeness error, forward interpolation error,
forward model error, observation‐operator error, aggregation error and sampling error are …

Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation in: Monthly
Weather Review Volume 144 Issue 12 (2016) Jump to Content Jump to Main Navigation …

Abstract In November 2021, the Royal Meteorological Society Data Assimilation (DA)
Special Interest Group and the University of Reading hosted a virtual meeting on the topic of …

To improve the quantity and impact of observations used in data assimilation, it is necessary
to take into account the full, potentially correlated, observation error statistics. A number of …

Data assimilation provides techniques for combining observations and prior model forecasts
to create initial conditions for numerical weather prediction (NWP). The relative weighting …

Atmospheric motion vectors (AMVs) are wind observations derived by tracking cloud or
water‐vapour features in consecutive satellite images. These observations are incorporated …

It has been common practice in data assimilation to treat observation errors as uncorrelated;
however, meteorological centres are beginning to use correlated inter-channel observation …

Data assimilation techniques are widely used to predict complex dynamical systems with
uncertainties, based on time-series observation data. Error covariance matrices modeling is …

Appropriate specification of the error statistics for both observational data and short-term
forecasts is necessary to produce an optimal analysis. Observation error stems from …

Model error covariances play a central role in the performance of data assimilation methods
applied to nonlinear state-space models. However, these covariances are largely unknown …