Assessment and tuning of data assimilation systems using passive observations
by G.J. Marseille (KNMI), J. Barkmeijer (KNMI), S. De Haan (KNMI), W. Verkley (KNMI),
Independent observations can be used to diagnose and tune a data assimilation (DA) system.
Analysis increments generally improve the model state near assimilated observations but
degrade it further away. High-resolution aircraft observations from Mode-S Enhanced
Surveillance (Mode-S EHS) are used as an independent data source to verify increment
degradation as a function of distance from assimilated observations. An adaptation of the
inherently imperfect gainmatrix inDA is proposed such that resulting analyses better fit the
independent data source and as such drawmodel simulations closer to the true atmospheric
state. It is found that the structure functions of the background-error covariance matrix
of the experimental mesoscale HARMONIE model are appropriate but too much weight is
given to observations relative to the model background. The ECMWF model is well tuned
with a slight overestimation of temperature information in the upper troposphere.
Finally, a caveat is highlighted when comparing model forecasts from different
experiments against observations. It is common practice to use the same observing system
both in the analysis and for forecast verification. However, forecast verification is prone to
sampling errors, yielding less favourable scores when using an independent data source.
Avoidance of biased conclusions on the impact of observing systems, e.g. in observing
system experiments (OSE), requires an independent data source (best practice) or a data
source used in all experiments (best pragmatic practice) for verification of forecasts from
Marseille, G.J., J. Barkmeijer, S. De Haan and W. Verkley, Assessment and tuning of data assimilation systems using passive observations