Evaluation of adjoint-based observation impacts as a function of forecast length using an Observing System Simulation Experiment

Adjoints of numerical weather prediction models may be employed for Forecast
Sensitivity to Observation (FSO) in order to monitor the contribution of ingested
observation data on short-term forecast skill. However, the calculation of short-term
forecast error is difficult due to the lack of a truly independent dataset for verification.
In an Observing System Simulation Experiment framework, the Nature Run is able to
provide a true and complete verification dataset and allows accurate evaluation of short
term forecast errors. In this work, an OSSE developed at the National Aeronautics and
Space Administration Global Modeling and Assimilation Office is used to explore the
impact of observational data on forecasts in the 6 to 48 hour range. An adjoint of the
Global Earth Observing System model is employed to compare the observation impacts
estimated using both self-analysis verification and the true Nature Run verification.
Self-analysis verification is found to inflate the estimated forecast error growth
during the early forecast period, resulting in overestimations of observation impacts,
particularly in the 6-12 hour forecast range. By 48 hours, the self-analysis verification
estimates of forecast error and observation impacts more closely match the true values.
The fraction of beneficial observations is also overinflated at short forecast times when
self-analysis verification is used. The progression of impacts of an individual observation
or data type depends on the character of the growth of the initial condition error that
each observation affects.