Observing System Simulation Experiments (OSSEs) are typically designed to use data assimilation ideas (chapter Mathematical Concepts in Data Assimilation, Nichols) to investigate the potential impacts of prospective observing systems (observation types and deployments). They may also be used to investigate current observational and data assimilation systems by testing the impact of new observations on them. The information obtained from OSSEs is generally difficult, or in some contexts impossible, to obtain in any other way. In an OSSE, simulated rather than real observations are the input to a data assimilation system (DAS for short). Simulated observational values are drawn from some appropriate source (several possibilities have been considered; see Section 3). These values are generally augmented by implicitly or explicitly estimating respective values of observational errors to make them more realistic (see Section 4). The resulting values are then ingested into a DAS (that may be as complex as an operational one) just as corresponding real observations would be. Simulations of both analyses and subsequent forecasts are then produced for several experiments, with each considering a distinct envisioned observing system; i.e., a distinct set of observation types and characteristics. The analysis and forecast products are then compared to evaluate the impacts of the various systems considered.
M Masutani, T Schlatter, R Errico, A Stoffelen, E Andersson, W Lahoz, JS Woollen, GD Emmitt, LP Riishojgaard, SJ Lord. Observing System Simulation Experiments
published, Data Assimilation - Making Sense of Observations, 2010, Springer, yes