A direct piece-by-piece data assimilation targeting strategy through observing system simulation experiments was used to examine the quality of the target area for forecast metrics with different strengths of nonlinearity in a mesoscale convective vortex associated rainfall event from both a deterministic and probabilistic perspective.
The target area was determined based on the impact of assimilating synthetic wind-profiler observations, piece-by-piece, around the initial vortex center on the forecast error of strongly nonlinear rainfall and weakly nonlinear total energy. The quality of the target area in terms of its effectiveness, distinctiveness, necessity and reliability was examined for members of a reasonable ensemble. Apparently different target areas were found for different members, even for those with very small differences for both forecast metrics, with a larger variability observed for rainfall than for total energy. This result indicated that target areas estimated in deterministic scenarios are likely unreliable.
Probabilistic target areas were created by averaging data-impact index values over the ensemble. Significant differences were also observed in the ensemble-based target areas for rainfall and total energy. For total energy, assimilating data in an inaccurate target area could decrease the forecast error at a similar magnitude as that in the target area. For rainfall, however, much less error reduction was obtained, the magnitude of which was almost comparable to the no-data-assimilation experiment. Overall, the results of this study suggest that designing a particular observation plan based on an estimated target area could be unnecessary for total energy and ineffective for rainfall, given the difficulty involved in accurately determining a target area in an operational setting.