Quality of the Target Area for Metrics with Different Strengths of Nonlinearity in a Mesoscale Convective Vortex

Targeted observation aims to locate an area of a certain weather system in which adding extra observation may produce more benefit to the forecast accuracy than in other area. Targeting studies have been focused mostly on large-scale system with total energy as the forecast metric, while the targeted observation on mesoscale convective systems have not been seen in literature. A major reason could be that most current targeted observation strategies are based on linear assumption which likely does not fit for mesoscale convective system. This work proposed a nonlinear method, namely a direct piece-by-piece data assimilation (PBPDA) targeting strategy through observing system simulation experiments to examine the quality of target area for forecast metrics with different nonlinearities for a mesoscale convective vortex (MCV)-associated heavy rainfall event from both a deterministic and probabilistic perspectives.

The target area was determined based on the impact of assimilating synthetic wind-profiler observations through WRF-3DVar, piece by piece, on the forecast error of strongly nonlinear rainfall and weakly nonlinear total energy around the initial MCV center. The quality of the target area in terms of its effectiveness and variability 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 indicates that target areas estimated in deterministic scenarios are likely unreliable.

Probabilistic target areas were created by averaging data-impact index values over the ensemble. The ensemble-based target areas for both the total energy and rainfall were found to be physically meaningful. However, significant differences existed in their quality in the verifying data assimilation experiments. For total energy, assimilating data in an inaccurate target area decreased the forecast error at a similar magnitude as that in the target area. The error reduction obtained by assimilating data in either the target area or inaccurate target area were both larger than that obtained in the no-data-assimilation experiment. For rainfall, however, much larger error reduction was obtained when assimilating data in target area than that in inaccurate target area. The magnitude of error reduction by assimilating data in inaccurate target area was almost comparable to that obtained in the no-data-assimilation experiment.

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 useless for rainfall, given the difficulty involved in accurately determining a target area in an operational setting. In other words, for total energy, what really decreases the error is likely the data assimilation procedure instead of assimilating data in the target area or non-target area, which is possibly what was happening in many assessment studies of operational targeting observation field experiments. For rainfall, only assimilating data in an exactly accurate target area can decrease the forecast error.

The sensitivity of the performance of this PBPDA method to a different case or using a different background error covariance were also examined.