enkf assimilation of simulated spaceborne doppler observations of vertical velocity: impact on the simulation of a supercell thunderstorm and implications for model-based retrievals

Recently, a number of investigations have been made that point to the robust effectiveness of the Ensemble Kalman Filter (EnKF) in convective-scale data assimilation. These studies have focused on the assimilation of ground-based Doppler radar observations (i.e. radial velocity and reflectivity). The present study differs from these investigations in two important ways. First, in anticipation of future satellite technology, the impact of assimilating spaceborne Doppler-retrieved vertical velocity is examined; second, the potential for the EnKF to provide an alternative to instrument-based microphysical retrievals is investigated.

It is shown that the RMS errors of the analyzed fields produced by assimilation of vertical velocity alone are in general better than those obtained in previous studies: in most cases assimilation of vertical velocity alone leads to analyses with small errors (e.g. <1 ms^-1 for velocity components) after only 3 or 4 assimilation cycles. The microphysical fields are notable exceptions, exhibiting lower errors when observations of reflectivity are assimilated together with observations of vertical velocity, likely a result of the closer relationship between reflectivity and the microphysical fields themselves. It is also shown that the spatial distribution of the error estimates improves (i.e. approaches the true errors) as more assimilation cycles are carried out, which could be a significant advantage of EnKF model-based retrievals.