Impact of Flow Dependence, Column Covariance, and Forecast Model Type on Surface-Observation Assimilation for Probabilistic PBL Profile Nowcasts

Abstract

Abstract A probabilistic verification and factor-separation analysis (FSA) elucidate skillful nowcasts of planetary boundary layer (PBL) temperature, moisture, and wind profiles with a single-column model (SCM) and ensemble filter (EF) assimilation of surface observations. Recently, an FSA showed the importance of surface assimilation versus advection and radiation on ensemble-mean skill. That work addressed the necessary complexity of the model and assimilation scheme for improving PBL nowcasts, relative to deterministic-mesoscale predictions. Here, probabilistic ensemble-based SCM forecasts are compared to a simple probabilistic postprocessing scheme termed climatological dressing (CD). CD adjusts a deterministic mesoscale forecast using surface-atmosphere 3D-climatological covariances, a 30-min persistence model, and surface-forecast errors. It also dresses the adjusted profile with an in-sample uncertainty distribution (obtained from archives) scaled by the 30-min forecast error. Superior deterministic skill from SCM/EF results during night when flow-dependent covariances are more accurate than climatological covariances. CD is deterministically more skillful for temperature and moisture profiles during daytime because SCM/PBL parameterization yields biased covariances. SCM/EF is most probabilistically skillful because (a) the EF covariances accommodate large seasonal variability, (b) the 30-min error persistence assumption fails during nighttime, and (c) vertical error covariance estimates from archived forecasts are generally poor estimates of actual error covariances. A probabilistic FSA of the SCM/EF shows the relative importance of surface assimilation, radiation parameterization, and advection during night. Results confirm surface assimilation as the most important factor. A factor can be deterministically beneficial and probabilistically detrimental, or vice versa, depending on its role in reducing mean error or improving sharpness. Assimilation results in notable probabilistic improvement for nowcasts of low-level jet structures.