The Roles of Surface-Observation Ensemble Assimilation and Model Complexity for Nowcasting of PBL Profiles: A Factor Separation Analysis

Abstract

AbstractRecent results showed the ability of surface-observation assimilation with a single-column model (SCM) and an ensemble filter (EF) to skillfully estimate the vertical structure of the PBL when only climatological information is provided for initialization and forcing. The present study quantifies the relative benefits of model complexity, compared to surface-observation assimilation, for making 30-min SCM ensemble predictions (nowcasts). The SCM is initialized and forced by timely mesoscale forecasts, making it capable of providing flow-dependent probabilistic very short-range forecasts of PBL profiles wherever surface observations are available. Factor separation (FS) analysis measures the relative contributions to skill from EF surface assimilation compared to selected SCM components: parameterized radiation and objectively scaled horizontal advection. Here, the SCM–EF system is presented and its deterministic skill (as represented by ensemble-mean error) is analyzed with FS. Results show that surface assimilation can more meaningfully contribute to the skill levels of temperature, wind, and mixing-ratio nowcasts than model enhancements under a wide range of flow scenarios. However, in the convective PBL regime surface assimilation can enhance the moist bias often observed in parameterized PBL mixing ratio profiles due to poor covariances estimated from the ensemble. Then, the SCM–EF proves useful in revealing a model deficiency. Externally imposed horizontal advection is required to provide skillful ensemble-mean forecasts when not assimilating surface observations, but can offset the benefit realized from assimilation by quickly sweeping the updated state out of the domain. The radiation scheme has a minor effect on forecast performance. It improves the nowcast surface temperature at night, and can act synergistically with assimilation to improve low-level jet predictions, but the effect above the surface steeply decreases with height. The results suggest that an SCM–EF may be helpful in wind-power and pollutant dispersion applications.