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Abstract There is a growing interest in the use of ground-based remote sensors for Numerical Weather Prediction (NWP), which is sparked by their potential to address the currently existing observation gap within the Planetary Boundary Layer (PBL). Nevertheless, open questions still exist regarding the relative importance of and synergy among various instrument types. To shed light on these important questions, the present study examines the forecast benefits associated with several different ground-based profiling networks using 10 diverse cases from the Plains Elevated Convection at Night (PECAN) field campaign. Aggregated verification statistics reveal that a combination of in situ and remote sensing profilers leads to the largest increase in forecast skill, both in terms of the parent mesoscale convective system and the explicitly resolved bore. These statistics also indicate that it is often advantageous to collocate thermodynamic and kinematic remote sensors. By contrast, the impacts of networks consisting of single profilers appear to be flow-dependent, with thermodynamic (kinematic) remote sensors being most useful in cases with relatively low (high) convective predictability. Deficiencies in the data assimilation method as well as inherent complexities in the governing moisture dynamics are two factors shown to limit the forecast value extracted from such networks. 

Using data from the 6 July 2015 PECAN case study, this paper provides the first objective assessment of how the assimilation of ground-based remote sensing profilers affects the forecasts of bore-driven convection. To account for the multiscale nature of the phenomenon, data impacts are examined separately with respect to (i) the bore environment, (ii) the explicitly resolved bore, and (iii) the bore-initiated convection. The findings from this work suggest that remote sensing profiling instruments provide considerable advantages over conventional in situ observations, especially when the retrieved data are assimilated at a high temporal frequency. The clearest forecast improvements are seen in terms of the predicted bore environment where the assimilation of kinematic profilers reduces a preexisting bias in the structure of the low-level jet. Data impacts with respect to the other two forecast components are mixed in nature. While the assimilation of thermodynamic retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) results in the best convective forecast, it also creates a positive bias in the height of the convectively generated bore. Conversely, the assimilation of wind profiler data improves the characteristics of the explicitly resolved bore, but tends to further exacerbate the lack of convection in the control forecasts. Various dynamical diagnostics utilized throughout this study provide a physical insight into the data impact results and demonstrate that a successful prediction of bore-driven convection requires an accurate depiction of the internal bore structure as well as the ambient environment ahead of it.

 

Abstract There has been a recent wave of attention given to atmospheric bores in order to understand how they evolve and initiate and maintain convection during the night. This surge is attributable to data collected during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. A salient aspect of the PECAN project is its focus on using multiple observational platforms to better understand convective outflow boundaries that intrude into the stable boundary layer and induce the development of atmospheric bores. The intent of this article is threefold: 1) to educate the reader on current and future foci of bore research, 2) to present how PECAN observations will facilitate aforementioned research, and 3) to stimulate multidisciplinary collaborative efforts across other closely related fields in an effort to push the limitations of prediction of nocturnal convection.