Search Results

You are looking at 1 - 6 of 6 items for :

  • Author or Editor: Samuel Degelia x
  • Plains Elevated Convection At Night (PECAN) x
  • Refine by Access: All Content x
Clear All Modify Search
Aaron Johnson
,
Xuguang Wang
, and
Samuel Degelia

Abstract

Multiscale ensemble-based data assimilation and forecasts were performed in real time during the Plains Elevated Convection At Night (PECAN) field experiment. A 20-member ensemble of forecasts at 4-km grid spacing was initialized daily at both 1300 and 1900 UTC, together with a deterministic forecast at 1-km grid spacing initialized at 1300 UTC. The configuration of the GSI-based data assimilation and forecast system was guided by results presented in Part I of this two-part study. The present paper describes the implementation of the real-time system and the extensive forecast products that were generated to support the unique interests of PECAN researchers. Subjective and objective verification of the real-time forecasts from 1 June through 15 July 2015 is conducted, with an emphasis on nocturnal mesoscale convective systems (MCSs), nocturnal convective initiation (CI), nocturnal low-level jets (LLJs), and bores on the nocturnal stable layer. Verification of nocturnal precipitation during overnight hours, a proxy for MCSs, shows both greater skill and spread for the 1300 UTC forecasts than the 1900 UTC forecasts. Verification against observed soundings reveals that the forecast LLJs systematically peak, veer, and dissipate several hours before the observations. Comparisons with bores that passed over an Atmospheric Emitted Radiance Interferometer reveal an ability to predict borelike features that is greatly improved at 1-km, compared with 4-km, grid spacing. Objective verification of forecast CI timing reveals strong sensitivity to the PBL scheme but an overall unbiased ensemble.

Full access
Samuel K. Degelia
,
Xuguang Wang
,
David J. Stensrud
, and
Aaron Johnson

Abstract

The initiation of new convection at night in the Great Plains contributes to a nocturnal maximum in precipitation and produces localized heavy rainfall and severe weather hazards in the region. Although previous work has evaluated numerical model forecasts and data assimilation (DA) impacts for convection initiation (CI), most previous studies focused only on convection that initiates during the afternoon and not explicitly on nocturnal thunderstorms. In this study, we investigate the impact of assimilating in situ and radar observations for a nocturnal CI event on 25 June 2013 using an ensemble-based DA and forecast system. Results in this study show that a successful CI forecast resulted only when assimilating conventional in situ observations on the inner, convection-allowing domain. Assimilating in situ observations strengthened preexisting convection in southwestern Kansas by enhancing buoyancy and locally strengthening low-level convergence. The enhanced convection produced a cold pool that, together with increased convergence along the northwestern low-level jet (LLJ) terminus near the region of CI, was an important mechanism for lifting parcels to their level of free convection. Gravity waves were also produced atop the cold pool that provided further elevated ascent. Assimilating radar observations further improved the forecast by suppressing spurious convection and reducing the number of ensemble members that produced CI along a spurious outflow boundary. The fact that the successful CI forecasts resulted only when the in situ observations were assimilated suggests that accurately capturing the preconvective environment and specific mesoscale features is especially important for nocturnal CI forecasts.

Full access
Samuel K. Degelia
,
Xuguang Wang
, and
David J. Stensrud

Abstract

Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.

Full access
Hristo G. Chipilski
,
Xuguang Wang
,
David B. Parsons
,
Aaron Johnson
, and
Samuel K. Degelia

Abstract

There is a growing interest in the use of ground-based remote sensors for numerical weather prediction, which is sparked by their potential to address the currently existing observation gap within the planetary boundary layer. Nevertheless, open questions still exist regarding the relative importance of and synergy among various instruments. 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, in terms of both 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 that can further limit the forecast value extracted from such networks.

Restricted access
Tammy M. Weckwerth
,
John Hanesiak
,
James W. Wilson
,
Stanley B. Trier
,
Samuel K. Degelia
,
William A. Gallus Jr.
,
Rita D. Roberts
, and
Xuguang Wang

Abstract

Nocturnal convection initiation (NCI) is more difficult to anticipate and forecast than daytime convection initiation (CI). A major component of the Plains Elevated Convection at Night (PECAN) field campaign in the U.S. Great Plains was to intensively sample NCI and its near environment. In this article, we summarize NCI types observed during PECAN: 1 June–16 July 2015. These NCI types, classified using PECAN radar composites, are associated with 1) frontal overrunning, 2) the low-level jet (LLJ), 3) a preexisting mesoscale convective system (MCS), 4) a bore or density current, and 5) a nocturnal atmosphere lacking a clearly observed forcing mechanism (pristine). An example and description of each of these different types of PECAN NCI events are presented. The University of Oklahoma real-time 4-km Weather Research and Forecasting (WRF) Model ensemble forecast runs illustrate that the above categories having larger-scale organization (e.g., NCI associated with frontal overrunning and NCI near a preexisting MCS) were better forecasted than pristine. Based on current knowledge and data from PECAN, conceptual models summarizing key environmental features are presented and physical processes underlying the development of each of these different types of NCI events are discussed.

Full access

Bore-ing into Nocturnal Convection

Kevin R. Haghi
,
Bart Geerts
,
Hristo G. Chipilski
,
Aaron Johnson
,
Samuel Degelia
,
David Imy
,
David B. Parsons
,
Rebecca D. Adams-Selin
,
David D. Turner
, and
Xuguang Wang

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.

Full access