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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.

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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.

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Samuel K. Degelia, Xuguang Wang, David J. Stensrud, and David D. Turner

Abstract

Nocturnal convection is often initiated by mechanisms that cannot be easily observed within the large gaps between rawinsondes or by conventional surface networks. To improve forecasts of such events, we evaluate the systematic impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected as part of the 2015 Plains Elevated Convection At Night (PECAN) experiment. For 13 nocturnal convection initiation (CI) events, we find small but consistent improvements when assimilating thermodynamic observations collected by Atmospheric Emitted Radiance Interferometers (AERIs). Through midlevel cooling and moistening, assimilating the AERIs increases the fractions skill score (FSS) for both nocturnal CI and precipitation forecasts. The AERIs also improve various contingency metrics for CI forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and radar wind profilers (RWPs) results in slight degradations to the forecast quality, including decreases in the FSS and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We also find large variability in the impact of assimilating these remote sensing profilers, likely due to the number of observing sites and the strength of the synoptic forcing for each case. We hypothesize that the lack of flow-dependent methods to diagnose observation errors likely contributes to degradations in forecast skill for many cases, especially when assimilating kinematic profilers.

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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.

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