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  • Author or Editor: Christopher J. Melick x
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John S. Kain
,
Michael C. Coniglio
,
James Correia
,
Adam J. Clark
,
Patrick T. Marsh
,
Conrad L. Ziegler
,
Valliappa Lakshmanan
,
Stuart D. Miller Jr.
,
Scott R. Dembek
,
Steven J. Weiss
,
Fanyou Kong
,
Ming Xue
,
Ryan A. Sobash
,
Andrew R. Dean
,
Israel L. Jirak
, and
Christopher J. Melick

The 2011 Spring Forecasting Experiment in the NOAA Hazardous Weather Testbed (HWT) featured a significant component on convection initiation (CI). As in previous HWT experiments, the CI study was a collaborative effort between forecasters and researchers, with equal emphasis on experimental forecasting strategies and evaluation of prototype model guidance products. The overarching goal of the CI effort was to identify the primary challenges of the CI forecasting problem and to establish a framework for additional studies and possible routine forecasting of CI. This study confirms that convection-allowing models with grid spacing ~4 km represent many aspects of the formation and development of deep convection clouds explicitly and with predictive utility. Further, it shows that automated algorithms can skillfully identify the CI process during model integration. However, it also reveals that automated detection of individual convection cells, by itself, provides inadequate guidance for the disruptive potential of deep convection activity. Thus, future work on the CI forecasting problem should be couched in terms of convection-event prediction rather than detection and prediction of individual convection cells.

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Adam J. Clark
,
Steven J. Weiss
,
John S. Kain
,
Israel L. Jirak
,
Michael Coniglio
,
Christopher J. Melick
,
Christopher Siewert
,
Ryan A. Sobash
,
Patrick T. Marsh
,
Andrew R. Dean
,
Ming Xue
,
Fanyou Kong
,
Kevin W. Thomas
,
Yunheng Wang
,
Keith Brewster
,
Jidong Gao
,
Xuguang Wang
,
Jun Du
,
David R. Novak
,
Faye E. Barthold
,
Michael J. Bodner
,
Jason J. Levit
,
C. Bruce Entwistle
,
Tara L. Jensen
, and
James Correia Jr.

The NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis and prediction of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new scientific concepts and tools from research to operations through the use of intensive real-time experimental forecasting and evaluation activities conducted during the spring and early summer convective storm period. The 2010 NOAA/HWT Spring Forecasting Experiment (SE2010), conducted 17 May through 18 June, had a broad focus, with emphases on heavy rainfall and aviation weather, through collaboration with the Hydrometeorological Prediction Center (HPC) and the Aviation Weather Center (AWC), respectively. In addition, using the computing resources of the National Institute for Computational Sciences at the University of Tennessee, the Center for Analysis and Prediction of Storms at the University of Oklahoma provided unprecedented real-time conterminous United States (CONUS) forecasts from a multimodel Storm-Scale Ensemble Forecast (SSEF) system with 4-km grid spacing and 26 members and from a 1-km grid spacing configuration of the Weather Research and Forecasting model. Several other organizations provided additional experimental high-resolution model output. This article summarizes the activities, insights, and preliminary findings from SE2010, emphasizing the use of the SSEF system and the successful collaboration with the HPC and AWC.

A supplement to this article is available online (DOI:10.1175/BAMS-D-11-00040.2)

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Adam J. Clark
,
Israel L. Jirak
,
Scott R. Dembek
,
Gerry J. Creager
,
Fanyou Kong
,
Kevin W. Thomas
,
Kent H. Knopfmeier
,
Burkely T. Gallo
,
Christopher J. Melick
,
Ming Xue
,
Keith A. Brewster
,
Youngsun Jung
,
Aaron Kennedy
,
Xiquan Dong
,
Joshua Markel
,
Matthew Gilmore
,
Glen S. Romine
,
Kathryn R. Fossell
,
Ryan A. Sobash
,
Jacob R. Carley
,
Brad S. Ferrier
,
Matthew Pyle
,
Curtis R. Alexander
,
Steven J. Weiss
,
John S. Kain
,
Louis J. Wicker
,
Gregory Thompson
,
Rebecca D. Adams-Selin
, and
David A. Imy

Abstract

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.

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