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contributor authorClark, Adam J.
contributor authorJirak, Israel L.
contributor authorDembek, Scott R.
contributor authorCreager, Gerry J.
contributor authorKong, Fanyou
contributor authorThomas, Kevin W.
contributor authorKnopfmeier, Kent H.
contributor authorGallo, Burkely T.
contributor authorMelick, Christopher J.
contributor authorXue, Ming
contributor authorBrewster, Keith A.
contributor authorJung, Youngsun
contributor authorKennedy, Aaron
contributor authorDong, Xiquan
contributor authorMarkel, Joshua
contributor authorGilmore, Matthew
contributor authorRomine, Glen S.
contributor authorFossell, Kathryn R.
contributor authorSobash, Ryan A.
contributor authorCarley, Jacob R.
contributor authorFerrier, Brad S.
contributor authorPyle, Matthew
contributor authorAlexander, Curtis R.
contributor authorWeiss, Steven J.
contributor authorKain, John S.
contributor authorWicker, Louis J.
contributor authorThompson, Gregory
contributor authorAdams-Selin, Rebecca D.
contributor authorImy, David A.
date accessioned2019-09-19T10:07:52Z
date available2019-09-19T10:07:52Z
date copyright1/15/2018 12:00:00 AM
date issued2018
identifier otherbams-d-16-0309.1.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261872
description abstractAbstractOne 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.
publisherAmerican Meteorological Society
titleThe Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment
typeJournal Paper
journal volume99
journal issue7
journal titleBulletin of the American Meteorological Society
identifier doi10.1175/BAMS-D-16-0309.1
journal fristpage1433
journal lastpage1448
treeBulletin of the American Meteorological Society:;2018:;volume 099:;issue 007
contenttypeFulltext


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