PPT-SI2-SSE: Pipeline Framework for Ensemble Runs on the Cloud

Beth Plale PI Indiana University Craig Mattocks CoPI University of Miami Goals The goal of the project is to develop andor assemble tooling to enable rapid and cost effective deployment of anywhere between 500 and 15000 small jobs on cloud resources This project develops a pipeline framework for running ensemble simulations on the cloud the framework has two key components . . Thorpex-Tigge. . and use in Applications. Tom Hopson. Outline. Thorpex. -Tigge. data set. Ensemble forecast examples:. a) Southwestern African . flooding. . TIGGE, the THORPEX Interactive Grand Global Ensemble. David Unger. Climate Prediction Center. Summary. A linear regression model can be designed specifically for ensemble prediction systems.. It is best applied to direct model forecasts of the element in question.. Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Are we still talking about diversity in classifier ensembles?. Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. SIMD architectures. A data parallel architecture. Applying the same instruction to many data. Save control logic. A related architecture is the vector architecture. SIMD and vector architectures offer high performance for . A jogger runs 145m in a direction 20.0 degrees east of north (vector A), and then runs 105m in a direction 35.0 degrees south of east (vector B). Using components, determine the magnitude and direction of the resultant vector C for these two displacements.. Slides to include. MODIS land use slide from Tanya with . Zo. Show time-lagged ensemble of updraft . helicity. – source . for idea. Beth . Plale. (PI), Indiana University | Craig Mattocks (Co-PI), University of Miami. . Figure: . Scheduling tasks in Azure using . Sigiri. Middleware. Climate change can have adverse impacts on strength of storms. Even modest changes in Ocean surface temperature can have a significant impact on hurricane strength, making the coastal regions increasingly vulnerable to storm surge. . Ludmila. I . Kuncheva. School of Computer Science. Bangor University, UK. Publications (580). Citations (4594). “CLASSIFIER ENSEMBLE DIVERSITY”. Search on 10 Sep 2014. MULTIPLE CLASSIFIER SYSTEMS 30. Lifeng. Yan. 1361158. 1. Ensemble of classifiers. Given a set . of . training . examples, . a learning algorithm outputs a . classifier which . is an hypothesis about the true . function f that generate label values y from input training samples x. Given . Molly Smith, Ryan Torn, . Kristen . Corbosiero. , and Philip . Pegion. NWS Focal Points: . Steve . DiRienzo. and Mike . Jurewicz. . Fall 2016 CSTAR Meeting. 2 . November, . 2016. Motivation. Landfalling. Better Predictions Through Diversity. Todd Holloway. ETech 2008. Outline. Building a classifier (a tutorial example). Neighbor method. Major ideas and challenges in classification. Ensembles in practice. Earl -- 2010. 45-km outer domain. 15-km moving nest. Best Track. Ensemble Members. Relocated Nest. COAMPS-TC Forecast Ensemble. Web Page Interface. http://www.nrlmry.navy.mil/coamps-web/web/ens?&spg=1. Zoli Herczeg. Evangelist. Microsoft Romania. zoli.herczeg@microsoft.com. http://zoli.herczeg.ro. *Inspired by Simon Guest’s ITS204 session @TechEd Europe 2009. This is Jim. Jim’s Boss (the CIO) has asked him to . Overview the Bayu-Undan Field (situated in the Joint Petroleum Development Area between Timor-Leste and Australia) to the Darwin liqueed natural gas (DLNG) plant near Darwin, Northern Territory