'Ensembles' presentation slideshows

An evaluation of the probabilistic information in multi-model ensembles

An evaluation of the probabilistic information in multi-model ensembles. David Unger Huug van den Dool , Malaquias Pena, Peitao Peng, and Suranjana Saha 30 th Annual Climate Prediction and Diagnostics Workshop October 25, 2005. Objective.

473 views • 37 slides

An evaluation of the probabilistic information in multi-model ensembles

An evaluation of the probabilistic information in multi-model ensembles. David Unger Huug van den Dool , Malaquias Pena, Peitao Peng, and Suranjana Saha 30 th Annual Climate Prediction and Diagnostics Workshop October 25, 2005. Objective.

379 views • 37 slides

NWS Flooding 101

352 views • 24 slides

Stability of Quantum Statistical Ensembles with Respect to Local Measurements Boris Fine

244 views • 24 slides

Connection between partition functions

259 views • 18 slides

Update: AFWA ensemble development and findings

Update: AFWA ensemble development and findings. J. Hacker / E. Kuchera Collaborators: C. Snyder, J. Berner , S.-Y. Ha, M. Pocernich , J. Schramm, WRF developers. Guiding interests. Primarily lower atmosphere (winds, shear) Multiple time scales (0-60 h) and fine spatial scales

293 views • 16 slides

ENSEMBLES RT8 Dissemination, education and training

ENSEMBLES RT8 Dissemination, education and training. Martin Beniston Department of Geosciences University of Fribourg, Switzerland Martin.Beniston@Unifr.CH. Primary aims. Internet-based project dissemination

280 views • 15 slides

Baroque Opera

543 views • 13 slides

What is CLIM-RUN?

Climate information for the wind energy industry in the Mediterranean Region: from ENSEMBLES to MED-CORDEX Alessandro Dell'Aquila, ENEA Sandro Calmanti, ENEA Melanie Davies, IC3 Peter Schmidt, PIK. What is CLIM-RUN?. CLIM-RUN (2011-2014) Objectives. www.climrun.eu.

265 views • 13 slides

A step back: ENSEMBLES DATA (DEL 2.4)

Climate information for the wind energy industry in the Mediterranean Region: from ENSEMBLES to MED-CORDEX Alessandro Dell'Aquila, ENEA Sandro Calmanti, ENEA Melanie Davies, IC3 Peter Schmidt, PIK. ENSEMBLES FP6 GCMs-RCMs MATRIX. A step back: ENSEMBLES DATA (DEL 2.4). ENSEMBLES domain.

257 views • 12 slides

Laboratory of Climate and Water Resources

204 views • 11 slides

Methods for Initial Ensembles

Methods for Initial Ensembles. Assimilated observation. Sea fog case. Observed sea fog (brown shades) evolution detected from MTSAT data from 6 to 7 March 2006. The light-blue areas indicate high clouds overcast. Model configuration. Experiments design.

180 views • 10 slides

Semi-Supervised Learning

Semi-Supervised Learning. Can we improve the quality of our learning by combining labeled and unlabeled data Usually a lot more unlabeled data available than labeled Assume a set L of labeled data and U of unlabeled data (from the same distribution)

333 views • 8 slides