'Model sensitivity' presentation slideshows

VOCALS

VOCALS. VOCALS THEME. To better understand and simulate how marine boundary layer cloud systems surrounding the Americas interact with the coupled ocean-atmosphere-land system on diurnal to interannual timescales. VOCALS in CLIVAR.

550 views • 36 slides

VOCALS

VOCALS. VOCALS THEME. To better understand and simulate how marine boundary layer cloud systems surrounding the Americas interact with the coupled ocean-atmosphere-land system on diurnal to interannual timescales. VOCALS in CLIVAR.

378 views • 36 slides

A Stategy for Predicting Climate Sensitivity Using Satellite Data Daniel B. Kirk-Davidoff

A Stategy for Predicting Climate Sensitivity Using Satellite Data Daniel B. Kirk-Davidoff University of Maryland Department of Meteorology dankd@atmos.umd.edu. Talk Structure Background on the Fluctuation Dissipation Theorem Experiments using a Toy Model Model Description Results

357 views • 26 slides

The TBEST Framework for Data Analysis and Forecasting

September 17, 2013. The TBEST Framework for Data Analysis and Forecasting. Center for Urban Transportation Research | University of South Florida. TBEST. Transit Demand Modeling/Analysis Software developed by

346 views • 25 slides

VOCALS Chris Bretherton, Univ. of Washington

VOCALS Chris Bretherton, Univ. of Washington. VOCALS THEME. To better understand and simulate how marine boundary layer cloud systems surrounding the Americas interact with the coupled ocean-atmosphere-land system on diurnal to interannual timescales. VOCALS in CLIVAR.

312 views • 18 slides

EKT

Education and Knowledge Transfer Objectives, Progress & Plans. EKT. Irina Overeem November 2009. Education of CSDMS modelers Knowledge transfer to industry/federal agencies Educational Repository Focus on Undergraduates? What about General Public? EKT short-term and long-term goals.

259 views • 11 slides

Urban impacts on summertime rainfall in Beijing

206 views • 8 slides

Assimilation of radar observations in mesoscale models using approximate

Assimilation of radar observations in mesoscale models using approximate background error covariance matrices. 1 UW-Madison AOS. Chang-Hwan Park 1 , Mark Kulie 1 and Ralf Bennartz 1. 3. HA Approximation. 1. Motivation. 6. Forecast Improvement. 1.. Introduction and Motivation.

142 views • 1 slides