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RMDCN Connections
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Classroom Contents
Numerical Weather Prediction - Facing the Future with C++
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- 1 Intro
- 2 Overview
- 3 Who are we and what do we do?
- 4 A basic description of our models
- 5 Major assimilated datasets
- 6 4D-Var Assimilation
- 7 Data Assimilation
- 8 Model Levels
- 9 Operational model grid
- 10 Grid Effect
- 11 Meteorological Fields
- 12 Chaos and weather prediction
- 13 RMDCN Connections
- 14 New Algorithms Challenge
- 15 Evalution of ECMWF scores comparison northern and southern hemispheres
- 16 Benefits of High Resolution
- 17 Planned Resolution Upgrades
- 18 Power Challenge
- 19 Platform Uncertainty Challenge
- 20 Power Matters
- 21 Hypothetical Solution
- 22 HPC Platform Restrictions
- 23 C++ Support on HPC
- 24 Scientists are reluctant to use C++
- 25 Big Data Challenge
- 26 Vis for Volume: Observations
- 27 Vis for Volume: Archive
- 28 CPU Power Growth
- 29 CPU Performance Growth (single-threaded)
- 30 Storage Density Growth Multiple Technologies
- 31 HDD Storage Growth
- 32 What does it imply?
- 33 Meteorological Archival and Retrieval System
- 34 MARS 2011 Migration
- 35 A meteorological language
- 36 Current IFS Model
- 37 Atlas capabilities
- 38 So what about Fortran?
- 39 C++ contains implementation
- 40 Fortran/C interface
- 41 Fortran Program
- 42 A look at the data-chain
- 43 Observations & Fields: Some similarities
- 44 Requirements
- 45 Use Case: Product Generation
- 46 Use Case: Observation Filters
- 47 Use Case Workstation Interpolation & Visualisation
- 48 Hermes
