High Performance Computing and Four-Dimensional Data Assimilation

This talk at CRPC Annual Meeting describes the NASA GRand Challenge in Data Assimilation. This application combines, using Kalman filter-like methos, models and atmospheric measurements to provide the best possible weather prediction. CRPC is represented by NPAC at Syracuse which is developing parallel versions of the key components of the application. We are developing both message-passing and High Performance Fortran implementations. The work involves the team responsible for current operational system at NASA Goddard, JPL particularly focussing on new Optimal interpolation algorithms and NPAC. Optimal interpolation terms the best way of combining data and models and is challenging because the of the complex model governed by sets of partial differential equations and large number of observations. The parallel implementation has load balancing and other interesting HPCC features. The talk was given by Miloje Makivic from NPAC who is leading the this work at NPAC.
The slides (Click on number for slide, or text for descriptions):

.1=..NASA grand challenge in atmospheric science - status of computer science issues.
.2=..Data assimilation and high performance computing.
.3=..General computational issues.
.4=..Block diagram of data assimilation system.
.5=..Comments on data assimilation software modules.
.6=..Basic optimal interpolation algorithms.
.7=..Local optimal interpolation features.
.8=..Global optimal interpolation features.
.9=..Eulerian Transport.
10=..Semi-lagrangian general circulation model - I.
11=..Semi-lagrangian general circulation model - II.
12=..Project milestones.
13=..Structure of grand challenge team.

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