High resolution numerical modeling and predictability of atmospheric flows
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High-resolution numerical modeling and predictability of atmospheric flows. M. Ehrendorfer, A. Gohm and G. J. Mayr Institut für Meteorologie und Geophysik Universität Innsbruck. Vortrag am Zweiter Mini-Workshop Konsortium Hochleistungsrechnen Universität Innsbruck, Austria 12. März 2004.

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High-resolution numerical modeling and predictability of atmospheric flows

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High resolution numerical modeling and predictability of atmospheric flows

High-resolution numerical modelingand predictability of atmospheric flows

M. Ehrendorfer, A. Gohm and G. J. Mayr

Institut für Meteorologie und Geophysik

Universität Innsbruck

Vortrag am

Zweiter Mini-Workshop

Konsortium Hochleistungsrechnen

Universität Innsbruck, Austria

12. März 2004

http://www2.uibk.ac.at/meteo


High resolution numerical modeling and predictability of atmospheric flows

High-Resolution Numerical Modeling and Predictability of Atmospheric Flows

Outline

  • Atmospheric models

  • Stability of flows

    • specific error structures: singular vectors, data assimilation

  • Additional remarks

  • High-resolution modeling

    • Past research: single-processor computing

    • Current research: multi-processor parallel computing

      • Introducing the numerical models

      • Introducing the computing facilities

      • An example: simulation of bora winds

      • Outlook: numerical weather prediction for the Winter Universiade 2005

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

7 Variables:wind v, densityr, potentialtemperatureq,

pressure p, temperature T

budget equations: momentum, mass, energy


High resolution numerical modeling and predictability of atmospheric flows

1922

P. Lynch, Met Éireann, Dublin


High resolution numerical modeling and predictability of atmospheric flows

European Centre for Medium-Range Weather Forecasts

ECMWF

Reading, UK

Operational models: 10^7 – 10^8 variables


High resolution numerical modeling and predictability of atmospheric flows

- sensitive dependence on i.c.

- preferred directions of growth

Lorenz

1984

model


High resolution numerical modeling and predictability of atmospheric flows

growing directions:

stability of the flow

correct for in initial condition

zid-cc


High resolution numerical modeling and predictability of atmospheric flows

o3800

NAG


High resolution numerical modeling and predictability of atmospheric flows

French storm 24/12/1999/1200

ZID-CC


High resolution numerical modeling and predictability of atmospheric flows

Nonlinear error growth

0.01%

tau_d = 12 h


High resolution numerical modeling and predictability of atmospheric flows

Optimized TL error growth

data assimilation

stability, error dynamics

tau_d = 4.9 h

o3800

12690^2


High resolution numerical modeling and predictability of atmospheric flows

Science Case

for Large-scale

Simulation

pnl.gov/scales

SIAM Rev. 2003


High resolution numerical modeling and predictability of atmospheric flows

High-Resolution Numerical Modeling and Predictability of Atmospheric Flows

Outline

  • Atmospheric models

  • Stability of flows

    • specific error structures: singular vectors, data assimilation

  • Additional remarks

  • High-resolution modeling

    • Past research: single-processor computing

    • Current research: multi-processor parallel computing

      • Introducing the numerical models

      • Introducing the computing facilities

      • An example: simulation of bora winds

      • Outlook: numerical weather prediction for the Winter Universiade 2005

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

flow around mountains

flow through mountain gaps

flow over mountains

orographically induced precipitation

High-Resolution Numerical Modeling of Atmospheric Flows

Past Research – Single-processor computing (Origin XL, o2000)

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

  • Numerical modeling with realistic orography

  • case studies

  • weather prediction

Flow around the Alps

Flow over the Alps

High-Resolution Numerical Modeling of Atmospheric Flows

Current Research – Multi-processor parallel computing (Origin o3800, ZID-CC)

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

Global Model (ECMWF*)

Limited Area Model (RAMS**)

boundary conditions

analysis or forecast

  • spectral technique

  • single global domain

  • x  40 km (TL511)

  • finite-difference technique

  • several nested domains, covering limited areas, centered near the location of interest

  • x  100 m – 1 km

* European Centre for Medium-Range Weather Forecasts (Reading, UK)

** Regional Atmospheric Modeling System (CSU, Ft. Collins, USA)

High-Resolution Numerical Modeling of Atmospheric Flows

We are using two models

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

Global Model @ ECMWF (UK)

RAMS @ ZID (IBK)

ftp

ZID-CC compute-cluster

16 servers (Transtec), each with 2 processors (2.2 GHz Intel Xeon)

IBM supercomputer

2 clusters, each with 30 servers (p690), each server having 32 processors (1.3 GHz Power4)

Origin o3800 compute-server

48 processors (600 MHz MIPS R14000)

High-Resolution Numerical Modeling of Atmospheric Flows

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

High-Resolution Numerical Modeling of Atmospheric Flows

An example: Simulation of bora winds to the lee of the Dinaric Alps

  • Parallel computing on ZID-CC cluster

  • 8 processors

  • master–slave configuration

  • domain decomposition technique

  • RAMS model setup

  • 5 nested grids

  • x = 267 m to 65 km

  • 56 vertical levels

  • 6443024 grid points

  • 1440 master time steps for 1-day forecast

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

Every hour:

data I/O from/to hard disk by master node

}

master

Every time step:

I/O communication

elapsed seconds

}

nodes

Every 20 minutes: update with radiative transfer model

number of time steps

High-Resolution Numerical Modeling of Atmospheric Flows

An example: Simulation of bora winds to the lee of the Dinaric Alps

  • Computing time for RAMS at ZID-CC cluster with 8 CPUs

  • ~180 seconds for a 60-second time step

  • 73.8 hours for a 24-hour simulation

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

Adriatic

Sea

Dinaric

Alps

DLR Falcon backscatter lidar

flow

bora

simulation

observation

High-Resolution Numerical Modeling of Atmospheric Flows

An example: Simulation of bora winds to the lee of the Dinaric Alps

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

  • Goal

  • Set up RAMS as NWP model for the Innsbruck region

  • Compute daily forecast on ZID-CC and/or Origin 3800

Benefit

Resolving various weather phenomena occurring in different spatial scales: between the Alpine scale (L~100 km) and the valley scale (L~1 km)

High-Resolution Numerical Modeling of Atmospheric Flows

Outlook: Numerical Weather Prediction (NWP) @ IMGI/ZID

IMGI HPC workshop 2004


High resolution numerical modeling and predictability of atmospheric flows

Ehrendorfer et al. 1999

80.000^2

iterative Lanczos

F. Rabier, Météo France


High resolution numerical modeling and predictability of atmospheric flows

A. Simmons, ECMWF

Heutige 5-Tages Prognose ebenso gut

wie 4-Tages Prognose for 6 Jahren


High resolution numerical modeling and predictability of atmospheric flows

Temperatur-

Unsicherheit

aus

Ensemble

von

50 Vorhersagen

(anfänglich

leicht

verschieden)

ECMWF


High resolution numerical modeling and predictability of atmospheric flows

amplification of 1-day forecast error

A. Simmons, ECMWF


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