High-resolution numerical modeling and predictability of atmospheric flows

1. 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

2. 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

3. 7 Variables:wind v, densityr, potentialtemperatureq, pressure p, temperature T budget equations: momentum, mass, energy

4. 1922 P. Lynch, Met Éireann, Dublin

5. European Centre for Medium-Range Weather Forecasts ECMWF Reading, UK Operational models: 10^7 – 10^8 variables

6. - sensitive dependence on i.c. - preferred directions of growth Lorenz 1984 model

7. growing directions: stability of the flow correct for in initial condition zid-cc

8. o3800 NAG

9. French storm 24/12/1999/1200 ZID-CC

10. Nonlinear error growth 0.01% tau_d = 12 h

11. Optimized TL error growth data assimilation stability, error dynamics tau_d = 4.9 h o3800 12690^2

12. Science Case for Large-scale Simulation pnl.gov/scales SIAM Rev. 2003

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

23. Ehrendorfer et al. 1999 80.000^2 iterative Lanczos F. Rabier, Météo France

24. A. Simmons, ECMWF Heutige 5-Tages Prognose ebenso gut wie 4-Tages Prognose for 6 Jahren

26. Temperatur- Unsicherheit aus Ensemble von 50 Vorhersagen (anfänglich leicht verschieden) ECMWF

27. amplification of 1-day forecast error A. Simmons, ECMWF