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Supercomputing for weather and climate modelling: convenience or necessity

Supercomputing for weather and climate modelling: convenience or necessity. Willem A. Landman Asmerom Beraki Francois Engelbrecht Stephanie Landman. 11 June 2009 Cut-off low over central SA. New Multi-Model Short-Range Ensemble System (precipitation). 24hour totals for:

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Supercomputing for weather and climate modelling: convenience or necessity

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  1. Supercomputing for weather and climate modelling: convenience or necessity Willem A. Landman Asmerom Beraki Francois Engelbrecht Stephanie Landman

  2. 11 June 2009Cut-off low over central SA

  3. New Multi-Model Short-Range Ensemble System (precipitation) • 24hour totals for: • day1 (14 members) and • day2 (6 members) • Unified Model (different configurations and resolutions) • 10 members: • 12km (xaana/ng/nj) • 15km (xaaha/hc) • WRF model • 2 members: • 12km • Non-hydrostatic mesoscale core • 2 members: • 15km • Advanced Research WRF core • In Test Phase • 1º NCEP model (15 members)

  4. Probability Maps Day 1 Day2 >5mm >10mm

  5. Are AGCMs useful? “trend” hits=27/33=82% The best model is the ECHAM4.5 AGCM

  6. Will uncertainties in forcing SST fields better estimate the probability of each outcome? Ensemble forecast from model 1 explores part of the future uncertainty Ensemble forecast from model 2, run from (even the) same set of initial states, typically explores additional future uncertainties Uncertainty in future atmospheric state Uncertainty in initial atmospheric state ??? Ensemble forecast from model 3, run from different ocean states may explore additional future uncertainties Uncertainty in SST state

  7. Shaded areas: forecast uncertainty as reflected by forecast ensemble; black line: ensemble mean; red line: model climatology

  8. The multi-models: Skill Differences Positive values where MM is better than best single model (ECMWFem) • 3 AGCM configurations: • Forced with ca_sst, ECMWFem and ECMWFsc • 2 AGCM configurations • Forced with ECMWFem and ECMWFsc By considering (some of) the certainties in forcing SST fields the probability of forecast outcomes is better estimated (over some areas)

  9. Coupled model on CHPC… • ECHAM4.5-MOM3 on CHPC using 8 processors, i.e., 4 processors for each model. • Simulation for one month (May 1982) • Time needed to fish the coupled run was 1074.32 sec (1.49 hrs) • Similar run for uncoupled ECHAM4.5 using 4 processors took 225.49 sec (19 min) • Seems that coupled run is slower than expected – usually double the time is assumed for coupled run (here, just one case) Total rainfall (mm; shaded) and 500 hPa geopotential height (m; contour)  

  10. High-resolution regional climate modelling Exp1: 60 km resolution over southern Africa • Forcing (wind nudging) from NCEP reanalysis data • Period simulated 1976-2005 • Time step 20 min • Data set size: 300 GB High-resolution panel: 40 S to 10 S 10 E to 40 E ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster Resolution over southern Africa is about 60 km

  11. High-resolution regional climate modelling: Exp2: 8 km resolution over the southwestern Cape • Forcing (wind nudging) from 60 km simulation • Period simulated 1976-2005 • Time step 3 min • Data set size: 600 GB High-resolution panel: 35.5 S to 31.5 S 17.5 E to 21.5 E ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster Resolution over Australia is about 60 km

  12. High-resolution regional climate modelling: Exp3: 1 km resolution over a portion of the southwestern Cape • Forcing (wind nudging) from 8 km simulation • Period simulated 1976-2005 • Time step 30 sek • Data set size: 1.8 TB High-resolution panel: 34.31 S to 33.81 S 28.28 E to 18.78 E ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster Resolution over Australia is about 60 km

  13. High-resolution regional climate modelling: Exp4: 200 m resolution over the Stellenbosch region • Forcing (wind nudging) from 1 km simulation • Period simulated 1976-2005 • Time step 6 sek • Data set size: 1.8 TB High-resolution panel: 33.89 S to 33.79 S 18.79 E to 18.89 E ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster Resolution over Australia is about 60 km

  14. A few machines...

  15. System Configuration The ES is a highly parallel vector supercomputer system of the distributed-memory type, and consisted of 160 processor nodes connected by Fat-Tree Network. Each Processor node is a system with a shared memory, consisting of 8 vector-type arithmetic processors, a 128-GB main memory system. The peak performance of each Arithmetic processors is 102.4Gflops. The ES as a whole thus consists of 1280 arithmetic processors with 20 TB of main memory and the theoretical performance of 131Tflops.

  16. Global Atmosphere Simulation with MSSG-A03-08AUG2003, Horizontal resolution: 1.9 km, 32 vertical layers

  17. Ocean Component of Multi-Scale Simulator for the Geoenvironment: MSSG-O The Northern Pacific Ocean Horizontal Resolution: 2.78km, Vertical Layers: 40 layers, 15 years integration Boundary condition: monthly data from NCAR monthly data from OFES simulation( 10km global simulation)

  18. Conclusion • High-resolution, large ensemble, many models, various configuration • All require dedicated high-speed computer for • Operational forecasts/projections • Research to better understand the weather/climate system

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