P Goswami Centre for Mathematical Modelling and Computer Simulation Bengaluru

1. Will HPC Ever Meet the Demand of Weather and Climate Forecasting REACH-2010 IIT-Kanpur P Goswami Centre for Mathematical Modelling and Computer Simulation Bengaluru

3. Why doubt the power of computing?!

4. By 2050 the cost of computing comparable to 1 Billion Human brains will be US$ 1000 By 2050 each human being will want customized personal forecast! What will such demand mean for computing?

5. The Grandest Challenge in Computing Atmosphere: A thermally active (water in three phases, with phase transition) mechanical system with interacting and dynamic boundary conditions External Persistent Forcing: Solar Radiation, Lower Boundary Random Forcing: Volcanoes, Forest Fire etc. Anthropogenic Forcing: Emissions, Land Use

7. These are Interacting Scales

8. Forecast of Weather and Climate:The Wish List On-Demand Forecast (Location, time, variable, resolution) Projections Backward and Forward in time: Paleo-climate and climate forecast Reliability: 90%, No False Positive, No False Negative Forecast (Hindcast) Period: Hour to decades Range of Forecast: Hours to decades and beyond Spatial Coverage: Station to global, and beyond

9. Forecasting Weather and Climate The Measure of our Understanding is our Ability to Forecast The ability to forecast depends on power to compute

10. Forecasting Weather and Climate The Route to Forecasting

11. MathematicalRepresentations Variables and Relations Simplifying Assumptions Parameterization Schemes Numerical Representation Code development Computing Platform Post Processing Error Management Initial and Boundary Data Simulation The Technology: A Generic Structure of Dynamical Forecasting Identification of Scales Mapping of small scales to large scales

12. Tropical Precip forecast made: 1Apr2006 India

13. The Promise of Weather Forecasting

14. NOAA NCEP CPC CAMS_OPI V0208 ANOMALY PRCP JUN-AUG 2007 Model JJA Rainfall Anomaly

15. The Orissa Super Cyclone: A Case Study ic: 26 Oct Wind Vector and Surface Pressure on 27th Oct Vector wind (m/s) over the Bay of Bengal region on 27 Oct 1999, 00 hour. The left panels represent ECMWF Analysis while the right panels represent model forecasts. The panels represent data for 925mb, 850mb and 200mb, respectively. Surface Pressure (hPa) over the Bay of Bengal region on 27 Oct 1999. The left panels represent ECMWF Analysis while the right panels represent model forecasts.

16. Cyclone Simulation AGCM LMDZ BoB ArbS WPac

17. Track Forecast Error Bay of Bengal (15 cases: 1980-2000) Lead -1 Forecast Time (hour) Forecast Time (hour) Lead 0 Lead +1

18. Multi-scale Forecasting: Heavy Rainfall Events Mumbai Heavy Rainfall on 26th July 2005 Forecast GCM (40km Resolution) (Satellite Observation, 10 km resolution)

19. Multi-scale Forecasting: Heavy Rainfall Events BANGALORE Heavy Rainfall on 24th October 2005 CHENNAI Heavy Rainfall on 27th October 2005 The circled areas indicate observed locations of heavy rainfall

20. Satellite observations at 10 Km resolution Satellite observations at 10 Km resolution

21. Compromise with Computing • Are we doing it right? Models are metaphors; need to use them carefully

22. Irreducible Model Error and Predictability Initial Data Boundary data Model Configuration Reducible Errors Resolution H P C Optimum Model Configuration False limits on predictability Intrinsic limits on predictability Nature is subtle; Reaching irreducible error configuration may require more computing than we can afford!

23. Lower Boundary Forcing may change depending on resolution

24. Monsoon and Extreme Rainfall Events: A Case of Tail Wagging the Dog? Daily Rainfall (Satellite) at 10 KM Resolution Weekly average time series of rainfall (red line) and number of ERE (blue line) >mm/day) both average over the region (70-85E; 5-30N). The CC between the weekly rainfall and ERE counts for each year is given in the respective panel. The blue dots represent distribution of daily counts of ERE. (Goswami and Ramesh, 2006)

25. Simulation of Weather and ClimateChallenges for Computing and Modelling • Resolving small scales in a global environment: Resolution • Removing Forecast uncertainties: Probabilistic Forecasts • Utilization of Observations: Data Assimilation • Customization: Sensitivity Experiments • Industrialization: Location-specific Forecast • Project with EID Parry: Forecast over sugar cane fields • Project with Govt. Karnataka: Hobli-level Forecast

26. Science and Cost of Customization

27. Customization An extremely computing-intensive proposition Sensitivity of limited area simulations to model domains

28. Spatial distribution of 30 Hr Accumulated ensemble mean rainfall (cm) for different Domains of 90km resolution

29. Reducing and Managing Forecast Uncertainty • The Problem of Forecast Dispersion • Intra-model Multi-lead/Multi-grid Ensemble • Inter-model Multi-model Ensemble (MI-ERMP) • Forecast dispersion may be addressed through ensemble forecasting => more computing Ensemble Forecasting: Instead of classical initial point to final point, initial neighborhood to final neighborhood An effective ensemble forecast may require hundreds of simulations for a given forecast!

30. What Type of Computing • Small-ensemble Long Runs • Climate Simulations • Impact Assessment • ………………………. • Large-ensemble Short Runs • Short-range Weather Forecasts • Probabilistic Forecasts • ………………………. Parallel Computing Simultaneous Multi-tasking We may need more than one type of computing architecture to generate the best forecast in an optimum configuration

31. Computational Requirement: An Example • Creation of Monsoon Climatology Integration Length: 6 months Number of Time steps: 104 Resolution: 20 km Number of Horizontal Grid Points: 105 Number of Vertical levels: 50 Ensemble Size: 100 Approximate Computing Time Required on ALTIX 3750 (SP): 100*6*10 = 6000 days ! With 30 processor multi-tasking, it is still 200 days of dedicated computing.

32. Simulation of Weather and Climate • A Cosmic Problem

33. Forecast Without Frontier • Habitat Planning(Location for Sustainability and Health) • Space Weather(Space Tourism and Freight Services) • Solar Flares(Satellite and terrestrial blackout Warnings) • Arctic Weather(Eco-Tourism and Habitat) • Martian Weather (For precision landings and future colonies)

34. Geo-Cosmological Computations Beyond Earth Simulator: Cosmo Simulator and beyond (Stars like Dust) The Sky is not the limit!!

35. HPC in Weather and Climate ForecastingSummary • As HPC grows, demand grows: • Higher Precision: Higher Resolution (larger grid) • Higher Reliability: Ensemble Forecasts (larger number of forecasts) • Customized Forecast:LargerNumber of Simulations • Coverage: Earth, Solar system and Beyond (domain size) • Longer Outlook: Increase in integration time (days to centuries) • Archival: Cumulative (New unit beyond petabytes!)

36. Looking ahead To simulate the Galaxy at a resolution of cyclonic vortex! Size: 1028 Number of Grid Points: 1028/103 Integration Time: Millions of years Time step: Decade Light Years of Computing before we stop; Happy Computing! Thank You