Using High-Performance Computing to Predict Extreme Weather

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<p>PowerPoint Presentation</p> <p>Using High-Performance Computing to Predict Extreme Weather</p> <p>Peter Bauer</p> <p>October 29, 2014</p> <p>1</p> <p>Independent intergovernmental organisation</p> <p>established in 1975</p> <p>with19 Member States15 Co-operating StatesEuropean Centre for Medium-Range Weather Forecasts</p> <p>October 29, 2014</p> <p>October 29, 2014</p> <p>For 2011:Age of ECMWF:36 yearsEmployees:227Supported by:34 StatesBudget: 43 million per annum Contributions by Member States and Co-operating States39.8 million per annum </p> <p>The success story of Numerical Weather Prediction: Wind storms</p> <p>28-29 October 2013 Christian wind gusts 5-day forecast of probabilities for wind gusts exceeding &gt; 10m/s</p> <p>October 29, 2014</p> <p>The success story of Numerical Weather Prediction: Heat wavesMaximum observed temperatureMaximum forecast temperature day-3 day-4Temperature anomaly ensemble forecast week-2 and week-3 Week 2-3 forecast of probabilities for temperature anomalies exceeding &gt; 5-10K</p> <p>October 29, 2014</p> <p>29-09-1530-09-1501-10-15Individual trajectories for JOAQUIN during the next 240 hourstracks: thick solid=HRES; thick dot=CTRL; thin solid=EPS members [coloured] The success story of Numerical Weather Prediction: Hurricanes</p> <p>October 29, 2014</p> <p>Day 0</p> <p>Day 2</p> <p>Southern Gaza Strip, on April18, 2012</p> <p>Day 4The success story of Numerical Weather Prediction: Dust storms</p> <p>October 29, 2014</p> <p>Previously on ?John von Neumann (1903-1957), Mathematician:Function theory, abstract algebra, quantum physicsLeader of Electronic Computer Project (1946-52)Jule Charney (1917-1981), Meteorologist:Set of equations for numerical prediction of planetary wavesFounder of theory of baroclinic instability</p> <p>Electronic Numerical Integrator and Computer (ENIAC)140 kW, 30 tons, 18,000 thermo-ionic valves1-layer model, resolution 400-700 km, North American domainSingle 24-hour forecast needed 24 hours compute timeENIAC 1950</p> <p>The same prediction needed 1 second on a Nokia 6300 mobile phone (2006)! </p> <p>Lewis Fry Richardson (1881-1953), Physicist, Meteorologist, Psychologist, Pacifist:Basics of numerical weather predictionFirst explicit calculation of weather on 20 May 1910</p> <p>October 29, 2014</p> <p>Computer powerModel complexityModel resolution</p> <p>TomorrowTodayMultiple dimensions</p> <p>Ensembles</p> <p>Long climate runs</p> <p>October 29, 2014</p> <p>8</p> <p>What is the challenge?</p> <p>ObservationsModelsVolume20 million = 2 x 1075 million grid points100 levels10 prognostic variables = 5 x 109Type98% from 60 different satellite instrumentsphysical parameters of atmosphere, waves, ocean</p> <p>ObservationsModelsVolume200 million = 2 x 108500 million grid points200 levels100 prognostic variables = 1 x 1013Type98% from 80 different satellite instrumentsphysical and chemical parameters of atmosphere, waves, ocean, ice, vegetation</p> <p>Today:Tomorrow:Factor 10Factor 2000per dayper time step(10-day forecast = 1440 time steps)</p> <p>October 29, 2014Today: 3 x IR spectrometers with IASI-NG (6 Mb/s) of which 300 channels are used: O(1000)Tomorrow: 5 x IR spectrometers with IASI-NG: 6 Mb/s of which 500 channels/PCs are used + 5 MTG-IRS (3 Gb/s) of which 500 channels/PCs will be used: O(5000)9</p> <p>scalability rangescalability rangeEnsemble</p> <p>SingleSimple compute projection (only resolution)20152025 M electricity/yearPower limit[Bauer et al. 2015]</p> <p>October 29, 20142015 Time critical21 TB/day written22 million fields85 million products11 TB/day sent to customers</p> <p>Non-time critical100 TB/day archived400 research experiments400,000 jobs / day20202025?Time critical128 TB/day written90 million fields450 million products60 TB/day sent to customers</p> <p>Non-time critical1 PB/day archived1,000 research experiments1,000,000 jobs / day</p> <p>Data projection Factor 5-10 every 5 years!time</p> <p>October 29, 2014</p> <p>[Schulthess 2015]</p> <p>Traditional science workflow</p> <p>October 29, 2014</p> <p>[Schulthess 2015]</p> <p>Future science workflow science specific code</p> <p> generic code</p> <p>Energy efficient SCalable Algorithms for weather Prediction at Exascale</p> <p>October 29, 2014</p> <p>13</p> <p>The quiet revolution of numerical weather prediction by P. Bauer, A.J. Thorpe, G. Brunet in Nature, 3 September 2015:The future of Numerical Weather Prediction[IPCC]</p> <p>October 29, 2014</p> <p>October 29, 2014</p> <p> May be one of the best medium-range forecasts of all times!The success story of Numerical Weather Prediction: Hurricanes</p> <p>October 29, 2014</p> <p>Analysis errorAnalysisForecast errorSingle forecastclimatologyTimePreviously on ?Shakespeare:for want a nail, the shoe was lost.for want a shoe, the horse was lost.for want a horse, the rider was lost.for want a rider, the battle was lost.for want a battle, the kingdom was lost.Ed Lorenz (1917-2008), Mathematician, Meteorologist:Impact of initial conditions on forecasts in non-linear systemsFounder of chaos-theory</p> <p>October 29, 2014[Michalakes et al. 2015]AVEC forecast model intercomparison: 3 kmNext-generation candidates models forUS national weather service:</p> <p> Some scale well but they are not efficient!Speed required to produce 7-day forecast in 1 hour</p> <p>October 29, 2014Required: Billion-way parallelismHardwarehybrid computing model seems to be here to stay;memory systems will become ever more complicated;hardware faults require fast adaptation strategies.</p> <p>Interconnection technology is seriously lagging behind computing power: 2-3 orders of magnitude gap!</p> <p>Softwaremessage-driven execution models;large-scale data analytics at the intersection of numerical linear algebra and data analytics;broad spectrum of programming languages; auto-tuning.</p> <p>[Dongarra et al. 2015]</p> <p>We believe that the time has come for the leaders of the computational science movement to focus their energies on creating software research centers to make progress on both hardware and software fronts simultaneously with a level of sustained, interdisciplinary collaboration among the core research communities.</p> <p>October 29, 2014</p> <p>18</p> <p>19</p> <p>Impact of climate variability on weather: El Nio</p> <p>October 29, 2014</p> <p>October 29, 2014</p>


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