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Global Modeling with NASA Supercomputing Technology: When Sandy Meets Lorenz Bo-Wen Shen, Ph

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Global Modeling with NASA Supercomputing Technology: When Sandy Meets Lorenz Bo-Wen Shen, Ph Global Modeling with NASA Supercomputing Technology: When Sandy Meets Lorenz Bo-Wen Shen, Ph. D. [email protected] Department of Mathematics and Statistics Center for Climate and Sustainability Studies Computational Science Research Center San Diego State University GMCS 214 SDiSttUiitSan Diego State University 21 November 2014 When Sandy Meets Lorenz 1 San Diego State Univ. Nov 21, 2014 Outline 1. ItIntrod uc tion 2. Navier-Stokes Equations, High-resolution Global Model, and Multiscale Modeling Framework (MMF) 3. Supercomputing and Visualization Technology 4. Simulations of Hurricanes Sandy and Others 5. Nonlinear Processes in Modified Lorenz Models 6. SdFtTkSummary and Future Tasks When Sandy Meets Lorenz 3 San Diego State Univ. Nov 21, 2014 Sandy: Tropical Cyclones with Multiscale Interactions Hurricane France Hurricane Howard Tropical Storm Phoebe Super typhoon Sonda When Sandy Meets Lorenz 4 San Diego State Univ. Nov 21, 2014 Lorenz: Chaos Theory with Butterfly Effect • The butterfly effect of first kind: sensitive dependence on initial conditions. • The butterfly effect of second kind: a metaphor for indicating that small perturbations can alter large-scale structure. • Lorenz’s studies suggested finite predictability and nonlinearity as the source of chaos. The studies by Lorenz (1963, 1972) laid the foundation for chaos theory, which was viewed as the third scientific revolution of the 20th century after relativity and quantum mechanics (e.g. Gleick, 1987; Anthes 2011). When Sandy Meets Lorenz 5 San Diego State Univ. Nov 21, 2014 When Sandy Meets Lorenz Sandy Lorenz Hurricane Dynamics Chaos Dynamics Earth Science Mathematics Complex Model Theoretical Model Real-world Model Idealized Model PDE ODE Complexities in Modeling Complexities in Mathematics Multiscale Processes Butterfly Effect Sandy (2012) Lorenz (1963) Can Sandy's ego be retained? (San Diego) When Sandy Meets Lorenz 6 San Diego State Univ. Nov 21, 2014 High-impact Tropical Weather: Hurricanes Each year tropical cyclones (TCs) cause tremendous economic losses and many fatalities throughout the world. Exampp()les include Hurricanes Katrina (2005) and Sandy (2012) . Hurricane Katrina (2005) (Shen et al., 2006b; 2013a) • Cat 5, 902 hPa, with two stages of rapid intensification • The sixth-strongest Atlantic hurricane ever recorded. • The third-strongest landfalling U.S. hurricane ever recorded. • The costliest Atlantic hurricane in history! ($100+ billion) Hurricane Sandy (2012) (Shen et al., 2013c) • The deadliest and the most destructive TC of 2012 Atlantic hurricane season • The second-costliest hurricane in United States history ($50 or 75$ billion) • The largest Atlantic hurricane on record When Sandy Meets Lorenz 7 San Diego State Univ. Nov 21, 2014 My Journey with Computational Science 08. 02. 2004 – Initial Columbia Promising When Sandy Meets Lorenz 8 San Diego State Univ. Nov 21, 2014 Computational Science July, 2004 June, 2005 Dec., 2007 • Computational Science (CS) is defined as an inter-disciplinary field with the goal s of und erst andi ng an d so lv ing comp lex pro blems us ing hig h-end computing facilities. • CS is identified as one of the most important fields of the 21st century to contribute to the scientific , economic , social and national security goals of USA by the President’s Information Technology Advisory Committee (PITAC). When Sandy Meets Lorenz 9 San Diego State Univ. Nov 21, 2014 Supercomputers @Top 500.org 1: Roadrunner Japan Earth 2: Jugar Simulator 3: NASA Pleiades (06/2002) (11/2008) (38.86 Tflops) 1: IBM/DOE BG/L Beta (70.72 Tflops) 2: NASA Columbia (51.87 Tflops) 3: Japan ES (38.86 Tflops) (11/2004) When Sandy Meets Lorenz 11 San Diego State Univ. Nov 21, 2014 NASA Supercomputing and Visualization Systems Pleiades Supercomputer (as June 2013) • one of a few petascale supercomputers •Rmax of 1,240 teraflops (LINPACK); Rpeak of 2, 880 tera flops • 162,496 cores in total; Intel Xeon processors, Nehalem, Westmere,Sandy Bridgg,e, Ivyyg, Bridge, • 417 TB memory • 3.1 PB disk space • Largest InfiniBand network. • Large-scale visualization system – 8x16 LCD tiled panel display – 245 million pixels • 128 nodes – 1024 cores, 128 GPUs • InfiniBand (IB) interconnect to Pleiades – 2D torus topology – High-bandwidth When Sandy Meets Lorenz 12 San Diego State Univ. Nov 21, 2014 My Journey with Computational Science 1. Ten (Five) awards from UMCP, SAIC and NASA/GSFC Since 2001 (2006). 2. Research results featured by Dr. Jack• Two Kaye major (Associate supercomputers: Director at Earth NASA/HQs) at the Interdepartmental Hurricane ConferencesSimulator and in 2012, NASA 2013, Columbia and 2014. 3. Researc h resu lts fea ture d in a recen• t Three Pres id modeling en t's Corner teams: ar tic one le o f from UCAR Magazine by Dr. Rick Anthes in 2011.NASA The article and two is entitled from Japan ``Turning the Tables on Chaos: Is the atmosphere more predictable than we assume?’’ 4. Research results featured in NASA News Stories (()07/2010 and 11/2010). It was also translated in Chinese by Science and Technology Division, Taipei Economic and Cultural Representative Office in the United States (駐美國台北 經濟文化代表處科技組). 5. Research results appeared in news medias, such as MSNBC, PhysOrg. com, National Geographic--Indonesia, ScienceDaily, EurekAlert, Yahoo News, TechNews Daily, Scientific Computing, HPCwire, etc. (2010) 6. Research projects selected as one of top 4 demonstrations at NASA Booth for Supercomputing Conferences (SC) in 2004, 2008, 2009, and 2010. 7.7. Selected Selected as as Journal Journal Highlights Highlights by by American American Geophysical Geophysical Union Union (07/2006) (07/2006) 8.8. Research Research results results featured featured in in Science Science magazine magazine (08/2006) (08/2006) “Is new science being produced or just really cool pictures?” which was raised by Mahlman and others who have reservations When Sandy Meets Lorenz 13 San Diego State Univ. Nov 21, 2014 Inspirational Comments When Sandy Meets Lorenz 14 San Diego State Univ. Nov 21, 2014 Inspirational Comments When Sandy Meets Lorenz 15 San Diego State Univ. Nov 21, 2014 Mathematical Models (math151) • The mathematical model often takes the form of a differential equation. • A differential equation is an equation that contains a unknown function and some of its derivatives. • An ordinary differential equation (ODE) is an equation containing a function of one independent variable and its derivatives. • A partial differential equation (PDE)differential equation that contains unknown multivariable functions and their partial derivatives. • Climate models use qu antitativ e methods to sim u late the interactions of the atmosphere, oceans, land surface, and ice. • A general circulation model (GCM), a type of climate model, is a mathema tica l mo de l o f the genera l c ircu la tion o f a p lane tary atmosphere or ocean and based on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). PDEs Æ GCM; PDEs –> ODEs (Lorenz model) When Sandy Meets Lorenz 16 San Diego State Univ. Nov 21, 2014 Navier-Stokes Equations The Navier–Stokes equations are strictly a statement of the conservation of momentum. Viscosity, 2v gravitational force, g When Sandy Meets Lorenz 17 San Diego State Univ. Nov 21, 2014 Navier-Stokes Equations + Other Equations • They may be used to model the weather, ocean currents, water flow in a pipe and air flow around a wing. • The Navier–Stokes equations in their full and simplified forms help with the design of aircraft and cars, the study of blood flow, the design of power stations, the analysis of pollution, and many other things. •Couppqyled with Maxwell's equations they can be used to model and study magnetohydrodynamics. To fully describe fluid flow, more information is needed. This additional information may include boundary data (e.g., no-slip), conservation of mass, conservation of energy, and/or an equation of state. ∂ρ + ∇ • ()ρv = 0 continuity equation, conservation of mass ∂t p = ρRT an equation of state • hydrostatic? ( dw/dt~0); incompressible? (d/dt ~0). • Source: Wikipedia When Sandy Meets Lorenz 18 San Diego State Univ. Nov 21, 2014 Idealized Equations for a Rotating Flow Coriolis Force d θ = 0 Thermodynamic equation Shen (1992, 1998) dt When Sandy Meets Lorenz 19 San Diego State Univ. Nov 21, 2014 Governing Equations (NCAR CAM 5.0) the momentum Eq. the hydrostatic Eq. π =p/ is the pseudo- density with eq. 3.1; is the general vertical coordinate the continuity Eq. The conservation of total air mass using π the thermodynamic Eq. virtual potential temperature the mass conservation q, water vapor law for tracer species When Sandy Meets Lorenz 20 San Diego State Univ. Nov 21, 2014 Parameterizations • Parametrization is the process of deciding and defining the parameters necessary for a complete or relevant specification of a model • Non-uniqueness: Parametrizations are not generally unique. • Dimensionality: the minimum number of parameters required to describe a model or geometric object is equal to its dimension, and the scope of the parameters—within their allowed ranges—is the parameter space. • Parameterization in a weather or climate model within numerical weather prediction refers to the method of replacing processes that are too small-scale or complex to be physically represented in the model (i.e., the so-called unresolved processes) by a simplified process (that can emulate the impact of unresolved processes) . • Associated with these parameterizations are various parameters used in the simplified processes. Parameterization are used to emulate the impact of unresolved processes. When Sandy Meets Lorenz 21 San Diego State Univ. Nov 21, 2014 NCAR CAM 5.0 dyypnamical processes, resolved ppyhysical processes , unresolved No direct interactions among different parameterizations! When Sandy Meets Lorenz 22 San Diego State Univ. Nov 21, 2014 Early Efforts with NCAR CAM March 1999 – September 2003 NCAR Community Atmosphere Model (CAM) • Unix System and Network Programming: Unix curses, device control (serial I/O), file system control, inter-process communication (pipes, 1992-1994 semaphore, shared memory, TCP/IP sockets), process control, signal handling.
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