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Skamarock

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Information about Skamarock
Science-Technology

Published on September 25, 2007

Author: Nickel

Source: authorstream.com

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Resolving Clouds in Atmospheric Models:  Resolving Clouds in Atmospheric Models Bill Skamarock NCAR/MMM Slide2:  Clouds in the Atmosphere:  Clouds in the Atmosphere Weather: Precipitation – rain, snow, hail Wind, radiation, visibility Climate Moisture redistribution and precipitation – hydrological cycle Radiation Chemistry/Air-Quality: Chemical processing (acid rain) Ozone chemistry Transport of pollutants Wet deposition Representation of Clouds in Atmospheric Models:  Representation of Clouds in Atmospheric Models Large-scale models: h andgt; 30 km The effects of the clouds are diagnosed (parameterized) from the predicted water vapor field precipitation vertical transport and redistribution of moisture and heat radiative effects turbulence Representation of Clouds in Atmospheric Models:  Representation of Clouds in Atmospheric Models Meso-scale models: 8 km andlt; x andlt; 30 km The effects of the clouds are partially prognosed from predicted fields: water vapor, cloud water and ice, and frozen and liquid precipitation. Some portions of the cloud effects are still diagnosed (parameterized). some precipitation some vertical transport and redistribution of moisture and heat turbulence Representation of Clouds in Atmospheric Models:  Representation of Clouds in Atmospheric Models Cloud-scale models: 100 m andlt; x andlt; 8 km The effects of the clouds are entirely prognosed from predicted fields: water vapor, cloud water and ice, and frozen and liquid precipitation. Problems with Modeled Clouds:  Problems with Modeled Clouds Large-scale models (clouds completely diagnosed): Poor diagnosis of cloud type, composition, and precipitation. Clouds and cloud-systems do not know about vertical wind shear. Implications: (1) Large uncertainty in climate-model predictions (2) A key limiting factor for weather-forecast accuracy Slide8:  Mobile AL Radar Meso-/Cloud-Scale Model (WRF) Hurricane Katrina Reflectivity at Landfall 29 Aug 2005 14 Z 4 km WRF, 62 h forecast Slide9:  Realtime WRF 4 km BAMEX Forecast Composite NEXRAD Radar Reflectivity Forecast 12 h forecast Initialized 5/24/03 00Z Slide10:  Realtime WRF 4 km BAMEX Forecast Composite NEXRAD Radar Reflectivity Forecast 12 h forecast Initialized 5/24/03 00Z Vertical Velocity at z = 5 km, t = 5 h:  Vertical Velocity at z = 5 km, t = 5 h Along-line cell spacing ~ 6 to 8 Dh until Dh andlt; 500 m (cell diameter is 3 to 4 km in converged solutions) (Courtesy of G. Bryan, NCAR/MMM) Simulations using x = 4 km to x = 250 m:  Simulations using x = 4 km to x = 250 m Weak-shear case: Vertical cross-section of tracer concentration at 6 h (not a line-average). x = 4000 m x = 1000 m x = 250 m (Courtesy of G. Bryan, NCAR/MMM) Surface rain rate, weak shear:  Surface rain rate, weak shear 250 m solution close to convergence 1, 2, 4 km solutions over-predict precipitation. (Courtesy of G. Bryan, NCAR/MMM) Problems with Cloud Models:  Problems with Cloud Models Solutions do not statistically converge until h andlt; O(100 m) - turbulence problem When will our applications get there? (assume comp. speed doubles every 18 months) Climate - not in my lifetime Weather - global (state-of-the-art h ~ 25 km) 36 years (maybe in my lifetime) Weather - regional (state-of-the-art h ~ 7 km) 19 years (hopefully in my lifetime – but will I be retired?) Cloud Models:  Cloud Models Cloud models solve the 3D Euler equations and transport equations for water vapor and liquid/solid water species with subgrid models for turbulence and other models (parameterizations) for everything else (moisture phase changes, radiation, land-surface, ocean-surface, etc.) Generally speaking, there are 2 flavors: (1) Semi-Implicit (implicit treatment of acoustic and gravity waves) usually found in global models on lat-long grids – pole problem. (2) Explicit (explicit treatment of acoustic and gravity waves) some form of splitting is usually used to advance acoustic and gravity waves with a shorter timestep. Slide16:  Terrain-following hydrostatic pressure vertical coordinate Arakawa C-grid 3rd order Runge-Kutta split-explicit time integration Conserves mass, momentum, entropy, and scalars using flux form prognostic equations 5th order upwind or 6th order centered differencing for advection Limited area (not global) WRF-ARW (more info - http://www.mmm.ucar.edu/wrf/users/) Why Explicit :  Why Explicit Explicit time integration with splitting is more efficient than implicit solvers (operations for a given level of accuracy). Solver needs little tuning for application at different grid resolutions and problem sizes. Easily parallelized for SM, DM and SM/DM architectures. Slide18:  Time Integration in ARW 3rd Order Runge-Kutta time integration advance Amplification factor Slide19:  Time-Split Runge-Kutta Integration Scheme dt is the RK3 timestep acoustic timestep (in this case dt/4) Slide20:  Time-Split Runge-Kutta Integration Scheme In DM applications: A small amount of data is communicated within each acoustic step. Slide21:  Time-Split Runge-Kutta Integration Scheme In DM applications: A small amount of data is communicated within each acoustic step. A larger amount is data is communicated after each RK substep. Parallelism in WRF: Multi-level Decomposition:  Model domains are decomposed for parallelism on two-levels Patch: section of model domain allocated to a distributed memory node Tile: section of a patch allocated to a shared-memory processor within a node; this is also the scope of a model layer subroutine. Distributed memory parallelism is over patches; shared memory parallelism is over tiles within patches Single version of code for efficient execution on: Distributed-memory Shared-memory Clusters of SMPs Vector and microprocessors Parallelism in WRF: Multi-level Decomposition Logical domain 1 Patch, divided into multiple tiles Inter-processor communication WRF Software Framework Overview:  WRF Software Framework Overview Implementation of WRF Architecture Hierarchical organization Multiple dynamical cores Plug compatible physics Abstract interfaces (APIs) to external packages Performance-portable Slide24:  Courtesy of J. Michalakes; see http://box.mmm.ucar.edu/wrf/WG2/bench/ for more info Petascale Computing and Clouds:  Petascale Computing and Clouds Many effects of clouds on climate and weather are largely unknown/uncertain (observations lacking, models at coarse resolution have poor representation of clouds). Most important problem confronting dynamicists and modelers today. Cloud-resolving (Dh ~ O(100 m)) simulations of cloud systems are needed to understand cloud dynamics and to improve parameterizations - a petascale computing challenge. cloud- mixing eddies clouds cloud systems planetary waves synoptic systems meters to 100’s meters 102 - 104 meters 105 - 106 meters andgt;106 meters Petascale Computing and Clouds:  Petascale Computing and Clouds Split-explicit cloud models are easiest to scale to peta-computing - no global data exchange or implicit solver needed, numerics are not scale dependent. We can scale our problems to bigger machines. Questions: What will new machine architectures look like? Will we maintain efficiency with scaling and changes in machine architecture? What code architecture changes will be needed? Other problems: load balancing, analysis, I/O.

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