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**Meso- to Macro-Scale Environmental Modeling**
NOAA, EPA, NASA, DOE, and NSF are involved in these Grand  Challenges and those in the next section onEcosystem Simulations.   These agencies collaborate on developing shared models of the  Earth'satmosphere and oceans that the individual agencies use for  global climate modeling andagency-specific applications.  NOAA  uses them for weather modeling (for use in weather forecasting), EPA for pollution modeling (using meteorological models that are  modified weather models), DOE foruse in groundwater management  and environmental remediation, and NSF for basic research in  subjectssuch as earthquake motion and land cover dynamics.  The  global climate modeling (An example of this is the "TOVS Ozone Picture of the 1994 Antarctic OzoneHole.") being conducted by these agencies builds on  these basic models, mission-specificapplications, and data.  This  work is now described in detail. 
***Massively Parallel Atmospheric Modeling Projects*
Atmospheric models are used in weather modeling (for example, in  understanding the mechanisms thatcontrol the development of  severe storms) and in environmental modeling (for example, in understanding the impact of emissions controls on air quality).  They  have been optimized for vectorsupercomputers, where they have  been run for years.  These computers have several limitations, notably limited memory, high cost, and uncertain future scalability.   Memory limits restrict thegeographical extent and resolution of the  simulations, and their cost prohibits the regionaldeployment of the  models that will be required for planning future air quality  measures. 
Parallel computing offers significant improvements in raw speed  and cost performance.  Speedimprovements allow scientists to  address larger problems, more complex model processes, and finer resolutions.  Cost performance improvements allow wider  deployment -- a local airport can run its ownweather model, a state  government its own air quality model.  Such a cost-effective  portablescalable parallel atmospheric circulation model has been  developed by researchers at Argonne andNCAR.  The model is a  parallel version of NCAR's Mesoscale Model 5 (MM5) that can resolve  distancescales ranging from continental to one kilometer or less  through the use of specialized numericalsolvers and support for  nested grids. 
The model incorporates new algorithms that scale to the number of  processors and address issues suchas load balancing that are unique  to massively parallel systems.  New programming techniques were developed to simplify the production of portable efficient parallel  models.  The model has been runon systems with hundreds to  thousands of processors that provide the large memory and  computationalpower needed for high resolution long duration  simulations; these include the Cray Research T3D, IBMSP, and Intel  Delta and Paragon.  It can be run on small clusters of workstations  connected by highspeed networks, providing cost effective  simulations when high resolution is not required. 
MM5 is a building block for EPA's next-generation air quality  modeling and decision support system,Models-3, that is being  developed to improve both the scientific accuracy and accessibility  ofmodeling tools and the data used in air quality management.  By  using distributed computingtechniques, a model can be run on high- end massively parallel computing systems in research labs aswell  as on small workstation clusters in local government offices.   Deployment is expected to resultin improved aircraft safety, air  quality control measures, and understanding of meteorological processes. 
HPCC funding comes from EPA and DOE; the U.S. Air Force provides  additional support.  This projectcomplements the DOE CHAMMP  (Computer Hardware, Advanced Mathematics, and Model Physics)  Program thatsupports development of the Parallel Community  Climate Model, a scalable global atmospheric model, atArgonne, Los  Alamos, and Oak Ridge. 
http://www.mcs.anl.gov/home/itf/epa.html
The SKYHI general circulation model developed by NOAA/GFDL has  been used fora decade to investigate the dynamical behavior of the  stratosphere and mesosphere.  The new versionis scalable and is  designed to run efficiently on both shared and distributed memory  systems undereither data parallel or message passing programming  models.  It allows scientists to incorporate newphysics software  modules of atmospheric features such as radiation and convection. 
A new scientific experiment is investigating how waves generated  by tropospheric disturbances, suchas thunderstorms and flow over  mountains, affect winds in the stratosphere and mesosphere.  This experiment will be run on the 1,024-node Thinking Machines CM-5 at  DOE/LANL and will use a gridresolution greater than can be achieved  on the supercomputer at NOAA/GFDL.  This collaboration issupported  by HPCC funds through NOAA and DOE/CHAMMP. 
("A Case Study Combining Granch Challenge and National ChallengeTechnologies:Weather Forecasting" is also presented.)
***Parallel Ocean Modeling*
The GFDL Modular Ocean Model (MOM) is public-domain software that  is being used by more than 200scientists in more than 20 countries.   Research topics include climate studies (both present day and paleoclimate), data assimilation, predictability, and basic research  into understanding world oceanprocesses on a number of space and  time scales.  A new version, MOM-2, is being released in FY 1995.  One feature of the new version is a flexible memory window that  allows solution for a group oflatitude rows at one time; as a result  MOM-2 can be run on a range of computer architectures with the number of latitude rows chosen to fit the particular system.  The  modular design of the model alsopermits alternate ways to treat  the atmosphere-ocean boundary at the top of the model and provides an improved way to represent the effects of rough bottom terrain. 
http://www.hpcc.noaa.gov/Ocean94 
The Parallel Ocean Program (POP) at DOE/LANL was developed to  perform high resolution oceansimulations.  Recent POP results are  shown in the lower image in the section "Advances in Ocean Modeling due to HPCC", where the significance of the calculations is also described.
http://www.acl.lanl.gov/ 
***Mathematical Modeling of Air Pollution Dynamics*
Massively parallel computing systems provide an avenue for  overcoming the computational requirementsin the study of  atmospheric chemical dynamics.  Implementation issues include  domain decompositionstrategies, algorithm design and evaluation,  portability, modularity, and buffering techniques usedin I/O  operations.  The Caltech urban air pollution model has been  implemented on distributed memoryMIMD systems including a 512- node Intel Paragon and a workstation cluster. 
The central challenge in developing a parallel air pollution model is  implementing the chemistry andtransport operators used to solve  the atmospheric reaction-diffusion equation.  The chemistry operator is generally the most computationally intensive step in  atmospheric air quality models, anda new method based on  Richardson extrapolation to solve the chemical kinetics has been  developed. The transport operator (advection equation) is the most  challenging to solve numerically.  Because ofits hyperbolic nature,  non-physical oscillations and/or negative concentrations appear  near steepgradient regions of the solution.  Six algorithms for  solving the advection equation have beenevaluated for their  suitability for use in parallel photochemical air quality models.  A  speedupfactor of 94.9 has been measured when chemistry,  transport, and I/O are done in parallel.  This workprovides the  computational infrastructure needed to incorporate new physico- chemical phenomena inthe next generation of urban- or regional- scale air quality models.  HPCC provides the toolsessential to  develop our understanding of air pollution further. 
Funding for this work came from EPA and the IBM Environmental  Research Program.  The research wasperformed at Caltech using the  Intel Delta and Paragon systems at Caltech's Concurrent Supercomputing Consortium. 
http://nicarao1.che.caltech.edu/~dabdub******