Time evolving meteorological variables of daily observations from multiple sources can be represented as simple 3-node tensors. Powerful analytic tools such as canonical polyadic (cp) tensor decomposition are available to factor streaming tensors into tensor approximations associated with dominant components. We show the potential of tensor decomposition for meteorological analysis by presenting multi-year experiments for inferring Planetary Boundary Layer Heights (PBLH) by employing cp tensor decomposition analytics applied to streaming ceilometer base Lidar data sets. The experiment compares the PBLH obtained from the 3 nodes tensor consisting of 5 minutes streams of hourly Lidar based aerosol backscatter profiles each day from 3 distinctly located ceilometers distributed along the East coast for a 1.5 years period from May 7, 2021-June 30, 2022. We compare the PBLH derived from the ceilometers with those inferred from a WRF model simulation for the same period with two nested intervals over the CONUS at 2 deg. and 0.9 deg., interpolated to the same locations as the ceilometers. The WRF model integration is forced by the interpolated hourly NOAA GFS reanalysis data set and annual averaged aerosols distributions specified geographically over the CONUS grid. We present graphs of the time dependence of the dominant tensor components of PBLH as well as the error of the dominant tensors for various assumptions of tensor rank. These dominant components show the correlations of PBLH over 24 hours over multiple regions.

We show how to leverage quantum annealers (QAs) to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making locally optimal choices at each stage, we use QAs that sample from the ground state of a problem-dependent Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables. More specifically, we look at each spin of the Ising model as a random variable and contract all problem variables whose corresponding uncertainties are negligible. Our empirical results on a D-Wave 2000Q quantum proces-sor demonstrate that the proposed quantum-assisted greedy algorithm (QAGA) scheme can find notably better solutions compared to the state-of-the-art techniques in the realm of quantum annealing.

Multiple satellites and ground based Lidar instruments are becoming increasingly available in providing near daily coverage of aerosol distributions over the US. Aerosols are very small particles, both liquid and solid, arising from both natural and human activities. Atmospheric aerosols directly influence incoming solar radiation by scattering and reflection and indirectly by serving as nuclei for cloud formation of rain, ice and graupel. In addition, aerosols are emitted in the surface layer and pollute the Planetary Boundary Layer (PBL) affecting human health. PBL height (PBLH) determines the vertical extent to which most pollutants effectively disperse and is a key variable in air quality studies. Incorporating aerosol emissions with microphysics and chemistry parameterizations can significantly impact sub-seasonal weather forecasts directly through model radiative transfer parametrization and indirectly can influences the cloud formation, precipitation and the onset of deep convective storms through microphysical processes. Cloud resolving regional forecast models at 0.01 0 (~ 1km) spatial resolution employing such parameterizations will require a fully dedicated petascale computer to effect sub-seasonal forecasts on a weekly basis. In this paper, we initially study the potential of Neural Architecture Searches (NAS) to accurately emulate aerosol and microphysical parameterizations at coarse spatial resolutions to assess potential speed ups over conventional computational approaches. We conduct two experiments, a coarse 1-year WRF model simulation conducted at 2.5° over N. America and 0.8° over Eastern US. with 30 levels including CHEM, Bulk 2-moment microphysics to explore the feasibility of using NAS nets for sub seasonal forecasts and a second short term 6-day high resolution simulation at 0.09°with a single domain over the US and a 0.03° inner domain over eastern US. The problem running the WRF model at cloud resolving resolutions (for example 1km - 4km) is that it will require large computational times and resources. By first studying a coarse WRF experiment, we only need 10 min./sim day on 18 nodes or ~ 14 hrs/sub-seasonal forecast compared with 4hrs /day or approximately 5 dedicated days /sub-seasonal prediction. Our 6 day forecast study is used to provide computing time estimate for testing scalability and takes 20 min./day to run the 0.09° sub-seasonal simulation when using chemical and microphysics components at 3km. We employ the Auto-Keras NAS model, which accurately emulates the 1 year coarse experiment as well as gains an expected 10x speedup. This NAS deep neural net model will be used to infer the aerosol types, sizes and distributions to enable microphysics inferences and realistic mixed boundary layer heights with expected speedups of an order of magnitude or more.

We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing. Since executing quantum machine instructions on a quantum annealer can result in an excited state, rather than the ground state of the given Hamiltonian, we use different penalty parameters to generate multiple distinct quadratic unconstrained binary optimization (QUBO) functions whose ground state(s) represent a potential solution of the original problem. We then employ the attained samples from minimizing all corresponding (different) QUBOs to estimate the solution of the problem of binary compressive sensing. Our experiments, on a D-Wave 2000Q quantum processor, demonstrated that the proposed ensemble scheme is notably less sensitive to the calibration of the penalty parameter that controls the trade-off between the feasibility and sparsity of recoveries.

Providing climate data records to infer seasonal and interannual variations from multiple heterogeneous sources is a challenging data fusion. We combine the Compressive Sensing (CS) and Deep Learning (DL) into a single framework for fusing data from multiple sensors to improve spatial and temporal resolution for long term analysis. CS is used as an initial guess to combine data from multiple sources. DL models, using Long Short-Term Memory Neural Network (LSTM/RNN), Convolutional Neural Network (CNN), refine and further improve the data fusion from CS algorithm's output. The proposed framework has been tested the using daily global observations from two satellites the NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT). Our framework achieves lower errors and high correlation compared with the original data. The quality of fused data is evaluated by comparing again AmeriFlux ground station's datasets. Long term trends using fused data over the United States indicate an increase of 8 parts per million (ppm) annually in XCO2 over four years with Root Mean Square Errors of 0.39 ppm and correlation of 0.98 compared with original data. Interannual variability of the seasonal cycle shows an increase in years 2015 - 2017, but a sharp decrease in 2018.

Inspired by the importance of Planetary Boundary Layer Heights (PBLH) for inferring Air Quality assessments and the disappointing performance of current weather forecasts of PBLH, this paper presents the proposed impact of using a Machine Learning derived PBLH (ML-PBLH) using ground-based Ceilometer observing systems. The PBLH is vital in air pollution studies to determine the extent of vertical mixing of pollutants (e.g., particles, trace gases, etc.). On the other hand, model forecasts of the nighttime collapse of the PBLH close to the surface, grossly underestimate the actual observations. We propose using machine learning methods, employing deep neural networks for denoising and image segmentation to detect PBLH from the Ceilometer backscatter signal from the LIDAR observations. The ML-PBLH detection algorithm shows promising early results when compared with conventional ground-based Ceilometer retrieval methods such as Wavelet methods used for LIDAR identification of PBLH under normal conditions. The use of conventional methods as well as the wavelet method for LIDAR backscatter retrievals is limited in the presence of dense clouds. However, the proposed machine learning approach is able to infer PBLH even under such conditions.

We describe an approach using dynamic topic modeling to model influence and predict future trends in a scientific discipline. Our study focuses on climate change and uses assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. We use a custom dynamic topic modeling algorithm to generate topics for both datasets and apply cross-domain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time. For the IPCC use case, the report topic model used 410 documents and a vocabulary of 5911 terms while the citations topic model was based on 200K research papers and a vocabulary more than 25K terms. We show that our approach can predict the importance of its extracted topics on future IPCC assessments through the use of cross domain correlations, Jensen-Shannon divergences and cluster analytics.

Machine learning and graph analytics typically process data in an iterative way, reading the same data multiple times and sharing intermediate results across the worker nodes in cluster. Hadoop MapReduce and Spark are two popular open source cluster compute frameworks for large scale data analytics. Apache Spark is currently the state-of-the-art in-memory computation model extending MapReduce by transforming data into RDDs stored in memory. One limitation of Spark, however, lies in the fact that data transformation and distribution is implicitly managed by HDFS. Data locality is not guaranteed for iterative machine learning algorithms which read the same data multiple times. For example, data needed for operations to one worker node might reside in RDDs stored in other worker nodes. The resulting data shuffling becomes a bottleneck when iteratively reading such RDDs. We propose YinMem, a parallel distributed indexed in-memory computation system, bridging the gap between Hadoop ecosystem and HPC by replacing MapReduce with MPI while obtaining the advantage of the distributed data storage. YinMem achieves fair load balancing prior to computation for large sparse matrix by scheduling and distributing indexed data from NoSQL database to the RAM of working nodes. YinMem explores Alluxio as the in-memory storage system and enables efficient data sharing of intermediate results. Preliminary results show that YinMem has achieved 3× speedup to Spark, for computing eigenvalue and eigenvectors of a 16-million scale sparse matrix.

LVFS is a virtual scalable file storage system developed in response to a class of scientific data systems that over time continue to collect petabytes of data that begin to seriously impact the response time to user request services. The system has been operational in a real use case, the NASA MODIS Adaptive Processing System (MODAPS), and shown to double data throughput compared to the original system thanks to better performance and easier load balancing. The MODAPS operational life has been extended over a decade as of now and contains over four petabytes of data in over billions of files on over 500 different disks attached to multiple storage nodes. MODAPS is the processing system for delivering calibrated Level 1 data from MODIS instruments on two NASA satellites, each containing 36 channel multi-spectral visible and infrared changes launched over a decade ago. These system's life cycle operations are typical of many scientific instruments and experiments that continue to generate useful archival data well beyond their originial expected lifetime capabilities to meet current scientific user needs. The Level 1 Atmosphere Archive and Distribution System (LAADS) is responsible for distribution of products produced by MODAPS. The LAADS Virtual File System (LVFS) has now replaced parts of LAADS and is responsible for the read only distribution of all LAADS data to the public. In this paper, we describe the unique design of LVFS and, additionally, describe our ongoing work to incorporate a Distributed Hash-based architecture into the LVFS design to transform LVFS into a full scientific storage architecture scalable to Exabyte sizes.

We have adapted the MODIS Observation Coverage (Obscov) algorithm to the gridding of the AIRS L1b calibrated brightness temperatures, and developed a “Numeric-Obscov” implementation capable of utilizing the AIRS Tophat PSF dataset [1, 2]. We show that Obscov significantly reduces AIRS gridded-artifacts from a day to a season over winter 2005. The Obscov algorithm provides a physically accurate gridding model using spatial response information within the IFOV. Previously, Obscov has been used with an approximate triangular PSF, to allow for billions of integrals to be calculated per daily grid. We evaluated the Obscov algorithm via inter-comparison with MODIS-Aqua. Over a day, RMS noise is improved at high latitudes at 0.5°×1°. When averaged over a week or more, RMS noise is reduced globally by almost 40%.

Aqua provides a direct measurement of the surface infrared radiation for a near decade (2003-2011) from a single satellite platform with two completely independent instruments (AIRS and MODIS). We produced a 0.5°×1.0° lat-lon daily gridded near fundamental decadal data record (FDDR) of AIRS all sky brightness temperatures (BT) for all 2378 AIRS channels including the 4μ and 12μ surface windows for the 9-year period. We show (i) that decadal difference trends between the spectrally convolved AIRS and MODIS IR surface channels 3.96μ, 4.05μ and 12.03μ are as small as 0.01K, 0.016K and 0.001K per year respectively, suitable to detect year to year decadal trends of AIRS global surface BTs as small as 0.1K over the decade. (ii) The analysis of gridded AIRS surface BT channel 4.16μ and 12.18μ reveals no global annual warming trend between 2003-2011 and negligible trends for both hemispheres in the 9-year period. (iii) Both AIRS channels 4.16μ and 12.18μ show a statistically significant global annual BT warming trend of 0.6K and 0.87K respectively in the Arctic (60N-90N) with yearly changes varying by as much as 1.6K. (iv) Finally, we show that AIRS global surface BT spectral channel 4.16μ has a correlation with annual, seasonal and monthly GISS global station surface temperature data records 0.96, 0.9, 0.82 respectively. We conjecture that direct satellite level 1B AIRS IR radiance sounding observations constitute a FDDR and that well calibrated IR surface BTs can serve as a surrogate for directly observing global climate surface temperatures.

The goal of this special issue is to address such issues by assembling some of the latest researches on efficiently utilizing multicore and many-core processors in real-world applications, and their strategies for coping with those challenges. Multicore (e.g., Intel Westmere and IBM Power7) and many-core (e.g., NVIDIA Tesla and AMD FireStream graphics processing units (GPUs)) microprocessors are enabling more compute-intensive and data-intensive computation in desktop computers, clusters, and leadership supercomputers. However, efficient utilization of these microprocessors is still a very challenging issue. Their differing architectures require significantly different programming paradigms when adapting real-world applications. The actual porting costs are actively debated, as well as the relative performance between GPUs and CPUs. The invited papers in this special issue represent amplified works originally presented at the Frontiers of Multicore Computing Conference 2010, held at the University of Maryland, Baltimore County in August 2010. The selected papers cover representative research addressing the issues above. There are four papers addressing the issues related to CPUs and two on GPUs. Seelam et al. report their experiences in building and scaling enterprise business analytics benchmark, report generation, and rendering, on an IBM Power7 multicore system with eight Power7 cores and 32 hardware threads 1. The paper by Tracy and Brown presents a multithreaded physics software design to eliminate overhead associated with bodies at rest and consequently accelerate physics simulation in large, continuous virtual environments on Intel multicore processors 2. The paper by Hammond et al. presents multilevel performance analysis for the computational chemistry software, NW Chem, in Blue Gene/P and on two large-scale clusters 3. The paper by Simon et al. provides a performance evaluation and investigation of the astrophysics code, FLASH, for a variety of Intel multicore processors 4. The paper by Blattner and Yang presents the key steps in porting one data assimilation algorithm to GPU 5. The paper by Malik et al. examines the programming paradigms of Compute Unified Device Architecture (CUDA), OpenCL, The Portland Group Accelerator Compiler (PGI), and MATLAB through developing kernels from the Numerical Aerodynamics Simulation (NAS) parallel benchmarking suite 6. The guest editors of this special issue would like to express their deep gratitude to all authors, external reviewers, and Geoffrey Fox for their efforts in making this issue possible.

In this paper, we present a novel approach that views social media (SM) data as a human sensor network. These data can serve as a low-cost augmentation to an observing system, which can be incorporated into geophysical models together with other scientific data such as satellite observations and sensor measurements. As a use case scenario, we analyze the Deepwater Horizon oil spill disaster. We gather SM data that mention sightings of oil from Flickr, geolocate them, and use them as boundary forcings in the General NOAA Oil Modeling Environment (GNOME) software for oil spill predictions. We show how SM data can be incorporated into the GNOME model to obtain improved estimates of the model parameters such as rates of oil spill, couplings between surface winds and ocean currents, diffusion coefficient, and other model parameters.

Hadoop is a distributed filesystem and MapReduce framework originally developed for search applications by Google and subsequently adopted by the Apache foundation as an open source system. We propose that this parallel computing framework is well suited for a variety of service oriented science applications and, in particular, for satellite data processing of remote sensing systems. We show that, by installing Hadoop on a cluster of IBM PowerPC blade clusters, we can efficiently process multiyear remote sensing data, expect to see speed performance improvements over conventional multi-processor methodologies, and have more memory efficient implementation allowing for finer grid resolutions. Moreover, these improvements can be met without significant changes in coding structure.

Abstract The call for ever‐increasing model resolutions and physical processes in climate and weather models demands a continual increase in computing power. The IBM Cell processor's order‐of‐magnitude peak performance increase over conventional processors makes it very attractive to fulfill this requirement. However, the Cell's characteristics, 256 kB local memory per SPE and the new low‐level communication mechanism, make it very challenging to port an application. As a trial, we selected the solar radiation component of the NASA GEOS‐5 climate model, which: (1) is representative of column‐physics components (half of the total computational time), (2) has an extremely high computational intensity: the ratio of computational load to main memory transfers, and (3) exhibits embarrassingly parallel column computations. In this paper, we converted the baseline code (single‐precision Fortran) to C and ported it to an IBM BladeCenter QS20. For performance, we manually SIMDize four independent columns and include several unrolling optimizations. Our results show that when compared with the baseline implementation running on one core of Intel's Xeon Woodcrest, Dempsey, and Itanium2, the Cell is approximately 8.8x, 11.6x, and 12.8x faster, respectively. Our preliminary analysis shows that the Cell can also accelerate the dynamics component (∼25% total computational time). We believe these dramatic performance improvements make the Cell processor very competitive as an accelerator. Copyright © 2009 John Wiley & Sons, Ltd.

Recent trends in computer microprocessor development have shifted from a single powerful core to multi- and many-cores. As a result, the continuous improvement in computing power fueled by the exponentially increasing speed of a single processor could be over. With applications such as Earth and space sciences, higher resolutions and more sophisticated treatments of physical processes make models even more computationally intensive. Moreover, the drastic increase of data collected by various instruments requires a significant increase in computing power for data processing and analysis. Therefore, it is crucial for the computational science and technology community to evaluate the impacts of this shift on computationally intensive modeling and data processing applications and to develop appropriate solutions. It is known that the computing power of conventional processors is limited by memory bandwidth. Adding more cores to the processors worsens the problem. It is necessary to adapt computing algorithms to effectively utilize the computing power of those conventional multi- and many-core processors. In the last two years, there have emerged few unconventional processors: IBM's Cell Broadband Engine (hereafter referred to as Cell) and NVIDIA's Graphics Processing Unit (GPU). Intel and AMD are also developing competing Cell- or GPU-like processors, in addition to conventional multi- and many-core processors. It has been demonstrated that certain computationally intensive applications with moderate data communication can benefit from both Cell and GPU with a significant performance improvement. However, these emerging processors require new programming paradigms, which increase the porting costs and impede their effective utilization. To address such issues with the unconventional multi- and many-core processors, this special issue assembles some of the latest research on assessing the impacts of multi- and many-core processors, developing appropriate solutions, and taking advantage of the abundant computing power. The invited papers in this special issue represent augmented works originally presented at the Frontiers of Multicore Computing Conference 2008, held at the University of Maryland, Baltimore County in August 2008. Of course, it is not possible for a single issue to include all the topics addressed by the conference. However, the selected papers cover representative research addressing the issues above. There are four and two papers addressing the issues related to Cell and multi-core processors, respectively. The paper by Germann et al. presents their approaches and optimization methods in porting a short-range parallel molecular dynamics code to the first petaflops computer, Roadrunner, which uses Cells as accelerators 1. The paper by Woodward et al. reports their initial experience with porting and optimizing gas dynamics simulation on Roadrunner 2. The paper by Zhou et al. presents a study on the impact of Cell on the programming paradigm for climate and weather models 3. The paper by Brown et al. presents a contemporary artwork, the ‘Scalable City,’ accelerated with Cell 4. The paper by Wang and JaJa presents a study on interactive direct volume rendering with Intel Clovertown quad-core processors 5. The paper by Simmon and McGalliard provides benchmark results for a variety of high-performance computing applications on the Cray XT3 and XT4, which use dual- and quad-core AMD Opteron processors, and discusses and analyzes multi-core effects 6. The guest editors of this special issue would like to express their deep gratitude to all authors, external reviewers, and Geoffrey Fox for their efforts in making this issue possible.

The Aqua spacecraft, launched on May 4, 2002, carries two well-calibrated independent infrared (IR) grating spectrometers Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectrometer (MODIS), which have been continuously returning upwelling IR spectral radiance measurements for over five years. Based on an Aqua Sr. Project Review, estimates of available flight fuel, power, and orbital projections assess the life span of the Aqua satellite, and these two instruments, to be reliable to 2013. Since launch, these instruments have generated petabytes of data, which are managed and made available by the Goddard Space Flight Center (GSFC) Earth Science Data and Information Services Center and GSFC MODAPS. Agencies such as NOAA, DOD, EPA, and USGS use the AIRS data mostly for weather-related applications, whereas MODIS data are used, in addition to some climate-related studies, for studies of weather, oceans, and land processes, aerosols, natural and man-made disasters, and earth ecology. The Science Investigator-led Processing Systems (SIPS) teams have made many of the desired products derived from these data sets available either as level 2 products and/or level 3 gridded product fields. However, no gridded level 3 data products of radiances, either averaged for a grid element, max, min, or as brightness temperatures (BTs), are provided directly by the SIPS. Thus, one impediment that the general community faces in accessing these MODIS produced petabytes of data is storing such large data sets, interpreting the multiformatted data, and transforming it into helpful data sets for climate-research needs. The Service-Oriented Atmospheric Radiance (SOAR) system has been designed to bridge these gaps and overcome the challenges of bringing this rich data source to the science community, by delivering applications that process these valuable radiance data into standard spatial–temporal grids as well as user-defined criteria on demand. SOAR can serve this community with aggregated, enriched, and thinned gridded data sets provided with access to the data on demand, with query and subsetting capabilities across many dimensions. In addition, SOAR provides online user-specified visualization and analysis requests, all accessible via a Web browser. The utility of SOAR is exposed via Web-service routines, using the Simple Object Access Protocol. The Web-service library and supporting technologies (Axis, PostgreSQL, and Tomcat) reside on a University of Maryland Baltimore Campus client server, which interfaces to and invokes algorithms on the process server, a high-performance computer cluster and storage system. These servers are connected to the sensor data stores at the GSFC via a high-speed fiber-optic network connection [10 Gb/s], providing reliable and fast on-demand access to a vast online library of AIRS and current monthly MODIS source data.

Launched on May 4, 2002, Aqua carries two well calibrated independent infrared (IR) instruments, Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectro- radiometer (MODIS), which together could provide a precise decadal measurement across the IR electro- magnetic spectrum from 3.7 um to 15um to form a fundamental climate data record. According to the National Academy of Science - Nation Research Council report,the notion of a fundamental climate data record (FCDR) is defined as "a time series of measurements of sufficient length and continuity to determine climate variability and change from sensor data (e.g., calibrated radiances, brightness temperatures (BT), radar backscatter) that have been improved and quality controlled over time." AIRS and MODIS have been continuously returning upwelling IR spectral radiance measurements while maintaining a nominal grid precision of ~0.2 - 0.4 K BT [2] with quality controlled calibration improvements for more than five years. Based on the Aqua senior Project Review of available flight fuel, power and orbital maneuvers, the assessed life span of the satellite Aqua is estimated to be 2013. AIRS and MODIS instrument teams also expect their passive radiometers to continue providing self-calibrating measurements with the same precision through the year 2013. With these assumptions, we show in this paper that with these two independent sensors on the same platform with on orbit relative validations, this data record would be of sufficient length and continuity to meet the FCDR definition to observe and determine the decadal variability of seasonal to interannual climate processes. In addition, NPOESS preparatory project will launch satellite systems carrying instruments similar to AIRS and MODIS which will enable production of an FCDR record for additional decades.

Atmospheric temperature and moisture profile sounding data spanning more than three decades comprise one of the longest continuous US polar orbiting satellite data records available today. NASA's Aqua satellite launched on May 4, 2002 carries two well calibrated independent IR grating spectrometers, AIRS and MODIS. Both AIRS and MODIS have been used for extensive research and are truly flagship instruments for the Earth Science community, but neither offers an operational product with gridded calibrated radiances. Radiance data are very useful and have been used for operational weather forecasting models, and to determine observed global trends in temperature, ozone, albedo, desertification, aerosols, etc. The radiances are only available in their native orbital swath form at full resolution that can be awkward and bulky to work with. Their co-location on Aqua provides a unique opportunity for common observation of the same air mass with two different instruments over a long time period.

Mass storage systems are finding greater use in scientific computing research environments for retrieving and archiving the large volumes of data generated and manipulated by scientific computations. This paper presents a queuing network model that can be used to carry out capacity planning studies of hierarchical mass storage systems. Measurements taken on a Unitree mass storage system and a detailed workload characterization provided the workload intensity and resource demand parameters for the various types of read and write requests. The performance model developed here is based on approximations to multiclass Mean Value Analysis of queuing networks. The approximations were validated through the use of discrete event simulation and the complete model was validated and calibrated through measurements. The resulting model was used to analyze three different scenarios: effect of workload intensity increase, use of file compression at the server and client, and use of file abstractions.

Five supercomputing problems are described: (1) determining the motions of the Earth's inner core boundary from outer-space measurements of the magnetic field; (2) simulating the global dynamics of the Earth's ocean currents, sea-ice cycles, land surface biology, and interannual atmospheric climates; (3) upper atmospheric chemistry; (4) modeling solar flare eruptions; and (5) the role of jet processes in the birth of stars. In addition, two examples indicating the growing importance of the use of supercomputers for scientific visualization are presented. The first deals with remote mapping from space of the Earth's topography or other planets. The second problem from astrophysics deals with mapping the cosmic background radiation in the presence of other astronomical signatures. A brief overview of interdisciplinary modeling plans under development for the next generation of supercomputers is given.>