Next Generation Earth System Prediction Strategies for Subseasonal to Seasonal Forecasts (2016) / Chapter Skim
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7 Cyberinfrastructure and Workforce Capacity Building
7 Cyberinfrastructure and Workforce Capacity Building
Pages 223-238
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From page 223... ...
creating professional incentive structures and workforce pipelines to ensure investment in pivotal yet currently underrepresented activities such as model development, moving research to operational systems, and meeting decisionmaker needs. Many of the barriers identified in these previous reports for weather forecasting and climate modeling are common to subseasonal to seasonal (S2S)
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Combining these factors into an example, improving model resolution from 100 km to 25 km and doubling the number of vertical levels as well as model complexity, while running 100 ensemble members, could easily result in a 1,000-fold increase in computational costs compared to today. Thus, the S2S modeling enterprise fundamentally relies on sustained, dramatic improvements in super computing capabilities and needs to strategically position itself to fully exploit them.
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Finding 7.2: The transition to new computing hardware and software through the next decade will not involve faster processing elements, but rather more elements with considerably more complex embodiments of concurrency. This transition will be highly disruptive.
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S2S Application Challenges For climate models generally, increasing numbers of processing elements combined with deep and abstruse memory hierarchies will continue to push the limits of application code design and parallel programming standards and will create a challenging environment for high-performance-computing (HPC) application programmers (NRC, 2012b)
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-- do not work well on memory systems with deep cache hierarchies, wide cache lines, and decreasing amounts of memory per processing element. The introduction of vector capabilities into many core processors creates challenges for the "branchy" physics codes1 typical of S2S applications.
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Finding 7.4: S2S models are not taking full advantage of current computing architectures, and improving their performance will likely require new algorithms with better data locality, as well as significant refactoring of existing ones for more parallelism. Shared Software Infrastructure Components Similar to the climate modeling community (NRC, 2012a)
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Finding 7.5: An integrative modeling environment presents an appealing option for addressing the large uncertainty about the evolution of hardware and programming models over the next two decades. Data Storage, Transfer, and Workflow for S2S Prediction In addition to the supercomputer/storage infrastructure and the forecasting models, a key element of the forecasting workflow includes data cyberinfrastructure, including the storage, transfer, analysis, and visualization workflows associated with big data sets.
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A dedicated and enhanced data-intensive cyberinfrastructure will be required to enable the distributed S2S community to access the enormous data sets generated from both simulation and observations. Data Analysis Workflow S2S data-intensive applications and workflows are likely to face data analysis challenges of scale and scope similar to those faced by the Coupled Model Inter omparison c Projects (CMIP)
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Finding 7.7: New approaches to data-centric workflow software that incorporate parallelism, remote analysis, and data compression will be required to meet the demands of the S2S forecasting community. Moving Forward with Building Capacity for S2S Cyberinfrastructure Advances in S2S forecast models will require dramatically increased computing c apacities, but the transition to new computing hardware and software during the next decade will be highly disruptive with the increasing concurrency of new HPC systems.
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The weather and climate forecasting community has never retreated from experimenting with leading-edge systems and programming approaches to achieve required levels of performance. The current HPC architectural landscape, however, is particularly challenging because it is not clear what direction future hardware and software paradigms may follow.
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The critical point is that development of atmospheric and environmental prediction models, for S2S and other ranges, must become an interdisciplinary effort involving scientists, software engineers, and applied mathematicians (NRC, 2008)
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Data on the numbers of students involved in S2S model development do not exist, and any proxy data and anecdotal evidence (NRC, 2012b) suggest that the pipeline for S2S model developers is not growing in a robust fashion.
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Here, the focus is on the skills needed to enable those connections. The potential scale of use dwarfs the current production of people trained in interdisciplinary research or research in the social and behavioral sciences focused on using weather or climate information in decision-making.
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Modeling centers outside of the United States, such as the European Centre for M edium-Range Weather Forecasts (ECMWF) , have attempted to attract and retain more people in S2S model development work by appointing model developers to 5-year terms, which is longer than typical research grant cycles in the United States (3 years)
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A number of avenues exist for decision makers to interact with experts working on S2S forecasting, through so-called "boundary organizations" and other interdisciplinary entities. Boundary organizations exist within the public sector (e.g., NOAA's Regional Integrated Sciences and Assessments program actively engages decision- akers through tailored products, educational programs, and efforts m to co- roduce climate products and services)
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From page 238... ...
• Improve incentives and funding to support existing professionals and to at tract new professionals to the S2S research community, especially in model development and improvement, and for those who bridge scientific disci plines and/or work at component interfaces. • Expand interdisciplinary programs to train a more robust workforce to be em ployed in boundary organizations that work in between S2S model developers and the users of forecasts.
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