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NASA Earth Exchange (NEX) Earth Science Division/NASA Advanced Supercomputing (NAS) Ames Research Center
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Project Goal To improve availability of Earth Science data, models, analysis tools and scientific results through a platform that fosters knowledge sharing, collaboration, innovation and direct access to compute resources. Project Goal To improve availability of Earth Science data, models, analysis tools and scientific results through a platform that fosters knowledge sharing, collaboration, innovation and direct access to compute resources. NASA Earth Exchange (NEX)
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NASA Earth Exchange components
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NEX Resources Computing Supercomputing and storage through NASA Advanced Supercomputing Division (NAS) Almost 3PB storage, 512 - 8,000 CPU cores Possible to extend far beyond 8,000 cores if needed Data Global MODIS, AVHRR, Landsat, GCM Scenarios, weather data, etc. Models Publicly available models Software Utilities Open source and commercial (pending licensing) Collaboration Facilitation/Knowledge Network Based on DASHlink social network site Audio/Visual Real-time Collaboration (Adobe Connect) Large-scale Data visualization at NAS on hyperwall-2 + building custom visualization facility Computing Supercomputing and storage through NASA Advanced Supercomputing Division (NAS) Almost 3PB storage, 512 - 8,000 CPU cores Possible to extend far beyond 8,000 cores if needed Data Global MODIS, AVHRR, Landsat, GCM Scenarios, weather data, etc. Models Publicly available models Software Utilities Open source and commercial (pending licensing) Collaboration Facilitation/Knowledge Network Based on DASHlink social network site Audio/Visual Real-time Collaboration (Adobe Connect) Large-scale Data visualization at NAS on hyperwall-2 + building custom visualization facility
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Knowledge Network Users can search for related activities Who is doing what where? Map interface Categorized by discipline Categorized by geographic region Users can create projects and link to other projects Users can control the visibility of their project Users can control access to the project and the level of sharing Users can share algorithms, data, knowledge Users can search for related activities Who is doing what where? Map interface Categorized by discipline Categorized by geographic region Users can create projects and link to other projects Users can control the visibility of their project Users can control access to the project and the level of sharing Users can share algorithms, data, knowledge
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Example End-to-End Usage User registers and specifies resource requirements: Data, tools, models and computing resources A custom environment is created containing the requested resources Within this environment user can: Run existing models Bring in new data, models, and algorithms Extend existing models Share models and data with community Provide access to the results and the environment When work is completed resources are recycled The specific environment (including models, data, etc.) can be completely saved and reloaded and re-run in the future reproducing the results User registers and specifies resource requirements: Data, tools, models and computing resources A custom environment is created containing the requested resources Within this environment user can: Run existing models Bring in new data, models, and algorithms Extend existing models Share models and data with community Provide access to the results and the environment When work is completed resources are recycled The specific environment (including models, data, etc.) can be completely saved and reloaded and re-run in the future reproducing the results
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Creating solutions for resource management Agricultural Water Management
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MODIS-LANDSAT integration For monitoring crop growth, irrigation scheduling, deforestation, and natural disasters Daily at 250/500m from MODIS Once every 8 days at 30m from Landsat
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DAY 144DAY 155DAY 185DAY 192 StarFM Algorithm MOSAT LANDSAT MODIS
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Forecasting California Agricultural Water Needs
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TOPS - Mobile Computing Interface
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KALPANA, Indian GOES 08001000 12001400
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NEX Benefits Lowers the barrier of entry. Faster algorithm/product development. Collaboration among multiple disciplines (remote sensing, machine learning). Scaling up, going global. Lowers the barrier of entry. Faster algorithm/product development. Collaboration among multiple disciplines (remote sensing, machine learning). Scaling up, going global.
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