2019 CSDMS meeting-115: Difference between revisions
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|CSDMS meeting abstract=Recent trends in Earth system modeling, climate data collection, and computing architecture have opened new opportunities for machine learning to improve ESMs. First, new and cheaper satellites are generating large volumes of observational data (e.g. Arctic and Antarctic DEMs), and massive climate modeling projects are generating large volumes of simulated climate data (e.g. CMIP5, CMIP6, CESM-LE). Second, machine learning applications are driving the design of next-generation computing architectures that will accelerate applications like neural nets without ameliorating the computational bottlenecks (ref: NOAA HPC position paper) that limit existing climate models. Third, the climate science community is becoming increasingly familiar with machine learning techniques. Here, I summarize opportunities for CSDMS practitioners to use machine learning techniques to improve Earth system and Earth surface models. | |CSDMS meeting abstract=Recent trends in Earth system modeling, climate data collection, and computing architecture have opened new opportunities for machine learning to improve ESMs. First, new and cheaper satellites are generating large volumes of observational data (e.g. Arctic and Antarctic DEMs), and massive climate modeling projects are generating large volumes of simulated climate data (e.g. CMIP5, CMIP6, CESM-LE). Second, machine learning applications are driving the design of next-generation computing architectures that will accelerate applications like neural nets without ameliorating the computational bottlenecks (ref: NOAA HPC position paper) that limit existing climate models. Third, the climate science community is becoming increasingly familiar with machine learning techniques. Here, I summarize opportunities for CSDMS practitioners to use machine learning techniques to improve Earth system and Earth surface models. | ||
|CSDMS meeting posterPDF=Kelly_Kochanski_CSDMS_Conference_Poster_final.pdf | |||
|CSDMS meeting posterPNG=Kelly_Kochanski_CSDMS_Conference_Poster_final.png | |||
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Revision as of 06:09, 28 May 2019
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Machine learning for climate prediction
Recent trends in Earth system modeling, climate data collection, and computing architecture have opened new opportunities for machine learning to improve ESMs. First, new and cheaper satellites are generating large volumes of observational data (e.g. Arctic and Antarctic DEMs), and massive climate modeling projects are generating large volumes of simulated climate data (e.g. CMIP5, CMIP6, CESM-LE). Second, machine learning applications are driving the design of next-generation computing architectures that will accelerate applications like neural nets without ameliorating the computational bottlenecks (ref: NOAA HPC position paper) that limit existing climate models. Third, the climate science community is becoming increasingly familiar with machine learning techniques. Here, I summarize opportunities for CSDMS practitioners to use machine learning techniques to improve Earth system and Earth surface models.
