Flywheel local dynamodb software#
We use statistics, data, and machine learning to identify architectural bottlenecks in our systems, improve software hygiene and raise velocity of our software teams.
Our works lies at the at the confluence of software development, machine learning, and business intelligence. Job summaryOur team provides the tools, training, and situational awareness for all engineering teams across Amazon to develop their teams, foster collaboration and inclusivity, and drive continuous improvement. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps.
We present a probabilistic, deep learning sea ice forecasting system, IceNet. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent.
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