The Arctic is undergoing profound environmental change at a time of increasing geopolitical interests in the region. Loss of the Arctic Ocean's sea ice cover is one of the most prominent signatures of global climate change. As a direct result of the sea ice loss is the increasing Arctic accessibility, in particular maritime shipping traffic. While the near-term future of maritime routes is uncertain, a polar route has the potential to reduce transit times of traditional shipping routes by up to two weeks. In addition, opportunities for potential resource extraction and expanding Arctic tourism offer new economic prospects for some of the US and Canada's most isolated northern communities. This research investigates the statistical relationship between navigational conditions and maritime traffic in the Arctic. Specifically, this research utilizes an eight-year observational dataset of Arctic vessel traffic from 2013 to 2020, together with sea ice and atmospheric reanalysis products, to understand the linkages between observed maritime vessel traffic and sea ice and environmental changes. The figure shows a heat map of the vessel traffic during the studied period. Spatial features and temporal trends of the Arctic vessel traffic are analyzed. Their correlation with navigational conditions like sea ice concentration, wind waves, and sea surface temperature will be modeled and quantified using Machine Learning algorithms. This policy and security-relevant research will improve our understanding of recent and future Arctic environmental change and its impacts on maritime transport.

Numeric weather prediction is undergoing a revolution resulting from the continuous advances in scientific knowledge and technologies. With dozens of weather models emerging that all generate different predictions from each other, forecasts have been gradually shifting from a deterministic form to a probabilistic form which shows the increasing concerns of, not just the absolute prediction values, but the confidence of predictions and the uncertainty of models. As a computational problem, generating uncertainty information can be an expensive task. Conventionally, prediction models are initiated with slightly perturbed parameters and then the diversion of model results can be a measure of model uncertainty. However, the multi-simulation approach drastically increases the computational requirement so that it can potentially exceed the ability of the state-of-art high-performance computing platforms. Meanwhile, if spatial and temporal resolutions are of concern, this approach is far from being efficient and viable. The Parallel Ensemble Forecast system is designed to generate probabilistic weather forecasts by using the revolutionary numerical weather prediction technique, Analog Ensemble. It is a data-driven method that derives probability information of a deterministic prediction model using past forecasts and observations without multiple simulation runs. Integrated with high-performance platforms, the system distributes computational tasks among nodes and therefore further boosts the data simulation process.

With the improvement in numerical weather prediction models and high-performance computing technology, ensemble modeling and probabilistic forecasts have taken on some of the most challenging tasks, such as weather model uncertainty estimation and the global climate projection. High-resolution model simulations that were deemed impossible to complete within a reasonable amount of time in the old days are now running as an ensemble to better characterize the model uncertainty. However, with advances in computation which make large parallel computing widely accessible, important questions are being increasingly addressed on how to interpret each forecast ensemble member, instead of relying on a summarization of all ensemble members. The analysis of individual ensemble members allows for an in-depth analysis of specific possible future outcomes. Thus, it is desirable to have the ability to generate a large forecast ensemble in order to help researchers understand the forecast uncertainty. But it is also crucial to determine which ensemble members are the better ones and to identify metrics to assess the uncertainty captured by each ensemble member. This work proposes the Empirical Inverse Transform (EITrans) function to address these questions. EITrans is a technique for ensemble transformation and member selection based on knowledge from historical forecasts and the corresponding observations. This technique is applied to a particular ensemble forecast to select ensemble members that would offer a sharper and more reliable distribution without compromising the accuracy of the prediction.