We introduce an efficient approach to mining multi-dimensional temporal streams of real-world data for ordered temporal motifs that can be used for prediction. Since many of the dimensions of the data are known or suspected to be irrelevant, our approach first identifies the salient dimensions of the data, then the key temporal motifs within each dimension, and finally the temporal ordering of the motifs necessary for prediction. For the prediction element, the data are assumed to be labeled. We tested the approach on two real-world data sets. To verify the generality of the approach, we validated the application on several subjects from the CMU Motion Capture database. Our main application uses several hundred numerically simulated supercell thunderstorms where the goal is to identify the most important features and feature interrelationships which herald the development of strong rotation in the lowest altitudes of a storm. We identified sets of precursors, in the form of meteorological Responsible editor:123 Identifying predictive multi-dimensional time series motifs 233 quantities reaching extreme values in a particular temporal sequence, unique to storms producing strong low-altitude rotation. The eventual goal is to use this knowledge for future severe weather detection and prediction algorithms.

Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for warm months. Most previous dynamic downscaling simulations significantly underestimate warm‐season precipitation in the region. This study aims to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that different cumulus schemes lead to more pronounced difference in simulated precipitation than other tested physics schemes. Simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. Spectral nudging ensures that large and synoptic‐scale circulations are faithfully reproduced while still allowing WRF to develop small‐scale dynamics, thus effectively suppressing the large‐scale circulation anomaly in the downscaling. As a result, a better precipitation downscaling is achieved. With the carefully validated configurations, WRF downscaling is conducted for 1980–2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans‐state Oologah watershed is also achieved.

Societies worldwide make large investments in the sustainability of integrated human-freshwater systems, but uncertainty about water supplies under climate change poses a major challenge. Investments in infrastructure, water regulation, or payments for ecosystem services may boost water availability, but may also yield poor returns on investment if directed to locations where water supply unexpectedly fluctuates due to shifting climate. How should investments in water sustainability be allocated across space and among different types of projects? Given the high costs of investments in water sustainability, decision-makers are typically risk-intolerant, and considerable uncertainty about future climate conditions can lead to decision paralysis. Here, we use mathematical optimization models to find Pareto-optimal satisfaction of human and environmental water needs across a large drought-prone river basin for a range of downscaled climate projections. We show how water scarcity and future uncertainty vary independently by location, and that joint consideration of both factors can provide guidance on how to allocate water sustainability investments. Locations with high water scarcity and low uncertainty are good candidates for high-cost, high-reward investments; locations with high scarcity but also high uncertainty may benefit most from low regret investments that minimize the potential for stranded assets if water supply increases. Given uncertainty in climate projections in many regions worldwide, our analysis illustrates how explicit consideration of uncertainty may help to identify the most effective strategies for investments in the long-term sustainability of integrated human-freshwater systems.

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Hands-on training, collaboration with scientists, and practice using real-world challenges give planners and decision-makers confidence to work with climate model information.