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Seasonal water supply forecasts are a critical component of regional water resources management planning. Across the United States, multiple modeling tools and operational protocols have evolved over time to address this need. Here, the authors document, assess, and recommend improvements to the current operational water supply forecasting protocols employed in managing flows through, and water levels across, Earth's largest lake system: the Laurentian Great Lakes. The water resources management actions for this massive system are critically linked to North America's economy, and to human health and safety, through planning and policy decisions related to hydropower management, commercial navigation, and water level fluctuations. The authors' assessment indicates that existing operational seasonal water supply forecasting systems for the Great Lakes have moderate skill and that there are several high-priority areas for improvement, including improved representation of initial hydrological conditions, aligning variability in future meteorological forcings with climate-scale projections, and robust representations of forecast uncertainty. [ABSTRACT FROM AUTHOR]

In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7-0.8 for both rain and snow, 0.2-0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models. [ABSTRACT FROM AUTHOR]