This study has investigated the spatiotemporal structure and changes in Lake Michigan snowfall for the period 1950–2013. With data quality caveats acknowledged, a larger envelope of stations was included than in previous studies to explore the data using time series analysis, principal component analysis, and geographic information systems. Results indicate warming in recent decades, a near-dearth of serial correlation, midwinter dependence on teleconnection patterns, strong sensitivity of snowfall to temperature, peak snowfall variability and dependence on temperature within the lake-effect belt, an increasing fraction of seasonal snowfall occurring from December to February, and temporal behavior consistent with the previously reported trend reversal in snowfall.

Previous studies have suggested that tropical cyclones (TCs) in deformation steering flows can be associated with large position errors and uncertainty. The goal of this study is to evaluate the sensitivity of position forecasts for three TCs within deformation wind fields [Debby (2012), Joaquin (2015), and Lionrock (2016)] using the ensemble-based sensitivity technique applied to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts. In all three cases, the position forecasts are sensitive to uncertainty in the steering wind within 500 km of the 0-h TC position. Subsequently, the TC moves onto either side of the axis of contraction due to the ensemble perturbation steering flow. As a TC moves away from the saddle point, the ensemble members subsequently experience different ensemble-mean steering winds, which act to move the TC away from the ensemble-mean TC position along the axis of dilatation. By contrast, the position forecasts appear to exhibit less sensitivity to the steering wind more than 500 km from the initial TC position, even though the TC may interact with these features later in the forecast. Furthermore, forecasts initialized at later times are characterized by significantly lower position errors and uncertainty once it becomes clear on which side of the axis of contraction the TC will move. These results suggest that TCs in deformation steering flow could be inherently unpredictable and may benefit from densely sampling the near-storm steering flow and TC structure early in their lifetimes.

This study has investigated the spatio‐temporal variability of November and March Lake Michigan snowfall for the period 1950–2013. Snowfall characteristics were assessed using time series analysis, geographic information systems, and visualization. Results indicate significant temporal decreases of November and March snowfall, peak inter‐annual variability within the lake‐effect belt, modest concurrent and lagged sensitivity to teleconnection indices, and strong dependence of snowfall on temperature. The decreased snowfall is in contrast to December–February snowfall and associated with a decreasing fraction of November and March precipitation days occurring as snow days, rather than changes in precipitation frequency. The decreasing fraction of precipitation days occurs as snowfall is consistent with synoptic‐scale disturbances producing rain rather than snow and lake‐effect rain falling in lieu of snow.

African easterly waves (AEWs) are the primary synoptic-scale weather feature found in sub-Saharan Africa during boreal summer, yet there have been few studies documenting the performance of operational ensemble prediction systems (EPSs) for these phenomena. Here, AEW forecasts in the 51-member ECMWF EPS are validated against an average of four operational analyses during two periods of enhanced AEW activity (July–September 2007–09 and 2011–13). During 2007–09, AEW position forecasts were mainly underdispersive and characterized by a slow bias, while intensity forecasts were characterized by an overintensification bias, yet the ensemble-mean errors generally matched the forecast uncertainty. Although 2011–13 position forecasts were still underdispersive with a slow bias, the ensemble-mean error is smaller than for 2007–09. In addition, the 2011–13 intensity forecasts were overdispersive and had a negligible intensity bias. Forecasts from 2007 to 2009 were characterized by higher precipitation in the AEW trough center and high correlations between divergence errors and intensity errors, suggesting the intensity bias is associated with errors in convection. By contrast, forecasts from 2011 to 2013 have smaller precipitation biases than those from 2007 to 2009 and exhibit a weaker correlation between divergence errors and intensity errors, suggesting a weaker connection between AEW forecast errors and convective errors.