Central New York State, located at the intersection of the northeastern United States and the Great Lakes basin, is impacted by snowfall produced by lake-effect and non-lake-effect snowstorms. The purpose of this study is to determine the spatiotemporal patterns of snowfall in central New York and their possible underlying causes. Ninety-three Cooperative Observer Program stations are used in this study. Spatiotemporal patterns are analyzed using simple linear regressions, Pearson correlations, principal component analysis to identify regional clustering, and spatial snowfall distribution maps in the ArcGIS software. There are three key findings. First, when the longterm snowfall trend (1931/32-2011/12) is divided into two halves, a strong increase is present during the first half (1931/32-1971/72), followed by a lesser decrease in the second half (1971/72-2011/12). This result suggests that snowfall trends behave nonlinearly over the period of record. Second, central New York spatial snowfall patterns are similar to those for the whole Great Lakes basin. For example, for five distinct regions identified within central New York, regions closer to and leeward of Lake Ontario experience higher snowfall trends than regions farther away and not leeward of the lake. Third, as compared with precipitation totals (0.02), average air temperatures had the largest significant (r , 0.05) correlation (20.56) with seasonal snowfall totals in central New York. Findings from this study are valuable because they provide a basis for understanding snowfall patterns in a region that is affected by both non-lake-effect and lake-effect snowstorms.

The Great Lakes region experiences anomalously high seasonal snowfall totals relative to similar latitudes. Although lake‐effect snowstorms are common in this region, snowfall occurs from a variety of storm types. This study examines snowstorms in a subsection of the Lake Ontario basin to develop a classification scheme to categorize the different types of snowstorms affecting the region. From 1985 to 2015, there were 11 different snowstorm types to affect the study area. The classification system was used to assess the frequency of, and snowfall produced by the different storm types within the eastern Great Lakes region. From the classification, snowstorms were categorized as either non‐direct cyclonic storms (NDCS) or direct cyclonic storms (DCS). Lake‐effect snowstorms, a type of NDCS, were the most frequent storm (35.1% of all storms) and accounted for approximately 39.4% of the snowfall. Most lake‐effect storms (37.7%) produced moderate snowfall totals (10.2–25.3 cm), yet heavy snowfall storms (≥25.4 cm) contributed significantly (ρ ≤ .05) more to seasonal snowfall totals than lighter snowfall storms. Direct cyclonic clippers forming over high latitudes of northwestern Canada, were the most frequent DCS in Central New York (11.3% of all storms), with nearly three quarters of the storms originating over Alberta. These storms only contributed 9.2% of the seasonal snowfall in the study area, compared to 12.7% from direct cyclonic Nor'easters forming near the east coast of North America. Although Nor'easters occur less frequently than clippers, when they do occur, they tend to produce heavy widespread snowfall across the region. The classification system proposed can be modified to accommodate snow basins across the globe. Classifying snowstorms will help determine the seasonal snowfall contribution from different storms and aid in future climate predictions, as individual snowstorm types may respond differently to a warming global climate.

Southern California's most extreme fire weather is caused by offshore Santa Ana winds, which commonly occur later in the year than the lightning which provides natural ignition. Examination of the specific dates of both lightning and Santa Ana winds over 25 years shows that Santa Ana winds are very rare during or even within ten days of lightning strikes. The median lag between the two phenomena is 52 days, and on those occasions when lightning does occur shortly before Santa Ana winds, the actual density of strikes is very low. The rarity of lightning as ignition for Santa Ana-driven fires suggests that the current fire regime dominated by such fires is largely a product of the abundance of human-caused ignition.

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Located at the eastern extent of the Great Lakes snowbelt, Central New York averages some of the highest annual snowfall totals east of the Rocky Mountains. This is in large part due to the variety of snowstorms that affect the region including lake-effect storms, coastal storms, and overrunning storms. Previous estimates suggest that lake-effect snowstorms account for approximately half of the seasonal snow in the Great Lakes basin, but ignore the spatial variability that exists within the region. Therefore, this study examines the seasonal snowfall contributions of the different snowstorm types to affect Central New York. Results suggest that although lake-effect snowstorms are the dominant snowstorm type in the region, their seasonal snowfall contributions vary between 13 and 48%. Although lake-effect snowstorms produce more snow during the peak and mid-seasons, their relative contribution is greatest during the early and mid-winter seasons. Generally, higher contributions occur near the Tug Hill Plateau, with lower contributions in southern Central New York. Instead, snowfall in southern Central New York is mostly dominated by Nor'easters (16–35%), with lesser contributions from Rocky lows (14–29%). Overrunning storms that originate in Canada (e.g., Alberta clippers) and non-cyclonic storms contribute the least to seasonal snowfall totals across Central New York; however, they are often the catalyst for lake-effect snowstorms in the region, as they advect continental polar air masses that destabilize across the lake. Understanding the actual snowfall contribution from different snowstorm types is needed for future climate predictions. Since the potential trajectory of future snowfall varies according to the type of storm, climate models must accurately predict the type of storm that is producing the snow.