Snow prediction for new york
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Snow Prediction for New York: Insights from Recent Research
Objective Forecasting Methods for Snow Prediction
Accurate snow prediction in New York City can be achieved using objective forecasting methods that incorporate atmospheric parameters at multiple levels. A study developed a forecasting guide based on order statistic properties, which utilizes both surface and upper-level data to provide highly accurate predictions for rain and snow occurrences in the area. This method is particularly effective in translating large-scale weather predictions into localized forecasts.
Trends in Extreme Snowfall Events
Recent research has focused on the trends and return levels of extreme snowfall events in New York City. Analysis of extreme snow data reveals that while the annual maximum snowfall shows an increasing trend, it is not statistically significant. However, the 87.5th percentile snowfall has significantly increased by 0.564 inches per decade, indicating more frequent large snowstorms. The 2016 blizzard, which brought up to 42 inches of snow, is considered an extreme event with a return level of approximately 40 years.
Mesoclimatology and Urban Heat Island Effect
The mesoclimatology of the New York metropolitan area shows significant variation in the probability of snow on winter precipitation days. The urban heat island effect in New York City reduces the likelihood of snowfall compared to suburban areas. Empirical probabilities based on precipitation type and daily maximum temperature at Central Park highlight the influence of urbanization on snowfall patterns.
Lake Effect Snowfall in Western and Central New York
Lake effect snowstorms are a significant source of heavy snowfall in western and central New York, particularly when Lakes Erie and Ontario are ice-free. The National Weather Service Office in Buffalo uses a specialized computer program to forecast these events, considering factors such as wind direction, temperature, and lake fetch. This program helps predict the depth and distribution of snowfall, which can vary significantly over short distances.
Snowfall Reflectivity and Fall Speeds
Comparisons between modeled and observed reflectivities and fall speeds during winter storms on Long Island reveal that different bulk microphysical parameterizations (BMPs) in weather models can capture varying degrees of riming intensity. While some models overpredict or underpredict snowfall speeds, they generally provide a reasonable match to observed reflectivity distributions during moderate riming periods.
Snowpack Estimation Approaches
Snowpack estimation is crucial for managing New York City's water supply. Various models, including the Generalized Watershed Loading Function (GWLF) and the NOAA Snow Data Assimilation System (SNODAS), are used to estimate snow water equivalent (SWE). These models perform well in estimating basin-wide SWE, with SNODAS providing better spatial variability estimates among snow survey sites.
Rain-on-Snow Runoff Events
Rain-on-snow (ROS) runoff events are significant for flood prediction and risk assessment in New York. These events are more common in high elevation areas like the Adirondack and Catskill regions. ROS events are associated with higher cumulative runoffs compared to rain-only events and are a major cause of annual peak flows in the study watersheds.
Impact of Snowfall on Traffic and Revenue
Snowfall significantly impacts traffic volume, crash risk, and toll revenue on the New York State Thruway. Inclement weather, particularly snow, is associated with 35-50% of crashes, with "unsafe speed" being the most common cause. Snowfall also leads to decreased passenger vehicle counts and revenue, while commercial vehicle counts and revenue are less affected.
Conclusion
Snow prediction in New York involves a combination of objective forecasting methods, analysis of extreme snowfall trends, and understanding the mesoclimatology influenced by urbanization. Accurate snowpack estimation and consideration of rain-on-snow events are essential for effective water management and flood risk assessment. Additionally, the impact of snowfall on traffic and revenue highlights the broader implications of snow events on daily life and economic activities.
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