The Snowstorm Database is a collection of over 500 snowstorms dating back to 1900 and updated operationally. Only storms having large areas of heavy snowfall (10-20 inches or greater) are included. The spatial extent includes the contiguous U.S. but the most storms are in the eastern two thirds of the U.S. This is the only comprehensive data set with starting and ending dates along with daily and total storm snowfall for large snowstorms from 1900 to the present. The data is archived in shapefile format, one shapefile per storm. Shapefiles are a non-proprietary spatial format widely used in Geographical Information Systems (GIS). Each shapefile contains daily and storm total snowfall for weather stations that were affected by the snowstorm. The snowfall data comes from the Global Historical Climatological Network - Daily (GHCN-D).

The number of snow days has been decreasing since the 1960s. According to Météo France, there would be five fewer snow days every 10 years. While in 1963, there were 200 days of snowfall, there were only 115 in 2017. The same is true for heavy snowfalls (one meter of snow and more), with a significant falling recurrence over the decade. In 2017, only one day with more than one meter of snow on the ground was recorded. The years without heavy snowfall have multiplied since the end of the 80s. This lack of snow in a mid-mountain resort is a high risk for all winter sports tourism professionals where many resorts have to plan for increasingly short snowy seasons. In order to measure snow cover in the mountain regions of France, Météo France relies on readings recorded at the Col de Porte station, located at an altitude of 1,325 meters in the heart of the Chartreuse Massif.

In 2024, the annual snowfall depth in Niigata, Japan amounted to 78 centimeters. Figures hit a decade-low in 2020, reaching only five centimeters. Niigata is located in the Chubu region, facing the Sea of Japan.

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.

Detailed hydrometeorological data from the mountain rain-to-snow transition zone are present for water years 2004 through 2014. The Johnston Draw watershed (1.8 km2), ranging from 1497 – 1869 m in elevation, is a sub-watershed of the Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho. The dataset includes continuous hourly hydrometeorological variables across a 372 m elevation gradient, on north- and south-facing slopes, including air temperature, relative humidity and snow depth from 11 sites in the watershed. Hourly measurements of solar radiation, precipitation, wind speed and direction, and soil moisture and temperature are available at selected stations. The dataset includes hourly stream discharge measured at the watershed outlet. These data provide the scientific community with a unique dataset useful for forcing and validating models in interdisciplinary studies and will allow for better representation and understanding of the complex processes that occur in the rain-to-snow transition zone. This version of the data set fixes errors in all data files and supersedes the earlier datasets https://doi.org/10.15482/USDA.ADC/1258769 and https://doi.org/10.15482/USDA.ADC/1245163. See the file inventory included with this dataset for more information on individual data files. For more information about this dataset contact: Clarissa L. Enslin: enslclar@gmail.com Sarah Godsey: godsey@isu.edu Danny G. Marks: ars.danny@gmail.com Resources in this dataset:Resource Title: Instrumentation Information. File Name: Instrumentation_Information.pdfResource Title: File name conventions. File Name: Naming_Convention.pdfResource Title: Precipitation from weather station 124. File Name: precipitation_from_weather_station_124.csvResource Title: Precipitation from weather station 125. File Name: precipitation_from_weather_station_125.csvResource Title: Weather Data from 124. File Name: weather_data_124.csvResource Title: Weather Data from 124b. File Name: weather_data_124b.csvResource Title: Weather Data from 125. File Name: weather_data_125.csvResource Title: Weather Data from jdt1. File Name: weather_data_jdt1.csvResource Title: Weather Data from jdt2. File Name: weather_data_jdt2.csvResource Title: Weather Data from jdt2b. File Name: weather_data_jdt2b.csvResource Title: Weather Data from jdt3. File Name: weather_data_jdt3.csvResource Title: Weather Data from jdt3b. File Name: weather_data_jdt3b.csvResource Title: Weather Data from jdt4. File Name: weather_data_jdt4.csvResource Title: Weather Data from jdt4b. File Name: weather_data_jdt4b.csvResource Title: Soil Moisture and Temperature Data from 124ba. File Name: rc.tg_.dc_.jd-124ba_stm.csvResource Title: Weather Data from jdt5. File Name: weather_data_jdt5.csvResource Title: Precipitation Data - original text file uploads. File Name: Precipitation Data.zipResource Title: Weather Data - original text file uploads. File Name: Weather Data.zipResource Title: Soil Moisture and Temperature Data from 124bs. File Name: rc.tg_.dc_.jd-124bs_stm.csvResource Title: Soil Moisture and Temperature Data from jdt1 . File Name: rc.tg_.dc_.jd-jdt1_stm.csvResource Title: Soil Moisture and Temperature Data from jdt2. File Name: rc.tg_.dc_.jd-jdt2_stm.csvResource Title: Soil Moisture and Temperature Data from jdt2b. File Name: rc.tg_.dc_.jd-jdt2b_stm.csvResource Title: Soil Moisture and Temperature Data from jdt3. File Name: rc.tg_.dc_.jd-jdt3_stm.csvResource Title: Soil Moisture and Temperature Data from jdt3b. File Name: rc.tg_.dc_.jd-jdt3b_stm.csvResource Title: Soil Moisture and Temperature Data from jdt4. File Name: rc.tg_.dc_.jd-jdt4_stm.csvResource Title: Soil Moisture and Temperature Data from jdt4b. File Name: rc.tg_.dc_.jd-jdt4b_stm.csvResource Title: Snow depth data from all Johnston Draw Stations. File Name: rc.tg_.dc_.jd_sc.csvResource Title: Soil moisture, temperature and Snow depth - original text file uploads. File Name: Soil_Moisture_Temperature_and_SnowDepth_Data.zipResource Title: README file for Johnston Draw Data Set. File Name: READMEJohnston_Draw_Dataset_20180206.pdfResource Title: Precipitation from weather station 124b. File Name: precipitation_from_weather_station_124b.csvResource Title: File inventory. File Name: File_Inventory_JDCatchmentV1-1.txt

As of October 2024, the annual snowfall depth in Aomori amounted to 460 centimeters. Figures hit a decade-low in 2020, reaching only 264 centimeters of snowfall depths. Aomori is the capital city of Aomori Prefecture, which is located in the Tohoku region of Japan. It is one of the Japanese cities with the heaviest snowfall.

This map displays the expected total accumulation of new snow over the next 72 hours across the contiguous United States. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.The dataset includes incremental and cumulative snowfall data in 6-hour intervals. In the ArcGIS Online map viewer you can enable the time animation feature and select either the amount by time (incremental) or accumulation by time (cumulative) layers to view a 72-hour animation of forecast precipitation. All times are reported according to your local time zone.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.snow.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

As of October 2024, the annual snowfall depth in Sapporo, Hokkaido, amounted to 459 centimeters. Sapporo is the capital city of Hokkaido, the northernmost prefecture of Japan. Because of its cold and temperate climate, it is a popular destination for winter sports.

The Monthly Climate Normals for 1991 to 2020 are 30-year averages of meteorological parameters that provide users the information needed to understand typical climate conditions for thousands of locations across the United States, as well as U.S. Territories and Commonwealths, and the Compact of Free Association nations. The stations used include those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the U.S. Climate Reference Network (USCRN) and other automated observation stations. In addition, precipitation normals for stations from the U.S. Snow Telemetry (SNOTEL) Network and the citizen-science Community Collaborative Rain, Hail and Snow (CoCoRaHS) Network are also available. The Monthly Climate Normals dataset includes various derived products such as air temperature normals (including maximum and minimum temperature normals, heating and cooling degree day normals, and others), precipitation normals (including precipitation and snowfall totals, and percentiles, frequencies and other statistics of precipitation, snowfall, and snow depth), and agricultural normals (growing degree days (GDDs)). All data utilized in the computation of the 1991-2020 Climate Normals were taken from the Global Historical Climatology Network-Daily and -Monthly datasets. Temperatures were homogenized, adjusted for time-of-observation, and made serially complete where possible based on information from nearby stations. Precipitation totals were also made serially complete where possible based using nearby stations. The source datasets (including intermediate datasets used in the computation of products) are also archived at NOAA NCEI. A comparatively small number of station normals sets (~50) have been added as Version 1.0.1 to correct quality issues or because additional historical data during the 1991-2020 period has been ingested.

Data include monthly snowfall and end-of-month snow depth for 140 stations across Canada. Stations that maintained at least 20 years of data were chosen. The original data are from Atmospheric Environment Service Canadian Climate Centre and from NOAA's National Climatic Data Center. Data are divided into eight files by region or province: British Columbia (17 stations), Yukon and Northwest Territories (33 stations), Alberta (12 stations), Saskatchewan (115 stations), Manitoba (10 stations), Ontario (19 stations), Quebec (20 stations) and Atlantic Provinces (14 stations). Each data record contains a variable indicator to identify data as snow on the ground on the last day of the month (centimeters) or total snowfall for the month (millimeters). A list of stations, with latitude and longitude to hundredths of a degree, is included in documentation for this data set.

These tiles are published and intended for use in the map Historic date of first snow.These base map tiles cover the North American extent and include data which represent the historic date by which there’s a 50% chance at least 0.1” of snow will have accumulated, based on each location’s snowfall history from 1981-2010. Map based on an analysis of the current U.S. Climate Normals by Mike Squires, National Centers for Environmental Information. White indicates places where there is a year-round chance of snow. Shades of blue and purple show places where the first day of snow historically falls between August 1st and December 31st, while dark gray shows places where, historically, the first snow doesn't take place until January 1st or later. Empty circles showing background gray indicate places where snow is so infrequent that there is not enough data to calculate a statistical first date of snow. While the map shows the historic date of first snow, the actual conditions this year may vary widely from this map because current weather patterns will determine the first snow of the year. For a more detailed assessment of the historic date of first snow, please see this Climate.gov blog post by Deke Arndt, NOAA NCEI scientist. For a broad overview of NOAA's 1981–2010 Climate Normals, see NOAA's 1981-2010 U.S. Climate Normals: An Overview published in the Bulletin of the American Meteorological Society, or for a detailed description of snow Normals, seeNOAA's 1981-2010 U.S. Climate Normals: Monthly Precipitation, Snowfall, and Snow Depth published in the Journal of Applied Meteorology and Climatology.

The U.S. Geological Survey (USGS), in cooperation with the New Hampshire Department of Environmental Services (NHDES) and the Department of Health and Human Services (NHDHHS), has developed data to assess the effects of short- and long-term climate change on hydrology in New Hampshire. A USGS Scientific Investigations Report (SIR) documents the datasets developed by the USGS. The data presented in this data release represent future hydrologic climate projections developed using a calibrated USGS Precipitation Runoff Modeling System (PRMS) model using precipitation and air temperature inputs from five general circulation models (GCMs) for two future climate scenarios for the period 2009 to 2099. The data sets include simulated current and future streamflow, groundwater recharge, and snowfall output datasets. Average monthly streamflow time series data sets are provided for 21 streamgages in New Hampshire, 14 of which also provide daily streamflow time series, Average monthly groundwater recharge and snowfall time series for the same reference time frame and future time frame are also provided for each of the 467 hydrologic response units (HRUs) that compose the model.

A daily gridded North American snowfall data with focus on the quality of the interpolated product is archived in this dataset. Daily snowfall amounts from National Weather Service Cooperative Observer Program stations and Meteorological Service of Canada surface stations are interpolated to 1 degree by 1 degree grids and examined for data record length and quality. The interpolation is validated spatially and temporally through the use of stratified sampling and k-fold cross-validation analyses. Interpolation errors average around 0.5 cm and range from less than 0.01 to greater than 2.5 cm. For most locations, this is within the measurement sensitivity. Grid cells with large variations in elevation experience higher errors and should be used with caution.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

This data set consists of snow observations for 18 stations in the western Italian Alps. Two types of data are included: monthly snowfall amounts and monthly snow cover duration in days. The period of record varies with each station, with the longest station record including data from 1877 to 1996. The average station record duration is approximately 61 years. Available stations range from 565 meters to 2720 meters in elevation. The data are summaries of snow stake measurements. Daily observations of total snow depth were generally made at 8 a.m. local time. Data were aquired using a snow measuring rod. Snowfall amount was defined as being any increase over the previous day's reading. This measurement may be an underestimate of as much as 10-20% due to snow pack settlement. However, this underestimation is fairly consistent for all measurements. The daily data were then totaled to yield the monthly values.

The National Snow and Ice Data Center hosts a time-series data set comprising annual snow cover data for the Northern Hemisphere (covering land primarily over 45 degrees North) from 1972 to 2000. Data are presented for land areas that exhibited snow cover in each of the 29 years. Variables are the week of snow disappearance, the week of snow cover onset, and the duration of the snow-free period. These variables were derived from operational NOAA weekly snow cover charts that have been quality controlled by the Rutgers University Global Snow Lab. For each year of the 29-year period, there are three binary files with data in an 89 x 89 cell grid. The week of the first detected snow cover in the fall is included in the WFS file, the week of the last observed snow cover in the spring is given in the WLS file, and the duration of snow-free period in weeks is given in the DSF file. Data are also provided in ASCII format summary files. In addition, summary statistics for each parameter are provided. These are grids of the mean and the standard deviation for the three parameters. Gridded latitude and longitude files are also included with this data set.

The Regional Snowfall Index (RSI) is an index of significant snowstorms that impact the eastern two thirds of the U.S. The RSI ranks snowstorm impacts on a scale from 1 to 5, similar to the Fujita scale for tornadoes or the Saffir-Simpson scale for hurricanes. NCEI has analyzed and assigned RSI values to over 500 storms going as far back as 1900. New storms are added operationally. As such, RSI puts the regional impacts of snowstorms into a century-scale historical perspective. The RSI differs from other indices because it includes population. RSI is based on the spatial extent of the storm, the amount of snowfall, and the juxtaposition of these elements with population. The area and population are cumulative values above regional specific thresholds. For example, the thresholds for the Southeast are 2", 5", 10", and 15" of snowfall while the thresholds for the Northeast are 4", 10", 20", and 30" of snowfall. Population information ties the index to societal impacts. Currently, the index uses population based on the 2000 Census. The RSI is an evolution of the Northeast Snowfall Impact Scale (NESIS) which NCDC (the precursor to NCEI) began producing operationally in 2005. While NESIS was developed for storms that had a major impact in the Northeast, it includes the impact of snow on other regions as well. It can be thought of as a quasi-national index that is calibrated to Northeast snowstorms. By contrast, the RSI is a regional index; a separate index is produced for each of the six NCDC climate regions in the eastern two-thirds of the nation. The indices are calculated in a similar fashion to NESIS, but our experience has led us to propose a change in the methodology. The new indices require region-specific parameters and thresholds for the calculations. For details on how RSI is calculated, see Squires et al. 2011.

This dataset contains key characteristics about the data described in the Data Descriptor Approaching 80 years of snow water equivalent information by merging different data streams. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows the snow cover data, referring primarily to the presence and total depth of a snow cover on the surface of the earth, across Canada. This is in contrast to data characteristics of snow cover depth, which increases by the occurrence of freshly fallen snow, but decreases by melting, wind action and settling. Two maps of these maps show the mean dates of the occurrence of first and last snow covers by one inch (2.54 cm) or greater. These are not necessarily the average dates to the beginning and ending of a continuous snow cover, since the snow cover may form and later disappear once or several times during a winter season. A third map showing the mean annual number of days with a snow cover of one inch (2.54 cm) or greater, only includes those days on which there was a snow cover. For the last map, the mean annual maximum depth of snow data was obtained by averaging the maximum depth reported for each snow season record. Snow cover data is mainly based on the ten-year period from 1941 to 1950.

These data are published and intended for use in the map Historic date of first snow.These data show the historic date by which there’s a 50% chance at least 0.1” of snow will have accumulated, based on each location’s snowfall history from 1981-2010, based on an analysis of the U.S. Climate Normals by Mike Squires, National Centers for Environmental Information. For a more detailed assessment of the historic date of first snow, please see this Climate.gov blog post by Deke Arndt, NOAA NCEI scientist. For a broad overview of NOAA's 1981–2010 Climate Normals, see NOAA's 1981-2010 U.S. Climate Normals: An Overview published in the Bulletin of the American Meteorological Society, or for a detailed description of snow Normals, seeNOAA's 1981-2010 U.S. Climate Normals: Monthly Precipitation, Snowfall, and Snow Depth published in the Journal of Applied Meteorology and Climatology.