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.

Pull weather data as collected at Raleigh-Durham International Airport by NOAA.

This dataset contains Raleigh Durham International Airport weather data pulled from the NOAA web service described at:

http://www.ncdc.noaa.gov/cdo-web/webservices/v2

We have pulled this data and converted the data to commonly used units.

This dataset contains Raleigh Durham International Airport weather data pulled from the NOAA web service described at Climate Data Online: Web Services Documentation. We have pulled this data and converted it to commonly used units. This dataset is an archive - it is not being updated.

Weather Data collected by CIMIS automatic weather stations. The data is available in CSV format. Station data include measured parameters such as solar radiation, air temperature, soil temperature, relative humidity, precipitation, wind speed and wind direction as well as derived parameters such as vapor pressure, dew point temperature, and grass reference evapotranspiration (ETo).

Historical Weather Data
This dataset falls under the category Environmental Data.
It contains the following data: Historical climate datasets
This dataset was scouted on 2022-02-12 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.worldweatheronline.com/ho-chi-minh-city-weather-history/vn.aspxSee URL for data access and license information.

Dataset

This dataset was created by Parthib Paul

Contents

World Weather Records (WWR) is an archived publication and digital data set. WWR is meteorological data from locations around the world. Through most of its history, WWR has been a publication, first published in 1927. Data includes monthly mean values of pressure, temperature, precipitation, and where available, station metadata notes documenting observation practices and station configurations. In recent years, data were supplied by National Meteorological Services of various countries, many of which became members of the World Meteorological Organization (WMO). The First Issue included data from earliest records available at that time up to 1920. Data have been collected for periods 1921-30 (2nd Series), 1931-40 (3rd Series), 1941-50 (4th Series), 1951-60 (5th Series), 1961-70 (6th Series), 1971-80 (7th Series), 1981-90 (8th Series), 1991-2000 (9th Series), and 2001-2011 (10th Series). The most recent Series 11 continues, insofar as possible, the record of monthly mean values of station pressure, sea-level pressure, temperature, and monthly total precipitation for stations listed in previous volumes. In addition to these parameters, mean monthly maximum and minimum temperatures have been collected for many stations and are archived in digital files by NCEI. New stations have also been included. In contrast to previous series, the 11th Series is available for the partial decade, so as to limit waiting period for new records. It begins in 2010 and is updated yearly, extending into the entire decade.

This dataset was kindly provided by OikoLab (https://oikolab.com). It contains eight time series representing the hourly climate data nearby Monash University, Clayton, Victoria, Australia from 2010-01-01 to 2021-05-31. The climate data include temperature (C), dewpoint temperature (C), wind speed (m/s), mean sea level pressure (Pa), relative humidity (0-1), surface solar radiation (W/m^2), surface thermal radiation (W/m^2) and total cloud cover (0-1).

This map displays the Quantitative Precipitation Forecast (QPF) for 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 precipitation 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) layer or the "Accumulation by Time" (cumulative) layer 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 forecast data 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.qpf.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!

This map displays the minimum and maximum air temperature forecast over the next 3 days across the Contiguous United States, Alaska, Guam, Hawaii, and Puerto Rico in daily increments. Minimum temperatures are typically at night, while maximum temperatures are typically afternoon. The original raster data has been processed into 1-degree contours and both Layers include a Time Series set to a 24-hour time interval.The minimum and maximum temperatures are the forecasted ambient air temperature in °F.See sister data product for Apparent and Expected Hourly TemperaturesRevisionsApr 21, 2022: Added Forecast Period Number 'Interval' field for an alternate query method to the Timeline of data.Apr 22, 2022: Set 'Min Temperature' layer visibility to False by default, so only Max temperature is visible when initially viewed.Sep 1, 2022: Updated renderer Arcade logic on layers to correctly symbolize on values greater than 120 and less than -60 degrees.DetailService Data update interval is: HourlyWhere 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).Overnight Minimum Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.mint.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.mint.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.mint.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.mint.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.mint.binDaytime Maximum Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.maxt.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.maxt.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.maxt.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.maxt.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.maxt.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 feature 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.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in 1895. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

The Storm Events Database is an integrated database of severe weather events across the United States from 1950 to this year, with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce. Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area. Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event. Data about a specific event is added to the dataset within 120 days to allow time for damage assessments and other analysis.

Documentation

https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00773

Update Frequency

Monthly

License

Open Data. There are no restrictions on the use of this data. Use of the data should cite NOAA and NESDIS/NCEI as the dataset creator and the Severe Weather Data Inventory as the dataset.

Data extracted from French Global Atmospheric model ARPEGEData until H+102 h maximum.https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=131&id_rubrique=51.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY) methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) files that can be used for building simulation to estimate the impact of climate scenarios on the built environment. This dataset contains fTMY files for 18 cities in the continental United States. The locations are representative cities for each climate zone. The data for each city is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6- ACCESS-CM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2059 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O’Neill et al. (2020). More information about the six selected CMIP6 GCMs: ACCESS-CM2 - http://dx.doi.org/10.1071/ES19040 BCC-CSM2-MR - https://doi.org/10.5194/gmd-14-2977-2021 CNRM-ESM2-1- https://doi.org/10.1029/2019MS001791 MPI-ESM1-2-HR - https://doi.org/10.5194/gmd-12-3241-2019 MRI-ESM2-0 - https://doi.org/10.2151/jmsj.2019-051 NorESM2-MM - https://doi.org/10.5194/gmd-13-6165-2020 Additional references: O’Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework. Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0 Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734 Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228. Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8338549, Sept 2023. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8335815, Sept 2023. [Data] Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [Data]

Current METAR weather stations and associated weather conditions based on Meteorological Terminal Aviation Routine Weather Report (METAR) data collected globally from either airports or permanent weather observation stations by NOAA’s NWS Aviation Weather Center (http://www.aviationweather.gov/metar). IGEMS reads this source data and updates the layer every 10 minutes.

This layer is a component of Interior Geospatial Emergency Management System (IGEMS) General Data.

This map presents the geospatial locations and additional information for global tide monitoring stations, and U.S. stream gages, weather stations and DOI managed lands. This map is part of the Interior Geospatial Emergency Management System (IGEMS) and is supported by the DOI Office of Emergency Management. This map contains data from a variety of public data sources, including non-DOI data, and information about each of these data providers, including specific data source and update frequency is available at: http://igems.doi.gov.

© DOI Office of Emergency Management

The "Unified Forecast System (UFS)" is a community-based, coupled, comprehensive Earth Modeling System. It supports " multiple applications" with different forecast durations and spatial domains. The UFS Short-Range Weather (SRW) Application figures among these applications. It targets predictions of atmospheric behavior on a limited spatial domain and on time scales from minutes to several days. The SRW Application includes a prognostic atmospheric model, pre-processor, post-processor, and community workflow for running the system end-to-end. The "SRW Application Users's Guide" includes information on these components and provides detailed instructions on how to build and run the SRW Application. Users can access additional technical support via the "UFS Community Forum"

This data registry contains the data required to run the “out-of-the-box” SRW Application case. The SRW App requires numerous input files to run, including static datasets (fix files containing climatological information, terrain and land use data), initial condition data files, lateral boundary condition data files, and model configuration files (such as namelists). The SRW App experiment generation system also contains a set of workflow end-to-end (WE2E) tests that exercise various configurations of the system (e.g., different grids, physics suites). Data for running a subset of these WE2E tests are also included within this registry.

Users can generate forecasts for dates not included in this data registry by downloading and manually adding raw model files for the desired dates. Many of these model files are publicly available and can be accessed via links on the "Developmental Testbed Center" website.

Documentation

https://ufs-srweather-app.readthedocs.io/en/develop/

Update Frequency

These are stable datasets for use with the SRW Application. They will not be updated frequently.

License

The UFS SRW Application license page can be found at: https://github.com/ufs-community/ufs-srweather-app/blob/develop/LICENSE.md

Numerical Weather Prediction data. Published by Met Éireann. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Near-real-time meteorological products from the HARMONIE atmospheric model. NWP computer models use high performance computers to solve a set of hydro-dynamical equations that mathematically describe motions in the atmosphere. NWP simulations are used along with the skill of experienced forecasters to predict future weather events. There are many inputs to our prediction model such as, previous model run, current weather observations, marine buoy data and satellite imagery to name a few. The two main components of any atmospheric model are known as the dynamics and the physics. For the dynamics, we divide the forecast region into a grid and use mathematical algorithms to solve the equations governing the motions of the atmosphere at each grid-point. Currently, this grid has a 2km horizontal resolution. The physics of the model considers the key processes which occur at scales smaller than this, and thus are not “seen” by the grid. These include solar radiation and turbulence. The data presented here are from the control member of the ensemble NWP system. Each file represents the next 60 steps of the forecast. Each hourly file is availavble for approximately 24 hours here. Every effort is made to have a complete model run in each fie, that is, all 60 steps of the forecast, however due to timing and processing occasionally a file may not have all steps. This data is released in response to the EU's open data directive. For official weather forecasts please see met.ie We will be removing this page in the coming weeks. Access to NWP data can now be found here https://opendata.met.ie...

Dataset

This dataset was created by Vick_K

Released under MIT

Contents

Weather significantly impacts sales and eCommerce, influencing consumer behavior and purchasing patterns. By analyzing weather forecast data alongside sales data, we have identified trends so that businesses can make strategic decisions to optimize their operations.

This data includes the forecast of weather-based demand for up to 10 days on daily level for a given ZIP code. In comparison to the full data set, this data sample provides information for one ZIP code.

The data can be found here: "PUBLIC"."FORECAST_GFK_VIEW_EXAMPLE”

The dataset has the following fields:

• date_forecast: Date on which the forecast was created
• country: country of the zip_code.
• zip_code: Zip code for which the index was calculated for a pollen type
• date: date for which the index was calculated
• model: industry / product type for which the indices were calculated
• value: actual impact of the forecasted weather on the given date and zip code. value of 1.15 means the demand is 15 % higher due to the weather than normally.
• class: the class to which level the weather actually influences the demand more generally

The definition of the class is: 1: weather reduces the demand on 10 % of the days 2: weather reduces the demand on 20 % of the days 3: weather has no influence on the demand on 40 % of the days 4: weather increased the demand on 30 % of the days

We offer the following models in this dataset:

• Car-Tyres
• DIY-Activity
• Fashion-Stationary
• Fashion-Ecommerce
• Fashion-Swimwear
• Fashion-Athletic Apparel
• Fashion-Sneakers
• Fashion-Outerwear
• Fashion-Umbrella
• FMCG-Beverages: Beer
• FMCG-Beverages: Coke
• FMCG-Beverages: Coffee
• FMCG-Beverages: Juice
• FMCG-Beverages: Tea
• FMCG-Beverages: Water
• FMCG-Beverages: Wine
• FMCG-Food: Bakery Goods
• FMCG-Food: BBQ
• FMCG-Food: Ice Cream
• FMCG-Food: Snacks
• FMCG-Food: Chocolate Goods
• FMCG-Food: Frozen Goods
• FMCG-Personal Care: Deodorant
• FMCG-Personal Care: Dry Skin
• FMCG-Personal Care: Insect Skin Protection
• FMCG-Personal Care: Body Care
• FMCG-Personal Care: Sun Protection
• FMCG-Pharma: Cold Medicines
• Garden Outdoor: Garden Furniture
• Garden Outdoor: Garden Tools
• Garden Outdoor: Grills Accessories
• Garden Outdoor: Outdoor Plants