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.

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.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation. For instance, in January 2025, the average temperature across the North American country stood at -1.54 degrees Celsius. Rising temperatures Globally, 2015, 2016, 2019 and 2021 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

AWIS Weather Services has delivered weather data from our small business in Auburn, Alabama to companies all over the world for over 25 years. We started with a few citrus growing clients in Florida and have expanded to worldwide offerings in both Historical Weather Data and Localized Human Weather Forecasts.

Our Extensive Historical Weather Database is full of 100% quality checked weather data from over 30,000 observation sites worldwide. The data is REAL WEATHER OBSERVATIONS and visually checked by humans each day.

This service is your access to that database as it gets updated.

You choose the variables you need. You choose the cities you need covered. We'll handle the data pulling, updating, and delivery. Most of the time, it's a simple .csv file saved to the Amazon S3 bucket system that only you have access to.

Variables for Live Weather Data Feed available for most locations are Max Temperature Min Temperature Total Precipitation Average Wind Speed Average Cloud Cover Average Temperature Max Relative Humidity Min Relative Humidity Evapotranspiration Potential Evapotranspiration Total Hours of Sunshine Solar Radiation Veg Wetting Max Soil Temperature Min Soil Temperature Average Soil Temperature Snow Fall Snow Depth

If a variable not listed is needed, contact us, we can likely generate the output from our many ingested inputs stored in our historical databases.

PRICING ESTIMATES: (The number of variables requested could change the price slightly) $1.50 per site, per month if you need less than 1000 sites. $1.25 per site, per month if you need 1001-5000 sites. $0.75 per site, per month if you need 5001-10000 sites. $0.25 per site, per month if you need over 10k sites.

Discounts available for long term deals. HISTORICAL DATA available upon request at a reduced rate. Reach out to us for more details and we can provide a targeted proposal within hours.

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 Atmospheric Layer Temperature Climate Data Record (CDR) dataset is a monthly analysis of the tropospheric and stratospheric data using temperature sounding microwave radiometers flown on polar-orbiting weather satellites providing an important record of upper atmosphere temperatures by merging data from the older Microwave Sounding Unit (MSU) and the new Advanced Microwave Sounding Units (AMSUs). The instruments measure the brightness temperature (Tb) for each channel and corresponds to an average temperature of the atmosphere averaged over that channel's weighting function. Global average and global anomaly maps on a 2.5 x 2.5 degree resolution are produced from each month's data from both the MSU and the AMSU for over a 31 year period (1978-2011) for each channel (layer); the Temperature Lower Troposphere (TLT); the Temperature Middle Troposphere (TMT); the Temperature Troposphere/Stratosphere (TTS) ; the Temperature Lower Stratosphere (TLS). UAT 4 Layer MW data is important to continue into the future to ensure the existence of a high-quality record of atmospheric temperatures for climate change detection and climate model verification activities. The data are available in netCDF-4 file format with variables containing standard deviation, quality flags, and projection information and will be updated monthly.

AWIS Weather Services has delivered weather forecasts from our small business in Auburn, Alabama to companies all over the world for over 25 years. We started with a few citrus growing clients in Florida and have expanded to worldwide offerings in both Historical Weather Data and Localized Human Weather Forecasts.

One backend, data driven option is our human-checked, quality controlled daily forecast files that can be generated for your specific needs and wants. We have a database stacked full of forecast variables that are generated with over 25 year of weather forecasting expertise that we can pull from. You choose the variables you need. You choose the cities you need covered. You choose how far out into the future you need information for. (We usually suggest 7-10 days) You choose the frequency of delivery. We'll handle the forecast and delivery. Most of the time, it's a simple .csv file saved to the Amazon S3 bucket system that only you have access to.

Variables for our Daily Weather Forecasts include: Max Temperature Min Temperature Total Precipitation Average Wind Speed Average Cloud Cover Average Temperature Max Relative Humidity Min Relative Humidity Evapotranspiration Potential Evapotranspiration Total Hours of Sunshine Solar Radiation Veg Wetting Max Soil Temperature Min Soil Temperature Average Soil Temperature

If a variable not listed is needed, contact us, we can likely generate the output from our many ingested inputs stored in our forecast databases.

Pricing for our Daily Weather Forecast data is fully dependent upon your needs. If you need one city, one variable for the next year the price is something close to $100 per month. If you need 100 cities, with all the variables, you're looking at something close to $1000.00 per month, with long term contract discounts available.

Reach out to us for more details and we can provide a targeted proposal within hours.

The radiosonde takes measurements at intervals of approximately 2 seconds. The high resolution data files contain all such data. The standard resolution data files contain measurements taken at standard and significant pressure levels of the atmosphere. Standard global radiosonde data is available from 1997 onwards.

          The data consists of vertical profiles of pressure, temperature,
          relative humidity, humidity mixing ratio, sonde position, wind speed
          and wind direction. Measurements are taken at 2 second intervals and
          the ascents extend to heights of approximately 20-30km. Two subsets of
          data are avaliable.

          Data from Aberporth (on the west coast of Wales) is available from
          April 1990 - present - At least one ascent per day up until April
          1996, 4 ascents per day thereafter. This data was obtained to support
          the work with the MST radar at Aberystwyth.

          Data from other UK stations is starting to arrive. Generally there are
          4 ascents per day from each station. The archive will have around 10
          stations with data from the 1990's.

          Link to the data set home page:
          http://badc.nerc.ac.uk/home/index.html

          [Summary Extracted from the BADC Home Page]

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the University of Illinois in Chicago, Illinois, on the meteorological tower near the greenhouse on campus. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. File naming convention includes the project (CROCUS), location (UIC), data level (raw, a1), date (year, month, day), and hour (0000).

This statistic shows cities in the United States with the highest average annual temperatures. Data is based on recordings from 1981 to 2010. In San Antonio, Texas the average temperature is 80.7 degrees Fahrenheit. Some cities that have the hottest maximum summer temperatures will not be included in this list due to their extreme temperature variance.

Climate affects many aspects of our daily lives. Transportation, industrial processes, tourism, environment, and agriculture are closely tied to climatic factors. Even our recreation and general lifestyles are influenced by the climate of our region. Because of this, one of the primary objectives of the SCO is to provide accurate and thorough climate information to all of North Carolina's citizens, including private industry, community economic development authorities, state agencies, schools, community colleges, universities, and other community organizations. The SCO provides a wealth of information which is available to everyone.

Climate Monitoring Services Available include:

• Climate Retrieval and Observations Network Of the Southeast (CRONOS)

enables the public to quickly and easily retrieve archived weather observations from 216 stations in and around North Carolina.

• AgNet

a network of automated weather stations located at most of the outlying research stations and field laboratories.

• Drought

Links to web sites with drought monitoring data and information.

• NC ECONet

North Carolina Environment and Climate Observing Network

• Station Density

  Analysis of Monitoring Station Density of the North Carolina Environment and Climate Observing System (NC ECONet)

• Storm Events Database

Access the National Climatic Data Center's Storm Events Database.

Access All Products: https://climate.ncsu.edu/cronos

[Summary Extracted from the State Climate Office of North Carolina Home Page]

This map displays the Apparent and Expected Air Temperature forecast over the next 72 hours across the Contiguous United States, Alaska, Guam, Hawaii, and Puerto Rico in 3 hour increments. The original raster data has been processed into 1-degree contours.Two layers are included: apparent and expected temperature, both include a Time Series set to a 3-hour time interval. The apparent temperature is the perceived (or feels like) temperature derived from either a combination of temperature and wind (wind chill) or temperature and humidity (heat index) for the indicated hour. When the temperature at a particular grid point falls to 50 °F or less, wind chill will be used for that point for the apparent temperature. When the temperature at a grid point rises above 80 °F, the heat index will be used for apparent temperature.
Between 51 and 80 °F, the apparent temperature will be the ambient air temperature.The expected temperature is the forecasted ambient air temperature in °F.See sister data product for Min and Max Daily TemperaturesRevisionsApr 21, 2022: Added Forecast Period Number 'Interval' field for an alternate query method to the Timeline of data. Disabled Time Series by default to improve initial Map Viewer exprience and added a Filter for 'interval = 1' to display initial forecast time data (current time period).Apr 22, 2022: Set 'Apparent Temperature' layer visibility to True by default, so content 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).Apparent Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.apt.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.apt.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.apt.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.apt.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.apt.binExpected Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.temp.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.temp.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.temp.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.temp.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.temp.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 or add a Filter using the 'Forecast Period Number'.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 National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Since the late 1950s, the USGS has maintained a long-term glacier mass-balance program at three North American glaciers. Measurements began on South Cascade Glacier, WA in 1958, expanding to Gulkana and Wolverine glaciers, AK in 1966, and later Sperry Glacier, MT in 2005. Additional measurements have been made on Lemon Creek Glacier, AK to compliment data collected by the Juneau Icefield Research Program (JIRP; Pelto and others, 2013). Direct field measurements are combined with weather data and imagery analyses to estimate the seasonal and annual mass balance at each glacier in both a conventional and reference surface format (Cogley and others, 2011). High-altitude measurements of meteorological data have been collected since the beginning of the USGS Benchmark Glacier Program adjacent to glaciers in order to support related science. This portion of the data release includes select weather data that has received basic quality control and assurance. Data is released at three different levels of processing, level 0, 1 and 2. Level 0 data contains compiled raw data, before QC procedures are applied, at the original timestep recorded by the instrument. Level 1 data has received a plausible value check, and minimal manual error identification (e.g. errors noted on field visits). Level 2 data has been through more extensive quality control procedures and is provided at both the original instrument timestep as well as aggregated hourly and daily values. However, beyond the procedures detailed in this document, no additional steps have been taken to manually assure quality of the data. Data outside the main record of temperature and precipitation at each site should be considered preliminary, and be utilized with increased scrutiny.

This data set consists of daily

The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2023. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.

Three datasets containing climate data, compiled in April 2011, have been purchased from the Bureau of Meteorology. These datasets include observations from stations in all Australian States and Territories. Each dataset includes a file which gives details of the stations where observations were made and a file describing the data. AWS Hourly Data contains hourly records of precipitation, air temperature, wet bulb temperature, dew point temperature, relative humidity, vapour pressure, saturated vapour pressure, wind speed, wind direction, maximum wind gust, mean sea level pressure, station level pressure. Each record for each parameter is also flagged to indicate the quality of the value.Synoptic Data contains records of air temperature, dew point temperature, wet bulb temperature, relative humidity, wind speed, wind direction, mean sea level pressure, station level pressure, QNH pressure, vapour pressure and saturated vapour pressure. Each record for each parameter is also flagged to indicate the quality of the value.Daily Rainfall Data contains records precipitation in the 24 hours before 9 am, number of days of rain within the days of accumulation and the accumulated number of days over which the precipitation was measured. Each precipitation record is flagged to indicate the quality of the value.

Model output for IPCC Fourth Assessment 20C3M experiment. These data represent monthly averaged values of selected variables for the Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC). (see also https://www.ipcc-data.org)

For specific scenario details see experiment summery.

These data are in GRIB format. They are also available in netCDF format.