This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (NClimDiv) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. As of November 2018, NClimDiv includes county data and additional inventory files.

The NOAA Regional Surface Data - 1989 (NCDC) Data Set contains hourly surface meteorological data for the FIFE area. Though the measurements presented in this data set were not taken precisely at the FIFE study area, it is hypothesized that they present a representative horizontal cross-section of meteorological variables and sky conditions in and around the site. It is also realized that many of the variables presented in this data set are somewhat subjective and dependent on the skill (and biases) of the observer, such as estimates of cloud amount and height. This data may be used as input data and/or verification data for numerical simulation models.

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.

The FIFE Historic Daily Meteorology Data Data Set is one of the historical data sets used for the FIFE project. The data set contains data back to January, 1900. This data set was prepared for input into models, therefore, no leap days (February 29) are included. Daily weather observations of air temperature and precipitation were made by Kansas State University. The observations are made according to the procedures outlined by the National Weather Service (Anonymous 1989).

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

SILO is a Queensland Government database containing continuous daily climate data for Australia from 1889 to present, in a number of ready-to-use formats, suitable for modelling and research applications. The SILO database contains two major classes of data: point (station) time series and spatial grids, both based on observed data from the Bureau of Meteorology ADAM (Australian Data Archive for Meteorology) database. For point data, interpolated or derived values are used where observations are missing. Gridded data are spatially interpolated from observations.

The Automated Weather Data Network (AWDN) was established in 1981 with five stations in Nebraska. Currently, there are 110 stations collecting data in North and South Dakota, Wyoming, Colorado, Nebraska, Kansas, Iowa, and Missouri.

Weather data are recorded every 60 seconds and are averaged over the hour. Data are then averaged (or totaled) over the 24-hour period to arrive at daily data. Parameters included in these data are temperature, winds, solar radiation, humidity, precipitation, and soil temperature.

"http://www.hprcc.unl.edu/awdn/"

Contact HPRCC: "http://hprcc.unl.edu/comments.html"

NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).

Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.

Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.

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]

KNMI operates automatic weather stations on land (incl. airports). These weather stations measures meteorological parameters such as temperature, precipitation, wind, air pressure and global radiation. On a daily basis all real-time collected observations and measurements (hourly) are validated on correctness and completeness. The validated data is archived in the Klimatologisch Informatie Systeem (KIS) of KNMI. The daily data is composed from hourly data and each day reference evaporation is calculated using the Makkink method. After the data has been processed and archived in KIS, changes are no longer possible. This assures data integrity.

This data set contains U.S. station surface observations that were digitized from the original forms by the Climate Database Modernization Program (CDMP). Data are available for more than 200 stations (mainly at airports, but also some city weather bureau offices) that made observations at hourly intervals at least during the daytime hours and often over the full 24-hour day. The general period of record for these stations is 1928-1948, though this varies by individual station. To find out what is available, see this inventory [https://rda.ucar.edu/datasets/ds506.0/inventories/sao_inventory.txt]. A significant effort was made by DSS to correct errors in the digitized data, especially dates, times, and pressures. For more information about this work, see this document [https://rda.ucar.edu/datasets/ds506.0/docs/qc-20040110.txt]. We have also received data for more than 130 U.S. stations that were digitized from Form 1001. These stations, which were usually city weather bureau offices, generally took observations once, twice, or four times daily. Some stations have data back as far as late 1892. An inventory [https://rda.ucar.edu/datasets/ds506.0/inventories/form1001_inventory.txt] can be viewed which shows stations and their date range coverage. These data will be made available to the community when errors in the digitized data have been corrected.

• Update Frequency: Weekly

Data from this dataset can be downloaded/accessed through this dataset page and Kaggle's API.

Context

Severe weather is defined as a destructive storm or weather. It is usually applied to local, intense, often damaging storms such as thunderstorms, hail storms, and tornadoes, but it can also describe more widespread events such as tropical systems, blizzards, nor'easters, and derechos.

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. The records in SWDI come from a variety of sources in the NCDC archive. SWDI provides the ability to search through all of these data to find records covering a particular time period and geographic region, and to download the results of your search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML.

Content

The current data layers in SWDI are:
- Filtered Storm Cells (Max Reflectivity >= 45 dBZ) from NEXRAD (Level-III Storm Structure Product)
- All Storm Cells from NEXRAD (Level-III Storm Structure Product)
- Filtered Hail Signatures (Max Size > 0 and Probability = 100%) from NEXRAD (Level-III Hail Product)
- All Hail Signatures from NEXRAD (Level-III Hail Product)
- Mesocyclone Signatures from NEXRAD (Level-III Meso Product)
- Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product)
- Tornado Signatures from NEXRAD (Level-III TVS Product)
- Preliminary Local Storm Reports from the NOAA National Weather Service
- Lightning Strikes from Vaisala NLDN

Disclaimer:
SWDI provides a uniform way to access data from a variety of sources, but it does not provide any additional quality control beyond the processing which took place when the data were archived. The data sources in SWDI will not provide complete severe weather coverage of a geographic region or time period, due to a number of factors (eg, reports for a location or time period not provided to NOAA). The absence of SWDI data for a particular location and time should not be interpreted as an indication that no severe weather occurred at that time and location. Furthermore, much of the data in SWDI is automatically derived from radar data and represents probable conditions for an event, rather than a confirmed occurrence.

Acknowledgements

Dataset Source: NOAA. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Cover photo by NASA on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. In January 2025, the National Centers for Environmental Information (NCEI) began summarizing the State of the Climate for Hawaii. This was made possible through a collaboration between NCEI and the University of Hawaii/Hawaii Climate Data Portal and completes a long-standing gap in NCEI's ability to characterize the State of the Climate for all 50 states. NCEI maintains monthly statewide, divisional, and gridded average temperature, maximum temperatures (highs), minimum temperature (lows) and precipitation data for Hawaii over the period 1991-2025. As of November 2018, NClimDiv includes county data and additional inventory files In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set.

As of November 2018, NClimDiv includes county data and additional inventory files.

This project compiled 463,530 instrumental observations of daily temperature, precipitation, and sunshine at up to 319 stations in China between 1912-51. The principal sources are monthly reports of the Institute of Meteorology, Nanjing, observatories in Japanese-occupied Manchuria, and the Japanese Army in North China. Gross errors, rounding errors, and inhomogeneities are identified. The new dataset is temporally and spatially consistent with existing datasets of monthly temperature, but reports higher precipitation and sunshine. Please refer to the readme.txt in the zip file for terms of use and description of individual files in the dataset.

Please ensure the related paper is cited appropriately when reusing this dataset: Png Paak Liang Ivan, Chen Yeh-Ning, Chu Junhong, Feng Yikang, Lin Kuanhui Elaine, Tseng Wan-Ling (in press). Temperature, Precipitation, and Sunshine Across China, 1912-51: A New Daily Instrumental Dataset. Geoscience Data Journal.

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!

These data are in addition to "Madison Wisconsin Daily Meteorological Data 1869-current." Additional variables added include: daily cloud cover, wind, solar radiation, vapor pressure, dew point temperature, total atmospheric pressure, and average relative humidity for Madison, Wisconsin. In addition, the adjustment factors which were applied on a given date to calculate the adjusted parameters in "Madison Wisconsin Daily Meteorological Data 1869-current" are also included in these data. Raw data, in English units, were assembled by Douglas Clark - Wisconsin State Climatologist. Data were converted to metric units and adjusted for temporal biases by Dale M. Robertson. For adjustments applied to various parameters see Robertson, 1989 Ph.D. Thesis UW-Madison. Adjusted data represent the BEST estimated daily data and may be raw data. Data collected at Washburn observatory, 8-1-1883 to 9-30-1904. Data collected at North Hall, 10-1-1904 to 12-31-1947 Data collected at Truax Field (Admin BLDG), 1-1-1948 to 12-31-1959. Data collected at Truax Field, center of field, 1-1-1960 to Present. Much of the data after 1990 were obtained in digital form from Ed Hopkins, UW-Meteorology. Data starting in 2002-2005 were obtained from Sullivan at http://www.weather.gov/climate/index.php?wfo=mkx%20 ,then go to CF6 and download monthly data to Madison_sullivan_conversion. Relative humidity data was obtained from 1986 to 1995 from CD's at the State Climatologist's Office. Since Robertson (1989) adjusted all historical data to that collected prior to 1989; no adjustments were applied to the recent data except for wind and estimated vapor pressure. Wind after January 1997, and only wind from the southwest after November 2007, was extended by Dale M. Robertson and Yi-Fang "Yvonne" Hsieh, see methods. Estimated vapor pressure after April 2002 was updated by Yvonne Hsieh, see methods.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2017), with the processed data through September 30, 2017. The primary data for each year is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2017) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) in thousandths of an inch is computed from average daily air temperature in degrees Fahrenheit (°F), average daily dewpoint temperature in degrees Fahrenheit (°F), daily total wind movement in miles (mi), and daily total solar radiation in Langleys per day (Lg/d) and disaggregated to hourly PET in thousandths of an inch using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as “backup”. Temporal variations in the statistical properties of the data resulting from changes in measurement and data storage methodologies were adjusted to match the statistical properties resulting from the data collection procedures that have been in place since January 1, 1989 (Over and others, 2010). The adjustments were computed based on the regressions between the primary data series from ANL and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. Each hourly value is assigned a corresponding data source flag that indicates the source of the value and its transformations. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2015) station at St. Charles, Illinois is used as "backup" for the air temperature, solar radiation and wind speed data. Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2017) station at Chicago O'Hare International Airport is used as "backup" for the dewpoint temperature and wind speed data. Each data source flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2017, Meteorological data, accessed on October 25, 2017, at URL http://gonzalo.er.anl.gov/ANLMET/. Bera, M., and Over, T. M., 2017, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/F7SJ1HS5. Midwestern Regional Climate Center, 2017, Meteorological data, accessed on December 5, 2017, at URL http://mrcc.isws.illinois.edu/CLIMATE/welcome.jsp. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, 2015, Illinois Climate Network: Champaign, Ill., Illinois State Water Survey, accessed on December 5, 2017, at http://dx.doi.org/10.13012/J8MW2F2Q.

These meteorological data were collected at Pitilla Bilogical Station, in the Guanacaste Conservation Area, Guanacaste, Costa Rica. The data were collected either manually ("manual" in variable name) or by a Campbell automated weather station ("stn" in variable name). The manual data was recorded manually each morning at approximately 0900 h with basic instruments. Manual temperature was measured with a battery operated max-min thermometer, and gives the maximum and minimum temperature over the preceding 24 hours. Manual precipitation (rainfall) data was physically collected in a rain gauge over the 24 hours preceding the recording of this data. Automated temperature data was collected by the Campbell station each minute with the MetSENS500-DS-17-PT sensor and communicated with a CR300 wireless data logger. The station outputs calculations of maximum and minimum temperature based on by the temperature data collected each minute from midnight (00:00) to midnight (24:00) on “Date”. The automated precipitation data was calculated from 06:01 the previous day to 06:00 on the day corresponding to “Date”, and utilized an automatically-emptying rain gauge. This rain gauge was found in October 2023 to have been colonized by ants, and the measures leading up to this period would have been affected by this. In general, automatic rain gauges may not be reliable for the amount of rain experienced during some tropical rain storms. We re-calculated the maximum and minimum temperatures to correspond to the same time window as the manual temperature data; specifically, starting the previous day at 09:01 h and ending the day corresponding to “Date” at 09:00 h. This staggering procedure can only be done for temperature, not rainfall, because rainfall is only reported by Campbell for 6 hour blocks. Staggered measures of temperature include "stag" in the variable name. All time measures in Central Standard Time (GMT-6). Variable names: "Date": yyyy-mm-dd format date of measurement "AirTemp_max_stag_stn": maximum air temperature from station, 09:01 previous day-0:900 "AirTemp_min_stag_stn" : minimum air temperature from station, 09:01 previous day-0:900 "Rain_Tot_stn": precipitation automatically measured by station, unreliable, 06:01 the previous day to 06:00 "AirTemp_min_raw_stn": minimum air temperature from station, 00:00 to 24:00 "AirTemp_max_raw_stn": maximum air temperature from station, 00:00 to 24:00 "AirTemp_min_manual": minimum air temperature measured manually, 09:01 previous day-0:900 "AirTemp_max_manual": maximum air temperature measured manually, 09:01 previous day-0:900 "Rain_Tot_manual": total precipitation measured manually, 09:01 previous day-0:900

Q: What was the average temperature for the month? A: Colors show the average monthly temperature across the contiguous United States. White and very light areas had average temperatures near 50°F. Blue areas on the map were cooler than 50°F; the darker the blue, the cooler the average temperature. Orange to red areas were warmer than 50°F; the darker the shade, the warmer the monthly average temperature. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments collect the highest and lowest temperature of the day at each station over the entire month, and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly average of daily mean temperatures, then plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Shades of blue show areas that had monthly average temperatures below 50°F. The darker the shade of blue, the lower the average temperature. Areas shown in shades of orange and red had average temperatures above 50°F. The darker the shade of orange or red, the higher the average temperature. White or very light colors show areas where the average temperature was near 50°F. Q: Why do these data matter? A: The 5x5km NClimGrid data allow scientists to report on recent temperature conditions and track long-term trends at a variety of spatial scales. The gridded cells are used to create statewide, regional and national snapshots of climate conditions. Energy companies use this information to estimate demand for heating and air conditioning. Agricultural businesses also use these data to optimize timing of planting, harvesting, and putting livestock to pasture. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Average Temperature References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions) NCEI Monthly National Analysis) Climate at a Glance - Data Information) NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-us-monthly-averageThis upload includes two additional files:* Temperature - US Monthly Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots.* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.