This Global Summaries dataset, known as GSOM for Monthly, contains a monthly resolution of meteorological elements from 1763 to present with updates applied weekly. The major parameters are: monthly mean maximum, mean minimum and mean temperatures; monthly total precipitation and snowfall; departure from normal of the mean temperature and total precipitation; monthly heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; extreme daily temperature and precipitation amounts; number of days with fog; and number of days with thunderstorms. The primary input data source is the Global Historical Climatology Network - Daily (GHCN-Daily) dataset. The Global Summaries datasets also include a yearly resolution of meteorological elements in the GSOY (for Yearly) dataset. See associated resources for more information. These datasets are not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which are not available in GSOM and GSOY. The GSOM and GSOY datasets replace the legacy U.S. COOP Summaries (DSI-3220), and have been expanded to include non-U.S. (global) stations. U.S. COOP Summaries (DSI-3220) only includes National Weather Service (NWS) COOP Published, or "Published in CD", sites.

The global summaries data set contains a monthly (GSOM) resolution of meteorological elements (max temp, snow, etc) from 1763 to present with updates weekly. The major parameters are: monthly mean maximum, mean minimum and mean temperatures; monthly total precipitation and snowfall; departure from normal of the mean temperature and total precipitation; monthly heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; and extreme daily temperature and precipitation amounts. The primary source data set source is the Global Historical Climatology Network (GHCN)-Daily Data set. The global summaries data set also contains a yearly (GSOY) resolution of meteorological elements. See associated resources for more information. This data is not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which will not be available in GSOM and GSOY. The GSOM and GSOY data set is going to replace the legacy DSI-3220 and expand to include non-U.S. (a.k.a. global) stations. DSI-3220 only included National Weather Service (NWS) COOP Published, or "Published in CD", sites.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview-National Overview summarizes observations of surface temperature and precipitation data for the United States by placing the data into historical perspective. The National Overview provides access to monthly, 3-month/seasonal, 6-month, 12-month and annual climate summaries by state, division and region. Topics include: Surface Temperature (rankings maps and time series), Precipitation (rankings maps and time series), Percent Area wet/dry cold/warm, and Primary Crop Region Maps with Season-to-Date Precipitation Time series. Additional information on current seasonal summaries for snow, fire, severe weather, and Atlantic and Pacific hurricanes, is also provided. The first annual report was presented in 1998, and monthly reports are available thereafter. Summary reports are often included with the final month of the period, for example, the spring report is issued along with May. "Year-to-date" reports are also often available for months that don't end a meteorological season.

This global summary of the day and month data set is obtained on a delayed monthly basis from the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP). CPC extracts surface synoptic weather observations from the Global Telecommunications System (GTS) and performs limited automated validation of the parameters. The data is then summarized for all reporting stations on a daily basis to current operational requirements related to the assessment of crop and energy production. Data coverage begins in 1979. In 1987 there is a format change and additional parameters were added. Major parameters include maximum temperature, minimum temperature, precipitation, vapor pressure, sea level pressure, maximum relative humidity, and minimum relative humidity. If the maximum or minimum temperatures are not reported, they are estimated from reported air temperatures in the regular synoptic reports when sufficient data exist. Starting in 1994, total sky cover, 3-hourly wind direction and speed, and total snow depth are included. There are approximately 8900 actively reporting stations. Periods of record vary widely among the stations. CAUTIONARY NOTE: NCEP incorrectly decoded the wind units indicator from February 1, 2001 until 1500 UTC on June 11, 2002, which caused a knots versus meters-per-second problem. Not all stations were affected. Users may, with caution, apply the knots or meters-per-second conversion where it appears to be the correct choice.

Monthly Summaries of Global Historical Climatology Network (GHCN)-Daily is a dataset derived from GHCN-Daily. The data are produced by computing simple averages or monthly accumulations of the daily observations. The meteorological elements calculated for the data set include, but are not limited to: monthly maximum and minimum temperature, monthly precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. Users of these monthly summaries have access to simple meteorological summaries for tens of thousands of stations worldwide.

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.

This map is part of Indicators of the Planet. Please see https://livingatlas.arcgis.com/indicatorsTemperature on Earth varies significantly each day. However, with each passing day, a pattern emerges: the planet is getting warmer. While the global average temperature has increased by approximately 1.2 degrees Celsius since 1880, the past 45 years have accounted for two-thirds of that increase. The National Oceanic and Atmospheric Administration (NOAA) has used detailed station, ship, and buoy data going back to the 1800s to analyze these changes and have all confirmed the warming of our planet.Such detailed data records allow climate scientists to compare current temperatures to historical averages. These historical averages are called climatologies. NOAA reports their monthly average temperature compared to a 20th Century climatology. Their latest analysis can be seen in the map, where red areas where temperatures were warmer than the 20th Century average and blue areas were cooler. Month to month, these patterns may vary, but when visualized across years or decades, the warming patterns become much clearer.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here.What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview-National Wildfires provides a summary of wildland fires in the U.S. and related weather and climate conditions. Statistical summaries such as the number of fires and acres burned are provided as are reports from the U.S. Drought Monitor and fire danger maps. Monthly reports for the summer "fire season" and annual summaries begin in July 2002. Depending on conditions, reporting was extended beyond the summer and fall seasons, and beginning in 2009 a summary was generated for each month. Following the July 2013 report, and until further notice, NCEI will no longer issue the Wildfire component of its Monthly Climate report. All previous Wildfire reports will be maintained online. Updated statistics will be updated on our Wildfire Societal Impacts webpage.

NOAA’s Global Historical Climatology Network (GHCN) is an integrated database of climate summaries from land surface stations across the globe that have been subjected to a common suite of quality assurance reviews. The data are obtained from more than 20 sources. Two GHCN datasets are available in BigQuery, the GHCN-D (daily) and the GHCN-M (monthly). The GHCN-Monthly is a temperature dataset that contains monthly mean temperatures and is used for operational climate monitoring activities. It is comprised of climate records from over 7,000 stations. For a complete description of data variables available in this dataset, see NOAA’s GHCN-M readme . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

This file contains additional resolutions of the same images as in https://www.datalumos.org/datalumos/project/233461/version/V2/view. Q: Where was the monthly temperature warmer or cooler than usual? A: Colors show where average monthly temperature was above or below its 1991-2020 average. Blue areas experienced cooler-than-usual temperatures while areas shown in red were warmer than usual. The darker the color, the larger the difference from the long-term average temperature. Q: Where do these measurements come from? A: Weather stations on every continent record temperatures over land, and ocean surface temperatures come from measurements made by ships and buoys. NOAA scientists merge the readings from land and ocean into a single dataset. To calculate difference-from-average temperatures—also called temperature anomalies—scientists calculate the average monthly temperature across hundreds of small regions, and then subtract each region’s 1991-2020 average for the same month. If the result is a positive number, the region was warmer than the long-term average. A negative result from the subtraction means the region was cooler than usual. To generate the source images, visualizers apply a mathematical filter to the results to produce a map that has smooth color transitions and no gaps. Q: What do the colors mean? A: Shades of red show where average monthly temperature was warmer than the 1991-2020 average for the same month. Shades of blue show where the monthly average was cooler than the long-term average. The darker the color, the larger the difference from average temperature. White and very light areas were close to their long-term average temperature. Gray areas near the North and South Poles show where no data are available. Q: Why do these data matter? A: Over time, these data give us a planet-wide picture of how climate varies over months and years and changes over decades. Each month, some areas are cooler than the long-term average and some areas are warmer. Though we don’t see an increase in temperature at every location every month, the long-term trend shows a growing portion of Earth’s surface is warmer than it was during the base period. 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. NOAA's Environmental Visualization Laboratory (NNVL) produces the source images for the Difference from Average Temperature – Monthly maps. To produce our images, we run a set of scripts that access the source images, re-project them into desired projections at various sizes, and output them with a custom color bar. Additional information Source images available through NOAA's Environmental Visualization Lab (NNVL) are interpolated from data originally provided by the National Center for Environmental Information (NCEI) - Weather and Climate. NNVL images are based on NOAA Merged Land Ocean Global Surface Temperature Analysis data (NOAAGlobalTemp, formerly known as MLOST). References NCEI Monthly Global Analysis NOAA View Temperature Anomaly Merged Land Ocean Global Surface Temperature Analysis Global Surface Temperature Anomalies Climate at a Glance - Data Information Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a... This upload includes two additional files: * Temperature - Global Monthly, Difference from Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...) * Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

This version has been superseded by a newer version. It is highly recommended for users to access the current version. Users should only access this superseded version for special cases, such as reproducing studies. If necessary, this version can be accessed by contacting NCEI. The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a blended product from two independent analysis products: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1880 to the present. The monthly product output is in gridded (5 degree x 5 degree) and time series formats. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. The changes from version 4 to version 5 include an update to the primary input datasets: ERSST version 5 (updated from v4), and GHCN-M version 4 (updated from v3.3.3). Version 5 updates also include a new netCDF file format with CF conventions. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

Q: Where was the monthly temperature warmer or cooler than usual? A: Colors show where average monthly temperature was above or below its 1991-2020 average. Blue areas experienced cooler-than-usual temperatures while areas shown in red were warmer than usual. The darker the color, the larger the difference from the long-term average temperature. Q: Where do these measurements come from? A: Weather stations on every continent record temperatures over land, and ocean surface temperatures come from measurements made by ships and buoys. NOAA scientists merge the readings from land and ocean into a single dataset. To calculate difference-from-average temperatures—also called temperature anomalies—scientists calculate the average monthly temperature across hundreds of small regions, and then subtract each region’s 1991-2020 average for the same month. If the result is a positive number, the region was warmer than the long-term average. A negative result from the subtraction means the region was cooler than usual. To generate the source images, visualizers apply a mathematical filter to the results to produce a map that has smooth color transitions and no gaps. Q: What do the colors mean? A: Shades of red show where average monthly temperature was warmer than the 1991-2020 average for the same month. Shades of blue show where the monthly average was cooler than the long-term average. The darker the color, the larger the difference from average temperature. White and very light areas were close to their long-term average temperature. Gray areas near the North and South Poles show where no data are available. Q: Why do these data matter? A: Over time, these data give us a planet-wide picture of how climate varies over months and years and changes over decades. Each month, some areas are cooler than the long-term average and some areas are warmer. Though we don’t see an increase in temperature at every location every month, the long-term trend shows a growing portion of Earth’s surface is warmer than it was during the base period. 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. NOAA's Environmental Visualization Laboratory (NNVL) produces the source images for the Difference from Average Temperature – Monthly maps. To produce our images, we run a set of scripts that access the source images, re-project them into desired projections at various sizes, and output them with a custom color bar. Additional information Source images available through NOAA's Environmental Visualization Lab (NNVL) are interpolated from data originally provided by the National Center for Environmental Information (NCEI) - Weather and Climate. NNVL images are based on NOAA Merged Land Ocean Global Surface Temperature Analysis data (NOAAGlobalTemp, formerly known as MLOST). References NCEI Monthly Global Analysis NOAA View Temperature Anomaly Merged Land Ocean Global Surface Temperature Analysis Global Surface Temperature Anomalies Climate at a Glance - Data Information Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...This upload includes two additional files:* Temperature - Global Monthly, Difference from Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...)* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

The Global Historical Climatology Network - Daily (GHCN-Daily/GHCNd) dataset integrates daily climate observations from approximately 30 different data sources. Version 3 was released in September 2012 with the addition of data from two additional station networks. Changes to the processing system associated with the version 3 release also allowed for updates to occur 7 days a week rather than only on most weekdays. Version 3 contains station-based measurements from well over 90,000 land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. Over 25,000 stations are regularly updated with observations from within roughly the last month. The dataset is also routinely reconstructed (usually every week) from its roughly 30 data sources to ensure that GHCNd is generally in sync with its growing list of constituent sources. During this process, quality assurance checks are applied to the full dataset. Where possible, GHCNd station data are also updated daily from a variety of data streams. Station values for each daily update also undergo a suite of quality checks.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service. The data updates monthly, usually around the 15th of the following month. For instance, the January data will become available on or about February 15th. The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report that summarizes the data is released each month (and end of the year) by NOAA NCEI is available here. GHCN monthly mean averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here. What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for the past month. Analysis: The full archive from 1880 – present is available here, and can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses.

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). The Global Historical Climatology Network - Daily (GHCN-Daily) dataset addresses the need for historical daily records over global land areas. Like its monthly counterpart (GHCN-Monthly), GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The meteorological elements measured for the data set include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. GHCN-Daily serves as the official archive for daily data from the Global Climate Observing System (GCOS) Surface Network (GSN) and is particularly well suited for monitoring and assessment activities related to the frequency and magnitude of extremes. Sources for the GHCN-Daily data set include, but are not limited, to U.S. Cooperative Summary of the Day, U.S. Fort data, U.S. Climate Reference Network, Community Collaborative Rain, Hail and Snow Network, and numerous international sources. The dataset contains measurements from over 75,000 stations worldwide,about two thirds of which are for precipitation measurement only. Approximately 8500 are regularly updated with observations from within the last month. While most of these sites report precipitation, daily maximum and minimum temperatures are available at more than 25,000 of them, and over 24,000 contain records of snowfall and/or snow depth. The process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: screening the source data for stations whose identity is unknown or questionable; classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and mingling the data from the different sources. The first two of these steps are performed whenever a new source dataset or additional stations become available, while the actual mingling of data is part of the automated processing that creates GHCN-Daily on a regular basis. GHCN-Daily data are subject to a suite of quality assurance checks. The checks consist of several types of carefully evaluated tests that detect duplicated data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of flagged values was used to set the threshold for each procedure such that the tests false-positive rate is minimized. In addition, the tests are performed in a deliberate sequence in an effort to enhance the performance of the later checks by detecting errors with the checks applied earlier in the sequence.

The GPCP Daily analysis is a companion to the GPCP Monthly analysis, and provides globally complete precipitation estimates at a spatial resolution of one degree latitude-longitude and daily time scale from October 1996 to the present. Although derived using both some of the same, but also different, data sets and methods than used in the GPCP Monthly analysis, the GPCP Daily "adds up" to the GPCP Monthly. The GPCP Daily V1.3 analysis is currently computed by the University of Maryland and submitted to NCEI. The routine update of the product takes place two months after the end of the month, once all input data sets become available. The data set is part of World Climate Research Program (WCRP) and GEWEX activities, being part of the array of data sets describing the water and energy cycles of the planet under the auspices of the GEWEX Data and Assessment Panel (GDAP). Details of input data sets and methods can be found in: Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis, R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. J. Hydrometeor., 2(1), 36-50.

This dataset contains the bias-corrected CPC MORPHing technique (CMORPH) global precipitation analyses, version 1, and is obtained from the NOAA Climate Data Record [https://www.ncei.noaa.gov/products/climate-data-records/precipitation-cmorph]. The following description is from the NOAA Climate Data Record CMORPH dataset page: This data set is for the bias-corrected, reprocessed CPC Morphing technique (CMORPH) high-resolution global satellite precipitation estimates. The CMORPH satellite precipitation estimates are created in two steps. First, the purely satellite-based global fields of precipitation are constructed through integrating Level 2 retrievals of instantaneous precipitation rates from all available passive microwave measurements aboard low earth orbiting platforms. Bias in these integrated satellite precipitation estimates is then removed through comparison against CPC daily gauge analysis over land and adjustment against the Global Precipitation Climatology Project (GPCP) merged analysis of pentad precipitation over ocean. The bias corrected CMORPH satellite precipitation estimates are created on an 8 km by 8 km grid over the global domain from 60 degrees S to 60 degrees N and in a 30-minute interval from January 1, 1998. Due to the delay of some input data sets, this formal version (Version 1) bias corrected CMORPH is produced manually once a month at a latency of 3-4 months. For the CDR production, the bias corrected CMORPH generated at its native resolution of 8 km by 8 km / 30-minute is upscaled to form THREE sets of data files of different time/space resolution for improved user experience: a) the full-resolution CMORPH data; Output variable: precipitation rate in mm/hour; spatial resolution: 8 km by 8km (at equator); spatial coverage: global (60S-60N); temporal resolution: 30min; data period: January 1, 1998 to the present b) Hourly CMORPH; Output variable: precipitation rate in mm/hour; spatial resolution: 0.25 degrees latitude/longitude;...

Local Climatological Data (LCD) are summaries of climatological conditions from airport and other prominent weather stations managed by NWS, FAA, and DOD. The product includes hourly observations and associated remarks, and a record of hourly precipitation for the entire month. Also included are daily summaries summarizing temperature extremes, degree days, precipitation amounts and winds. The tabulated monthly summaries in the product include maximum, minimum, and average temperature, temperature departure from normal, dew point temperature, average station pressure, ceiling, visibility, weather type, wet bulb temperature, relative humidity, degree days (heating and cooling), daily precipitation, average wind speed, fastest wind speed/direction, sky cover, and occurrences of sunshine, snowfall and snow depth. The source data is global hourly (DSI 3505) which includes a number of quality control checks.

Q: How much rain and snow fell through the month? A: Colors show monthly precipitation totals across the contiguous United States. The darker the color, the higher the total precipitation. Q: Where do these measurements come from? A: Daily measurements of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gather the data 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 total precipitation and 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: Areas shown in white received little or no measurable precipitation for the month. Areas shown in the lightest green received less than one inch of water from rain or snow. The darker the color on the map, the higher the precipitation for the month. Areas shown in dark blue received eight inches or more of precipitation that fell as either rain or snow. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation. Q: Why do these data matter? A: Farmers and gardeners who depend on rain for their plants want to know if enough precipitation has fallen to support plant growth. Similarly, forest managers and ranchers check monthly precipitation to monitor the status of the environment. Water managers who work to ensure that towns and cities have enough water for drinking, washing, and industrial uses are also interested in how much precipitation falls each month. 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 climate data (NClimGrid) 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. Q: Data Format Description A: NetCDF (Version: 4) 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 Total Precipitation 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/precipitation-monthly-total This upload includes two additional files:* Precipitation - Monthly Total _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-total )* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.