In 2024, the United States registered its highest temperature anomaly since records began in 1895, at *** °F higher than the mean temperature from 1901 to 2000. During the same year, the average annual temperature in the U.S. was **** °F.

Q: Was the month cooler or warmer than usual? A: Colors show where and by how much the monthly average temperature differed from the month’s long-term average temperature from 1991-2020. Red areas were warmer than the 30-year average for the month, and blue areas were cooler. White and very light areas had temperatures close to the long-term average. 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). To calculate the difference-from-average temperatures shown on these maps—also called temperature anomalies—NCEI scientists take the average temperature in each 5x5 km grid box for a single month and year, and subtract its 1991-2020 average for the same month. If the result is a positive number, the region was warmer than average. A negative result means the region was cooler than usual. Q: What do the colors mean? A: Shades of blue show places where average monthly temperatures were below their long-term average for the month. Areas shown in shades of pink to red had average temperatures that were warmer than usual. The darker the shade of red or blue, the larger the difference from the long-term average temperature. White and very light areas show where average monthly temperature was the same as or very close to the long-term average. Q: Why do these data matter? A: Comparing an area’s recent temperature to its long-term average can tell how warm or how cool the area is compared to usual. Temperature anomalies also give us a frame of reference to better compare locations. For example, two areas might have each had recent temperatures near 70°F, but 70°F could be above average for one location while below average for another. Knowing an area is much warmer or much cooler than usual can encourage people to pay close attention to on-the-ground conditions that affect daily life and decisions. People check maps like this to judge crop progress, estimate energy use, consider snow and lake ice melt; and to understand impacts on wildfire regimes. 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. 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 Average Temperature NClimGrid Temperature Normals 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 Cl

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.

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.

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 ****. 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.

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.

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the annual average frequency of anomalies of SST from 2000-2013, with values presented as fraction of a year. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The SST average annual frequency of anomalies was calculated by taking the average number of weeks that exceeded the maximum monthly climatological SST value from 2000-2013 for each pixel.

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the annual average of the maximum anomaly of SST (degrees Celsius) from 2000-2013.

Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013.

The SST average annual maximum anomaly was calculated by taking the average of the annual maximum SST values in exceedance of the maximum monthly climatological SST from 2000-2013 for each pixel.

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.

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 coldest temperature anomaly for 2019 was seen in Bozeman, Montana, which was on average *** degrees Celsius below the normal between 1981 and 2010. Lower than average temperatures were seen in the northern plains and north Midwest.

Title: Dataset: Temperatures and flow rates for some springs in New England, 2017-18

Authors: Dallas Abbott1, William Menke1, Juliette Lamoureux2, Dionne Hutson2 and Alyssa Marrero3

1Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York 2City College of New York, New York, New York 3Kingsborough Community College, Brooklyn, New York Summary: In 2017-2018, we visited a suite of about 80 springs in New York and New England (USA). We measured water temperature with a Lascar EL-WIFI-TP digital temperature logger (0.1°C precision) at the closest accessible point to the source, which was usually the reservoir inside a spring house or the outflow pipe from a spring house. When both reservoir and outflow pipe were accessible, we found that temperatures agreed to within ±0.2°C. We also measured the flow rate of the spring with a bucket and a stopwatch, with a repeatability of about ±10%.

A temperature anomaly ∆T was determined for each spring by subtracting the annual average temperature at the spring site. Annually averaged temperatures are rarely available for spring sites but are available for airports via the National Oceanic and Atmospheric Administration’s (NOAA’s) National Center for Environmental Information. We therefore used the annually averaged temperature for the nearest airport (typically ~10-20 km away), corrected to the elevation of the spring using the dry adiabatic lapse rate of 9.8°C/km.

Data was used in the following paper:

Menke, W., Lamoureux, J., Abbott, D., Hopper, E., Hutson, D. and Marrero, A., 2018. Crustal heating and lithospheric alteration and erosion associated with asthenospheric upwelling beneath southern New England (USA). Journal of Geophysical Research: Solid Earth, 123(10), pp.8995-9008.

Notice: this is not the latest Heat Island Anomalies image service. For 2023 data visit https://tpl.maps.arcgis.com/home/item.html?id=e89a556263e04cb9b0b4638253ca8d10.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2019 and 2020.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the annual average of the maximum anomaly of SST (degrees Celsius) from 1985-2018. These SST dataset are derived from CoralTemp 5-km gap-free analyzed blended sea surface temperature over the global ocean. CoralTemp is derived from three different but related 5-km daily gap-free SST data sets and provides an internally consistent SST product that stretches from 1985 to present. 1) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature Reanalysis (1985-2002). 2) Geo-Polar Blended Night-Only Sea Surface Temperature Reanalysis (2002-2016). 3) Geo-Polar Blended Night-Only Sea Surface Temperature Near Real-Time (2017 to present). The 8-day composites are generated from daily Coral Reef Watch (CRW) files by OceanWatch Central Pacific. The SST average annual maximum anomaly was calculated by taking the average of the annual maximum SST values in exceedance of the maximum monthly climatological SST from 1985-2018 for each pixel. Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0_8day.graph

2021 was one of the warmest years on record in the United States. The city of Bismarck, North Dakota recorded a temperature anomaly of *** degrees Celsius above average conditions. That year, more than a dozen cities across the country either tied or broke records for warmest years to date.

The U.S. daily temperature analyses are maps depicting various temperature quantities utilizing daily maximum and minimum temperature data across the US. Maps are available for the daily maximum and minimum temperatures and anomalies, mean temperatures, and mean temperature anomalies averaged over various time scales (daily, 5-day, 7-day, 30-day, 90-day, and current month-to-date). Each of these quantities are available in terms of degrees Celsius and Fahrenheit. The graphics are updated daily and the graphics reflect the updated analyses including the latest daily data available. Archived graphics are rotated every year, making the previous and current year (to present) graphics available.

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. This Climate Data Record (CDR) includes lower tropospheric, mid-tropospheric, and lower stratospheric temperatures over land and ocean derived from microwave radiometers on NOAA and NASA polar orbiting satellites. The temperatures are from measurements produced by Microwave Sounding Units (MSU) since 1978 and Advanced Microwave Sounding Unit-A (AMSU-A) since 1998 flying on NOAA polar orbiting satellites, on NASA Aqua satellite (operating since mid-1998) and on the European MetOp satellite (operating since late 2006). The instruments are cross-track through-nadir scanning externally-calibrated passive microwave radiometers. Brightness temperature measurements are derived at microwave frequencies within the 50-60 GHz oxygen absorption complex, and (in the case of AMSU-A) at a few microwave frequencies above and below that absorption complex. There are three atmospheric layers for which intermediate products are processed: (1) lower-tropospheric (TLT) deep-layer average temperature, computed as a weighted difference between view angles of AMSU-A channel 5, whose heritage comes from MSU channel 2, (2) mid-tropospheric (TMT) deep-layer temperature, computed as an average of the central portion of the scan of AMSU-A channel 5, whose heritage also comes from MSU channel 2, and (3) lower-stratospheric (TLS) deep layer temperatures, computed from the central portion of the scan of AMSU channel 9, whose heritage comes from MSU channel 4. This CDR includes several products. The global monthly anomaly data data are averaged onto a 2.5 x 2.5 degree latitude-longitude grid for each of the three atmospheric layers. Monthly anomalies are averaged for each of the three atmospheric layers over multiple regions, including Global, hemispheric, tropic, extratropic, polar and contiguous U.S. A mean annual cycle of monthly mean layer temperatures is also included. Anomalies are deviations from 1981-2010 mean. The datasets have been converted from the native ASCII format to CF-compliant netCDF-4 format.

This dataset includes monthly gridded temperature anomalies on a global 2.5 x 2.5 degree grid derived from Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) radiance data since December 1978. In addition, there are monthly regional anomalies and monthly mean annual cycle temperatures. All products are derived for four bulk layers of the atmosphere: the Lower Troposphere (TLT), Mid-Troposphere (TMT), Tropopause (TTP) and Lower Stratosphere (TLS). Version 6.0 is the latest UAH version archived at NOAA and is updated monthly. It utilizes the linear calibration equation with hot-target correction for the MSU series (TIROS-N through NOAA-14) rather than other non-linear calibration equations. Gridded values of absolute temperature are calculated from a polynomial fit in the vertical coordinate of all view angle temperatures binned into each grid over a month. The selected temperature is calculated from a prescribed view-angle where it intersects the polynomial fit of the temperature vs. view-angle relationship or each grid. The diurnal adjustment is completely empirical, calculated by comparing a diurnally-drifting spacecraft against one that is not drifting during their overlap comparison period (for a.m. spacecraft, NOAA-15 vs. (non-drifting) AQUA, and for p.m., NOAA-18 vs. (non-drifting) NOAA-19 during 4 years). The calculated diurnal relationship of temperature change vs. time of day is then applied to all drifting satellites. The Lower Troposphere is calculated from a linear combination of TMT, TTP and TLS rather than from a linear combination of view-angles from the single channel (MSU2 or AMSU5) as was done in versions 5.6 and earlier. A new bulk layer centered on the Tropopause was added in version 6.0. These products were converted from the native text file format to netCDF-4 following CF metadata conventions, and they are accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

This Climate Data Record (CDR) includes lower tropospheric, mid-tropospheric, and lower stratospheric temperatures over land and ocean derived from microwave radiometers on NOAA and NASA polar orbiting satellites. The temperatures are from measurements produced by Microwave Sounding Units (MSU) since 1978 and Advanced Microwave Sounding Unit-A (AMSU-A) since 1998 flying on NOAA polar orbiting satellites, on NASA Aqua satellite (operating since mid-1998) and on the European MetOp satellite (operating since late 2006). The instruments are cross-track through-nadir scanning externally-calibrated passive microwave radiometers. Brightness temperature measurements are derived at microwave frequencies within the 50-60 GHz oxygen absorption complex, and (in the case of AMSU-A) at a few microwave frequencies above and below that absorption complex. There are three atmospheric layers for which intermediate products are processed: (1) lower-tropospheric (TLT) deep-layer average temperature, computed as a weighted difference between view angles of AMSU-A channel 5, whose heritage comes from MSU channel 2, (2) mid-tropospheric (TMT) deep-layer temperature, computed as an average of the central portion of the scan of AMSU-A channel 5, whose heritage also comes from MSU channel 2, and (3) lower-stratospheric (TLS) deep layer temperatures, computed from the central portion of the scan of AMSU channel 9, whose heritage comes from MSU channel 4. This CDR includes several products. The global monthly anomaly data data are averaged onto a 2.5 x 2.5 degree latitude-longitude grid for each of the three atmospheric layers. Monthly anomalies are averaged for each of the three atmospheric layers over multiple regions, including Global, hemispheric, tropic, extratropic, polar and contiguous U.S. A mean annual cycle of monthly mean layer temperatures is also included. Anomalies are deviations from 1981-2010 mean. The datasets have been converted from the native ASCII format to CF-compliant netCDF-4 format.