The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

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.

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.

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.

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.

The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. In March 2015, new Alaska data was included in the nClimDiv dataset. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS. It includes maximum temperature, minimum temperature, average temperature and precipitation. 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.

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.

The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 2 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.0: http://worldclim.org/version2 Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global Aridity Index_ET0 geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals. The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator.

Climate Data Online is a collection of climatic data that offers public access and consumption via discovery and ordering services. The data available through CDO is available at no charge and can be viewed online or ordered and delivered to your email inbox.

The Nordic Gridded Climate Dataset (NGCD) is a high resolution, observational, gridded dataset of daily minimum, maximum and mean temperatures and daily precipitation totals, covering Finland, Sweden and Norway. The time period covered begins in January 1961 and continues to the present. The dataset is regularly updated every 6 months, in March and in September. In addition, there are daily, provisional updates. Spatial interpolation methods are applied to observational datasets to create gridded datasets. In general, there are three types of such methods: deterministic (type 1), stochastic (type 2) and pure mathematical (type 3). NGCD applies both a deterministic kriging (type 1) interpolation approach and a stochastic Bayesian (type 2) interpolation approach to the same in-situ observational dataset collected by weather stations. For more details on the algorithms, users are advised to read the product user guide. The input data is provided by the National Meteorological and Hydrological Services of Finland, Norway and Sweden. The time-series used for Finland and Sweden are the non-blended time-series from the station network of the European Climate Assessment & Dataset (ECA&D) project. For Norway, time-series are extracted from the climate database of the Norwegian Meteorological Institute.

The Ontario in-filled climate data collection includes information from 339 monitoring stations maintained by the Meteorological Service of Canada. Historical climate data commonly has missing hourly and daily records due to equipment malfunctions, temporary site maintenance or other reasons. “In-filling” is a technical process that draws on data from nearby stations to fill in these missing records. This collection contains fully in-filled precipitation and temperature records for Ontario from 1950 to 2005. It is organized into eight separate databases in Microsoft Access format. One of the databases contains daily in-filled climate records. The remaining seven databases contain hourly in-filled climate records divided into regions for manageability.

The Climate Data Analytics Market report segments the industry into By Type (Climate Model Evaluation, Climate Data Processing and Visualization, Climate Data Formats, and more), By End-User Industry (Government and Public Sector, Energy and Utilities, Agriculture, and more), and Geography (North America, Europe, Asia, and more).

Time series of monthly minimum and maximum temperature, precipitation, and potential evapotranspiration were derived for 1,469 watersheds in the conterminous United States for which stream flow measurements were also available from the national streamflow database, termed the Hydro-Climatic Data Network (HCDN), developed by Slack et al. (1993a,b). Monthly climate estimates were derived for the years 1951-1990.The climate characteristic estimates of temperature and precipitation were estimated using the PRISM (Daly et al. 1994, 1997) climate analysis system as described in Vogel, et al. 1999.Estimates of monthly potential evaporation were obtained using a method introduced by Hargreaves and Samani (1982) which is based on monthly time series of average minimum and maximum temperature data along with extraterrestrial solar radiation. Extraterrestrial solar radiation was estimated for each basin by computing the solar radiation over 0.1 degree grids using the method introduced by Duffie and Beckman (1980) and then summing those estimates for each river basin. This process is described in Sankarasubramanian, et al. (2001). Revision Notes: This data set has been revised to update the number of watersheds included in the data set and to updated the units for the potential evapotranspiration variable. Please see the Data Set Revisions section of this document for detailed information.

Goals: To quantify SOA yield and gas-phase product yields from humid ozonolysis of g-terpinene, which will serve as a "control" experiment to subtract the ozonolysis contribution to the nighttime chemistry of g-terpinene as measured in the experiment on 6/14/22. ... Summary: The chamber was humidified, after which H2O2, and O3 were injected in the chamber and allowed to equilibrate. Ammonium sulfate seed particles were injected. Then g-terpinene was injected and decayed by reaction with ozone (plus some contribution from OH). Gas phase organics were monitored by CF3O- CIMS (units: normalized counts), particle formation was monitored by SMPS, and RH / T / O3 / NOx were monitored throughout. O3/NOx data sometimes require post-correction not applied here, including subtraction of an ozone interference from H2O2 (1 ppm H2O2 is detected as about 12 ppb ozone) and correction for an in-line HEPA filter (see timeline for applicable timing) that scrubs about 10% of NO2 and 45% of ozone. Organization: Nguyen Group Lab Affiliation: UC Davis Chamber: Tran's big bag of air Experiment Category: Gas phase chemical reaction, Aerosol formation Oxidant: Ozone Reactants: gamma-Terpinene, Ammonium Sulfate, Ozone, Hydrogen Peroxide Reaction Type: Dark oxidation Relative Humidity: 63 Temperature: 22 Seed Name: Ammonium sulfate Pressure: approximate 1 atm

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.

The Global Aridity Index (Global-AI) and Global Reference Evapo-Transpiration (Global-ET0) datasets provided in Version 3 of the Global Aridity Index and Potential Evapo-Transpiration (ET0) Database (Global-AI_PET_v3) provide high-resolution (30 arc-seconds) global raster data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon implementation of the FAO-56 Penman-Monteith Reference Evapotranspiration (ET0) equation.

Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global-AI geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals. The Readme File is provided with a detailed description of the dataset files, and the following article for a description of the methodology and a technical validation.The Global-AI_PET_v3 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator.

NOAA NEXRAD Quantitative Precipitation Estimation (QPE) Climate Data Record (CDR) is created from the Radar Multi-Radar/Multi-Sensor (MRMS) Reanalysis to produce severe weather and precipitation products for improved decision-making capability to improve severe weather forecasts and warnings, hydrology, aviation, and numerical weather prediction. The data cover a time period from 2002-01-01 to 2011-12-31. NOAA's NEXRAD reanalysis consists of two primary components; (1) Severe weather and radar-reflectivity data generation, (2) Quantitative Precipitation Estimate (including associated precipitation variables and merged rain gauge and radar estimation). This document focuses on the second component of NOAA's NEXRAD reanalysis - the Quantitative Precipitation Estimate (QPE). The primary files generated within this data set are radar-only and radar- gauge (ROQPE, GCQPE, and MOS2D) merged precipitation products as well as ancillary information on precipitation type (PRATE and PFLAG) and radar quality (RQIND). The initial data set covers the time period from January 2002 - December 2011. Radar-only reflectivity, Gauge, Precipitation Flag, and Radar Quality Index for 5-minute data at 1km regular grid over CONUS. Radar only Radar-Gauge Quantitative Precipitation Estimates at hourly scale at 1km regular grid over CONUS. MRMS Quantitative Precipitation Estimation (QPE) uses the most advanced radar technologies and provides high-resolution information about precipitation types and amounts for the nation. The data are stored in netCDF version 4.0 files that include the necessary metadata and supplementary data fields. Data set provides information that can be useful for identification of various types of precipitation, estimation of radar reflectivity, recognition of storm patterns, forecasting technologies for rainfall estimation, and associating different phases of precipitation such as hail freezing rain and snow with radar observations.

Abstract

Compilation of Earth Surface temperatures historical. Source: https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data

Documentation

Data compiled by the Berkeley Earth project, 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

%3C!-- --%3E

• LandAverageTemperature: global average land temperature in celsius

%3C!-- --%3E

• LandAverageTemperatureUncertainty: the 95% confidence interval around the average

%3C!-- --%3E

• LandMaxTemperature: global average maximum land temperature in celsius

%3C!-- --%3E

• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature

%3C!-- --%3E

• LandMinTemperature: global average minimum land temperature in celsius

%3C!-- --%3E

• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature

%3C!-- --%3E

• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius

%3C!-- --%3E

• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

%3C!-- --%3E

**Other files include: **

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)

%3C!-- --%3E

• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)

%3C!-- --%3E

• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)

%3C!-- --%3E

• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

%3C!-- --%3E

The raw data comes from the Berkeley Earth data page.

This data set contains U.S. station surface observations that were digitized from the original forms by the Climate Database Modernization ... (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. 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. 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 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.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Other Collections. The data include parameters of database with a geographic _location of . The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study _location details. Please cite this study when using the data.