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.

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.

Documentation

hhttps://www.ncdc.noaa.gov/cdr

Update Frequency

Climate Data Records are updated independently. For update frequency for a specific CDR, please refer to the Climate Data Record website.

License

Open Data. There are no restrictions on the use of this data.

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 South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. Geospatial data provided in an ArcGIS shapefile are described herein. The shapefile contains polygons of climate regions including their acreages. As part of this project, best models from each downscaled climate dataset are determined for climate regions in South Florida and South-Central Florida based on how well they reproduce historical climate extreme indices. Table 1 of Datasets_station_information.xlsx lists the NOAA Atlas 14 stations located within each climate region. See Best_model_lists.xlsx for a list of best models for each climate region in each downscaled climate dataset and when all downscaled climate datasets are considered together.

This data has been superseded by a newer version of the dataset. Please refer to NOAA's Climate Divisional Database for more information. The U.S. Climate Divisional Dataset provides data access to current U.S. temperature, precipitation and drought indeces. Divisional indices included are: Precipitation Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Modified Palmer Drought Severity Index, Temperature, Palmer Z Index, Cooling Degree Days, Heating Degree Days, 1-Month Standardized Precipitation Index (SPI), 2-Month (SPI), 3-Month (SPI), 6-Month (SPI),12-Month (SPI) and the 24-Month (SPI). All of these Indices, except for the SPI, are available for Regional, State and National views as well. There are 344 climate divisions in the CONUS. For each climate division, monthly station temperature and precipitation values are computed from the daily observations. The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. The indices were computed using daily station data from 1895 to present.

Context

The monthly data sets include all parameters of the CLIMAT reports, which are routinely disseminated by the National Meteorological Services all over the world for their stations. The correctness of month and year has been quality controlled.

Content

The CLIMAT dataset contains a collection of sensor data from weather stations, collected worldwide from 04/2003 to 08/2020. 123 sensor data have been recorded in addition to the year, month, WMO station-identifier, and group index. The raw data consisted of 212 text files. Which were downloaded from the DWD FTP server.

The station dataset contains extended information about the respective weather station. This contains the id, the name, latitude, longitude, height, and the country.

Columns

The section and group index G1, G2, G3, and G4 referring t to the coding of the CLIMAT reports appears several times, within the sequence but is explained only once below. Temperature values consist of sign and absolute value, which are stored in separate entries. The sign sn is below coded as 0 - positive or zero, 1 - negative. In the dataset, sn columns have additionally a numeric identifier. Which is not shown in the table below. |Column identifer|Discription| |---|---| |IIiii|WMO Station-identifer| |G1|Group index within section 1| |Po|Monthly mean pressure at 1/10 hPA station level| |P|Monthly mean sea level pressure or for high located stations (in mountainous regions): meters| |sn|Sign of Monthly mean air temperature| |T|Monthly mean air temperature| |st|Standard deviation of daily mean values| |sn|Sign of mean daily maximum air temperature of the month| |Tx|Mean daily maximum air temperature of the month| |sn|Sign of mean daily minimum air temperature| |Tn|Mean daily minimum air temperature of the month| |e|Mean vapor pressure for the month| |R1|Total precipitation for the month| |Rd|Frequency group (quintile) within which R1 falls nr Number of days in the month with precipitation equal to or greater than 1 mm| |S1|Total sunshine for the month| |ps|Percentage of total sunshine duration relative to the normal| |mP|Number of days missing from the records for pressure| |mT|Number of days missing from the records for air temperature| |mTx|Number of days missing from the record for daily maximum air temperature| |mTn|Number of days missing from the record for daily minimum air temperature| |me|Number of days missing from the records for vapor pressure| |mR|Number of days missing from the records for precipitation| |mS|Number of days missing from the records for sunshine duration| G2|Group index within section| |Yb|Year of beginning of the reference period| |Yc|Year of ending of the reference period| |P0|Monthly mean pressure at station level| |P|Monthly mean sea level pressure or for high located stations (in mountainous regions): meters| |sn|Sign of T| |T|Abs(Monthly mean air temperature)| |st|Standard deviation of daily mean values relative to the monthly mean air temperature| |sn|Sign of Tx| |Tx|abs(Mean daily maximum air temperature of the month)| |sn|Sign of Tn| |Tn|Abs(Mean daily minimum air temperature of the month)| |e|Mean vapor pressure for the month| |R1|Total precipitation for the month| |nr|Number of days in the month with precipitation equal to or greater than 1 mm| |S1|Total sunshine for the month| | |Number of missing years within the reference period from the calculation of normal for ...| |yP|... air pressure| |yR|... precipitation| |yS|... sunshine duration| |yT|... mean air temperature| |yTx|... mean extreme air temperature| |ye|... vapor pressure| |G3|group index within section Number of days in the month with maximum air temperature equal to or more than ...| |T25|... 25°C| |T30|... 30°C| |T35|... 35°C| |T40|... 40°C| | |Number of days in the month with minimum air temperature ...| |Tn0|... less than 0°C Number of days in the Month with maximum air temperature ...| |Tx0|... less than 0°C| | |Number of days in the month with precipitation equal to or more than ...| |R01|... 1.0 mm| |R05|... 5.0 mm| |R10|... 10.0 mm| |R50|... 50.0 mm| |R100|... 100.0 mm| |R150|... 150.0 mm| | |Number of days in the month with snow depth ...| |s00|... more than 0 cm| |s01|... more than 1 cm| |s10|... more than 10 cm| |s50|... more than 50 cm| | |Number of days in the month with observed or recorded wind speed equal to or more than ...| |f10|... 10 meters per second or 20 knots| |f20|... 20 meters per second or 40 knots| |f30|... 30 meters per second or 60 knots| | |Number of days in the month with observed or recorded visibility of ...| |V1|... less than 50 m| |V2|... less than 100 m| |V3|... less than 1000 m| |G4|Group index within section sn sign of Highest daily mean air temperature of the month| |Txd|Abs(Highest daily mean air temperature of the month)| |yx|Day of highest daily mean air temperature during the month| |sn|Sign of Lowest daily mean ...

Les anomalies de précipitations mensuelles, saisonnières et annuelles ont été interpolées à partir des données ajustées (c.-à-d. les ensembles de données DCCAH) de précipitations totales quotidiennes, à une résolution de 50 km sur l'ensemble du Canada. Les données homogénéisées de précipitations comportent des ajustements aux données initialement enregistrées aux stations afin de compenser les discontinuités causées par des facteurs non climatiques, par exemple le remplacement d'un instrument ou la relocalisation d'une station. Les anomalies correspondent au pourcentage de différence entre la quantité de précipitations totales d'une année ou saison donnée et une valeur de référence (définie comme la moyenne de la période de référence, soit de 1961 à 1990). Les anomalies de précipitations relatives annuelles et saisonnières ont été calculées pour la période 1948-2014. Les données seront mises à jour lorsque le temps le permettra. Gridded monthly, seasonal and annual anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

TerraClimate est un ensemble de données sur le climat et l'équilibre hydrique climatique mensuels pour les surfaces terrestres mondiales. Il utilise une interpolation assistée par le climat, combinant des normales climatologiques à haute résolution spatiale de l'ensemble de données WorldClim, avec des données à résolution spatiale plus grossière, mais à évolution temporelle, de CRU Ts4.0 et de la réanalyse japonaise sur 55 ans (JRA55). Conceptuellement, la procédure applique des valeurs interpolées …

The Climate Data Analytics Market Report is Segmented by Type (Climate Model Evaluation, Climate Data Processing and Visualization, Climate Data Formats, and Statistical Methods), End-User Industry (Government and Public Sector, Energy and Utilities, Agriculture, Insurance and Risk Management, Infrastructure and Transportation, and Healthcare), and Geography (North America, Europe, Asia Pacific, Middle East and Africa, and Latin America). The Market Sizes and Forecasts Regarding Value (USD) for all the Above Segments are Provided.

Ontario-focused high resolution climate information and its application. The data contains the 50th percentile high resolution probabilistic projections of annual averaged temperature and precipitation over the province. It covers the: * 1970s * 2030s * 2050s * 2080s This data is provided in partnership with the University of Regina. More user-friendly visualizations and downloads of this data are also available at: * The Ontario Climate Change Data Portal (OCCDP) by the University of Regina * The Ontario Climate Change Projections (OCCP) page by York University This dataset's technical documentation contains a metadata record of projects funded by the Ministry of the Environment, Conservation and Parks alongside final reports and associated data.

Providing files in pdf or jpeg format, obtained from digitisation, of monthly climate tables summarising the meteorological observations recorded daily in metropolitan France.

Weather-France retains its climate archives to ensure its missions of conservation of climate memory and scientific characterisation of past and future climate.

These are mainly surveys of meteorological observations gathered on metropolitan territory, ultramarine territory or former French colonies, and documentation on meteorological stations, observation techniques and measuring instruments, since the creation of the first French meteorological service in 1855.

Météo-France offers access to these funds through a website that provides several thousand inventory descriptions and offers access to some of these archives in digital form (JPEG or PDF format). The search can be done by weather station, rating, weather parameter and/or date.

Website address: http://archives-climat.fr

The size and share of the market is categorized based on Type (Climate Model Evaluation, Climate Data Processing and Visualization, Climate Data Formats, Statistical Methods) and Application (Government Agencies and Research Institutions, Energy Sectors, Agriculture and Food Industry, Insurance and Risk Management, Transportation and Logistics) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Climatic data for Welmera district from 2013 to 2018

The dataset 'climate_change_agriculture_dataset.csv' comprises 1000 rows and 10 columns, mimicking the relationship between climate change and agricultural facets. Each row represents distinct data points while columns depict various aspects. The columns encompass diverse parameters vital to agriculture under changing climates. 'Temperature' and 'Precipitation' denote weather elements influencing crops, while 'CO2 Levels' simulate atmospheric variations. 'Crop Yield' gauges agricultural productivity, whereas 'Soil Health' indicates ground fertility. 'Extreme Weather Events' portrays occurrences like droughts or storms affecting crops, while 'Crop Disease Incidence' measures susceptibility to ailments. 'Water Availability,' 'Food Security,' and 'Economic Impact' highlight crucial factors influencing agriculture's stability and socio-economic repercussions. This dataset, although synthetically generated, simulates a spectrum of conditions and impacts tied to climate change, offering a simulated view of how varying environmental factors might affect agricultural outcomes.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

CustomWeather houses one of the world's most comprehensive troves of Historical Weather Data. This climate data is sourced directly from 8,500 global weather stations.

The global weather data is used in many verticals including Agriculture, Energy, Event Planning, Food and Grocery Planning, Oil and Gas, Supply Chains, Climate Data Studies, Wind Power Studies, and research on Natural Disasters.

The Historical Climate Data is part of CustomWeather's trove of historical, real-time, and forecast data sets covering the entire life cycle of weather - past, present, and future.

The Historical Weather data includes information on Places Data, Precipitation Data, Rainfall Data, Severe Weather Data, Storm Data, Temperature Data, and Wind Data.

The hourly Historical Weather Data is available directly through the CustomWeather API for the past 365 days. We send over files for the hourly data going back in time more than the past year.

The daily Historical Weather Data is also available directly from the CustomWeather API, in yearly call increments, dating back many years.

Hourly Climate Data: Normalized values can be delivered for various time periods. Fields include: daylight status, sky descriptor, precipitation descriptor, temperature, wind speed, wind direction, wind gusts, humidity, dew point, barometric tendency, sea level pressure, sky conditions, precipitation totals for various time periods.

Ongoing Hourly Climate Updates Updated daily before 7am PST, this file includes the hourly observed data values for the past 3 days in the same format as the hourly climate data file. The file contains 3 days of data to help capture any missing data that becomes available during the 3 day period.

On-going Daily Summary File Updated each morning, this file contains the daily summary of the previous days data in the same file format as Climate file.

On-going Year-to-date Daily Summary File Updated 3 times per week, this product contains a year to date summary of the official values for each historical data location. The purpose of this file is to capture any data that was previously not available that has become available after the fact. For example, data that was not delivered due to a communication error at the reporting station that is later gathered and made available. This product will run through January and February of the following year to capture any delayed data over that period for the previous year.

Daily Climate Data Daily climate records go back to the late 1940s for some locations with our standard product sets being 10, 20 or 30 years of historical data. Normalized values can be delivered across each of these periods. Fields include: station id, valid date time, maximum temperature, minimum temperature, average temperature, heating degree days, cooling degree days, precipitation, average relative humidity, average wind speed, average dew point, average visibility, average sea level pressure.

For non-weather stations, CustomWeather houses robust, satellite-derived Historical Climate Data sets including CFSR, NMME, and ERA-5.

In essence we can deliver hourly and daily Climate Data for any location on earth at high resolution with data available going back many years.