Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

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.

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

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

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

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

In this dataset, we have include several files:

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

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

Other files include:

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

The raw data comes from the Berkeley Earth data page.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

This resource demonstrates the workflow developed to prepare downscaled GCM data for input to Model My Watershed (ModelMyWatershed.org). GCM data for the Delaware River Basin was assembled from 19 GCMs including each model's RCP4.5 and RCP8.5; this was performed by Dr. Tim Hawkins, Shippensburg University (http://www.ship.edu/geo-ess/). Downscaled precipitation data from global climate models (GCM) does not accurately retain the magnitude and frequency of individual storm events for a given location. This lack of predictive resolution of event magnitude and frequency limits realism of rainfall-runoff models used to for predicting watershed hydrology under future climate scenarios. To address this problem, Maimone et al (2019) developed a method for summarizing the statistical distribution of precipitation event magnitude and frequency that could be applied to downscaled GCM precipitation predictions. Application of the methods here to down-scaled GCM scenarios requires that the those predictions do not include an increase in the number of days of precipitation per year. Maimone et al (2019) state this requirement: "Because GCM projections for the Philadelphia region do not indicate an increase in the number of wet days per year, future increases in precipitation are the result of the existing number and distribution of wet days becoming more intense."

I developed a workflow to replicate Maimone et al's methods and provide an example of it in this Resource. There are three sections of the R Markdown document. The first section seeks to replicate the synthetic weather generator developed by Maimone et al (2019) using an example dataset. The second section applies those methods to the downscaled GCM ensemble average conditions for the Delaware River Basin provided by Dr. Hawkins. The third section develops depth-duration-frequency statistics for the 24 hour storm event relevant to the 2080-2100 predictions. To open the R Markdown document and execute the workflow yourself, find the Open With dropdown list in the upper right hand corner of this Resource and select CUAHSI JupyterHub.

The first section uses an example precipitation dataset from the Philadelphia Airport for the period 01 January 1995 through 31 December 2013. The data were downloaded from NOAA's Climate Data Online Search portal: https://www.ncdc.noaa.gov/cdo-web/search.

The downloaded data and metadata for this NOAA Climate Data are available on Hydroshare here: http://www.hydroshare.org/resource/60058ceda8334e68be141516c5b8de3f. Additional data on precipitation frequency at the Philadelphia Airport was downloaded from the NOAA Hydrometeorological Design Studies Center: https://hdsc.nws.noaa.gov/hdsc/pfds/index.html.

An example of working with this type of NOAA Climate Data is provided on the NEON website here: https://www.neonscience.org/da-viz-coop-precip-data-R.

References: Maimone, M., S. Malter, J. Rockwell, and V. Raj. 2019. Transforming Global Climate Model Precipitation Output for Use in Urban Stormwater Applications. Journal of Water Resources Planning and Management 145:04019021.

This dataset contains Raleigh Durham International Airport weather data pulled from the NOAA web service described at Climate Data Online: Web Services Documentation. We have pulled this data and converted it to commonly used units. This dataset is an archive - it is not being updated.

subject to appropriate attribution.

Surface temperatures and thickness-derived temperatures from a 54-station, globally distributed radiosonde network have been used to estimate global, hemispheric, and zonal annual and seasonal temperature deviations. Most of the temperature values used were column-mean temperatures, obtained from the differences in height (thickness) between constant-pressure surfaces at individual radiosonde stations. The pressure-height data before 1980 were obtained from published values in Monthly Climatic Data for the World. Between 1980 and 1990, Angell used data from both the Climatic Data for the World and the Global Telecommunications System (GTS) Network received at the National Meteorological Center. Between 1990 and 1995, the data were obtained only from GTS, and since 1995 the data have been obtained from National Center for Atmospheric Research files. The data are evaluated as deviations from the mean based on the interval 1961-1990. Time series for the earth's surface, and the 850-300mb, 300-100mb and 100-50mb layers are presented for north polar (60-90N), north temperate (30-60N), tropical (30S-30N), south temperate (30-60S) and south polar (60-90S) climate zones, as well as for the Northern and Southern hemispheres and the globe. The data presentation is more compact than in the case of Angell's 63-station network, with two fewer layers and three fewer climate zones, for a total of eight time series.CITE AS: Angell, J.K. 2012. Global, hemispheric, and zonal temperature deviations derived from a 54-station radiosonde network. In Trends Online: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. doi: 10.3334/CDIAC/cli.005

This dataset contains gridded monthly rainfall from 1990 to 2019 at 250 m resolution for seven of the eight main Hawaiian Islands (18.849°N, 154.668°W to 22.269°N, 159.816°W; the island of Ni‘ihau is excluded due to lack of data). The gridded data use a World Geographic Coordinate System 1984 (WGS84) and are stored as individual GeoTIFF files for each month-year, as indicated by the GeoTIFF file name. Contained in the dataset is a statewide complete 30-year partially gap filled monthly rainfall dataset for all stations for the entire date range with station names, ID and location. Also included are month year statewide files for rainfall kriging input files which contain station rainfall, station rainfall transformations, station transformed anomaly, and denotation of inclusion in per county kriging process, statewide gridded rainfall, statewide standard error, statewide gridded rainfall anomaly, statewide gridded rainfall anomaly standard errors, and statewide meta-data that contain per county as well as statewide cross validation statistics, station counts, and readable data quality statement. Monthly rainfall grids were created using an optimized geostatistical kriging approach to interpolate relative rainfall anomalies which are then combined with long-term means to develop the climatologically aided gridded estimates. Optimization of the kriging algorithm consists of: 1) determining an offset (constant) to use when log-transforming data; 2) quality controlling data prior to interpolation; 3) using machine learning to detect erroneous maps; and 4) identifying the most appropriate parametrization scheme for fitting the model used in the interpolation. At present, the data are available from 1990 to 2019, but datasets will be updated as new gridded monthly rainfall data become available. Rainfall products and error metrics are also available by county and can be accessed online for easy download through the Hawaiʻi Data Climate Portal available at http://www.hawaii.edu/climate-data-portal.

This data package provides daily measurements of snow depth at 195 National Weather Service (NWS) first-order climatological stations in the United States. The data have been assembled and made available by the National Climatic Data Center (NCDC) in Asheville, North Carolina. The 195 stations encompass 388 unique sampling locations in 48 of the 50 states; no observations from Delaware or Hawaii are included in the database. Station selection criteria emphasized the quality and length of station records while seeking to provide a network with good geographic coverage. Snow depth at the 388 locations was measured once per day on ground open to the sky. The daily snow depth is the total depth of the snow on the ground at measurement time. The time period covered by the database is 1893-1992; however, not all station records encompass the complete period. While a station record ideally should contain daily data for at least the seven winter months (January through April and October through December), not all stations have complete records. Each logical record in the snow depth database contains one station's daily data values for a period of one month, including data source, measurement, and quality flags. The snow depth data have undergone extensive manual and automated quality assurance checks by NCDC and the Carbon Dioxide Information Analysis Center (CDIAC). These reviews involved examining the data for completeness, reasonableness, and accuracy, and included comparison of some data records with records in NCDC's Summary of the Day First Order online database. Since the snow depth measurements have been taken at NWS first-order stations that have long periods of record, they should prove useful in monitoring climate change. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp059.html

This layer shows the aggregated emissions resulting from energy consumption in buildings across different neighborhoods and sectors (i.e., residential, commercial and industrial). The data is mapped to census tracts. This layer has been populated with utility energy consumption data procured directly from Seattle City Light (electricity), aggregated and anonymized by sector, quarter, and census tract. Some tracts have their data combined and averaged with neighboring tracts for privacy purposes. If data is aggregated in a tract, the "grouped flag" field will read "true".For more information please visit the One Seattle Climate Portal item description page.

Climate Watch: Data for Climate Action

Climate Watch is an online platform designed to empower policymakers, researchers, media and other stakeholders with the open climate data, visualizations and resources they need to gather insights on national and global progress on climate change.

Climate Watch is managed by World Resources Institute. It is a contribution to the NDC Partnership.

Encompassing data on Historical emissions by country, region, industry, and gas by year (1850-2018) Nationally Determined Contributions (NDCs); Linkages between Nationally Determined Contributions (NDCs) and the Sustainable Development Goals (SDGs) Emissions scenario pathways for major emitting countries, derived from a growing library of models;

Acknowledgements

The CDP Kaggle project prompted me to seek out datasets on climate change and I stumbled upon this one in my research. Climate watch is a great platform and their website offers many opportunities to explore and visualize the data.

I encourage everyone to check out their website: https://www.climatewatchdata.org/

Weather At Barre Montpelier Knapp State Airport VT US 1948 - 2015

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.

This dataset contains global historical GHG Emissions 1850-2023 from World Resources Institute.

CAIT Climate Data Explorer. Washington, DC: World Resources Institute. Available online at: http://cait.wri.org

This layer shows total trips by mode and their corresponding emissions across different neighborhoods in Seattle. The data is mapped to census tracts. The data in this layer has been populated using an output from the Puget Sound Regional Council's (PSRC's) regional travel demand model. This model is updated only once every few years and is therefore not ideal for frequent data updates. The City is working on procuring more frequent measured travel data from alternate sources. For more information please visit the One Seattle Climate Portal item description page.

The "Unified Forecast System (UFS)" is a community-based, coupled, comprehensive Earth Modeling System. It supports " multiple applications" with different forecast durations and spatial domains. The UFS Short-Range Weather (SRW) Application figures among these applications. It targets predictions of atmospheric behavior on a limited spatial domain and on time scales from minutes to several days. The SRW Application includes a prognostic atmospheric model, pre-processor, post-processor, and community workflow for running the system end-to-end. The "SRW Application Users's Guide" includes information on these components and provides detailed instructions on how to build and run the SRW Application. Users can access additional technical support via the "UFS GitHub Discussions"

This data registry contains the data required to run the “out-of-the-box” SRW Application case. The SRW App requires numerous input files to run, including static datasets (fix files containing climatological information, terrain and land use data), initial condition data files, lateral boundary condition data files, and model configuration files (such as namelists). The SRW App experiment generation system also contains a set of workflow end-to-end (WE2E) tests that exercise various configurations of the system (e.g., different grids, physics suites). Data for running a subset of these WE2E tests are also included within this registry.

Users can generate forecasts for dates not included in this data registry by downloading and manually adding raw model files for the desired dates. Many of these model files are publicly available and can be accessed via links on the "Developmental Testbed Center" website.

Several nations are currently experiencing a significant increase in coronavirus (COVID-19), including Indonesia. A total of 34,874,744 confirmed cases with 1,097,497 deaths (case fatality rate (CFR) 3.1%) were reported in 216 countries based on data from World Health Organization. COVID-19 remains public health problem around the world. It is possible the climate could affect the transmission of COVID-19. The wind is one of the climate factors besides temperature, humidity, and rainfall. Wind speed data can be used to study the spread of COVID-19 cases., The wind speed data was taken from the Meteorology, Climatology, and Geophysics Agency's data website (https://dataonline.bmkg. go.id/home). The wind speed (maximum and mean) in Jakarta from the pandemic inception, specifically between March and September 2020. These records were obtained from the website of the Jakarta Provincial Health Office and the website of the Indonesian Meteorology, Climatology, and Geophysics Agency. Subsequently, the general information was converted into 31-week documentation. Furthermore, a basic map of Jakarta with neighboring community boundaries was obtained using the GADM Map and Data site. Coordinates for the weather monitoring stations were accessed online from (https://www.gps-latitude-longitude.com/).,

Daily rainfall data was collect by the Bureau of Meteorology (BoM). This dataset contains data collected from 1950 to 2009. The stations that are represented in this data set are Carnarvon Airport, Exmouth Gulf, Gnaraloo, Learmonth Airport, Ningaloo, Quobba, Vlaming Head and Warroora. This data was downloaded from the Bureau of Meteorology's Climate Data Online website as separate csv files for each year, they were then collated into one MS Excel spreadsheet per station. A text file containing notes on the data was also downloaded at the same time. This data was obtained for the Ningaloo Project.

This layer shows the aggregated emissions resulting from energy consumption in buildings across different neighborhoods and sectors (i.e., residential, commercial and industrial). The data is mapped to census tracts. This layer has been populated with utility energy consumption data procured directly from Puget Sound Energy (gas), aggregated and anonymized by sector, quarter, and census tract. Some tracts have their data combined and averaged with neighboring tracts for privacy purposes. If data is aggregated in a tract, the "grouped flag" field will read "true". For more information please visit the One Seattle Climate Portal item description page.