This dataset contains 3010 daily time series representing the variations of four weather variables: rain, mintemp, maxtemp and solar radiation, measured at the weather stations in Australia. The series were extracted using R bomrang package.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias. This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.

[ NOTE – 2022/05/06: this dataset supersedes the earlier versions https://doi.org/10.15482/USDA.ADC/1482548 and https://doi.org/10.15482/USDA.ADC/1526329 ]. This dataset contains 15-minute mean weather data from the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) for all days in each year. The data are from sensors placed at 2-m height over a level, grass surface mowed to not exceed 12 cm height and irrigated and fertilized to maintain reference conditions as promulgated by Allen et al. (2005, 1998). Irrigation was by surface flood in 1989 through 1994, and by subsurface drip irrigation after 1994. Sensors were replicated and intercompared between replicates and with data from nearby weather stations, which were sometimes used for gap filling. Quality control and assurance methods are described by Evett et al. (2018). Data from a duplicate sensor were used to fill gaps in data from the primary sensor using appropriate regression relationships. Gap filling was also accomplished using sensors deployed at one of the four large weighing lysimeters immediately west of the weather station, or using sensors at other nearby stations when reliable regression relationships could be developed. The primary paper describes details of the sensors used and methods of testing, calibration, inter-comparison, and use. The weather data include air temperature (C) and relative humidity (%), wind speed (m/s), solar irradiance (W m-2), barometric pressure (kPa), and precipitation (rain and snow in mm). Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is

This map displays projected visible surface smoke across the contiguous United States for the next 48 hours in 1 hour increments. It is updated every 24 hours by NWS. Concentrations are reported in micrograms per cubic meter.Where is the data coming from?The National Digital Guidance Database (NDGD) is a sister to the National Digital Forecast Database (NDFD). Information in NDGD may be used by NWS forecasters as guidance in preparing official NWS forecasts in NDFD. The experimental/guidance NDGD data is not an official NWS forecast product.Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndgd/GT.aq/AR.conus/ds.smokes01.binSource data archive can be found here: https://www.ncei.noaa.gov/products/weather-climate-models/national-digital-guidance-database look for 'LXQ...' files by date. These are the Binary GRIB2 files that can be decoded via DeGRIB tool.Where can I find other NDGD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.RevisionsJuly 11, 2022: Feed updated to leverage forecast model change by NOAA, whereby the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) forecast model system was replaced with the Rapid Refresh (RAP) forecast model system. Key differences: higher accuracy with RAP now concentrated at 0-8 meter detail vs HYSPLIT at 0-100 meter; earlier data delivery by 6 hrs; forecast output extended to 51 hrs.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This data set provides Daymet Version 3 model output data as gridded estimates of daily weather parameters for North America and Hawaii: including Canada, Mexico, the United States of America, Puerto Rico, and Bermuda. The island areas of Hawaii and Puerto Rico are available as files separate from the continental land mass. Daymet output variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The data set covers the period from January 1, 1980 to December 31 of the most recent full calendar year. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are continuous surfaces provided as individual files, by variable and year, at a 1-km x 1-km spatial resolution and a daily temporal resolution. Data are in a Lambert Conformal Conic projection for North America and are distributed in a netCDF file (version 1.6) format compliant to Climate and Forecast (CF) metadata conventions. https://daymet.ornl.gov/overview.html Reference: Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2016. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1328

GC-Net Level 1 automated weather station data In Memory of Dr. Konrad (Koni) Steffen Author: B. Vandecrux Contact: bav@geus.dk Last update: 2023-09-01 Citation Steffen, K.; Vandecrux, B.; Houtz, D.; Abdalati, W.; Bayou, N.; Box, J.; Colgan, L.; Espona Pernas, L.; Griessinger, N.; Haas-Artho, D.; Heilig, A.; Hubert, A.; Iosifescu Enescu, I.; Johnson-Amin, N.; Karlsson, N. B.; Kurup Buchholz, R.; McGrath, D.; Cullen, N.J.; Naderpour, R.; Molotch, N.P.; Pederson, A. Ø.; Perren, B.; Philipps, T.; Plattner, G.K.; Proksch, M.; Revheim, M. K.; Særrelse, M.; Schneebli, M.; Sampson, K.; Starkweather, S.; Steffen, S.; Stroeve, J.; Watler, B.; Winton, Ø. A.; Zwally, J.; Ahlstrøm, A., 2023, "GC-Net Level 1 automated weather station data", https://doi.org/10.22008/FK2/VVXGUT, GEUS Dataverse, V3 as described and processed by: Vandecrux, B., Box, J. E., Ahlstrøm, A. P., Andersen, S. B., Bayou, N., Colgan, W. T., Cullen, N. J., Fausto, R. S., Haas-Artho, D., Heilig, A., Houtz, D. A., How, P., Iosifescu Enescu, I., Karlsson, N. B., Kurup Buchholz, R., Mankoff, K. D., McGrath, D., Molotch, N. P., Perren, B., Revheim, M. K., Rutishauser, A., Sampson, K., Schneebeli, M., Starkweather, S., Steffen, S., Weber, J., Wright, P. J., Zwally, H. J., and Steffen, K.: The historical Greenland Climate Network (GC-Net) curated and augmented Level 1 dataset, Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, 2023. Description The Greenland Climate Network (GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen on the Greenland Ice Sheet (GrIS). This first station, "Swiss Camp" or the "ETH-CU" camp, was initiated in 1990 by A. Ohmura et al. (1991, 1992) with K. Steffen taking over the site from 1995 and expending the network from that year to 31 stations at 30 sites in Greenland (Steffen et al., 1996, 2001). The GC-Net was supported by multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado. Provided in this dataset are the 25 two-level stations from 24 sites on the Greenland ice sheet and 3 experimental stations in Antarctica. The remaining 6 Greenland stations have a different design and will be added once quality checked. Although the GC-Net AWS transmitted their data near-real time through satellite communication, the present dataset was made from uncorrupted datalogger files, retrieved every 1-2 years during maintenance. Full dataset description publication will be forthcoming. The Geological Survey of Denmark and Greenland (GEUS) has undertaken the continuation of multiple GC-Net sites through the Programme for Monitoring of the Greenland Ice Sheet (PROMICE.dk). The level 1 data is provided in the newly described csv-compatible NEAD format, which is a csv file with added metadata header. The format is documented at https://doi.org/10.16904/envidat.187 and a python package is available to read and write NEAD files: https://github.com/GEUS-Glaciology-and-Climate/pyNEAD . The GC-Net stations measure: - Air temperature from four sensors at two heights above the surface - Relative humidity at two heights above the surface - Wind speed and direction at two heights above the surface - Air pressure - Surface height from two sonic sounders - Incoming and outgoing shortwave radiation - Net radiation (long- and short-wave)* - Firn or ice temperatures at 10 levels below the surface In the L1 dataset, these measurements are cleaned from sensor, station or logger malfunctions, adjusted and/or filtered when and where possible. Additionally, the L1 dataset contains the following derived variables: - Surface height (corrected from the shifts in sonic sounder height) - Instrument heights (derived from sonic sounder height and station geometry) - Inter- or extrapolated temperature, relative humidity and wind speed at respectively 2, 2, and 10 m above the surface - Estimated depth of the subsurface temperature measurements (adjusted for snow accumulation, ice ablation and instrument replacement) - Interpolated firn or ice temperature at 10 m below the surface - Calculated solar an azimuth angles - Sensible and latent heat fluxes calculated after Steffen and Demaria (1996) Important links: - The level 1 processing scripts and discussion page for Q&A and issue reporting (under "issues" tab) is available at: https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing - The level 0 data (from which the L1 data was built from) is available at: https://www.doi.org/10.16904/envidat.1. - The compilation of handheld GPS coordinates for each site and for multiple years is available here: Vandecrux, B. and Box, J.E.: GC-Net AWS observed and estimated positions (Version v1) [Data set]. Zenodo....

[NOTE - 2022-09-07: this dataset is superseded by an updated version https://doi.org/10.15482/USDA.ADC/1526433 ] This dataset consists of weather data for each year when maize was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The weather data include solar irradiance, barometric pressure, air temperature and relative humidity, and wind speed determined using sensors placed at 2-m height over a level, grass surface mowed to not exceed 12 cm height and irrigated and fertilized to maintain reference conditions as promulgated by ASCE (2005) and FAO (1996). Irrigation was by surface flood in 1989 through 1994, and by subsurface drip irrigation after 1994. Sensors were replicated and intercompared between replicates and with data from nearby weather stations, which were sometimes used for gap filling. Quality control and assurance methods are described by Evett et al. (2018). These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on maize ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data. Resources in this dataset:

Resource Title: 1989 Bushland, TX, standard 15-minute weather data. File Name: 1989_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: The weather data are presented as 15-minute mean values of solar irradiance, air temperature, relative humidity, wind speed, and barometric pressure; and as 15-minute totals of precipitation (rain and snow). Daily total precipitation as determined by mass balance at each of the four large, precision weighing lysimeters is given in a separate tab along with the mean daily value of precipitation. Data dictionaries are in separate tabs with names corresponding to those of tabs containing data. A separate tab contains a visualization tool for missing data. Another tab contains a visualization tool for the weather data in five-day increments of the 15-minute data. An Introduction tab explains the other tabs, lists the authors, explains data time conventions, explains symbols, lists the sensors, and datalogging systems used, and gives geographic coordinates of sensing locations.

Resource Title: 1990 Bushland, TX, standard 15-minute weather data. File Name: 1990_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1990.

Resource Title: 1994 Bushland, TX, standard 15-minute weather data. File Name: 1994_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1994.

Resource Title: 2013 Bushland, TX, standard 15-minute weather data. File Name: 2013_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 2013.

Resource Title: 2016 Bushland, TX, standard 15-minute weather data. File Name: 2016_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 2016.

Resource Title: 2018 Bushland, TX, standard 15-minute weather data. File Name: 2018_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 2018.

Resource Title: 1996 Bushland, TX, standard 15-minute weather data. File Name: 1996_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1996.

Resource Title: 1997 Bushland, TX, standard 15-minute weather data. File Name: 1997_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1997.

Resource Title: 1998 Bushland, TX, standard 15-minute weather data. File Name: 1998_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1998.

Resource Title: 1999 Bushland, TX, standard 15-minute weather data. File Name: 1999_15-min_weather_SWMRU_CPRL.xlsx. Resource Description: As above for 1999.

This dataset contains Australian Community Climate and Earth-System Simulator (ACCESS) Numerical Weather Prediction (NWP) model surface data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Darwin, Australia. The data is from the Australian Bureau of Meteorology (BoM) and is in digital gridded binary format netCDF files. The files are grouped into .tar files by month.

ClimateForecasts is a database that provides environmental data for 15,504 weather station locations and 49 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the TreeGOER (Tree Globally Observed Environmental Ranges) database that is available from https://doi.org/10.5281/zenodo.7922927. Details on the preparations of these layers are provided by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914. A similar extraction process was used for the CitiesGOER database that is also available from Zenodo via https://zenodo.org/doi/10.5281/zenodo.8175429.

ClimateForecasts (as the CitiesGOER) was designed to be used together with TreeGOER and possibly also with the GlobalUsefulNativeTrees database (Kindt et al. 2023) to allow users to filter suitable tree species based on environmental conditions of the planting site. One example of combining data from these different sets in the R statistical environment is available from this Rpub: https://rpubs.com/Roeland-KINDT/1114902.

The identities including the geographical coordinates of weather stations were sourced from Meteostat, specifically by downloading (17-FEB-2024) the ‘lite dump’ data set with information for active weather stations only. Two weather stations where the country could not be determined from the ISO 3166-1 code of ‘XA’ were removed. If weather stations had the same name, but occurred in different ISO 3166-2 regions, this region code was added to the name of the weather station between square brackets. Afterwards duplicates (weather stations of the same name and region) were manually removed.

Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the BiodiversityR::ensemble.envirem.run function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes future grid layers available from WorldClim 2.1).

Maps were added in version 2024.03 where locations of weather stations were shown on a map of the Climatic Moisture Index (CMI). These maps were created by a similar process as in the TreeGOER Global Zones Atlas from the environmental raster layers used to create the TreeGOER via the terra package (Hijmans et al. 2022, version 1.7-46) in the R 4.2.1 environment. Added country boundaries were obtained from Natural Earth as Admin 0 – countries vector layers (version 5.1.1). Also added after obtaining them from Natural Earth were Admin 0 – Breakaway, Disputed areas (version 5.1.0, coloured yellow in the atlas) and Roads (version 5.0.0, coloured red in the atlas). For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from https://github.com/tdwg/wgsrpd. Check Brummit 2001 for details such as the maps shown at the end of this document.

Maps for version 2024.07 modified the dimensions of the sheets to those used in version 2024.06 of the TreeGOER Global Zones Atlas. Another modification was the inclusion of Natural Earth boundaries for Lakes (version 5.0.0, coloured darkblue in the atlas).

Version 2024.10 includes a new data set that documents the location of the city locations in Holdridge Life Zones. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) that are available for download from DRYAD and were created for the following article: Elsen et al. 2022. Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962. Version 2024.10 further includes Holdridge Life Zones for the climates available from the previously included climates, calculating biotemperatures and life zones with similar methods as used by Holdridge (1947; 1967) and Elsen et al. (2022) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: https://zenodo.org/records/14020914.

Version 2024.11 includes a new data set that documents the location of the weather stations in Köppen-Geiger climate zones. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6.

Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the GlobalUsefulNativeTrees database. One of these zones are the Whittaker biome types, available as a polygon from the plotbiomes package (see also here). Whittaker biome types were extracted with similar R scripts as described by Kindt 2025 (these were also used to calculate environmental ranges of TreeGOER species, as archived here).

Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. 2024).

When using ClimateForecasts in your work, cite this depository and the following:

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086

Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. https://doi.org/10.1111/ecog.02880

Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021

Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

Meteostat (2024) Weather stations: Lite dump with active weather stations. https://github.com/meteostat/weather-stations (accessed 17-FEB-2024)

When using information from the Holdridge Life Zones, also cite:

Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962

When using information from Köppen-Geiger climate zones, also cite:

Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. https://doi.org/10.1038/s41597-023-02549-6

When using information on the Whittaker biome types, also cite:

Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.

Whittaker, R. H. (1970). Communities and ecosystems.

Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7145245

When using information on the steady state water table depth, also cite:

Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. https://gmd.copernicus.org/articles/17/275/2024/

The development of ClimateForecasts and its partial integration in version 2024.03 of the GlobalUsefulNativeTrees database was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso, by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

R code and tutorial for downloading and processing agrometeorological data from API client sources. Last update on March 18, 2022.

Supplementary material, data, and R code for the article "When the election rains out and how bad weather excludes marginal voters from turning out", forthcoming in Electoral Studies. Not replication code, strictly speaking, because some (registry) data cannot be made public, and most of the code therefore will not run.

These files were created in response to growing demand for weather data suitable for exploring the impact of climate change on the built environment.

These datasets consist of 996 text files. The files contain hourly weather data for 83 Australian locations under 3 future climate scenarios (RCP2.6, RCP4.5, and RCP8.5) and for 4 future years (2030, 2050, 2070, and 2090).

The dataset is available in two formats:

• In .epw format that can be used by building simulation software such as EnergyPlus, ESP-r, and IESVE.

• In a weather file format suitable for building simulations using Nationwide House Energy Rating Scheme (NatHERS) software such as AccuRate, BERSPro, FirstRate5, and HERO in non-regulatory mode. Lineage: The predictive weather data is based on a typical meteorological year of historical weather data drawn from Bureau of Meteorology weather data from the years 1990 to 2015. Global Climate Models and morphing were applied to this data to predict the future values under each climate scenario at each location.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains the individual-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 2 and the representative concentration pathway (RCP) used was RCP 4.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).

Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 -
http://dx.doi.org/10.1071/ES19040
BCC-CSM2-MR -
https://doi.org/10.5194/gmd-14-2977-2021
CNRM-ESM2-1-
https://doi.org/10.1029/2019MS001791
MPI-ESM1-2-HR -
https://doi.org/10.5194/gmd-12-3241-2019
MRI-ESM2-0 -
https://doi.org/10.2151/jmsj.2019-051
NorESM2-MM -
https://doi.org/10.5194/gmd-13-6165-2020

Additional references:
O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.
Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Please cite the following if this data is used in any research or project:

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637

Cross-Model Version:

Representative Cities Version:

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a

This dataset provides Daymet Version 4 daily data on a monthly cycle as 1-km gridded estimates of daily weather variables for minimum temperature (tmin), maximum temperature (tmax), precipitation (prcp), shortwave radiation (srad), vapor pressure (vp), snow water equivalent (swe), and day length. Data are derived from the Daymet version 4 software where the primary inputs are daily observations of near-surface maximum and minimum air temperature and daily total precipitation from weather stations. The main algorithm to estimate primary Daymet variables (tmax, tmin, and prcp) at each Daymet grid is based on a combination of interpolation and extrapolation, using inputs from multiple weather stations and weights that reflect the spatial and temporal relationships between a Daymet grid and the surrounding weather stations. Secondary variables (srad, vp, and swe) are derived from the primary variables (tmax, tmin, and prcp) based on atmospheric theory and empirical relationships. The day length (dayl) estimate is based on geographic location and time of year. Data are available for the Continental North America, Puerto Rico, and Hawaii as separate spatial layers in a Lambert Conformal Conic projection and are distributed in standardized Climate and Forecast (CF)-compliant netCDF file formats.

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

This is version v3.4.0.2023f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.

This update (v3.4.0.2023f) to HadISD corrects a long-standing bug which was discovered in autumn 2023 whereby the neighbour checks (and associated [un]flagging for some other tests) were not being implemented. For more details see the posts on the HadISD blog: https://hadisd.blogspot.com/2023/10/bug-in-buddy-checks.html & https://hadisd.blogspot.com/2024/01/hadisd-v3402023f-future-look.html

The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information.

The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20240101_v3.4.1.2023f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.

To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.

For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/

References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) :

Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.

Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.

Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012

Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1

For a homogeneity assessment of HadISD please see this following reference

Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

Historical rainfall and temperature forecast and observations hourly data (2016-05 to 2017-04), used to compare and verify forecasting. Observations data is from a sample of 518 automatic weather stations (AWS) over land, and is at the surface level. Data has been aggregated from one-minute readings into hourly values, for forecast comparison purposes. This observations data is partly QC'd.\r \r Forecasted weather elements include temperature, maximum and minimum temperature, rainfall probabilities and rainfall amounts. Different forecast products have different time resolutions, e.g. temperature forecasts are made for each hour, while maximum and minimum temperature forecasts are made for each day.\r \r This dataset is approximately 650 Mb in size.

This data set contains weather data from the Arctic Tundra Long Term Ecological Research Program (LTER) site at Toolik Lake. Only the sensors that are measured every 10 minutes and averaged every three hours are included, i.e. soil temperatures, lake temperature, lake depth, and evaporation pan depth and pan water temperature. For more information, please see the readme file.

This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade-1) than maximum (-0.1 - 0.2 ℃ decade-1), more for winter (-0.1 - 0.3 ℃ decade-1) than summer (-0.1 - 0.1 ℃ decade-1), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade-1 or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.

The GOES-R PLT Mission Reports dataset consists of various reports filed by the scientists during the GOES-R Post Launch Test (PLT) field campaign including flight reports, weather forecasts, mission scientist reports, and plan-of-day reports. The campaign took place from March to May of 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The GOES-R PLT Mission Reports dataset contains reports from March 13 through May 17, 2017 in PDF, PNG, Microsoft Excel and Word (.xlsx and .docx) format, and KMZ format for display in Google Earth.