The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The GFS data files stored here can be immediately used for OAR/ARL’s NOAA-EPA Atmosphere-Chemistry Coupler Cloud (NACC-Cloud) tool, and are in a Network Common Data Form (netCDF), which is a very common format used across the scientific community. These particular GFS files contain a comprehensive number of global atmosphere/land variables at a relatively high spatiotemporal resolution (approximately 13x13 km horizontal, vertical resolution of 127 levels, and hourly), are not only necessary for the NACC-Cloud tool to adequately drive community air quality applications (e.g., U.S. EPA’s Community Multiscale Air Quality model; https://www.epa.gov/cmaq), but can be very useful for a myriad of other applications in the Earth system modeling communities (e.g., atmosphere, hydrosphere, pedosphere, etc.). While many other data file and record formats are indeed available for Earth system and climate research (e.g., GRIB, HDF, GeoTIFF), the netCDF files here are advantageous to the larger community because of the comprehensive, high spatiotemporal information they contain, and because they are more scalable, appendable, shareable, self-describing, and community-friendly (i.e., many tools available to the community of users). Out of the four operational GFS forecast cycles per day (at 00Z, 06Z, 12Z and 18Z) this particular netCDF dataset is updated daily (/inputs/yyyymmdd/) for the 12Z cycle and includes 24-hr output for both 2D (gfs.t12z.sfcf$0hh.nc) and 3D variables (gfs.t12z.atmf$0hh.nc).

Also available are netCDF formatted Global Land Surface Datasets (GLSDs) developed by Hung et al. (2024). The GLSDs are based on numerous satellite products, and have been gridded to match the GFS spatial resolution (~13x13 km). These GLSDs contain vegetation canopy data (e.g., land surface type, vegetation clumping index, leaf area index, vegetative canopy height, and green vegetation fraction) that are supplemental to and can be combined with the GFS meteorological netCDF data for various applications, including NOAA-ARL's canopy-app. The canopy data variables are climatological, based on satellite data from the year 2020, combined with GFS meteorology for the year 2022, and are created at a daily temporal resolution (/inputs/geo-files/gfs.canopy.t12z.2022mmdd.sfcf000.global.nc)

This dataset contains the Coded Surface Bulletin (CSB) dataset reformatted as netCDF-4 files. The CSB dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.

Each netCDF file contains one year of CSB fronts data represented as spatial map data grids. The times and geospatial locations for the data grid cells are also included. The front data is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded. The front polylines from the CSB dataset are rasterized into the appropriate data grids. Each file conforms to the Climate and Forecast Metadata Conventions.

There are two large groupings of the CSB netCDF files. One group uses a data grid based on the North American Regional Reanalysis (NARR) grid, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.

The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.

There are a number of files within each group. The files all follow the naming convention codsus_[masked]_

The codsus_

The

Within each grid group, there are five subsets of files:

• codsus_masked_
• codsus_masked_
• codsus_
• codsus_
• codsus_

The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the Iowa Environmental Mesonet.

Datasets of VPD, and PAR in NetCDF (.nc) format with Geographic Lat/Long projection.

The region over Europe.

Resolution: 500mt.

Year: 2020

8-day composites

From http://northweb.hpl.umces.edu/LTRANS.htm. CHECK FOR UPDATES. NEWER VERSION MAY BE AVAILABLE.

PDF of original LTRANS_v.2b website [2016-09-14]

LTRANS v.2b Model Description

The Larval TRANSport Lagrangian model (LTRANS v.2b) is an off-line particle-tracking model that runs with the stored predictions of a 3D hydrodynamic model, specifically the Regional Ocean Modeling System (ROMS). Although LTRANS was built to simulate oyster larvae, it can easily be adapted to simulate passive particles and other planktonic organisms. LTRANS v.2 is written in Fortran 90 and is designed to track the trajectories of particles in three dimensions. It includes a 4th order Runge-Kutta scheme for particle advection and a random displacement model for vertical turbulent particle motion. Reflective boundary conditions, larval behavior, and settlement routines are also included. A brief description of the LTRANS particle-tracking model can be found here (68 KB .pdf). For more information on LTRANS and the application of LTRANS to oyster larvae transport, see a summary web page with animations, the publications North et al. (2008, 2011), and the LTRANS v.2 User's Guide. Please cite North et al. (2011) when referring to LTRANS v.2b. The updates that were made for LTRANS v.2b are listed here.

The Lagrangian TRANSport (LTRANS v.2b) model is based upon LTRANS v.1 (formerly the Larval TRANSport Lagrangian model). Ian Mitchell made the bug fixes in LTRANS v.2b. Zachary Schlag completed signigicant updates to the code in LTRANS v.2 with input from Elizabeth North, Chris Sherwood, and Scott Peckham. LTRANS v.1 was built by Elizabeth North and Zachary Schlag of University of Maryland Center for Environmental Science Horn Point Laboratory. Funding was provided by the National Science Foundation Biological and Physical Oceanography Programs**, Maryland Department of Natural Resources, NOAA Chesapeake Bay Studies, NOAA Maryland Sea Grant College Program, and NOAA-funded UMCP Advanced Study Institute for the Environment.

A beta version of LTRANS v2b which uses predictions from the circulation model ADCIRC is available here.

LTRANS Code

LTRANS v.2b Open Source Code. We would appreciate knowing who is using LTRANS. If you would like to share this information with us, please send us your name, contact information, and a brief description of how you plan to use LTRANS to enorth@umces.edu. To refer to LTRANS in a peer-reviewed publication, please cite the publication(s) listed in the Description section above.

External Dependencies and Programs. LTRANS v.2b requires NetCDF libraries and uses the following programs to calculate random numbers (Mersenne Twister) and fit tension splines (TSPACK). Because LTRANS v.2 reads-in ROMS-generated NetCDF (.nc) files, it requires that the appropriate NetCDF libraries be installed on your computer (see files and links below). Also, please note that although the Mersenne Twister and TSPACK programs are included in the LTRANS v.2b in the Random_module.f90 and Tension_module.f90, respectively, they do not share the same license file as LTRANS v.2b. Please review and respect their permissions (links and instructions provided below).

LTRANS v.2b Example Input Files. These files can be used to test LTRANS v.2b. They include examples of particle location and habitat polygon input files (.csv) and ROMS grid and history files (.nc) that are needed to run LTRANS v.2b. Many thanks to Wen Long for sharing the ROMS .nc files. The LTRANS v.2b code above is configured to run with these input files. Note: please download the tar (LTRANSv2.tgz) history files (clippped_macroms_his_*.nc) files between the hours of 5 pm and 6 am Eastern Standard Time because of their large size.

This dataset is a processed data in NetCDF (.nc) files, that used in our study. We used the SPI to determine meteorological drought conditions in the study area, that calculated by using the open-source module Climate and Drought Indices in Python.

Data Summary: US states grid mask file and NOAA climate regions grid mask file, both compatible with the 12US1 modeling grid domain. Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data. These files can be used with CMAQ-ISAMv5.3 to track state- or region-specific emissions. See Chapter 11 and Appendix B.4 in the CMAQ User's Guide for further information on how to use the ISAM control file with GRIDMASK files. The files can also be used for state or region-specific scaling of emissions using the CMAQv5.3 DESID module. See the DESID Tutorial and Appendix B.4 in the CMAQ User's Guide for further information on how to use the Emission Control File to scale emissions in predetermined geographical areas. File Location and Download Instructions: Link to GRIDMASK files Link to README text file with information on how these files were created File Format: The grid mask are stored as netcdf formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on the model projection and grid structure is contained in the header information of the netcdf file. The output files can be opened and manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other software programs that can read and write netcdf formatted files (e.g. Fortran, R, Python). File descriptions These GRIDMASK files can be used with the 12US1 modeling grid domain (grid origin x = -2556000 m, y = -1728000 m; N columns = 459, N rows = 299). GRIDMASK_STATES_12US1.nc - This file containes 49 variables for the 48 states in the conterminous U.S. plus DC. Each state variable (e.g., AL, AZ, AR, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that state. GRIDMASK_CLIMATE_REGIONS_12US1.nc - This file containes 9 variables for 9 NOAA climate regions based on the Karl and Koss (1984) definition of climate regions. Each climate region variable (e.g., CLIMATE_REGION_1, CLIMATE_REGION_2, etc.) is a 2D array (299 x 459) providing the fractional area of each grid cell that falls within that climate region. NOAA Climate regions: CLIMATE_REGION_1: Northwest (OR, WA, ID) CLIMATE_REGION_2: West (CA, NV) CLIMATE_REGION_3: West North Central (MT, WY, ND, SD, NE) CLIMATE_REGION_4: Southwest (UT, AZ, NM, CO) CLIMATE_REGION_5: South (KS, OK, TX, LA, AR, MS) CLIMATE_REGION_6: Central (MO, IL, IN, KY, TN, OH, WV) CLIMATE_REGION_7: East North Central (MN, IA, WI, MI) CLIMATE_REGION_8: Northeast (MD, DE, NJ, PA, NY, CT, RI, MA, VT, NH, ME) + Washington, D.C.* CLIMATE_REGION_9: Southeast (VA, NC, SC, GA, AL, GA) *Note that Washington, D.C. is not included in any of the climate regions on the website but was included with the “Northeast” region for the generation of this GRIDMASK file.

This repository contains the dataset for the manuscript

Pierzyna, M, et al. "Intercomparison of flux, gradient, and variance-based optical turbulence (Cn2) parameterizations." Applied Optics, 2024. https://doi.org/10.1364/AO.519942

The data is organized in the following structure:

met_cn2_*_10m.nc: netCDF files containing Cn2 estimated from meteorological data obtained at the CESAR site using the flux-based and gradient-based methods at the 10 m level.

wrf_cn2_*.nc: netCDF files containing Cn2 estimated from WRF model output using the variance-based method (80m) and flux, gradient, and variance-based methods (10m).

wrf_meteo_*.nc: netCDF files containing a cross-section of CESAR site extracted from WRF model output. This data serves as input for wrf_cn2_*.nc files.

Datasets of GPP, AirTemp, VPD, and PAR in NetCDF (.nc) format with Geographic Lat/Long projection.

The region over Europe.

Resolution: 500mt.

Year: 2020

8 day composites

This dataset contains the netCDF files used to run mizuRoute across continental Chile.
Each folder includes the following files:

• [Catchment_BNACode]_grid.gpkg: the VIC model grid used for the catchment.
• mapping_[Catchment_BNACode].nc: the mapping file that links the VIC grid to the GIS features.
• ntopo_[Catchment_BNACode].nc: a river network netCDF file containing river reach-to-reach topology, reach-to-HRU topology, and physical parameters for both rivers and HRUs.

This research was funded by the Fondecyt Project 11200142 “Robust estimates of current and future water resources across a hydroclimatic gradient in Chile” (Principal Investigator: Pablo A. Mendoza).

The use of these files requires citing this dataset, and the paper that describes the approach used to produce the data:
Cortés-Salazar, N., Vásquez, N., Mizukami, N., Mendoza, P. A., & Vargas, X. (2023). To what extent does river routing matter in hydrological modeling?. Hydrology and Earth System Sciences, 27(19), 3505-3524. (doi.org/10.5194/hess-27-3505-2023).

The research is important for the Great Barrier Reef (GBR) water quality management. The data was collected for the quantification of the contribution of Trichodesmium to the nitrogen budget of the GBR. Linux operating system, C compiler and NETCDF library were used to build the modified EMS applications on AIMS HPC. The EMS version used is 1.2.1. The modified EMS was derived from the eReefs model (https://ereefs.org.au/ereefs) and the model descriptions are found in Baird et al. (2020). Methods for collecting the data include the following: Hydrodynamic model forcing available in https://research.csiro.au/ereefs/models/models-about/models-hydrodynamics/; Biogeochemical (BGC) model forcing (Simulated hydrodynamic model output, regional wave model data, 2019 catchment conditions of nutrient and sediment loads available in https://svnserv.csiro.au/svn/CEM/projects/eReefs/model/gbr4_bgc_hindcast/gbr4_H2p0_B3p2_Cb/); Initialisation file: GBR4 BGC 3p1 initialisation data. The 4km resolution grid of the EMS was run on AIMS HPC from 1/12/2010 to 30/11/2012 and the data was collected on 17/02/2022. Software-specific information needed to interpret the data are R Software version 3.5.1, GNU Compiler Collection (GCC) version 6.1.0, network Common Data Form (NetCDF-cxx) version 4.2.1, Open Message Passing Interface (OpenMPI-gcc) version 1.10.2 and NetCDF Operators (NCO) version 4.5.5. R scripts for post-processing simulated data are available in https://github.com/Chinenyeani1986/Trichodesmium-N-budget.

Here we present data on vegetation and climate conditions in Syria, including some nc files. The nc files describe the spatial status of Syria, including land cover in 2010, trends in temperature and precipitation, EVI mean and trend, EVI residual analysis and water use efficiency. Detailed information can be found in the paper by Chen et al.

a hindcast reanalysis of ocean circulation in the mid-atlantic bight and gulf of maine has been computed using the regional ocean modeling system (roms) with 4-dimensional variational (4d-var) assimilation of data from satellites, land-based ocean surface current measuring radar, and all available in situ observations from the maracoos (maracoos.org) and neracoos (neracoos.org) regional associations of the u.s. integrated ocean observing system (ioos). this reanalysis is version dopanv2r3-ini2007 (version 2, release 3, initialized january 2007).the analysis covers the period 2-jan-2007 to 30-aug-2021 on a 7-km horizonal grid with 40 vertical terrain-following s-coordinate levels. ocean state variables computed are sea level, velocity, temperature, and salinity. air-sea fluxes of heat and momentum, and surface and bottoms stresses, are included.results are provided on the roms model native 3-dimensional grid as (i) 1-hourly interval snapshots (roms “history” files), (ii) 1-day averages, (iii) monthly averages, (iv) yearly averages, and (v) ensemble monthly averages (i.e., the mean of all days in the same month from all years). the output files are in netcdf format and data and metadata follow cf-1.4 conventions for the description of coordinates and variables.the files uploaded here are examples of one time record from each of these 5 collections. outputs for the full reanalysis, which comprises 6.8 terabytes fo data, are made available for download via a thredds (thematic real-time environmental distributed data services) web service to facilitate user geospatial or temporal sub-setting.the thredds catalog urls and example filenames available here, for the respective collections, are: 1-hourly history snapshots 2007-01-02 01:00 through 2021-08-31 00:00: ttps://tds.marine.rutgers.edu/thredds/roms/doppio/catalog.html?dataset=dopanv2r3-ini2007_da_history example file uploaded here is his_dopanv2r3_20140516t0100.nc for 2014-05-06 01:00 24-hour averages 2007-01-02 12:00 through 2021-08-30 12:00 https://tds.marine.rutgers.edu/thredds/roms/doppio/catalog.html?dataset=dopanv2r3-ini2007_da_average example file uploaded here is avg_dopanv2r3_20140516t1200.nc for 2014-05-06 monthly averages 2007-01-17 through 2020-12-16 https://tds.marine.rutgers.edu/thredds/roms/doppio/catalog.html?dataset=dopanv2r3-ini2007_da_monthly_averages example file uploaded here is mon_dopanv2r3_201405.nc for 2014-05 yearly averages 2007 through 2020: https://tds.marine.rutgers.edu/thredds/roms/doppio/catalog.html?dataset=dopanv2r3-ini2007_da_yearly_averages example file uploaded here is year_dopanv2r3_2014.nc for 2014 monthly ensemble averages: https://tds.marine.rutgers.edu/thredds/roms/doppio/catalog.html?dataset=dopanv2r3-ini2007_da_monthly_ensemble_means example file uploaded here is ensmon_dopanv2r3_05.nc for maythe underlying ocean circulation model configuration is described by lopez et al (2020). the observations that are assimilated and the error hypotheses and other aspects of the 4d-var assimilation implementation are described by levin et al. (2020; 2021).lópez, a. g., j. l. wilkin and j. c. levin, (2020) doppio – a roms (v3.6)-based circulation model for the mid-atlantic bight and gulf of maine: configuration and comparison to integrated coastal observing network observations, geosci. model dev., 13, 3709–3729, doi: 10.5194/gmd-13-3709-2020levin, j., h. arango, b. laughlin, e. hunter, j. wilkin and a. moore, (2020), observation impacts on the mid-atlantic bight front and cross-shelf transport in 4d-var ocean state estimates, part i – multiplatform analysis, ocean modelling, 156, 101721, doi: 10.1016/j.ocemod.2020.101721levin, j., h. g. arango, b. laughlin, j. wilkin and a. m. moore, (2021), the impact of remote sensing observations on cross-shelf transport estimates from 4d-var analyses of the mid-atlantic bight, advances in space research, 68, 553-570, doi: 10.1016/j.asr.2019.09.012

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

The research is important for the Great Barrier Reef (GBR) water quality management. The data was collected for the parameterisation of the influence of tuft-shaped Trichodesmium colonies on the vertical movement of Trichodesmium in the GBR. Linux operating system, C compiler and NETCDF library were used to build the modified EMS applications on AIMS HPC. The EMS version used is 1.2.1. The modified EMS was derived from the eReefs model (https://ereefs.org.au/ereefs) and the model descriptions are found in Baird et al. (2020). Methods for collecting the data include the following:

Hydrodynamic model forcing available in https://research.csiro.au/ereefs/models/models-about/models-hydrodynamics/;

Biogeochemical (BGC) model forcing (Simulated hydrodynamic model output, regional wave model data, 2019 catchment conditions of nutrient and sediment loads available in https://svnserv.csiro.au/svn/CEM/projects/eReefs/model/gbr4_bgc_hindcast/gbr4_H2p0_B3p2_Cb/); Initialisation file: GBR4 BGC 3p1 initialisation data.

The 4km resolution grid of the EMS was run on AIMS HPC from 1/12/2010 to 30/11/2012 and the data was collected on 26/06/2023. Software-specific information needed to interpret the data are R Software version 3.5.1, GNU Compiler Collection (GCC) version 6.1.0, network Common Data Form (NetCDF-cxx) version 4.2.1, Open Message Passing Interface (OpenMPI-gcc) version 1.10.2 and NetCDF Operators (NCO) version 4.5.5. R scripts for post-processing simulated data are available in Chinenyeani1986/Trichodesmium-buoyancy (github.com)

This dataset is a raw data in NetCDF (.nc) files, that used in our study.

This dataset is for the draft "Explaining the Forcing Efficacy with Pattern Effect and Feedback Nonlinearity".

The netcdf file "last150yravg_picontrol.nc" represents the time-average fields for the last 150 years of PI-control experiment. Netcdf files beginning with "last20yravg" represent the time-average fields for the last 20 years (Year131-150) of abrupt forcing experiments. Note that "0p5co2" in the file name denotes 0.5xCO2, "4psolar" denotes +4% solar radiation, and "m2psolar" denotes -2% solar radiation.

The netcdf files beginning with "fixedsst" represent the time-average fields for fixed-SST experiments, where the file "fixedsst_control.nc" denotes the fixed SST control experiment.

The netcdf files beginning with "uni" represent the time-average fields for uniform warming/cooling experiments, where the magnitude of SST change is indicated by the file names (m2K denotes -2K).

The text file "GFA_partialR_over_partial_SST" denote the value of partial_R over partial_SST for each grid, and its grid (96x144) is same as other netcdf files in this datasets .

This dataset contains the UNCALIBRATED Conductivity-Temperature-Depth (CTD) data collected on Southern Surveyor voyage SS 11/2004. The voyage took place in the Coral Sea and the Pacific Ocean between 27-Oct-2004 to 23-Nov-2004. NO HYDROCHEMISTS WERE ON THIS VOYAGE SO this UNCALIBRATED ctd dataset has been processed as far as possible. IT will NOT be included in the processed data delivered via Data Trawler. Notes re the CTD averaged data files for ss2004/06 & 11 ======================================================= The dataset includes both 'normal' (*Avg.nc) and 'ToYo' (*Avt.n) averaged files. General comments: * Both datasets have been subjected to basic QC (removal of spikes), * The temperature data is accurately calibrated. * Salinity uses the manufacturer's conductivity calibration, as no calibration samples were collected during the voyage. This data should not therefore be used for serious oceanographic calculations. * An arbitrary calibration was applied to the transmission data, giving it a range of 0 - 100 (%) in air The 'Normal' averaged files: * Have a 2 dB averaging interval. * Only include the downcast data * Pressure reversals have been removed. The ToYo files have the following attributes: * Averaging interval = 0.5dB (or 10 secs, if the package stays within a particular depth bin for an interval > 10 secs) * Pressure reversals are not removed. * The profiles may include out-of-water or 'pumps off' data * The files include Lat, Long and depth, but the only non-NaN values are for the start, bottom & end of the cast. This is of no real consequence, as this data is only required for 'towed' ToYos. We did standard CTD casts, which have been averaged as ToYos.

Summary:

There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.

The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.

How to cite this dataset:

Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. Earth System Science Data, 14(9), pp.4397-4418.

Update history:

I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)

Version 1.03: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.

Version 1.02: New data summary, add Netcdf data format

Version 1: Initial dataset for peer-review, CSV format only

Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.

SPAM2010:

Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.

GAEZv4:

FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021

The dataset of Ricciardi et al.'s:

Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.

The global dominant field size dataset:

Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.

GLC-Share:

Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.

CAAS-IFPRI cropland extent map:

Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.

There are two datasets in this data repository: the first one, named “RH.RF.720.360.1980.2016.Yearly.nc”, is a global heterotrophic respiration (RH) product with a spatial resolution of with 0.5 degree and a time resolution of one year. The global RH product is modelled by Random Forest algorithm with field observations and environmental variables. The environmental variables include temperature, precipitation, diurnal temperature range from CRU TS v.4.01 from 1901 to 2016; shortwave radiation; soil organic carbon content from soil grid data (Hengl et al., 2017); soil nitrogen content from ORNL DAAC; nitrogen deposition data from the Earth System Models of GISS-E2-R, CCSM-CAM3.5 and GFDL-AM3 from the 1850s to 2000s; Palmer Drought Severity Index (PDSI); and soil water content.The RH product is provided in network Common Data Form, version 4 (netCDF-4, short name: nc) data format (https://www.unidata.ucar.edu/software/netcdf/). The RH product is named using the following regulation:"RH.modelling approaches.spatial resolution.start YYYY. end YYYY.temporal resolution.nc"“RH.RF.720.360.1980.2016.Yearly.nc” means modelled RH flux (g C m-2 yr-1) by Random Forest (RF) with a 0.5° spatial resolution (size 720 along longitude and 360 along latitude) from start year 1980 to end year 2016 with a yearly temporal resolution.The second file, named “dataset.xlsx”, is the field observation from peer review publications combining Global Soil Respiration Database (SRDB), (version 3, Bond-Lamberty and Thomson, 2014), which is publicly available at https://github.com/bpbond/srdb. Besides, the database was further updated using observations collected from the China Knowledge Resource Integrated Database (www.cnki.net) up to March 2018 according to the criteria of SRDB. This dataset is provided in Microsoft Excel in format of “.xlsx”.R codes to produce main results and land area (named land.area.nc, km^2) are available.

This repository (DOI: 10.11583/DTU.13160606) contains the publicly available data generated during the TotalControl project: https://www.totalcontrolproject.eu/The dataset has been created using EllipSys3D and Flex5 by Niels Troldborg (ORCID 0000-0003-4508-4837, niet@dtu.dk) and Søren Juhl Andersen (ORCID 0000-0002-5935-751X, sjan@dtu.dk). It contains both LES precursor simulation data and LES wind farm flow data. The LES wind farm flow data is organized in separate folder for each flow scenario and wind farm orientation. The layout of the TotalControl Reference Wind Farm is shown for all directions in "TotalControlReferenceWindFarmLayout.pdf", where all turbines are numbered. The folders are named according to type of boundary layer, e.g. conventional neutral boundary layer ("cnbl"), the roughness length of for instance z0 = 2e-3 m denoted as "z02e3m", and finally the rotation of the TotalControl reference wind farm within the flow, i.e. "rot90". The public database contains the following cases with corresponding DOI:1. "cnblz02e3m": Precursor dataset using z0 = 2e-3 m without wind farm. DOI: 10.11583/DTU.133531612. "cnblz02e3m_rot00": Reference wind farm simulation using with 0 deg rotation. DOI: 10.11583/DTU.131222243. "cnblz02e3m_rot90": Reference wind farm simulation using with 90 deg rotation. DOI: 10.11583/DTU.13090113Files in each folder are given the corresponding naming where “TCal” corresponds to actuator line (AL) simulations performed in TotalControl(TC), while “TCad” corresponds to actuator disc (AD) simulations. The dataset contains the following three datatypes with example names: 1. Horizontal (xy) planes of flow fields (u, v, and w) extracted at hub height in standard NetCDF format. Naming: TCal_horiz.nc 2. Vertical inflow (yz) planes of flow field (u, v, and w) extracted 1R upstream each of the 32 wind turbine (wt) in standard NetCDF format. Naming: TCal_inflow_wt01.nc 3. Time series of each of the 32 wind turbines and their performance and loads in ascii format. The files contain time, u velocity at hub height, power, flapwise bending moment, for the three blades, tower bottom bending moment fore-aft and side-side, thrust force, blade pitch, and rotational speed. Naming: TCal_wt01.dat Additionally, there is data for the two benchmark cases on wind farm control for FarmConners:https://www.windfarmcontrol.info/The two cases has identical setup as the baseline, but two turbines have been intentionally yawed -20deg and -30deg, where negative corresponds to counterclockwise rotation as seen from above. The two cases are:1. The flow is identical to "z02e3m_rot00", but turbines 5 and 25 have been intentionally yawed -20deg and -30deg, respectively, where negative corresponds to counterclockwise rotation as seen from above. DOI: 10.11583/DTU.132745492. The flow is identical to "z02e3m_rot90", but turbines 32 and 29 have been intentionally yawed -20deg and -30deg, respectively, where negative corresponds to counterclockwise rotation as seen from above. DOI: 10.11583/DTU.13414922Additional data can be made available upon request.