NDWI as netcdf file

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 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]_.nc, where [masked] indicates that the presence of the word masked is optional and is either merra2-1deg or narr-96km. The element is either the word mask or the sequence wide_, where is the front width and is the year for the data stored in the file.

The codsus_mask.nc file is a file containing a single data grid that delineates the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year. The WPC meteorologists don't attempt to provide equal levels of attention to every grid cell displayed on their workstations. The files of the form codsus_masked_wide_.nc have all had the mask described above applied to exclude parts of fronts that extend past the envelope. The files of the form codsus_wide_.nc have no masking applied.

The wide portion of the file names takes two forms—1wide and 3wide. The fronts in the1wide files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the 3wide files were rasterized by drawing the front polylines with a width of 3 grid cells.

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

codsus_masked_1wide_.nc

codsus_masked_3wide_.nc

codsus_1wide_.nc

codsus_3wide_.nc

codsus_mask.nc

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.

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)

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 dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset paper (public preprint)

A description of this dataset, including the methodology and validation results, is available at:

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the Soil Moisture Climate Data Records from satellites community

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.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Feature Names Relationship File (FEATNAMES.dbf) contains a record for each feature name and any attributes associated with it. Each feature name can be linked to the corresponding edges that make up that feature in the All Lines Shapefile (EDGES.shp), where applicable to the corresponding address range or ranges in the Address Ranges Relationship File (ADDR.dbf), or to both files. Although this file includes feature names for all linear features, not just road features, the primary purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute, which can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature is identified by the linear feature identifier (LINEARID) attribute, which can be used to relate the address range back to the name attributes of the feature in the Feature Names Relationship File or to the feature record in the Primary Roads, Primary and Secondary Roads, or All Roads Shapefiles. The edge to which a feature name applies can be determined by linking the feature name record to the All Lines Shapefile (EDGES.shp) using the permanent edge identifier (TLID) attribute. The address range identifier(s) (ARID) for a specific linear feature can be found by using the linear feature identifier (LINEARID) from the Feature Names Relationship File (FEATNAMES.dbf) through the Address Range / Feature Name Relationship File (ADDRFN.dbf).

The NC files housed within this DOI represent the Data the Machine Learning Models were Trained/Validated/Tested on, during the execution of the work in the thesis, " Machine Learning for Energy Consumption Prediction of Numerically Controlled Programs." Theses files were created by Samuel D. Stencel, a Gradute Research Assistant at Purdue University.

CO2_netCDF.zip includes 3 files, readme_nc.txt is the descrition file of the rest 2 files. The format of data file is .nc, and demension description of the netCDF file is .xlsx. CO2_mat.zip includes 5 files, readme_mat.txt is the descrition file of the rest 4 data files. The format of data files is .mat, which could be load using matlab.

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.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

Amazon Rainforest Gross Primary Productivity (GPP) Dataset 2020 Product: Gross Primary Productivity (GPP) MODIS Tiles No: h11v09 and h12v09 Year: 2020 Region: Amazon Rainforest Temporal Scale: 8 Days Spatial Resolution: 500 meters Format: NetCDF (.nc) Projection: Geographic Lat/Long Method: Light-use-efficiency (LUE) approach. Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149 https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261

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).

HRES is a high-resolution (100m) synthetic bed elevation terrain for the whole Antarctic continent. The synthetic bed surface preserves topographic roughness characteristics of airborne and ground-based ice-penetrating radar data from the Bedmap1 compilation and the ICECAP consortium. Broad-scale features of the Antarctic landscape are incorporated from a lowpass filter of the Bedmap2 bed elevation data. The data are available in NetCDF classic format on a 100m resolution grid in a Polar Stereographic Projection (Central Meridian 0 degrees, Standard Parallel 71 degrees S) with respect to the WGS84 geoid. The 100m grid is 66661 rows by 66661 columns, where the corner of the lower left cell is located at a polar stereographic easting and northing of -3333000 m and -3333000 m, respectively. The value for missing data is -9999.

A minor update was made to the dataset on 2021-12-23 to correct an error in the data.

The issue was that while the x and y dimensions were defined in the netcdf file (i.e. how many points in each dimension) the corresponding variables defining the actual locations were not defined. Therefore, when the file was loaded, the software was defaulting to using the index count to be the coordinate location, which would look like 1m data spacing

To fix this, the coordinate variables have been defined, and also the order the data was written in to correspond to what viewing programs such as ferret expect has been swapped.

The actual post-processing on the original data (hres.nc) was

gdal_translate -of netCDF -co WRITE_BOTTOMUP=NO NETCDF:"hres.nc":bed_elevation hres_temp_1.nc

ncpdq -a -y,x hres_temp_1.nc hres_temp_2.nc ncap2 -S hres_nco.txt hres_temp_2.nc hres_revised.nc

Where the file hres_nco.txt contains the following 8 lines y=array(-3331000f,100,$y); x=array(-3331000f,100,$x); y@long_name="Northing"; y@Standard_name="Northing"; y@units="m"; x@long_name="Easting"; x@Standard_name="Easting"; x@units="m";

The second processing step (ncpdq) requires a system with a bit over 40GB of memory.

Abstract: These netCDF files provide CTD data from surveys in 2018. The Long Island Sound Mapping and Research Collaborative (LISMaRC) is a joint effort of the University of Connecticut, the University of New Haven and the US Geological Survey. Funding provided by the Long Island Sound Seafloor Mapping Fund administered cooperatively by the EPA Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection (DEEP).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

The twin satellites of the Gravity Recovery and Climate Experiment (GRACE), launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km. The monthly land mass grids contain terrestrial water storage anomalies (in aquifers, river basins, etc.) from GRACE time-variable gravity data relative to a time-mean. The storage anomalies are given in 'equivalent water thickness' (in NetCDF format). The time coverage for the monthly grids are determined by GRACE months. For the list of GRACE month dates visit http://grace.jpl.nasa.gov/data/grace-months/ . For information please visit http://grace.jpl.nasa.gov/data/get-data/monthly-mass-grids-land/ .

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

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.