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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Biomass sorghum production in Iowa, USA

This dataset includes field measurement and model simulation data from Josh Bendorf's (former MS student with Drs. VanLoocke & Heaton, ISU Dept. of Agronomy) thesis project focusing on the simulation of biomass sorghum ( Sorghum bicolor L. Moench) in the state of Iowa. Measured field data are provided in CSV file format for 2019 & 2020. Model output files are provided as NetCDF file format for the state of Iowa (1998-2007) & the larger US region (1969-2018).

Agricultural production systems simulation↗
DOE OSTI2023

Agro-IBIS/THMB Modeled Yield, Nitrogen Loss, and Profitability Maps for the Raccoon River Basin, Iowa, USA

This data was used to assess the potential for improving water quality in this watershed when integrating miscanthus, a bioenergy perennial grass, on to areas of low profit under current corn-soybean management and areas of high nitrogen leaching under both current and future climate conditions. This dataset includes all observations (yield, streamflow, nitrogen loads) and model simulation data used from Dr. Kelsie Ferin’s (former PhD student with Dr. Andy VanLoocke, ISU Dept. of Agronomy) dissertation and publication in Global Change Biology – Bioenergy (DOI: 10.1111/gcbb.13078). Modeled output included in this dataset includes corn, soybean, and miscanthus yields, nitrogen leaching rates, net mineralization rates, land use cover fractions with and without the inclusion of miscanthus, profitability maps, additional soil property maps, and simulated streamflow and nitrogen loads near the outlet of the Raccoon River Basin (Van Meter, Iowa) for both historical and future climate conditions. This dataset includes a mixture of netCDF, .tiff, .csv, and .xlsx files. See README.pdf for more information.

Ferin, Kelsie↗
DOE OSTI2019

Eddy Flux and Meteorology over Deciduous Forest, Prairie, and Soybean Ecosystems in Missouri, USA, during the Total Solar Eclipse of 2017

Eddy fluxes and meteorology data are reported at high temporal resolution for three flux tower sites in Mid-Missouri before, during, and after the Total Solar Eclipse of August 21, 2017. Mid-Missouri experienced up to 2 minutes and 40 seconds of totality at around solar noon during the eclipse (Fig. 1). The sites are located in deciduous oak-hickory forest, native prairie, and soybean ecosystems (Table 1 and Fig. 3). Eddy fluxes were computed using a wavelet-based approach that permitted the calculation of two-minute mean fluxes without losing low frequency flux contributions (Fig. 2). During the eclipse, standard meteorological variables were sampled and recorded at 5 second intervals. The two-minute means of fluxes and meteorology are reported. This data product includes three data files in NetCDF (*.nc) format, three data dictionary files and a file-level metadata file in comma separate (*.csv) format, and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiscale ACI Satellite Database

The SATELLITE_EAGLES_PNNL NetCDF dataset contains a suite of satellite- and reanalysis-derived atmospheric and surface parameters on a regular latitude–longitude grid. The dataset includes core geophysical fields such as land fraction, aerosol optical depth at multiple wavelengths (465, 550, 667, and 865 nm), sea surface temperature, estimated inversion strength, and various thermodynamic and dynamic quantities (e.g., relative humidity, vertical velocity, boundary-layer height, and surface fluxes) from both MERRA and ERA reanalysis products, provided as daily-mean and instantaneous values. A major component of the dataset consists of MODIS-retrieved cloud microphysical properties, including cloud droplet number concentration, cloud effective radius, optical thickness, and liquid water path, provided for three compositing regimes (“All,” “Q06,” and “G18”). Corresponding cloud-top parameters—temperature, height, and pressure—along with total and domain-mean cloud fraction fields are also included. The file further integrates additional satellite data from AMSR-E (for cloud water, rain water, and surface precipitation retrievals) and CERES (for top-of-atmosphere radiative fluxes, cloud fractions, and albedo). This dataset is designed to evaluate aerosol–cloud interactions in warm clouds, emphasizing the use of MODIS for deriving cloud droplet number concentration and liquid water path statistics. The complementary satellite and reanalysis fields are co-located and time-matched to the same instantaneous MODIS observations, enabling consistent comparisons between cloud properties, aerosol loading, and large-scale meteorological conditions. The dataset is recently featured in Christensen et al. (2025), Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3850, 2025.

Christensen, Matthew [Pacific Northwest National L↗
DOE OSTI2025

Multiscale ACI Satellite Database

The SATELLITE_EAGLES_PNNL NetCDF dataset contains a suite of satellite- and reanalysis-derived atmospheric and surface parameters on a regular latitude–longitude grid. The dataset includes core geophysical fields such as land fraction, aerosol optical depth at multiple wavelengths (465, 550, 667, and 865 nm), sea surface temperature, estimated inversion strength, and various thermodynamic and dynamic quantities (e.g., relative humidity, vertical velocity, boundary-layer height, and surface fluxes) from both MERRA and ERA reanalysis products, provided as daily-mean and instantaneous values. A major component of the dataset consists of MODIS-retrieved cloud microphysical properties, including cloud droplet number concentration, cloud effective radius, optical thickness, and liquid water path, provided for three compositing regimes (“All,” “Q06,” and “G18”). Corresponding cloud-top parameters—temperature, height, and pressure—along with total and domain-mean cloud fraction fields are also included. The file further integrates additional satellite data from AMSR-E (for cloud water, rain water, and surface precipitation retrievals) and CERES (for top-of-atmosphere radiative fluxes, cloud fractions, and albedo). This dataset is designed to evaluate aerosol–cloud interactions in warm clouds, emphasizing the use of MODIS for deriving cloud droplet number concentration and liquid water path statistics. The complementary satellite and reanalysis fields are co-located and time-matched to the same instantaneous MODIS observations, enabling consistent comparisons between cloud properties, aerosol loading, and large-scale meteorological conditions. The dataset is recently featured in Christensen et al. (2025), Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3850, 2025.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PNNL-ANL Hydrometeorological Super Ensemble

The current dataset contains data upload links to the following **hydrologic (water balance), river routing (water management), and hydropower simulation** data over CONUS: * Climate Forcing: **Livneh** (https://www.nature.com/articles/sdata201542) * Simulation Scenario: **Historical** * Simulation Period: **1971-2013 (1972-2013 for Hydropower)** * Simulation Models: **VIC (Variable Infiltration Capacity)**, **mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python)**, and **PNNL B1Hydro** * Output Format: **NetCDF** and **CSV**

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2025

PNNL-ANL Hydrometeorological Super Ensemble

The current dataset contains data upload links to the following hydrologic (water balance), river routing (water management), and hydropower simulation data over CONUS: Climate Forcing: ClimRR (https://climrr.anl.gov/climrrdata) Simulation Scenario: Historical, Mid-Century, End-Century Simulation Period: 1995-2004, 2045-2054, 2085-2094 Simulation Models: VIC (Variable Infiltration Capacity), mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python), and PNNL B1Hydro Output Format: NetCDF and CSV

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2025

TGW Hydrology, River Routing, and Hydropower Simulation Datasets

he current dataset contains data upload links to the following hydrologic (water balance), river routing (water management), and hydropower simulation data over CONUS: Climate Forcing: TGW (https://tgw-data.msdlive.org/) Simulation Scenario: Historical, Mid-Century, End-Century Simulation Period: 1980-2024, 2020-2059, 2060-2099 Simulation Models: VIC (Variable Infiltration Capacity), mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python), and PNNL B1Hydro Output Format: NetCDF and CSV

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2019

Input files for irrigation and paddy field implementation over South Asia in WRF-CLM4

The dataset consists of NetCDF files containing irrigation and paddy field input data for WRF-CLM4 simulations over South Asia for years 1990 to 2014. The fields are the irrigation water use for paddy and non-paddy crops and the fraction of water use coming from ground and surface water sources. The files also contain grid cell area fractions of irrigated crops, irrigation paddy crops, and rainfed paddy crops. The dataset is created by combining the global map of irrigated area dataset, irrigation water use estimation of Huang et al. (2018) and agricultural census based estimates over northern India.

58 GEOSCIENCES↗
DOE OSTI2025

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. This data record spans from 1981 to 2018 using data from eight NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18 and -19. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR Version 5 products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗
DOE OSTI2026

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This data record spans from 1981 to 2024 using data from NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18, -19 and S-NPP. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗
DOE OSTI2019

SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France

SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, temperature, and Penman–Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate provides an ensemble of 25 spatially coherent gridded multivariate time series. It is derived from the statistical downscaling of the Twentieth Century Reanalysis (20CR) by the SCOPE method, which is based on the analogue approach. SCOPE Climate performs well in comparison to both dependent and independent data for precipitation and temperature. The ensemble aspect corresponds to the uncertainty related to the SCOPE method. SCOPE Climate is the first century-long gridded high-resolution homogeneous dataset available over France and thus has paved the way for improving knowledge on specific past meteorological events or for improving knowledge on climate variability, since the end of the 19th century. This dataset has also been designed as a forcing dataset for long-term hydrological applications and studies of the hydrological consequences of climate variability over France. SCOPE Climate is freely available for any non-commercial use and can be downloaded as NetCDF files from https://doi.org/10.5281/zenodo.1299760 for precipitation, https://doi.org/10.5281/zenodo.1299712 for temperature, and https://doi.org/10.5281/zenodo.1251843 for reference evapotranspiration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Special Observing Period (SOP) data for the Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP)

The rapid changes occurring in the polar regions require an improved understanding of the processes that are driving these changes. At the same time, increased human activities such as marine navigation, resource exploitation, aviation, commercial fishing, and tourism require reliable and relevant weather information. One of the primary goals of the World Meteorological Organization's Year of Polar Prediction (YOPP) project is to improve the accuracy of numerical weather prediction (NWP) at high latitudes. During YOPP, two Canadian “supersites” were commissioned and equipped with new ground-based instruments for enhanced meteorological and system process observations. Additional pre-existing supersites in Canada, the United States, Norway, Finland, and Russia also provided data from ongoing long-term observing programs. These supersites collected a wealth of observations that are well suited to address YOPP objectives. In order to increase data useability and station interoperability, novel Merged Observatory Data Files (MODFs) were created for the seven supersites over two Special Observing Periods (February to March 2018 and July to September 2018). All observations collected at the supersites were compiled into this standardized NetCDF MODF format, simplifying the process of conducting pan-Arctic NWP verification and process evaluation studies. This paper describes the seven Arctic YOPP supersites, their instrumentation, data collection and processing methods, the novel MODF format, and examples of the observations contained therein. MODFs comprise the observational contribution to the model intercomparison effort, termed YOPP site Model Intercomparison Project (YOPPsiteMIP). All YOPPsiteMIP MODFs are publicly accessible via the YOPP Data Portal (Whitehorse: https://doi.org/10.21343/a33e-j150, Huang et al., 2023a; Iqaluit: https://doi.org/10.21343/yrnf-ck57, Huang et al., 2023b; Sodankylä: https://doi.org/10.21343/m16p-pq17, O'Connor, 2023; Utqiagvik: https://doi.org/10.21343/a2dx-nq55, Akish and Morris, 2023c; Tiksi: https://doi.org/10.21343/5bwn-w881, Akish and Morris, 2023b; Ny-Ålesund: https://doi.org/10.21343/y89m-6393, Holt, 2023; and Eureka: https://doi.org/10.21343/r85j-tc61, Akish and Morris, 2023a), which is hosted by MET Norway, with corresponding output from NWP models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The OCEAN ICE mooring compilation: a standardised, pan-Antarctic database of ocean hydrography and current time series

Continuous moored time series of temperature, salinity, pressure and current speed and direction are of great importance for understanding the continental shelf and under-ice-shelf dynamics and thermodynamics that govern water mass transformations and ice melting in and around Antarctic marginal seas. In these regions, icebergs and sea ice make ship-based mooring deployment and recovery challenging. Nevertheless, over decades, expeditions around the fringe of Antarctica sporadically deployed and recovered hundreds of moored instruments, including those facilitated through ice shelves boreholes. These datasets tend to be archived in a wide range of data centres, with, to our knowledge, no clear format standardisation. As a result, systematic analysis of historical mooring time series in the marginal seas is often challenging. Here we present the first version of a standardised pan-Antarctic moored hydrography and current time series compilation, with broad international contributions from data centres, research institutes and individual data owners. The mooring records in this compilation span over five decades, from the 1970s to the 2020s, providing an opportunity for a systematic study of the pan-Antarctic water mass transport and shelf connectivity. As a demonstration of the utility of this compilation, we present spectral analysis of the compiled current velocity time series, which unsurprisingly shows the dominating presence of tidal variability within most records. This component of the variability is fitted using multi-linear regression to tidal frequencies, and the tidal fit is removed from the original time series to leave de-tided variability. Given the limited record durations to months to years, de-tided variability is dominated by synoptic (3–10 d period), intraseasonal (10–80 d) and seasonal (∼6 months–1 year) signals. The spatial distribution of the kinetic energy integrated within frequency bands is presented and discussed within respective regional contexts, and future avenues of research are proposed. This data compilation is assembled under the endorsement of Ocean-Cryosphere Exchanges in ANtarctica: Impacts on Climate and the Earth System (OCEAN ICE) project (https://ocean-ice.eu/, last access: 23 October 2025) funded by the European Commission and UK Research and Innovation. It is available and regularly updated in NetCDF format with the SEANOE database at https://doi.org/10.17882/99922 (Zhou et al., 2024a).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The compression–error trade-off for large gridded data sets

The netCDF-4 format is widely used for large gridded scientific data sets and includes several compression methods: lossy linear scaling and the non-lossy deflate and shuffle algorithms. Many multidimensional geoscientific data sets exhibit considerable variation over one or several spatial dimensions (e.g., vertically) with less variation in the remaining dimensions (e.g., horizontally). On such data sets, linear scaling with a single pair of scale and offset parameters often entails considerable loss of precision. We introduce an alternative compression method called "layer-packing" that simultaneously exploits lossy linear scaling and lossless compression. Layer-packing stores arrays (instead of a scalar pair) of scale and offset parameters. An implementation of this method is compared with lossless compression, storing data at fixed relative precision (bit-grooming) and scalar linear packing in terms of compression ratio, accuracy and speed. When viewed as a trade-off between compression and error, layer-packing yields similar results to bit-grooming (storing between 3 and 4 significant figures). Bit-grooming and layer-packing offer significantly better control of precision than scalar linear packing. Relative performance, in terms of compression and errors, of bit-groomed and layer-packed data were strongly predicted by the entropy of the exponent array, and lossless compression was well predicted by entropy of the original data array. Layer-packed data files must be "unpacked" to be readily usable. The compression and precision characteristics make layer-packing a competitive archive format for many scientific data sets.

58 GEOSCIENCES↗
DOE OSTI2018

iiasa/emissions_downscaling

This software tool, written in R, performs manipulations necessary to translate Human-Earth system model regional/sector emission outputs, such as those from PNNL's Global Change Assessment Model (GCAM), to gridded form needed by Earth System Models, such as DOEs E3SM (Energy Exascale Earth System Model) model. The tool takes as its primary input emissions in a standard community format, and produces CF-compliant netCDF gridded emission as its primary output. As intermediate output the tool produces downscaled emissions at the country level.

Feng, Leyang↗
DOE OSTI2024

Climate Model Output Rewriter

The Climate Model Output Rewriter (CMOR) software was first developed by LLNL’s PCMDI program in early 2000s and was formally released with v1.0 (July 2006), v2.0 (January 2011), and v3.1(June 2016). CMOR is used to produce Climate and Forecast Convention (http://cfconventions.org/) CF-compliant netCDF files, in the standard format required to satisfy the World Climate Research Program (WCRP) Coupled Model Intercomparison Project (CMIP). The software has been used across multiple phases of the Earth System Modeling (ESM) project CMIP (CMIP3, CMIP5, CMIP6, and planned use in CMIP7) along with numerous parallel projects focused on preparation observations for use in model evaluation (obs4MIPs) and forcing datasets (input4MIPs) to guide ESM simulations to meet strict experimental protocols. More information can be obtained from the CMOR website and code repositories: https://cmor.llnl.gov/; https://github.com/pcmdi/cmor; https://github.com/PCMDI/cmor3_documentation The ESM variable definitions used as input for CMOR can also be viewed in code repositories: https://github.com/PCMDI/cmip3-cmor-tables/; https://github.com/PCMDI/cmip5-cmor-tables/; https://github.com/PCMDI/cmip6-cmor-tables/

Mauzey, ChristopherF↗
DOE OSTI2014

Cloud Property Retrieval Products for Graciosa Island, Azores

The motivation for developing this product was to use the Dong et al. 1998 method to retrieve cloud microphysical properties, such as cloud droplet effective radius, cloud droplets number concentration, and optical thickness. These retrieved properties have been used to validate the satellite retrieval, and evaluate the climate simulations and reanalyses. We had been using this method to retrieve cloud microphysical properties over ARM SGP and NSA sites. We also modified the method for the AMF at Shouxian, China and some IOPs, e.g. ARM IOP at SGP in March, 2000. The ARSCL data from ARM data archive over the SGP and NSA have been used to determine the cloud boundary and cloud phase. For these ARM permanent sites, the ARSCL data was developed based on MMCR measurements, however, there were no data available at the Azores field campaign. We followed the steps to generate this derived product and also include the MPLCMASK cloud retrievals to determine the most accurate cloud boundaries, including the thin cirrus clouds that WACR may under-detect. We use these as input to retrieve the cloud microphysical properties. Due to the different temporal resolutions of the derived cloud boundary heights product and the cloud properties product, we submit them as two separate netcdf files.

54 ENVIRONMENTAL SCIENCES↗