The Global Data Assimilation System (GDAS) is the system used by the Global Forecast System (GFS) model to place observations into a gridded model space for the purpose of starting, or initializing, weather forecasts with observed data. GDAS adds the following types of observations to a gridded, 3-D, model space: surface observations, balloon data, wind profiler data, aircraft reports, buoy observations, radar observations, and satellite observations. GDAS data are available as both input observations to GDAS and gridded output fields from GDAS. Gridded GDAS output data can be used to start the GFS model. Due to the diverse nature of the assimilated data types, input data are available in a variety of data formats, primarily Binary Universal Form for the Representation of meteorological data (BUFR) and Institute of Electrical and Electronics Engineers (IEEE) binary. The GDAS output is World Meteorological Organization (WMO) Gridded Binary (GRIB).

These NCEP FNL (Final) operational global analysis and forecast data are on 0.25-degree by 0.25-degree grids prepared operationally every six hours. This product is from the Global Data Assimilation System (GDAS), which continuously collects observational data from the Global Telecommunications System (GTS), and other sources, for many analyses. The FNLs are made with the same model which NCEP uses in the Global Forecast System (GFS), but the FNLs are prepared about an hour or so after the GFS is initialized. The FNLs are delayed so that more observational data can be used. The GFS is run earlier in support of time critical forecast needs, and uses the FNL from the previous 6 hour cycle as part of its initialization. The analyses are available on the surface, at 26 mandatory (and other pressure) levels from 1000 millibars to 10 millibars, in the surface boundary layer and at some sigma layers, the tropopause and a few others. Parameters include surface pressure, sea level pressure, geopotential height, temperature, sea surface temperature, soil values, ice cover, relative humidity, u- and v- winds, vertical motion, vorticity and ozone. The archive time series is continuously extended to a near-current date. It is not maintained in real-time.

Data is from NCEP initialized analysis (2x/day). It consists of most variables interpolated to pressure surfaces from model (sigma) surfaces.

This dataset contains land surface parameters simulated by the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System version 2 (FLDAS2) Central Asia model. The FLDAS2 Central Asia model is a custom instance of the NASA Land Information System that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. The data are produced using the Noah Multi-Parameterization (Noah-MP) version 4.0.1 Land Surface Model (LSM) forced by Global Data Assimilation System (GDAS) meteorological data.The FLDAS2 Central Asia dataset is produced daily with a one-day latency. Data are available from October 1, 2000 to present. The dataset contains 27 parameters at a 0.01 degree spatial resolution over the Central Asia region (30-100°E, 21-56°N).

The GraphCast Global Forecast System (GraphCastGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The horizontal resolution is a 0.25 degree latitude-longitude grid (about 28 km). The model runs 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, specific humidity, and vertical velocity, are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2.

The GraphCastGFS system is an experimental weather forecast model built upon the pre-trained Google DeepMind’s GraphCast Machine Learning Weather Prediction (MLWP) model. The GraphCast model is implemented as a message-passing graph neural network (GNN) architecture with “encoder-processor-decoder” configuration. It uses an icosahedron grid with multiscale edges and has around 37 million parameters. This model is pre-trained with ECMWF’s ERA5 reanalysis data. The GraphCastGFSl takes two model states as initial conditions (current and 6-hr previous states) from NCEP 0.25 degree GDAS analysis data and runs GraphCast (37 levels) and GraphCast_operational (13 levels) with a pre-trained model provided by GraphCast. Unit conversion to the GDAS data is conducted to match the input data required by GraphCast and to generate forecast products consistent with GFS from GraphCastGFS’ native forecast data.

The GraphCastGFS version 2 made the following changes from the GraphcastCastGFS version 1.

1. The 37 vertical levels model is removed due to the storage restriction and limited accuracy.
   
2. The 13 levels graphcast ML model was fine-tuned with NCEP’s GDAS data as inputs and ECMWF ERA5 data as ground truth from 20210323 to 20220901, validated from 20220901 to 20230101. Evaluation is done with forecasts from 20230101-20240101. The new weights created from the training are used to create global forecasts. It is important to note that the GraphCastGFS v1 model weights obtained from Google’s DeepMInd were provided based on 12 timesteps training with ERA5 data, while the GraphCastGFS v2 model weights resulted from training with 14 timesteps with GDAS and ERA5 data that significantly increased the accuracy of the forecasts compared with GraphCastGFS V1.
   
   The input data generated from the GDAS data as GraphCast input is provided under input/ directory. An example of file names is shown below
   
   source-gdas_date-2024022000_res-0.25_levels-13_steps-2.nc
   
   The files are under forecasts_13_levels/. There are 40 files under each directory covering a 10 day forecast. An example of file name is listed below
   
   graphcastgfs.t00z.pgrb2.0p25.f006
   
   

1. Starting from 06 cycle on 20240710, the forecast length is increased from 10 days to 16 days.
   
   Please note that this NOAA GraphCastGFS Model was produced using a code package released by Google DeepMind. For information on Google DeepMind, please visit their github page listed in the documentation and license sections of this page.

NOTE - Upgrade NCEP Global Forecast System to v16.3.0 - Effective November 29, 2022 See notification HERE

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 entire globe is covered by the GFS at a base horizontal resolution of 18 miles (28 kilometers) between grid points, which is used by the operational forecasters who predict weather out to 16 days in the future. Horizontal resolution drops to 44 miles (70 kilometers) between grid point for forecasts between one week and two weeks.

The NOAA Global Forecast Systems (GFS) Warm Start Initial Conditions are produced by the National Centers for Environmental Prediction Center (NCEP) to run operational deterministic medium-range numerical weather predictions.
The GFS is built with the GFDL Finite-Volume Cubed-Sphere Dynamical Core (FV3) and the Grid-Point Statistical Interpolation (GSI) data assimilation system.
Please visit the links below in the Documentation section to find more details about the model and the data assimilation systems. The current operational GFS is run at 64 layers in the vertical extending from the surface to the upper stratosphere and on six cubic-sphere tiles at the C768 or 13-km horizontal resolution. A new version of the GFS that has 127 layers extending to the mesopause will be implemented for operation on February 3, 2021. These initial conditions are made available four times per day for running forecasts at the 00Z, 06Z, 12Z and 18Z cycles, respectively. For each cycle, the dataset contains the first guess of the atmosphere states found in the directory ./gdas.yyyymmdd/hh-6/RESTART, which are 6-hour GDAS forecast from the last cycle, and atmospheric analysis increments and surface analysis for the current cycle found in the directory ./gfs.yyyymmdd/hh, which are produced by the data assimilation systems.

This dataset contains a series of land surface parameters simulated from the Noah 3.6.1 model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), adapted from Land Information System (LIS7). The dataset contains 28 parameters in a 0.10 degree spatial resolution and from January 2019 to present. The temporal resolution is monthly and the spatial coverage is global (60S, 180W, 90N, 180E). The simulation was forced by a combination of the Global Data Assimilation System (GDAS) data and Climate Hazards Group InfraRed Precipitation with Station Preliminary (CHIRPS-PRELIM) 6-hourly rainfall data that has been downscaled using the NASA Land Data Toolkit, restarted from CHIRPS-FINAL of the previous month. The simulation was initialized on January 1, 2019 using soil moisture and other state fields from a FLDAS/Noah model climatology for that day of the year.

This composition appears in the As-Gd-Pd region of phase space. It's relative stability is shown in the As-Gd-Pd phase diagram (left). The relative stability of all other phases at this composition (and the combination of other stable phases, if no compound at this composition is stable) is shown in the relative stability plot (right)

Radiance products used in the NCEP Global Data Assimilation System can be found in this set. Data types include: Atmospheric InfraRed Sounder, HSB processed brightness temperatures, Advanced Microwave Sounding Unit-A, Advanced Microwave Sounding Unit-B, High resolution InfraRed Sounder-3, High resolution InfraRed Sounder-4, and Microwave Humidity Sounder NCEP processed brightness temperatures. Due to operational issues, data files for different times may vary in size. This is especially true starting summer 2014 and continuing to present.

jacobbieker/gdas-kerchunk dataset hosted on Hugging Face and contributed by the HF Datasets community

gdas data used for hysplit model

The NCEP operational Global Data Assimilation System surface flux grids are on a T574 Gaussian global grid. Grids include analysis and forecast time steps at a 3 hourly interval from ... 0 to 9 hours. Model runs occur at 00, 06, 12, and 18 UTC daily. For real-time data access please use the NCEP data server.

GdAs is Halite, Rock Salt structured and crystallizes in the cubic Fm-3m space group. The structure is three-dimensional. Gd3+ is bonded to six equivalent As3- atoms to form a mixture of edge and corner-sharing GdAs6 octahedra. The corner-sharing octahedral tilt angles are 0°. All Gd–As bond lengths are 2.95 Å. As3- is bonded to six equivalent Gd3+ atoms to form a mixture of edge and corner-sharing AsGd6 octahedra. The corner-sharing octahedral tilt angles are 0°.

Data obtained from computational DFT calculations on Hexagonal GdAs is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

Two winter wheat (Triticum aestivum L.) populations, i.e. 180 genetic resources and 210 elite varieties, were compared in a field trial to analyse how grain number and grain yield distribution along the spike changed during the breeding process and how this associates to yield-related traits. Elites showed in average 38% more yield compared to resources. This breeding improvement mainly derived from an increase in grains and yield per spike in addition to grains and yield per spikelet. These increments corresponded to 19, 23, 21 and 25%, respectively. Not much gain in thousand grain weight (4%) was observed in elites as compared to resources. The number of spikelets per spike was not, or even negatively, correlated with most traits, except of grains per spike, which suggests that this trait was not favoured during breeding. The grain number and grain yield distributions along the spike (GDAS and GYDAS) were measured and compared by using a novel mathematical tool. GDAS and GYDAS measure the deviation of a spike of interest from the architecture of a model spike with even grain and yield distribution along all spikelets, respectively. Both traits were positively correlated. Elites showed in average only a 1% improvement in GDAS and GYDAS values compared to resources. This comparison revealed that breeding increased grain number and yield uniformly along the spike without changing relative yield input of individual spikelets, thereby, maintaining the general spike architecture.

This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The data are in 0.25 degree resolution and range from January 2001 to present. The temporal resolution is monthly and the spatial coverage is Southern Africa (34.75S, 5.75E, 6.75N, 51.25E). The files are in NetCDF format.

This simulation was forced by a combination of NCEP's Global Data Assimilation System (GDAS) atmospheric analysis fields and spatially and temporally disaggregated NOAA Climate Prediction African Rainfall Estimate Algorithm version 2 (RFE2) fields.

The simulation was initialized on 1 January 2001 using soil moisture and other state fields from a FLDAS VIC model climatology for that day of the year.

Note: Since this dataset now has a duplicate copy in AWS, we will stop continuous updates of this dataset in the 2025. The ... NCEP operational Global Data Assimilation System surface flux grids are on a T574 Gaussian global grid. Grids include analysis and forecast time steps at a 3 hourly interval from 0 to 9 hours. Model runs occur at 00, 06, 12, and 18 UTC daily. For real-time data access please use the NCEP data server.

Global Land Data Assimilation System Version 2 (hereafter, GLDAS-2) has two components: one forced entirely with the Princeton meteorological forcing data (hereafter, GLDAS-2.0), and the other forced with a combination of model and observation based forcing data sets (hereafter, GLDAS-2.1).

This data set, GLDAS-2.1 Noah 1.0 degree 3-hourly, simulated with the Noah Model 3.3 in Land Information System (LIS) Version 7, contains 36 land surface fields from January 2000 to present. GLDAS-2.1 simulation is forced by a combination of National Oceanic and Atmospheric Administration/National Center for Environmental Prediction's Global Data Assimilation System (GDAS) atmospheric analysis fields, spatially and temporally disaggregated Global Precipitation Climatology Project (GPCP) precipitation fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET). This data set supersedes GLDAS-1 products, in which improvements are made in the use of GPCP and the disaggregation scheme, and quality control for the AGRMET dataset. The GPCP 1-degree Daily (1DD) dataset is used and disaggregated to 3-hourly intervals, whereas GLDAS-1 used the NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) pentad dataset and disaggregated to 6-hourly. The gaps and irregularity in the AGRMET shortwave downward flux are alleviated by additional filtering and bias correction to the Surface Radiation Budget (SRB) dataset. Furthermore, the spatial aggregation scheme of GDAS dataset is revised in GLDAS-2.1.

The simulation started on 1 January 2000 using the conditions from the GLDAS-2.0 simulation and was forced with GDAS and the disaggregated GPCP. The AGRMET radiation forcing is added for 1 March 2001 onwards.

The simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30), as well as the Noah model default land cover (Modified IGBP MODIS 20-category classification) and soil texture (Hybrid STATSGO/FAO) datasets.

The GLDAS-2.1 data are archived and distributed in NetCDF format.

This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The data are in 0.25 degree resolution and range from January 2001 to present. The temporal resolution is daily and the spatial coverage is Eastern Africa (12.00S, 21.75E, 23.25N, 51.25E). The files are in NetCDF format.

This simulation was forced by a combination of NCEP's Global Data Assimilation System (GDAS) data and NOAA CPC Africa Rainfall Estimation Algorithm v2 (RFE2) data.

The simulation was initialized on 1 January 2001 using soil moisture and other state fields from a FLDAS/VIC model climatology for that day of the year.

Data obtained from computational DFT calculations on Tetragonal GdAs is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.