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)

DESCRIPTION: The data files contain analysis and forecast data from the forecasting model MEPS. MEPS is an ensemble forecasting system with a total of 15 members over a three-hour period. This means that MEPS produces 15 different forecasts for each forecast occasion where the different members have equal initial states based on boundary values from selected ensemble perturbation from the European Weather Centre model. By using the information in all ensemble members, different probabilities can be calculated, e.g. how uncertain the forecast is. If you are only interested in a deterministic forecast, MEPS member 0 is used. Each model member runs every 3 hours with forecasts 66 hours ahead of time for the member. All mid-March data is generated in grib2 format, where only a limited number of parameters are retrieved from the members for each time they are generated. For member 0 and 1 all parameters are retrieved in grib1 format for every six hours, 00, 06, 12 and 18 UTC.

The forecast is made over an area covering Scandinavia, Finland, Denmark, the Baltics, the North Sea and parts of north-eastern Europe. The area is divided into grid squares with 2.5km resolution and 65 vertical levels. There are a large number of parameters that describe temperature, precipitation, wind, humidity, cloudiness, etc.

Use: At SMHI, data from MEPS is used as a basis for weather forecasts and as input to other models in areas such as hydrology and oceanography.

FORMAT: The data is in GRIB format.

The version of ARPEGE submitted to YOPPsiteMIP was a pre-operational version based on the cy43t2_op1 operational system but coupled with the 1D sea-ice model GELATO. The resolution of the model used for these simulations is the same as is used operationally at Meteo France which is variable (using a stretching factor of 2.2) with the pole (highest resolution of 7.5 km) over France for SOP1 and SOP2 and over Antarctica in SOP-SH and 105 vertical levels. The horizontal resolution is about 8-9 km over the North-Pole and timeseries have been provided for the three SOPs in the MMDF format for the 21 YOPP observatories with an hourly output for both state variables (instantaneous) and fluxes (accumulated).

Near-real-time meteorological products from the HARMONIE atmospheric model. NWP computer models use high performance computers to solve a set of hydro-dynamical equations that mathematically describe motions in the atmosphere. NWP simulations are used along with the skill of experienced forecasters to predict future weather events. There are many inputs to our prediction model such as, previous model run, current weather observations, marine buoy data and satellite imagery to name a few. The two main components of any atmospheric model are known as the dynamics and the physics. For the dynamics, we divide the forecast region into a grid and use mathematical algorithms to solve the equations governing the motions of the atmosphere at each grid-point. Currently, this grid has a 2km horizontal resolution. The physics of the model considers the key processes which occur at scales smaller than this, and thus are not “seen” by the grid. These include solar radiation and turbulence. The data presented here are from the control member of the ensemble NWP system. Each file represents the next 60 steps of the forecast. Each hourly file is availavble for approximately 24 hours here. Every effort is made to have a complete model run in each fie, that is, all 60 steps of the forecast, however due to timing and processing occasionally a file may not have all steps. This data is released in response to the EU's open data directive. For official weather forecasts please see met.ie We will be removing this page in the coming weeks. Access to NWP data can now be found here https://opendata.met.ie

The "Unified Forecast System (UFS)" is a community-based, coupled, comprehensive Earth Modeling System. It supports " multiple applications" with different forecast durations and spatial domains. The UFS Short-Range Weather (SRW) Application figures among these applications. It targets predictions of atmospheric behavior on a limited spatial domain and on time scales from minutes to several days. The SRW Application includes a prognostic atmospheric model, pre-processor, post-processor, and community workflow for running the system end-to-end. The "SRW Application Users's Guide" includes information on these components and provides detailed instructions on how to build and run the SRW Application. Users can access additional technical support via the "UFS GitHub Discussions"

This data registry contains the data required to run the “out-of-the-box” SRW Application case. The SRW App requires numerous input files to run, including static datasets (fix files containing climatological information, terrain and land use data), initial condition data files, lateral boundary condition data files, and model configuration files (such as namelists). The SRW App experiment generation system also contains a set of workflow end-to-end (WE2E) tests that exercise various configurations of the system (e.g., different grids, physics suites). Data for running a subset of these WE2E tests are also included within this registry.

Users can generate forecasts for dates not included in this data registry by downloading and manually adding raw model files for the desired dates. Many of these model files are publicly available and can be accessed via links on the "Developmental Testbed Center" website.

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

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

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

More information about the six selected CMIP6 GCMs:

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

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

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

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

Cross-Model Version:

Representative Cities Version:

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

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.

This dataset is global, covering the years 2000, 2003, 2004, 2005 and 2006. Data is from a long model integration, NOT initialized on April 1st as described in the grads ctl file accompanying each data file. The files are ieee files, written out for fortran sequential reading. The model's grid is 2x2.5 degrees.

A global configuration provides the large-scale weather forecast and also supports the nested higher resolution regional models with boundary data. More detailed short-range forecasts are provided by these high-resolution models which are able to represent certain atmospheric processes more accurately, as well as having a more detailed representation of surface features such as coastlines and orography. This dataset contains UK atmospheric high resolution data from the UK Met Office operational NWP (Numerical Weather Prediction) Unified Model (UM). A post-processed regional downscaled configuration of the Unified Model, covering the UK and Ireland, is used with hourly forecast data covering the period T+0 to T+120 hours is used. With a resolution of approximately 0.018 degrees are able to produce hourly data at surface level and at standard pressure levels up to eight times a day. The model’s initial state is kept close to the real atmosphere using incremental 3D-Var data assimilation. This archive currently holds data from April 2016 onwards. An issue has been identified with the representation of the Transverse Mercator projection in the GRIB files in which some of the projection metadata values were set incorrectly. This error has been corrected in all data since 15th Jan 2020. The values that were corrected are as follows: Up to 15/01/2020: Source of Grid Definition: 12 Latitude of True Origin: 49 Longitude of True Origin: -2 From 15/01/2020: Source of Grid Definition: 0 Latitude of True Origin: 49.0e06 Longitude of True Origin: -2.0e06

Overview

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

Data Details

Initial conditions, lateral-boundary conditions, WRF namelists, and output graphics were archived from three real-time modeling frameworks: 1) RAP-ESRL: the experimental RAP (run hourly) 2) HRRR-ESRL: the experimental HRRR (run hourly) 3) HRRR-WFIP2: the experimental, WFIP2-provisional version of the HRRR, run twice daily at 0600 and 1800 UTC. The real-time HRRR-WFIP2 also ran with a concurrent 750-m nest (i.e., the HRRR-WFIP2 nest) that was initialized at 1 h into the HRRR forecast (i.e., 0700 and 1900 UTC).

Each of these frameworks should be considered experimental, subject to intermittent production outages (sometimes persistent), data-assimilation outages, and changes to data-assimilation procedures and physical parameterizations.

The archive of real-time data from these modeling frameworks consists of the following two zip-file aggregations: 1) files containing initial conditions, lateral boundary conditions, and WRF namelists: For RAP-ESRL and HRRR-ESRL runs, three files are compressed in a single zip file: i) wrfinput_d01: initial conditions (netCDF) ii) wrfbdy_d01: lateral-boundary conditions (netCDF) iii) namelist.input: the WRF-ARW namelist (plain text) The HRRR-WFIP2 archive also includes these files, but with the addition of "wrfinput_d02", the nested-domain initial conditions (netCDF). Note that while the archived HRRR-WFIP2 namelist specifies a 15-h forecast, lateral-boundary conditions for most runs are available for a 24-h forecast. 2) files containing output graphics (png). Given the large number of graphics files that are produced, a detailed description of the zip-file contents is not given here.

subject to appropriate attribution.

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

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

The dataset is available in two formats:

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

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

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

A selection of near surface weather model parameters of the UWC-West HARMONIE-AROME Cy43 model, for the Caribbean domain. For this weather forecast model, KNMI works closely with Iceland, Denmark and Ireland on local short-term weather forecasts under the name "United Weather Centres-West" (UWC-West). An international project team is working on a joint Numerical Weather Prediction model (NWP), procurement and management of the HPC (supercomputer) and infrastructure. Every hour, 9 files are available, each containing the full forecast depth for 1 parameter. The parameters are: wind gusts at 10m, wind speed at 10m, wind as a vector at 10m (both speed and direction), air temperature at 2m, air pressure at MSL (mean sea level), relative humidity, 3 precipitation parameters. The forecast time resolution is 1 hour, with a forecast horizon of 60 hours.

The National Water Model (NWM) is a water forecasting model operated by the National Water Center (NWC) of the NOAA National Weather Service. The NWM continually forecasts flows on 2.7 million stream reaches covering 3.2 million miles of streams and rivers in the continental United States [1]. It operates as part of the national weather forecasting system, with inputs from NOAA numerical weather prediction models, and from weather and water conditions observed through the US Geological Survey's National Water Information System. Reference materials for the computational framework behind NWM is published by NCAR [9] [10].

The NWC generates NWM streamflow forecasts for the continental US (CONUS) with multiple forecast horizons and time steps. Due to the output file sizes, these are normally not available for download more than a couple days at a time [2]. However, for a time a 40-day rolling window of these forecasts was maintained by HydroShare at RENCI [3], and a complete retrospective (August 2016 to the present) of the NWM Analysis & Assimilation outputs is maintained as well (contact help@cuahsi.org for access).

An archive of all NWM forecasts for the period Aug 18 to Sept 10, 2017 has been compiled at RENCI [4] [5], available as netCDF (.nc) files totaling 8TB. These can be browsed, subsetted, visualized, and downloaded (see [6] [7] [8]). In addition to these output files, we have uploaded to this HydroShare resource the input parameter files needed to re-run the NWM for the Harvey period, or for any time period covered by NWM v1.1 and 1.2 (August 2016 to this publication date in August 2018). These parameter files are also made available at [1].

See README for further details and usage guidance. Please see NOAA contacts listed on [1] for questions about the NWM data contents, structure and formats. Contact help@cuahsi.org if any questions about HydroShare-based tools and data access.

References [1] Overview of the NWM framework and output files [http://water.noaa.gov/about/nwm] [2] Free access to all National Water Model output for the most recent two days [http://water.noaa.gov/about/nwm - scroll down to links under "Downloading Output"] [3] NWM outputs for rolling 40-day window, maintained by HydroShare [link is no longer available] [4] Archived Harvey NWM outputs via RENCI THREDDS server [http://thredds.hydroshare.org/thredds/catalog/nwm/harvey/catalog.html] [link is no longer accessible] [5] RENCI is an Institute at the University of North Carolina at Chapel Hill [6] Live map for National Water Model forecasts [http://water.noaa.gov/map] [7] NWM Forecast Viewer app [no longer available] [8] CUAHSI JupyterHub example scripts for subsetting NWM output files [https://hydroshare.org/resource/3db192783bcb4599bab36d43fc3413db/] [9] WRF-Hydro Overview [https://ral.ucar.edu/projects/wrf_hydro/overview] [10] WRF-Hydro User Guide 2015 [https://ral.ucar.edu/sites/default/files/public/images/project/WRF_Hydro_User_Guide_v3.0.pdf]

The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The four-times-daily, 9-month control runs, consist of all 6-hourly forecasts, and the monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format.

The ECMWF DDH (Diagnostique des Domaines Horizontaux) 72 Hour Forecast Data is one of several model data sets collected by the National Center For Atmospheric Research/Earth Observing Laboratory (NCAR/EOL) as part of the Dynamics and Chemistry of Marine Stratocumulus Phase II: Entrainment Studies (DYCOMS-II) project. The data include ECMWF DDH (Diagnostique des Domaines Horizontaux) 72 hour forecast averaged values over the target region corresponding to the box spanning 236.5-240.0 E, 30.0-32.5 N and point values which locate at 238.4 E and 31.08 N. The data cover the period from 1-31 July 2001 and are available once per day at 00 UTC. The data were acquired from the European Centre for Medium Range Weather Forecasting (ECMWF). These are gzipped tar files containing ASCII data files.

Merged model Data Files (MMDFs) were produced with the HARMONIE-AROME (HIRLAM–ALADIN Research on Mesoscale Operational NWP in Euromed–Application of Research to Operations at Mesoscale) model configuration for operational weather forecasting for the European Arctic with the name AROME-Arctic. AROME-Arctic MMDFs are based on the operational forecasts (cy40h.1) and are available for the SOP1 and SOP2 at Sodankylä and Ny-Ålesund. Lateral Boundary Conditions are derived from the ECMWF IFS-HRES. The data archived in the MMDFs are provided hourly for the single model grid-point closest to the site.

The Community Radiative Transfer Model (CRTM) is a JCSDA developed and distributed community model. It is used within the JCSDA JEDI framework, but is also a commonly used stand-alone radiative transfer model used across various federal agencies, research organizations, and universities. CRTM is licensed under the Creative Commons Zero license, and is fully within the public domain, including the dataset to be hosted here.

The present dataset is the "binary files" that are used within the CRTM to enable it to compute clear-sky transmittances for various satellite sensors. There will be either 1 "tarball" file (tar.gz) with a total size of approximately 6 GB or less, or a structured directory consisting of 4139 files binary files consisting of big endian, little endian, netCDF3/4, pdf, and assorted binary assets totaling no more than 10GB (uncompressed). There will be occasion to add individual files, but no more than a weekly occurrence in general.

This dataset contains Australian Community Climate and Earth-System Simulator (ACCESS)-DN Numerical Weather Prediction (NWP) model surface data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Darwin, Australia. The data is 2D surface analysis and forecast data from the Australian Bureau of Meteorology (BoM) and is in digital gridded binary format netCDF files. The files are grouped into .tar files by day.