This collection provides a seamlessly merged, hydrologically robust Digital Elevation Model (DEM) for the Murray Darling Basin (MDB), Australia, at 5 m and 25 m grid cell resolution.

This composite DEM has been created from all the publicly available high resolution DEMs in the Geoscience Australia (GA) elevation data portal Elvis (https://elevation.fsdf.org.au/) as at November 2022. The input DEMs, also sometimes referred to as digital terrain models (DTMs), are bare-earth products which represent the ground surface with buildings and vegetation removed. The DEMs were either from lidar (0.5 to 2 m resolution) or photogrammetry (5 m resolution) and totalled 852 individual DEMs.

The merging process involved ranking the DEMs, pairing the DEMs with overlaps, and adjusting and smoothing the elevations of the lower ranked DEM to make the edge elevations compatible with the higher-ranked DEM. This method is adapted from Gallant 2019 with modifications to work with hundreds of DEMs and have a variable number of gaussian smoothing steps.

Where there were gaps in the high-resolution DEM extents, the Forests and Buildings removed DEM (FABDEM; Hawker et al. 2022), a bare-earth radar-derived, 1 arc-second resolution global elevation model was used as the underlying base DEM. FABDEM is based on the Copernicus global digital surface model.

Additionally, hillshade datasets created from both the 5 m and 25 m DEMs are provided.

Note: the FABDEM dataset is available publicly for non-commercial purposes and consequently the data files available with this Collection are also available with a Creative Commons NonCommercial ShareAlike 4.0 Licence (CC BY-NC-SA 4.0). See https://data.bris.ac.uk/datasets/25wfy0f9ukoge2gs7a5mqpq2j7/license.txt Lineage: For a more detailed lineage see the supporting document Composite_MDB_DEM_Lineage.

DATA SOURCES 1. Geoscience Australia elevation data (https://elevation.fsdf.org.au/) via Amazon Web Service s3 bucket. Of the 852 digital elevation models (DEMs) from the GA elevation data portal, 601 DEMs were from lidar and 251 were from photogrammetry. The latest date of download was Nov 2022. The oldest input DEM was from 2008 and the newest from 2022.

1. FABDEM - Forests and buildings removed DEM based on the 1 arc-second Copernicus global digital surface model. Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17, 024016. https://doi.org/10.1088/1748-9326/ac4d4f

METHODS Part I. Preprocessing The input DEMs were prepared for merging with the following steps: 1. Metadata for all input DEMs was collated in a single file and the DEMs were ranked from finest resolution/newest to coarsest resolution/oldest 2. Tiled input DEMs were combined into single files 3. Input DEMs were reprojected to GA LCC conformal conic projection (EPSG:7845) and bilinearly resampled to 5 m 4. Input DEMs were shifted vertically to the Australian Vertical Working Surface (AVWS; EPSG:9458) 5. The input DEMs were stacked (without any merging and/or smoothing at DEM edges) based on rank so that higher ranking DEMs preceded the lower ranking DEMs, i.e. the elevation value in a grid cell came from the highest rank DEM which had a value in that cell 6. An index raster dataset was produced, where the value assigned to each grid cell was the rank of the DEM which contributed the elevation value to the stacked DEM (see Collection Files - Index_5m_resolution) 7. A metadata file describing each input dataset was linked to the index dataset via the rank attribute (see Collection Files - Metadata)

Vertical height reference surface https://icsm.gov.au/australian-vertical-working-surface

Part II. DEM Merging The method for seamlessly merging DEMs to create a composite dataset is based on Gallant 2019, with modifications to work with hundreds of input DEMs. Within DEM pairs, the elevations of the lower ranked DEM are adjusted and smoothed to make the edge elevations compatible with the higher-ranked DEM. Processing was on the CSIRO Earth Analytics and Science Innovation (EASI) platform. Code was written in python and dask was used for task scheduling.

Part III. Postprocessing 1. A minor correction was made to the 5 m composite DEM in southern Queensland to replace some erroneous elevation values (-8000 m a.s.l.) with the nearest values from the surrounding grid cells 2. A 25 m version of the composite DEM was created by aggregating the 5m DEM, using a 5 x 5 grid cell window and calculating the mean elevation 3. Hillshade datasets were produced for the 5 m and 25 m DEMs using python code from https://github.com/UP-RS-ESP/DEM-Consistency-Metrics

Part IV. Validation Six validation areas were selected across the MDB for qualitative checking of the output at input dataset boundaries. The hillshade datasets were used to look for linear artefacts. Flow direction and flow accumulation rasters and drainage lines were derived from the stacked DEM (step 5 in preprocessing) and the post-merge composite DEM. These were compared to determine whether the merging process had introduced additional errors.

OUTPUTS 1. seamlessly merged composite DEMs at 5 m and 25 m resolutions (geotiff) 2. hillshade datasets for the 5 m and 25 m DEMs (geotiff) 3. index raster dataset at 5 m resolution (geotiff) 4. metadata file containing input dataset information and rank (the rank column values link to the index raster dataset values) 5. figure showing a map of the index dataset and 5m composite DEM (jpeg)

DATA QUALITY STATEMENT Note that we did not attempt to improve the quality of the input DEMs, they were not corrected prior to merging and any errors will be retained in the composite DEM.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on pressure levels from 1940 to present".

Version 3 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 3. The goal of this data set is to create and archive a Level 2 SO2 Earth Science Data Record (ESDR) from backscatter Ultraviolet (BUV) measurements from Total Ozone Mapping Spectrometer (TOMS) flown on NASA's Nimbus - 7 satellite in 1978-1993. We apply TOMS ozone team calibration techniques and consistent MEaSUREs SO2 (MS_SO2) algorithm, to obtain the best measurement-based ESDR of volcanic and anthropogenic SO2 masses and emissions. As part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, the Goddard Earth Science (GES) Data and Information Data Center (DISC) has released a new SO2 Earth System Data Record (ESDR), TOMSN7SO2, re-processed using new 4 UV wavelength bands MS_SO2 algorithm that spans the full Nimbus 7 data period. TOMSN7SO2 is a Level 2 orbital swath product, which will be used to study the fifteen-year SO2 record from the Nimbus-7 TOMS and to expand the historical database of known volcanic eruptions. Sulfur Dioxide (SO2) is a short-lived gas primarily produced by volcanoes, power plants, refineries, metal smelting and burning of fossil fuels. Where SO2 remains near the Earth s surface, it is toxic, causes acid rain, and degrades air quality. Where SO2 is lofted into the free troposphere, it forms aerosols that can alter cloud reflectivity and precipitation. In the stratosphere, volcanic SO2 forms sulfate aerosols that can result in climate change.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset contains the raw .dat versions of the structural geological model for the Hunter subregion. RMS geomodelling was used to construct the geological model for the Hunter subregion. The data set contains the depth to basement horizons, reference horizons, eroded horizons, isochores and well markers extracted from the completed geological model. The model was built using data extracted from well completions reports published by mining companies and consultants, which record the depth of various formations encountered during drilling works. These data were compiled into model input files (See data set: Hunter deep well completion reports - f2df86d5-6749-48c7-a445-d60067109f08) used to build the RMS model.

Nine geological formations and their depths from the surface are included covering a grid across the Basin. The geological model is based on measured depths recorded in well completion reports published by mining companies and consultancies.

The naming convention refers to the geological age and depth (TVD ss = total vertical depth subsea reported to the Australian Height Datum) of the various formations as follows:

Regional horizon name Age (geological stage) Newcastle Coalfield Hunter Coalfield Western Coalfield Central or Southern coalfields

M600 Top Anisian Top Hawkesbury Sandstone Top Hawkesbury Sandstone Top Hawkesbury Sandstone Base Wianamatta Group

M700 Top Olenekian Base Hawkesbury Sandstone Base Hawkesbury Sandstone Base Hawkesbury Sandstone Base Hawkesbury Sandstone

P000 Top Changhsingian Base Narrabeen Group Base Narrabeen Group Base Narrabeen Group Base Narrabeen Group

P100 Upper Wuchiapingian Base Newcastle Coal Measures Base Newcastle Coal Measures Top Watts Sandstone Top Bargo Claystone

P500 Mid Capitanian Base Tomago Coal Measures Base Wittingham Coal Measures Base Illawarra Coal Measures Base Illawarra Coal Measures

P550 Top Wordian Base Mulbring Siltstone Base Mulbring Siltstone Base Berry Siltstone Base Berry Siltstone

P600 Mid Roadian Base Maitland Group Base Maitland Group Base Shoalhaven Group Base Shoalhaven Group

P700 Upper Kungurian Top Base Greta Coal Measures Top Base Greta Coal Measures

P900 Base Serpukhovian Base Seaham Formation Base Seaham Formation

with 'M' referring to Mesozoic and 'P' to Paleozoic

Dataset History

RMS geomodelling was used to construct the geological model for the Hunter subregion. The data set contains the layers in the completed geological model. The model was built using data extracted from well completions reports published by mining companies and consultants which record the depth of various formations encountered during drilling works. These data were compiled into model input files (See dataset: Hunter deep well completion reports - f2df86d5-6749-48c7-a445-d60067109f08) used to build the RMS model.

This model has a horizontal resolution of 2000 x 2000 m (x y), with 109 vertical layers for a total of 511,118 cells. The depth ranges between 1185m above sea level and 5062 m below sea level.

Data originally sourced from 44 well completion reports and incorporated into the geological model. The reference horizons were exported from RMS software as .dat files.

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN RMS Output Dat Files v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/c975d250-b699-4585-b32f-cbfde4d8d436.

Dataset Ancestors

• Derived From Geoscience Australia, 3 second SRTM Digital Elevation Model (DEM) v01

• Derived From Australian Coal Basins

• Derived From Bathymetry GA 2009 9sec v4

• Derived From Hunter Groundwater Model extent

• Derived From South Sydney Deep Well Completion Reports V02

• Derived From Hunter deep well completion reports

• Derived From HUN GW modelling DEM v01

The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs (https://earthdata.nasa.gov/about/competitive-programs/measures)) SRTM, which includes the global 1 arc second (~30 meter) combined (merged) image data product (See User Guide Section 2.2.2). The combined image data set contains mosaicked one degree by one degree images/tiles of uncalibrated radar brightness values at 1 arc second. To create a smooth mosaic image, each pixel in an output is an average of all the image pixels for a location. Pixels with a value of zero (voids) were not counted. Because SRTM imaged a given location with two like-polarization channels (VV = vertical transmit and vertical receive, and HH = horizontal transmit and horizontal receive) and at a variety of look and azimuth angles, the quantitative scattering information was lost in the pursuit of a smoother image product unlike the SRTM swath image product SRTMIMGR (https://doi.org/10.5067/MEaSUREs/SRTM/SRTMIMGR.003), which preserved the quantitative scattering information. The NASA SRTM data sets result from a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA - previously known as the National Imagery and Mapping Agency, or NIMA), as well as the participation of the German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the Space Shuttle Endeavour, which launched February 11, 2000 and flew for 11 days. The SRTM swaths extended from ~30 degrees off-nadir to ~58 degrees off-nadir from an altitude of 233 kilometers (km), creating swaths ~225 km wide, and consisted of all land between 60° N and 56° S latitude to account for 80% of Earth’s total landmass. Known Issues Known issues in the NASA SRTM are described in the following publication: Rodriguez, E., C. S. Morris, and J. E. Belz (2006), A global assessment of the SRTM performance, Photogramm. Eng. Remote Sens., 72, 249–260. https://doi.org/10.14358/PERS.72.3.249 Improvements/Changes from Previous Versions Voids in the Version 3.0 products have been filled with ASTER Global Digital Elevation Model (GDEM) Version 2.0, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), and the National Elevation Dataset (NED).

Global cloud dataset from combined spaceborne radar and lidar.

This repository contains the 3S-GEOPROF-COMB product, a globally-gridded dataset for cloud vertical structure retrieved from hybrid active remote sensing (CloudSat radar and CALIPSO lidar) reported at 240 m vertical resolution. Science variables include vertical cloud fraction and vertically-integrated cloud cover for various geometrical criteria (i.e. high, middle, low, and thick clouds, along with with unique high, middle, and low cloud cover variants).

A Python notebook showing how to work with the dataset is available on GitHub, as is the source code used to produce the data product.

Our product is calculated from the latest release (R05) of per-orbit (level 2) combined cloud mask profiles in 2B-GEOPROF-LIDAR with additional data from 2B-GEOPROF. Validation and a complete description of the data product is given in the paper "A Global Gridded Dataset for Cloud Vertical Structure from Combined CloudSat and CALIPSO Observations" (Earth System Science Data).

Please cite "Bertrand, L., Kay, J. E., Haynes, J., and de Boer, G.: A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations, Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, 2024."

The files contained in each folder are given via the following format:

instruments_frequency_resolution.zip

• instruments:
  • radarlidar: the standard product, computed from merged geometrical profiles of hydrometeor occurrence
  • radaronly: computed solely from CloudSat radar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.
  • lidaronly: computed solely from CALIPSO lidar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.
• frequency:
  • monthly: data files report fields aggregated over a 1-month period
  • seasonal: data files report fields aggregated over a 3-month period (DJF, MAM, JJA, SON)
• resolution:
  • 2.5x2.5: each grid box spans 2.5 degrees latitude and 2.5 degrees longitude
  • 5x5: each grid box spans 5 degrees latitude and 5 degrees longitude
  • 10x10: each grid box spans 10 degrees latitude and 10 degrees longitude

Each folder contains a netCDF data file and a cloud cover quicklook plot image file for each time period over the 2006-2019 data record. Individual files are named according to the following format:

timeperiod_instruments_datastream_version.nc (or .png)

• timeperiod: the time step at the given frequency, either e.g. 2006-08 (August 2006) or 2012-DJF (December 2012 to February 2013).
• instruments: the instruments used in the data product as a whole, always CSCAL (CloudSat and CALIPSO).
• datastream: either 3S-GEOPROF-COMB (COMBined radar and lidar), 3S-GEOPROF-COMB-RO (the auxiliary Radar Only variant of the product), or 3S-GEOPROF-COMB-LO (the auxiliary Lidar Only variant of the product)
• version: current release is v8.4

The product handles the 2011 CloudSat battery anomaly, after which the satellite only collects data in the sunlit portion of its orbit, by allowing users to subsample the pre-anomaly period to mimic the post-anomaly collection patterns. This allows users to estimate the effect of the reduced sampling on their analyses or apply a consistent sampling mode to the entire dataset. This option is provided to users via the "doop" dimension. Dimension coordinate value "All cases" reports variables computed using all observations, while "DO-OP observable" reports variables using only input data that either were or would have been collected in DO-OP mode (i.e. the pre-DO-OP period is subsampled to DO-OP collection patterns).