DOI: 10.5287/ora-korkq2z7n
title: Atmospheric mineral dust emission and climatological variables for Etosha Pan, Namibia (2000-2022)

description: CSV data files containing records of mineral dust plume events (dust point source locations lat/long, start and end time, duration, plume movement direction, and sensor used for detection), extrapolated monthly, seasonal and annual dust plume event and dust days (i.e. count of days in which a dust plume was observed) and dust optical depth (DOD) data, and associated records of meteorological and hydrological variables for dust plume events (i.e. 10 m wind speed, lake area extents, catchment precipitation totals and specific source point surface wetting frequencies by precipitation and ephemeral flooding, and El Niño Southern Oscillation [ENSO 3.4] and South Indian Ocean Dipole [SIOD] index values) for Etosha Pan, Namibia for the analysis period from July 1999 to January 2023.
All datasets are readable using CSV file viewer software.
Dust plume event data were analysed manually by the author Natasha S. Wallum. Data used for detection were sourced from Terra and Aqua satellites MODIS level 1b and Aerosol data acquired from the Atmospheric Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland (https://ladsweb.nascom.nasa.gov/) and SEVIRI data procured from the EUMETSAT Data Store (https://data.eumetsat.int/search?query=). Analysis of SEVIRI imagery utilised the Clear Sky Differencing (CSD) algorithm developed by Jon Murray and colleagues (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JD025221).
Lake area extent data were derived by density thresholding of near-infrared (NIR) reflectance data from the MODIS Terra satellite obtained from NASA’s LAADS DAAC data portal (https://ladsweb.modaps.eosdis.nasa.gov/) and verified using Level-2 (8-day) images collected from Landsat 5 TM (1984–2012) and Landsat 8 OLI (2013 – present day) sensors were acquired through the USGS Earth Explorer data portal (www.earthexplorer.com).
The contributing catchment (Cuvelai-Etosha Basin) was derived from the HydroBASINS (Lehner and Grill, 2013) catchment database (https://hydrosheds.org/products/hydrobasins), and this area was used to derive daily precipitation inputs for 2000–2022 (July – June hydrological year) from The Integrated Multi-Satellite Retrievals for GPM (GPM-IMERG) and The Tropical Rainfall Measuring Mission (TRMM) gridded time-series of precipitation available from the Goddard Earth Science Data and Information Services Center (http://disc.gsfc.nasa.gov/).
These data were augmented by limited monthly precipitation records (2000–2022) from 10 local weather stations (Mahenene, Ondjiva, Namacunde, Oshaambelo, Ogongo, Ondangwa, Okashana, Okapya, Okaukuejo, and Mannheim) provided by the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL; https://sasscal.org/) and continuous rain gauge measurements recorded at Windpoort located in close proximity to Etosha Pan within the Cuvelai-Etosha Basin.
Near-surface (10 m) wind speeds (m/s) and cubed wind speed anomaly data were derived from ERA5-Land reanalysis model data product available from the Copernicus Climate Change Service Data Store (https://cds.climate.copernicus.eu/).
Surface wetting frequencies and time since wetting for dust event source points were calculated by the author Natasha S. Wallum using ArcGIS Pro (education licence on behalf of the University of Oxford).
Global climate indices of SST anomalies data (ENSO 3.4 and SIOD) were obtained from the Climate Diagnostics Centre (CDC) online archives (http://psl.noaa.gov/data/climateindices) and the Indian Ocean dipole (IOD) site maintained by the Frontier Research System for Global Change (FRSGC)/Japan Agency for Marine-Earth Science and Technology (JAMSTEC) Climate Variations Research Program (http://www.jamstec.go.jp).