Macquarie Island meteorological data at 30 minutes resolution from 2016-11-19 to 2018-03-14
From the project summary: Both satellite products and climate models have large biases in the energy and water budgets over the Southern Ocean (SO), which is not surprising given this environment's unique nature. The air is free of dust and pollution, and the surface is governed by strong winds...
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Format: | Dataset |
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Australian Antarctic Data Centre
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Online Access: | https://researchdata.edu.au/macquarie-island-meteorological-03-14/1444373 https://doi.org/10.26179/5e449b4a33fc1 https://data.aad.gov.au/metadata/records/AAS_4340_macquarie_met_30min_2016-2018_1 http://nla.gov.au/nla.party-617536 |
Summary: | From the project summary: Both satellite products and climate models have large biases in the energy and water budgets over the Southern Ocean (SO), which is not surprising given this environment's unique nature. The air is free of dust and pollution, and the surface is governed by strong winds, large waves and heavy sea spray. These conditions lead to the greatest fractional cloud cover over any place on the globe. Much of these biases are a direct consequence of a poor understanding of the structure and dynamics of the SO atmospheric boundary layer, which in turn is a consequence of the sparse observations being available due to the harsh conditions. This proposals call for employing unmanned aerial vehicles/systems from Macquarie Island to make unprecedented observations of the boundary layer processes over the SO. These observations will be used to both model the boundary layer dynamics and clouds and evaluate satellite products and numerical simulations of surface fluxes, cloud properties and sea spray. The data was recorded at lat: -54.5, lon:158.935. The observations include Absolute Humidity, Relative Humidity, Ambient Temperature, Potential Temperature, 3D wind speed, and Carbon concentration. The data is in netcdf4 format with medium compression, and have all available information in the attributes of each variable. The data can be easily previewed with an application like Panoply (https://www.giss.nasa.gov/tools/panoply/). The variable names are: Ah_7500_Avg Cc_7500_Avg RH_HMP_Avg Ta_HMP_Avg Tpanel_Avg Tv_CSAT_Avg Ux_CSAT_Avg Uy_CSAT_Avg Uz_CSAT_Avg Vbat_Avg WD_CSAT_Avg WD_CSAT_Compass_Avg WD_CSAT_Sd WS_CSAT_Avg ps_7500_Avg rho_a_Avg time time_YYYYmmDDHHMMSS |
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