Data

Derived Optimal Linear Combination Evapotranspiration

Also known as: DOLCE
ARC Centre of Excellence for Climate System Science
ARC Centre of Excellence for Climate Extremes Data Manager (Managed by) ARC Centre of Excellence for Climate System Science Data Manager (Managed by) Sanaa Hobeichi (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/41/58980b55b0495&rft.title=Derived Optimal Linear Combination Evapotranspiration&rft.identifier=10.4225/41/58980b55b0495&rft.publisher=ARC Centre of Excellence for Climate System Science&rft.description=The Derived Optimal Linear Combination Evapotranspiration (DOLCE) dataset consists of monthly Evapotranspiration (ET) and their associated uncertainty on a global scale. DOLCE is derived at 0.5 spatial resolution and monthly temporal resolution over the period 2000-2009. DOLCE is derived by weighting six existent global ET products based on their ability to match site-level data from 159 globally distributed flux tower sites. The six evapotranspiration products are: GLEAM V2A, GLEAM V2B, GLEAM V3A, MOD16, MPIBGC and PML . DOLCE is a mosaic of three tiers, we derive each tier from a different weighting ensemble and subset of FLUXNET data. The dominant part (i.e. tier1) involves six ET products and data from 138 flux towers, tier2 involves 5 products and 151 flux towers, while tier3 involved 2 products and 159 sites.&rft.creator=ARC Centre of Excellence for Climate System Science&rft.date=2017&rft.relation=10.5194/hess-22-1317-2018&rft.coverage=-180,-90 -180,90 180,90 180,-90 -180,-90&rft_rights=Access to this dataset is free, the users are free to download this dataset and share it with others and adapt it as long as they credit the dataset owners, provide a link to the license, and if changes were made, indicate it clearly and distribute their contributions under the same license as the original, commercial use is not permitted.&rft_rights=Creative Commons - Attribution - Non Commercial - No Derivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode&rft_subject=Meteorology&rft_subject=EARTH SCIENCES&rft_subject=ATMOSPHERIC SCIENCES&rft_subject=Climatology (excl. Climate Change Processes)&rft_subject=Surface Processes&rft_subject=PHYSICAL GEOGRAPHY AND ENVIRONMENTAL GEOSCIENCE&rft_subject=Land Surface Models&rft_subject=Evapotranspiration&rft.type=dataset&rft.language=English Access the data
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CC-BY-NC-ND

Creative Commons - Attribution - Non Commercial - No Derivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

Access to this dataset is free, the users are free to download this dataset and share it with others and adapt it as long as they credit the dataset owners, provide a link to the license, and if changes were made, indicate it clearly and distribute their contributions under the same license as the original, commercial use is not permitted.

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Dataset is available online via NCI thredds catalogue

Brief description

The Derived Optimal Linear Combination Evapotranspiration (DOLCE) dataset consists of monthly Evapotranspiration (ET) and their associated uncertainty on a global scale. DOLCE is derived at 0.5 spatial resolution and monthly temporal resolution over the period 2000-2009.

DOLCE is derived by weighting six existent global ET products based on their ability to match site-level data from 159 globally distributed flux tower sites.

The six evapotranspiration products are: GLEAM V2A, GLEAM V2B, GLEAM V3A, MOD16, MPIBGC and PML .

DOLCE is a mosaic of three tiers, we derive each tier from a different weighting ensemble and subset of FLUXNET data. The dominant part (i.e. tier1) involves six ET products and data from 138 flux towers, tier2 involves 5 products and 151 flux towers, while tier3 involved 2 products and 159 sites.

Created: 01 02 2017

Data time period: 2000-01-01 to 2009-12-31

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Subjects
Atmospheric Sciences | Climatology (Excl. Climate Change Processes) | Earth Sciences | Evapotranspiration | Land Surface Models | Meteorology | Physical Geography and Environmental Geoscience | Surface Processes |

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