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Footprints for Zurich-Hardau at 30-min resolution - 2023-03-13

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2023-03-13
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Daily flux footprints for the Zurich-Hardau flux-tower in Switzerland at 30-minute temporal resolution. Each dataset contains up to 48 footprints with the following variables each: Time, X, Y, Boundary Layer Height Quality Flag, and Footprint Climatology. The flux footprints are computed using FFP as described in Kljun et al. (2015), https://doi.org/10.5194/gmd-8-3695-2015, at 10-meter spatial resolution using ETC L2 Fluxes from Hardau, PID: 11676/Lg2RRHAdsLee0lsF-9KsTC-r. The projection can be customized, but the footprints were calculated in UTM 32N.

This work is part of the ICOS Cities/PAUL Pilot Applications in Urban Landscapes project. In this version, footprints have been calculated using the ETC L2 Flux dataset for Hardau PID: 11676/Lg2RRHAdsLee0lsF-9KsTC-r.

2023-03-13 00:00:00
2023-03-13 21:30:00
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Molinier, B., Kljun, N. (2026). Footprints for Zurich-Hardau at 30-min resolution - 2023-03-13, 2023-03-13, ICOS Cities, https://hdl.handle.net/11676/mJ-UGEcY4SQnDEFHgfmWD5OJ
BibTex
@misc{https://hdl.handle.net/11676/mJ-UGEcY4SQnDEFHgfmWD5OJ,
  author={Molinier, Betty and Kljun, Natascha},
  title={Footprints for Zurich-Hardau at 30-min resolution - 2023-03-13, 2023-03-13},
  year={2026},
  note={Daily flux footprints for the Zurich-Hardau flux-tower in Switzerland at 30-minute temporal resolution. Each dataset contains up to 48 footprints with the following variables each: Time, X, Y, Boundary Layer Height Quality Flag, and Footprint Climatology. The flux footprints are computed using FFP as described in Kljun et al. (2015), https://doi.org/10.5194/gmd-8-3695-2015, at 10-meter spatial resolution using ETC L2 Fluxes from Hardau, PID: 11676/Lg2RRHAdsLee0lsF-9KsTC-r. The projection can be customized, but the footprints were calculated in UTM 32N.

This work is part of the ICOS Cities/PAUL Pilot Applications in Urban Landscapes project. In this version, footprints have been calculated using the ETC L2 Flux dataset for Hardau PID: 11676/Lg2RRHAdsLee0lsF-9KsTC-r.},
  keywords={Flux footprints, atmospheric modelling, urban flux, ICOS Cities},
  url={https://hdl.handle.net/11676/mJ-UGEcY4SQnDEFHgfmWD5OJ},
  publisher={Carbon Portal},
  copyright={http://meta.icos-cp.eu/ontologies/cpmeta/icosLicence},
  pid={11676/mJ-UGEcY4SQnDEFHgfmWD5OJ}
}
RIS
TY - DATA
T1 - Footprints for Zurich-Hardau at 30-min resolution - 2023-03-13, 2023-03-13
ID - 11676/mJ-UGEcY4SQnDEFHgfmWD5OJ
PY - 2026
AB - Daily flux footprints for the Zurich-Hardau flux-tower in Switzerland at 30-minute temporal resolution. Each dataset contains up to 48 footprints with the following variables each: Time, X, Y, Boundary Layer Height Quality Flag, and Footprint Climatology. The flux footprints are computed using FFP as described in Kljun et al. (2015), https://doi.org/10.5194/gmd-8-3695-2015, at 10-meter spatial resolution using ETC L2 Fluxes from Hardau, PID: 11676/Lg2RRHAdsLee0lsF-9KsTC-r. The projection can be customized, but the footprints were calculated in UTM 32N.

This work is part of the ICOS Cities/PAUL Pilot Applications in Urban Landscapes project. In this version, footprints have been calculated using the ETC L2 Flux dataset for Hardau PID: 11676/Lg2RRHAdsLee0lsF-9KsTC-r.
UR - https://hdl.handle.net/11676/mJ-UGEcY4SQnDEFHgfmWD5OJ
PB - Carbon Portal
AU - Molinier, Betty
AU - Kljun, Natascha
KW - Flux footprints
KW - atmospheric modelling
KW - urban flux
KW - ICOS Cities
ER - 
zurich_footprint_230313.nc
48 MB (50150986 bytes)
3

Production

2026-02-04 11:00:00

Statistics

0

Submission

2026-02-12 15:17:00
2026-02-12 15:16:56

Technical information

989f94184718e124270c414781f9960f93896469dae3bec5398f77ea15a205b1
mJ+UGEcY4SQnDEFHgfmWD5OJZGna477FOY936hWiBbE
S: 47.32634, W: 8.429202, N: 47.436353, E: 8.591174
Flux footprints ICOS Cities atmospheric modelling urban flux
3 pilot cities
15 countries
15 cities
30 scientific partners
13M€ funding
2021-2025
EU flag PAUL, Pilot Applications in Urban Landscapes - Towards integrated city observatories for greenhouse gases (ICOS Cities), has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 101037319.