Articles | Volume 11, issue 10
Atmos. Meas. Tech., 11, 5421–5438, 2018
https://doi.org/10.5194/amt-11-5421-2018
Atmos. Meas. Tech., 11, 5421–5438, 2018
https://doi.org/10.5194/amt-11-5421-2018

Research article 02 Oct 2018

Research article | 02 Oct 2018

Uncertainty of eddy covariance flux measurements over an urban area based on two towers

Leena Järvi et al.

Related authors

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
Geosci. Model Dev., 14, 7411–7424, https://doi.org/10.5194/gmd-14-7411-2021,https://doi.org/10.5194/gmd-14-7411-2021, 2021
Short summary
Carbon sequestration potential of street tree plantings in Helsinki
Minttu Havu, Liisa Kulmala, Pasi Kolari, Timo Vesala, Anu Riikonen, and Leena Järvi
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-242,https://doi.org/10.5194/bg-2021-242, 2021
Preprint under review for BG
Short summary
Sensitivity of spatial aerosol particle distributions to the boundary conditions in the PALM model system 6.0
Mona Kurppa, Pontus Roldin, Jani Strömberg, Anna Balling, Sasu Karttunen, Heino Kuuluvainen, Jarkko V. Niemi, Liisa Pirjola, Topi Rönkkö, Hilkka Timonen, Antti Hellsten, and Leena Järvi
Geosci. Model Dev., 13, 5663–5685, https://doi.org/10.5194/gmd-13-5663-2020,https://doi.org/10.5194/gmd-13-5663-2020, 2020
Short summary
Simulation of the radiative effect of haze on the urban hydrological cycle using reanalysis data in Beijing
Tom V. Kokkonen, Sue Grimmond, Sonja Murto, Huizhi Liu, Anu-Maija Sundström, and Leena Järvi
Atmos. Chem. Phys., 19, 7001–7017, https://doi.org/10.5194/acp-19-7001-2019,https://doi.org/10.5194/acp-19-7001-2019, 2019
Short summary
Implementation of the sectional aerosol module SALSA2.0 into the PALM model system 6.0: model development and first evaluation
Mona Kurppa, Antti Hellsten, Pontus Roldin, Harri Kokkola, Juha Tonttila, Mikko Auvinen, Christoph Kent, Prashant Kumar, Björn Maronga, and Leena Järvi
Geosci. Model Dev., 12, 1403–1422, https://doi.org/10.5194/gmd-12-1403-2019,https://doi.org/10.5194/gmd-12-1403-2019, 2019
Short summary

Related subject area

Subject: Gases | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Importance of the Webb, Pearman, and Leuning (WPL) correction for the measurement of small CO2 fluxes
Katharina Jentzsch, Julia Boike, and Thomas Foken
Atmos. Meas. Tech., 14, 7291–7296, https://doi.org/10.5194/amt-14-7291-2021,https://doi.org/10.5194/amt-14-7291-2021, 2021
Short summary
Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors
Seán Schmitz, Sherry Towers, Guillermo Villena, Alexandre Caseiro, Robert Wegener, Dieter Klemp, Ines Langer, Fred Meier, and Erika von Schneidemesser
Atmos. Meas. Tech., 14, 7221–7241, https://doi.org/10.5194/amt-14-7221-2021,https://doi.org/10.5194/amt-14-7221-2021, 2021
Short summary
An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations
Alex Resovsky, Michel Ramonet, Leonard Rivier, Jerome Tarniewicz, Philippe Ciais, Martin Steinbacher, Ivan Mammarella, Meelis Mölder, Michal Heliasz, Dagmar Kubistin, Matthias Lindauer, Jennifer Müller-Williams, Sebastien Conil, and Richard Engelen
Atmos. Meas. Tech., 14, 6119–6135, https://doi.org/10.5194/amt-14-6119-2021,https://doi.org/10.5194/amt-14-6119-2021, 2021
Short summary
Mobile atmospheric measurements and local-scale inverse estimation of the location and rates of brief CH4 and CO2 releases from point sources
Pramod Kumar, Grégoire Broquet, Camille Yver-Kwok, Olivier Laurent, Susan Gichuki, Christopher Caldow, Ford Cropley, Thomas Lauvaux, Michel Ramonet, Guillaume Berthe, Frédéric Martin, Olivier Duclaux, Catherine Juery, Caroline Bouchet, and Philippe Ciais
Atmos. Meas. Tech., 14, 5987–6003, https://doi.org/10.5194/amt-14-5987-2021,https://doi.org/10.5194/amt-14-5987-2021, 2021
Short summary
SIBaR: a new method for background quantification and removal from mobile air pollution measurements
Blake Actkinson, Katherine Ensor, and Robert J. Griffin
Atmos. Meas. Tech., 14, 5809–5821, https://doi.org/10.5194/amt-14-5809-2021,https://doi.org/10.5194/amt-14-5809-2021, 2021
Short summary

Cited articles

Ao, X., Grimmond, C., Chang, Y., Liu, D., Tang, Y., Hu, P., Wang, Y., Zou, J., and Tan, J.: Heat, water and carbon exchanges in the tall megacity of Shanghai: challenges and results, Int. J. Climatol., 36, 4608–4624, https://doi.org/10.1002/joc.4657, 2016. a
Auvinen, M., Järvi, L., Hellsten, A., Rannik, Ü., and Vesala, T.: Numerical framework for the computation of urban flux footprints employing large-eddy simulation and Lagrangian stochastic modeling, Geosci. Model Dev., 10, 4187–4205, https://doi.org/10.5194/gmd-10-4187-2017, 2017. a
Barlow, J., Harrison, J., Robins, A., and Wood, C.: A wind-tunnel study of flow distortion at a meteorological sensor on top of the BT Tower, London, UK, J. Wind Eng. Ind. Aerod., 99, 899–907, https://doi.org/10.1016/j.jweia.2011.05.001, 2011. a, b, c
Billesbach, D.: Estimating uncertainties in individual eddy covariance flux measurements: A comparison of methods and a proposed new method, Agr. Forest Meteorol., 151, 394–405, https://doi.org/10.1016/j.agrformet.2010.12.001, 2011. a
Brümmer, B., Lange, I., and Konow, H.: Atmospheric boundary layer measurements at the 280 m high Hamburg weather mast 1995–2011: mean annual and diurnal cycles, Meteorol. Z., 21, 319–335, https://doi.org/10.1127/0941-2948/2012/0338, 2013. a
Download
Short summary
Identical EC systems on two sides of a building in central Helsinki were used to assess the uncertainty of the vertical fluxes on the single measurement point from July 2013 to September 2015. Sampling at only one point yielded up to 12% underestimation in the cumulative carbon fluxes; for sensible and latent heat the respective values were up to 5 and 8%. The commonly used statistics, kurtosis and skewness, are not necessarily suitable for filtering out data in a densely built urban area.