2cdf599e-5f25-4065-9e1f-821b17ee2dc8 Mauro Mazzola CNR mauro.mazzola@cnr.it 2025-05-12T14:24:58 ISO 19139 1.0 WGS84 Climate Change Tower Turbulence Data 2022-11-15 2022-11-15 2022-11-15 https://metadata.iadc.cnr.it/geonetwork/srv/api/records/2cdf599e-5f25-4065-9e1f-821b17ee2dc8 The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere. Turbulent parameters are measured at the Amundsen-Nobile Climate Change Tower (CCT) by means of a Gill R3 sonic anemometer installed at 7.5 m from the ground since 2010. It measures the three components of the wind (u, v and w) and the sonic temperature at a rate of 20 Hz. These micro-meteorological measurements are complemented by standard meteorological ones at 4 levels: 2, 5, 10 and 33 m (acquisition time step equal to 1 minute). From these measurements, sensible heat flux, friction velocity and roughness length are calculated. Wind components and sonic temperature measurements were used to estimate friction velocity and kinematic heat flux. Before computing the micrometeorological parameters, a preliminary analysis is applied in order to assess the data quality and to remove low quality records. After the quality analysis application, mean values of the turbulence statistics were computed following two coordinate rotations to ensure the mean lateral and vertical velocities were zero (McMillen, 1988). Half-hour turbulent statistics (heat fluxes and friction velocity) were derived using two time-scales: a standard averaging time of 30 min and a reduced one (2 min) necessary for filtering out submeso motions contributions that can greatly alter the estimation of turbulent fluxes in a strong and long-lived stable BL. The short averaging time scale was evaluated on the basis of spectral analysis of data in order to include all turbulent scales, but excluding submeso motions (larger than turbulence). The turbulent statistics evaluated over the short subsets and then re-averaged over 30 min following Vickers and Mahrt (2006). Turbulent parameter relative to unfavorable wind direction ([150÷270] degrees) for which the tower was upwind of the sonic anemometer were not discarded but are flagged (flagdir=1) in the final dataset. More, the percentage of NaNs relative to each run is indicated. The wind speed vertical profile measured by slow response standard meteorological anemometers at 2, 5, 10 and 33 m was used for estimating the roughness length assuming a typical log wind profile under statically neutral conditions. Mahrt, L., 1998. Flux Sampling Errors for aircraft and towers. J. Atmos. Ocean. Technol. 15, 416-429. Mc Millen, R.T., 1988. An Eddy correlation technique with extended applicability to non-simple terrain. Boundary-Layer Meteorol. 43, 231-245. Vickers D, Mahrt L. 2006. A solution for flux contamination by mesoscale motions with very weak turbulence. Boundary-Layer Meteorol. 118: 431–447. https://doi.org/10.1007/s10546-005-9003-y. Zahn, E., Chor, T.L., Dias, N. L., 2016. A Simple Methodology for Quality Control of Micrometeorological Datasets. American Journal of Environmental Engineering 6(4A): 135-142 DOI: 10.5923/s.ajee.201601.20. These boundary layer (BL) parameters describe the interaction of the mean flow with the surface and the energy and momentum exchanges with the atmosphere. Apart from being essential to the definition of the stability function in the Monin-Obukhov similarity theory, these local measurements can be used in meteorological or climatological reanalyzes (e.g. ERA5). Further, global and regional climate models are extremely sensitive to a correct representation of atmospheric energy fluxes, especially over snow-covered polar surfaces, where atmospheric conditions can become strongly and persistently stable. Angelo Pietro Viola CNR-ISP angelopietro.viola@cnr.it Mauro Mazzola CNR-ISP mauro.mazzola@cnr.it Vito Vitale CNR-ISP vito.vitale@cnr.it Daniela Cava CNR-ISAC d.cava@isac.cnr.it Luca Mortarini CNR-ISAC l.mortarini@isac.cnr.it Francesco Tampieri CNR-ISAC f.tampieri@isac.cnr.it https://metadata.iadc.cnr.it/geonetwork/srv/api/records/2cdf599e-5f25-4065-9e1f-821b17ee2dc8/attachments/turbulence.jpg SIOS Atmospheric conditions GEMET - INSPIRE themes, version 1.0 2008-01-01 geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme AIR TEMPERATURE BOUNDARY LAYER WINDS HUMIDITY ATMOSPHERIC PRESSURE SURFACE WINDS TURBULENCE ATMOSPHERIC WINDS BOUNDARY LAYER TEMPERATURE SURFACE ROUGHNESS HEAT FLUX GCMD - Science Keywords 2015-01-01 geonetwork.thesaurus.external.theme.gcmd-sciencekeywords Hygrometers BAROMETERS THERMOMETERS SONIC ANEMOMETER ANEMOMETERS GCMD - Instruments 2023-10-17 geonetwork.thesaurus.external.instrument.gcmd-instruments SVALBARD AND JAN MAYEN ARCTIC GCMD - Locations 2023-10-17 geonetwork.thesaurus.external.place.gcmd-locations ITALY GCMD - Providers 2023-10-17 geonetwork.thesaurus.external.dataCentre.gcmd-providers Chemistry and physics of the atmosphere IADC Research Activities 2023-10-17 geonetwork.thesaurus.external.theme.iadc-research-activities   Creative-Commons CC BY-NC-SA 4.0         IADC Italian Arctic Data Center 1 climatologyMeteorologyAtmosphere 11.86 11.88 78.921 78.924 2009-10-15 2050-12-31 ISO19139 1 IADC IADC info@iadc.cnr.it https://iadc.cnr.it https://metadata.iadc.cnr.it/geonetwork/srv/api/records/2cdf599e-5f25-4065-9e1f-821b17ee2dc8 WWW:LINK-1.0-http--link Landing page https://data.iadc.cnr.it/erddap/tabledap/turbulence_cct WWW:LINK-1.0-http--opendap OPeNDAP URL Climate Change Tower Turbulence Data https://data.iadc.cnr.it/erddap/tabledap/turbulence_cct.nc WWW:DOWNLOAD-1.0-http--download NetCDF Direct Download https://www.researchinsvalbard.no/project/20000000-0000-0000-0000-000000006507/project-info WWW:LINK-1.0-http--link Project RIS COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 This data set is conformant with the INSPIRE Implementing Rules for the interoperability of spatial data sets and services true Wind components and sonic temperature measurements were used to estimate friction velocity and kinematic heat flux. Before computing the micrometeorological parameters, a preliminary analysis is applied in order to assess the data quality and to remove low quality records. After the quality analysis application, mean values of the turbulence statistics were computed following two coordinate rotations to ensure the mean lateral and vertical velocities were zero (McMillen, 1988). Half-hour turbulent statistics (heat fluxes and friction velocity) were derived using two time-scales: a standard averaging time of 30 min and a reduced one (2 min) necessary for filtering out submeso motions contributions that can greatly alter the estimation of turbulent fluxes in a strong and long-lived stable BL. The short averaging time scale was evaluated on the basis of spectral analysis of data in order to include all turbulent scales, but excluding submeso motions (larger than turbulence). The turbulent statistics evaluated over the short subsets and then re-averaged over 30 min following Vickers and Mahrt (2006). Turbulent parameter relative to unfavorable wind direction ([150÷270] degrees) for which the tower was upwind of the sonic anemometer were not discarded but are flagged (flagdir=1) in the final dataset. More, the percentage of NaNs relative to each run is indicated.