{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T13:34:12Z","timestamp":1783172052915,"version":"3.54.6"},"reference-count":156,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T00:00:00Z","timestamp":1529625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["727890"],"award-info":[{"award-number":["727890"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called \u201cA European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction\u201d. Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting\/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.<\/jats:p>","DOI":"10.3390\/s18072016","type":"journal-article","created":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T10:56:28Z","timestamp":1529664988000},"page":"2016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["European In-Situ Snow Measurements: Practices and Purposes"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3215-4592","authenticated-orcid":false,"given":"Roberta","family":"Pirazzini","sequence":"first","affiliation":[{"name":"Finnish Meteorological Institute (FMI), FI-00101 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leena","family":"Lepp\u00e4nen","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute (FMI), FI-00101 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1475-5853","authenticated-orcid":false,"given":"Ghislain","family":"Picard","sequence":"additional","affiliation":[{"name":"UGA, CNRS, Institut des G\u00e9osciences de l\u2019Environnement (IGE), UMR 5001, F-38041 Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7270-9313","authenticated-orcid":false,"given":"Juan Ignacio","family":"Lopez-Moreno","sequence":"additional","affiliation":[{"name":"Instituto Pirenaico de Ecolog\u00eda, CSIC, 50059 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-6253","authenticated-orcid":false,"given":"Christoph","family":"Marty","sequence":"additional","affiliation":[{"name":"WSL Institute for Snow and Avalanche Research (SLF), CH-7260 Davos Dorf, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-7939","authenticated-orcid":false,"given":"Giovanni","family":"Macelloni","sequence":"additional","affiliation":[{"name":"CNR, Institute of Applied Physics \u201cNello Carrara\u201d (IFAC), 50019 Sesto Fiorentino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Kontu","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute (FMI), FI-00101 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annakaisa","family":"Von Lerber","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute (FMI), FI-00101 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0678-2608","authenticated-orcid":false,"given":"Cemal Melih","family":"Tanis","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute (FMI), FI-00101 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2872-4409","authenticated-orcid":false,"given":"Martin","family":"Schneebeli","sequence":"additional","affiliation":[{"name":"WSL Institute for Snow and Avalanche Research (SLF), CH-7260 Davos Dorf, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7374-3820","authenticated-orcid":false,"given":"Patricia","family":"De Rosnay","sequence":"additional","affiliation":[{"name":"European Centre for Medium Range-Weather Forecasts (ECMWF), Reading RG2 9AX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1256-0163","authenticated-orcid":false,"given":"Ali Nadir","family":"Arslan","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute (FMI), FI-00101 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/532017a","article-title":"Snow sensors seek best way to track the white stuff","volume":"532","author":"Witze","year":"2016","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2124","DOI":"10.1175\/2008JCLI2665.1","article-title":"The Response of Northern Hemisphere Snow Cover to a Changing Climate","volume":"22","author":"Brown","year":"2009","journal-title":"J. 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