S. Noe, A. Krasnova, D. Krasnov, H. Peter, E. Cordey, A. Kangur
{"title":"Facilitating long-term 3D sonic anemometer measurements in hemiboreal forest ecosystems","authors":"S. Noe, A. Krasnova, D. Krasnov, H. Peter, E. Cordey, A. Kangur","doi":"10.2478/fsmu-2021-0016","DOIUrl":null,"url":null,"abstract":"Abstract Estimations of forests’ carbon sequestration capacity relies on proper assessment of the eddy covariance measurement mast’s footprint. Harsh winter temperatures in Estonia lead to ice formation on 3D sonic anemometer sensor heads and thus induce measurement gaps in the data. To maximise data availability, we use a smart heating algorithm to minimise ice formation on the anemometer sensor heads. Here, we studied the temperature distribution of ice formation on the measurement instruments. Three major temperature ranges were found, between 0°C and −3°C, which is the most abundant temperature range for ice formation, and two temperature regions with peaks around −10°C and −20°C. Our algorithm to prevent ice formation led to very short median heating intervals of about 25 to 30 seconds.","PeriodicalId":35353,"journal":{"name":"Forestry Studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forestry Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/fsmu-2021-0016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Estimations of forests’ carbon sequestration capacity relies on proper assessment of the eddy covariance measurement mast’s footprint. Harsh winter temperatures in Estonia lead to ice formation on 3D sonic anemometer sensor heads and thus induce measurement gaps in the data. To maximise data availability, we use a smart heating algorithm to minimise ice formation on the anemometer sensor heads. Here, we studied the temperature distribution of ice formation on the measurement instruments. Three major temperature ranges were found, between 0°C and −3°C, which is the most abundant temperature range for ice formation, and two temperature regions with peaks around −10°C and −20°C. Our algorithm to prevent ice formation led to very short median heating intervals of about 25 to 30 seconds.