Winter air temperature and wind speed data from paired open and forest low-cost meteorological stations (Q8751)

The diurnal cycle of both air temperature and wind speed is reflected by considerable differences if open site conditions are compared to forests. This new two-hourly, open dataset covering a high spatial and temporal variability, enables multiple purposes and capabilities due to its diversity and sample size. The dataset provides station pairs, each consisting of one station in the open field and one related station in the forest, located in central Europe, more precisely in southern Germany in the Black Forest (Kinzig; Breg; Brugga) and the Bavarian Alps (Dreisularbach; Nationalpark Berchtesgaden) as well as the Austrian Alps (Brixenbachtal). Associated meta data specify parameters tocharacterizethe environment and the reference between the paired stations. The air temperature measurements consist of128 station pairs from6 winter seasons and6 different study sites witha total amount of 173682 (time steps with availability of open and forest values). Thewind speed measurements consist of64 station pairs from3 winter seasons and4 different study sites witha total amount of 115211. The dataset was initially collected to study the spatio-temporal variability of micrometeorological variables describing the energy balance of the snowpack, but is provided for multiple purposes as examining forest effects on micrometeorological data, validating climate or snow models as well as developing new transfer functions. Boundary conditions are given below and a comprehensive description of the dataset including analyses and applications follows in the open access article: Klein, M.; Garvelmann, J.; Frster, K. Revisiting Forest Effects on Winter Air Temperature and Wind SpeedNew Open Data and Transfer Functions.Atmosphere2021,12, 710. https://doi.org/10.3390/atmos12060710 Meta data The meta data consists of 12 descriptive characteristics.Pair_IDgives an identification name which includes the year of sampling and the acronym of the study site as well as both stations. TheLocationparameter is a local description of the study site.Elevation,ExposureandSlopehave values for the open and forest stations, whileEffective_LAI,Canopy_OpennessandDistance_Forest_Edgestands for the forest station. WithDistance_Open_Stationthe distance between both stations is designated. TheExposureparameter is defined counterclockwise as follows: 0 and 360 is north, 90 is west and consequently 180 is south and 270 east. Only a few parameters ofDistance_Forest_EdgeandDistance_Open_Stationare not available. These values are marked with NA. Pair_ID: Identification of the station pair [-] Location:Local description [-] Elevation_Open: Elevation in the open field [ma.s.l.] Elevation_Forest: Elevation in the forest [ma.s.l.] Exposure_Open: Exposure in the open field counterclockwise (0/360 = north; 90 = west, etc.) Exposure_Forest: Exposure in the forest counterclockwise (0/360 = north; 90 = west, etc.) Slope_Open: Slope in the open field [] Slope_Forest: Slope in the forest [] Effective_LAI: Effective leaf area per ground area [-] Canopy_Openness: Openness of the forest canopy [%] Distance_Forest_Edge:Distance of the forest station to the closed forest edge [m] Distance_Forest_Station: Distance between the paired stations [m] Time series data The time series data consists of air temperature datasets and wind speed datasets, which are named after thePair_IDdescribed above. According to the two-hour intervals, there are 12 measurements per day. The datasets are structured in the same way as follows: The time stamp (Heading:Date), the measurement in the open (Heading:Air_Temp_Open;Wind_Open) and the measurement in the forest (Heading:Air_Temp_Forest;Wind_Forest). Missing values are marked with NA.Remaining information in terms of number of stations, distribution of observations concerning the study sites and winter seasons, the absolute number of available measurements of both stations as well asadditional information arelisted following. Air temperature 128 station pairs (73 open; 59 forest) Kinzig KIN (9 station pairs/2012; 10 station pairs/2013) Breg BRE (7 station pairs/2012; 9 station pairs/2013; 8 station pairs/2014) Brugga BRU (5 station pairs/2013; 14 station pairs/2014; 5station pairs/2015) Brixenbachtal BRX (3 station pairs/2015) Dreisulerbach DSB (7 station pairs/2016; 3 station pairs/2017) Nationalpark Berchtesgaden NPB (8 station pairs/2015; 26station pairs/2016; 14 station pairs/2017) 173 682 total measurements with both values available Variables: Date [yyyy-MM-dd hh:mm:ss]; Air_Temp_Open [C]; Air_Temp_Forest [C] 2 h time interval between measurements Indication for missing value: NA Additional information: Air temperature values measured at open stations corrected for radiative heating. Near surface wind speed is measured at 2 m above surface. Wind speed 64 station pairs (27 open; 34 forest) Brugga BRU (5 station pairs/2015) Brixenbachtal BRX (3 station pairs/2015) Dreisulerbach DSB (7 station pairs/2016; 3 station pairs/2017) Nationalpark Berchtesgaden NPB (7 station pairs/2015; 25 station pairs/2016; 14 station pairs/2017) 115 211 total measurements with both values available Variables: Date [yyyy-MM-dd hh:mm:ss]; Wind_Open [ms-1]; Wind_Forest [ms-1] 2 h time interval between measurements Indication for missing value: NA Additional information: Near surface wind speed is measured at 2 m above surface. Author Contributions JG led and supervised the field work to collect the data and compiled the dataset. Editing and preparation referring to the publication by JG, MK and KF. Acknowledgements The presented data was collected during the following research projects: Field Observations and Modelling of Spatial and Temporal Variability of Processes Controlling Basin Runoff during Rain on Snow Events funded by the German Research Foundation (DFG) and carried out at the Chair of Hydrology (PI Stefan Pohl), University of Freiburg, Germany; Alpine water resources research: Observing and modeling the spatio-temporal variability of snow dynamics and water- and energy fluxes funded by Helmholtz Water Alliance and carried out at the Institute of Meteorology and Climate Research (IMK-IFU, PI Jakob Garvelmann, research group Harald Kunstmann), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany. Technical infrastructure from TERENO; Storylines of Socio-Economic and Climatic drivers for Land use and their hydrological impacts in Alpine Catchments (STELLA) funded by the Austrian climate and energy fond and carried out at the Institute of Geography (PI Ulrich Strasser), University of Innsbruck, Austria. Many thanks to Daniel Gnther, Franziska Zieger, Michael Warscher and others for assistance in field work and Emil Blattmann and the staff from KIT-Campus Alpin for technical support. At the University of Innsbruck Elisabeth Mair led the field work within the STELLA-project. Furthermore, we would like to thank Nationalpark of Berchtesgaden for supporting the micrometeorological and snow hydrological measurement campaign.