What makes the data different?

Limitations of the data

Radiation data from November to March is not reliable because the sensors lack blowers to prevent snow accumulation. In addition, during most of December and January shade from distant buildings and trees affects the radiation sensors most of the the day as the sun is very low in the sky even at noon. Wind measurements are only useful for research at the field, as tall buildings even if a couple of 100 m away from the anemometer affect the wind flow. Precipitation as snow or sleet is not detected by the weather sensor, and also wind measurements could have been potentially affected by snow and ice accumulation before the summer of 2022 when heating was enabled. Heating was enabled for the BF5 sensor at the same time.

Other variables can be trusted year-round. The intention of this station is mainly to take readings during the growing season. The data in the files include winter values for all variables, including those which are not reliable on many days between mid November and late March, and a few days in April.

Data availability

Available on request from Pedro J. Aphalo through a project repository at the Open Science Foundation site (https://osf.io/e4vau/). If data are used in publications an acknowledgement of source is requested. Collaborations are welcome.

Variables acquired and logged

Here we list the variables that are stored in the logger’s memory at different time intervals, plus time and date-related variables that are computed and adjusted to UTC when the data are imported into R. The data made available at the OSF project site are already processed to correct for time shifts and apply sensor calibrations.

Quantities logged once per minute

Table Data logged once per minute available before computation of derived quantities. In most cases with calibrations applied in the logger. Column ‘summary’ shows how the data are summarised by the datalogger before being stored. (1) Since 2022-06-09, mm per minute instead of accumulated total. (2) Each value from the WTX350 is the mean of 6 measurements, except for wind.
Quantity Summary Units
“TIMESTAMP” sample
“PAR_Den_Avg” mean of 12 values µmol/s/m²
“PAR_BF_tot_Avg” mean of 12 values µmol/s/m²
“PAR_BF_diff_Avg” mean of 12 values µmol/s/m²
“Solar_irrad_Avg” mean of 12 values W/m²
“PTemp_C” mean of 12 values C
“WindSpd_S_WVT” sample (2) m/s
“WindDir_D1_WVT” sample (2) Deg
“WindDir_SD1_WVT” sample (2) Deg
“AirTemp_Avg” sample (2) C
“RelHumidity” sample (2) %
“AirDewPoint” sample (2) C
“AirPressure” sample (2) hPa
“Ramount_Tot” sample (2) mm (1)
“Hamount_Tot” sample (2) hits/cm2/h
“Red_Den_cal_Avg” mean of 12 values µmol/s/m²
“Far_red_Den_cal_Avg” mean of 12 values µmol/s/m²
“RFR_rat_Avg” mean of 12 values mol / mol
“Blue_Den_Avg” mean of 12 values mV
“UVA_Den_Avg” mean of 12 values mV
“UVB_Den_Avg” mean of 12 values mV
“SurfTemp_grnd_Avg” mean of 12 values C
“SurfTemp_veg_Avg” mean of 12 values C
“T107_C_Avg(1-4)” mean of 12 values C
On import to R some quantities are computed and added to the data set. Based on the “TIMESTAMP” stored by the datalogger (and its difference from UTC time) and the geographic coordinates of the station, the sun position and solar time are computed and added to the data. “TIMESTAMP” is deleted and replaced by the quantities in this table. Depending on the date of data acquisition, some calibrations are applied at this stage.
Quantity Summary Units
“time” sample yyyy-mm-dd hh:mm:ss UTC
“day_of_year” sample numeric
“month_of_year” sample 1..12
“month_name” sample character
“calendar_year” sample numeric
“time_of_day” sample numeric
“solar_time” sample numeric
“sun_elevation” sample degrees
“sun_azimuth” sample degrees

Quantities logged once per hour

Table Data logged once per hour available before computation of derived quantities. In most cases with calibrations applied in the logger. Column ‘summary’ shows how the data are summarised by the datalogger before being stored. As for data logged at 1 min intervals, the TIMESTAMP is converted to UTC time. Sun angles are not computed. (1) Since 2022-06-09, mm per minute instead of accumulated total. (2) Each value from the WTX350 is the mean of 6 measurements. (3) Mean of 4 values since 2022-xx-xx.
Quantity Summary Units
“TIMESTAMP” sample
“PAR_Den_Avg” mean of 720 values µmol/s/m²
“PAR_Den_Std” standard deviation of 720 values µmol/s/m²
“PAR_BF_tot_Avg” mean of 720 values µmol/s/m²
“PAR_BF_tot_Std” standard deviation of 720 values µmol/s/m²
“PAR_BF_diff_Avg” mean of 720 values µmol/s/m²
“PAR_BF_diff_Std” standard deviation of 720 values µmol/s/m²
“Solar_irrad_Avg” mean of 720 values W/m²
“Solar_irrad_Std” standard deviation of 720 values W/m²
“Red_Den_cal_Avg” mean of 720 values µmol/s/m²
“Far_red_Den_cal_Avg” mean of 720 values µmol/s/m²
“RFR_rat_Avg” mean of 720 values mol/mol
“RFR_rat_Min” minimum of 720 values mol/mol
“RFR_rat_TMn” time at minimum of 720 values yyyy-mm-dd hh:mm:ss
“RFR_rat_Max” maximum of 720 values mol/mol
“RFR_rat_TMx” time maximum of 720 values yyyy-mm-dd hh:mm:ss
“Blue_Den_Avg” mean of 720 values mV
“UVA_Den_Avg” mean of 720 values mV
“UVB_Den_Avg” mean of 720 values mV
“WindSpd_S_WVT” mean of 60 values (2) m/s
“WindDir_D1_WVT” mean of 60 values (2) Deg
“WindDir_SD1_WVT” mean of 60 values (2) Deg
“AirTemp_Avg” mean of 60 values (2) C
“RelHumidity_Avg” mean of 60 values (2) %
“AirDewPoint_Avg” mean of 60 values (2) C
“AirPressure” mean of 60 values (2) hPa
“Ramount” sum of 60 values (2) mm/h (1)
“Hamount” sum of 60 values (2) hits/cm2/h
“SurfTemp_grnd_Avg” mean of 720 values C
“SurfTemp_veg_Avg” mean of 720 values C
“VWC_[2-8]_Avg” sample or mean of 4 values (3) m3/m3
“EC_[2-8]_Avg” sample or mean of 4 values (3)
“T_[2-8]_Avg” sample or mean of 4 values (3) C
“VWC_5cm_[1-3]” sample or mean of 4 values (3) m3/m3
“Ka_5cm_[1-3]” sample or mean of 4 values (3)
“T_5cm_[1-3]” sample or mean of 4 values (3) C
“BulkEC_5cm_[1-3]” sample or mean of 4 values (3)
“VWC_10cm_[1-3]” sample or mean of 4 values (3) m3/m3
“Ka_10cm_[1-3]” sample or mean of 4 values (3)
“T_10cm_[1-3]” sample or mean of 4 values (3) C
“BulkEC_10cm_[1-3]” sample or mean of 4 values (3)
“VWC_20cm_[1-3]” sample or mean of 4 values (3) m3/m3
“Ka_20cm_[1-3]” sample or mean of 4 values (3)
“T_20cm_[1-3]” sample or mean of 4 values (3) C
“BulkEC_20cm_[1-3]” sample or mean of 4 values (3)
“VWC_30cm_[1-3]” sample or mean of 4 values (3) m3/m3
“Ka_30cm_[1-3]” sample or mean of 4 values (3)
“T_30cm_[1-3]” sample or mean of 4 values (3) C
“BulkEC_30cm_[1-3]” sample or mean of 4 values (3)
“VWC_40cm_[1-3]” sample or mean of 4 values (3) m3/m3
“Ka_40cm_[1-3]” sample or mean of 4 values (3)
“T_40cm_[1-3]” sample or mean of 4 values (3) C
“BulkEC_40cm_[1-3]” sample or mean of 4 values (3)
“VWC_50cm_[1-3]” sample or mean of 4 values (3) m3/m3
“Ka_50cm_[1-3]” sample or mean of 4 values (3)
“T_50cm_[1-3]” sample or mean of 4 values (3) C
“BulkEC_50cm_[1-3]” sample or mean of 4 values (3)

Quantities logged once per day

Table Data logged once per day available before computation of derived quantities. In most cases with initial calibrations applied in the logger. Column ‘summary’ shows how the data are summarized by the datalogger before being stored. As for data logged at 1 h intervals, the TIMESTAMP is converted to UTC time. Sun angles are not computed. For daily data midnight is set at UTC + 2h year round in the logger consistently since 2020, occasionally, in previous years in summer it has been at UTC + 3 h. These periods are known. (These data are not in the repository).
Quantity Summary Units
“TIMESTAMP” sample yyyy-mm-dd
“PAR_Den_Hst(1-25)” histogram with 25 bins µmol/s/m²
“Solar_irrad_Hst(1-25)” histogram with 25 bins µmol/s/m²
“PAR_DenLog_Hst(1-12)” histogram with 12 bins log(µmol/s/m²)
“Solar_irradLog_Hst(1-10)” histogram with 10 bins log(µmol/s/m²)
“PAR_Den_Min” minimum of 172680 values µmol/s/m²
“PAR_Den_TMn” time at minimum of 172680 values yyyy-mm-dd hh:mm:ss
“PAR_Den_Max” maximum of 172680 values µmol/s/m²
“PAR_Den_TMx” time at maximum of 172680 values yyyy-mm-dd hh:mm:ss
“PAR_BF_tot_Min” minimum of 172680 values µmol/s/m²
“PAR_BF_tot_TMn” time at minimum of 172680 values yyyy-mm-dd hh:mm:ss
“PAR_BF_tot_Max” maximum of 172680 values µmol/s/m²
“PAR_BF_tot_TMx” time at maximum of 172680 values yyyy-mm-dd hh:mm:ss
“PAR_BF_diff_Min” minimum of 172680 values µmol/s/m²
“PAR_BF_diff_TMn” time at minimum of 172680 values yyyy-mm-dd hh:mm:ss
“PAR_BF_diff_Max” maximum of 172680 values µmol/s/m²
“PAR_BF_diff_TMx” time at maximum of 172680 values yyyy-mm-dd hh:mm:ss
“AirTemp_Avg” mean of values C
“AirDewPoint_Avg” mean of values C
“AirPressure_Avg” mean of values hPa
“AirTemp_Min” minimum of values C
“AirTemp_TMn” time at minimum of values yyyy-mm-dd hh:mm:ss
“AirTemp_Max” maximum of 172680 values C
“AirTemp_TMx” time at maximum of values yyyy-mm-dd hh:mm:ss
“AirDewPoint_Min” minimum of values C
“AirDewPoint_TMn” time at minimum of values yyyy-mm-dd hh:mm:ss
“AirDewPoint_Max” maximum of values C
“AirDewPoint_TMx” time at maximum of values yyyy-mm-dd hh:mm:ss
“AirPressure_Min” minimum of values C
“AirPressure_TMn” time at minimum of values yyyy-mm-dd hh:mm:ss
“AirPressure_Max” maximum of values C
“AirPressure_TMx” time at maximum of values yyyy-mm-dd hh:mm:ss
“T107_C_Min(1-4)” minimum of 172680 values C
“T107_C_TMn(1-4)” time at minimum of 172680 values yyyy-mm-dd hh:mm:ss
“T107_C_Max(1-4)” maximum of 172680 values C
“T107_C_TMx(1-4)” time at maximum of 172680 values yyyy-mm-dd hh:mm:ss
“SurfTemp_grnd_Min” minimum of 172680 values C
“SurfTemp_grnd_TMn” time at minimum of 172680 values yyyy-mm-dd hh:mm:ss
“SurfTemp_grnd_Max” maximum of 172680 values C
“SurfTemp_grnd_TMx” time at maximum of 172680 values yyyy-mm-dd hh:mm:ss
“SurfTemp_veg_Min” minimum of 172680 values C
“SurfTemp_veg_TMn” time at minimum of 172680 values yyyy-mm-dd hh:mm:ss
“SurfTemp_veg_Max” maximum of 172680 values C
“SurfTemp_veg_TMx” time at maximum of 172680 values yyyy-mm-dd hh:mm:ss
“BattV_Min” minimum of 172680 values V
“PTemp_C_Min” minimum of 172680 values C
“PTemp_C_Max” maximum of 172680 values C

Data including some derived variables

Based on the logged data some additional derived quantities are computed. The position of the sun, local solar time, time of day, estimates of solar irradiance at the top of the atmosphere and estimates of reference evapotranspiration for a short canopy, a tall canopy and according to FAO publication 56 (R package ‘photobiology’, Aphalo 2013-2022). By combining readings from the UVB, UVA and blue broadband sensors we also obtain good estimates of UVA1 and UVA2 irradiances. In the 2020 and 2022 campaigns several times during the snow-free season spectral measurements were done with two different Maya 2000 Pro spectrometers (Ocean Optics) equipped with DH7-SMA cosine diffusers (Bentham) using the protocol of Ylianttila as implemented in R package ‘ooacquire’ (Aphalo 2015-2022). For calibration of the broadband sensors the irradiances corresponding to the different wavelength bands were computed from the measured spectral irradiances in R using packages ‘photobiology’ and ‘photobiologyWavebands’ (Aphalo 2013-2022).

As the data have been logged for different lengths of time for the different variables, when merging the data into a single set, some variables are filled-in with a marker of “not available” (NA). We use R but the processed data are available both as R data files (.rda) and as comma separated values (CSV) as text files compressed with gzip (.csv.gz) at the Open Science Foundation internet site in project High frequency weather data for Viikki, Helsinki, Finland. Text files with metadata (.met) are also available in the repository.

The data set at 1 min interval has 2298362 rows and 58 columns. The variables are series_start, time, day_of_year, month_of_year, month_name, calendar_year, time_of_day, solar_time, sun_elevation, sun_azimuth, PAR_umol, PAR_umol_CS, PAR_umol_BF, PAR_diff_fr, global_watt, red_umol, far_red_umol, red_far_red, blue_umol, blue_sellaro_umol, blue_red, UVA_umol, UVA_PAR, UVA1_PAR, UVA2_PAR, UVB_umol, UVA1_umol, UVA2_umol, UVB_PAR, wind_speed, wind_direction, air_temp_C, air_temp_min_C, air_temp_max_C, air_vp, air_RH, air_DP, air_pressure, rain_mm_min, surf_temp_C, surf_temp_sensor_delta_C, was_sunny, SupplyVoltage_Max, ReferenceVoltage_Min, ReferenceVoltage_Max, BattV_Min, BattV_Max, logged_air_temp_C, air_temp_run_median, temp_surf2air_C, R_0, R_rel, Rn_sw_ref, Rn_ref, ET_ref_FAO56, ET_ref_short, ET_ref_tall, air_vpd.

The data set at 1 h interval has 45079 rows and 22 variables. The variables are time, PAR_umol, PAR_umol_sd, PAR_umol_CS, PAR_umol_CS_sd, PAR_umol_BF, PAR_umol_BF_sd, global_watt, global_watt_sd, wind_speed, wind_direction, air_temp_C, air_RH, air_DP, air_pressure, rain, hail, day_of_year, month_of_year, month_name, calendar_year, solar_time.

The soil data set at 1 h interval has 23190 rows and 103 variables. The variables are time, PAR_umol, PAR_umol_sd, PAR_umol_BF, PAR_umol_BF_sd, global_watt, global_watt_sd, wind_speed, wind_direction, air_temp_C, air_RH, air_DP, air_pressure, rain, hail, day_of_year, month_of_year, month_name, calendar_year, solar_time, VWC_5cm_1, Ka_5cm_1, T_5cm_1, BulkEC_5cm_1, VWC_5cm_2, Ka_5cm_2, T_5cm_2, BulkEC_5cm_2, VWC_5cm_3, Ka_5cm_3, T_5cm_3, BulkEC_5cm_3, VWC_10cm_1, Ka_10cm_1, T_10cm_1, BulkEC_10cm_1, VWC_20cm_1, Ka_20cm_1, T_20cm_1, BulkEC_20cm_1, VWC_30cm_1, Ka_30cm_1, T_30cm_1, BulkEC_30cm_1, VWC_40cm_1, Ka_40cm_1, T_40cm_1, BulkEC_40cm_1, VWC_50cm_1, Ka_50cm_1, T_50cm_1, BulkEC_50cm_1, VWC_10cm_2, Ka_10cm_2, T_10cm_2, BulkEC_10cm_2, VWC_20cm_2, Ka_20cm_2, T_20cm_2, BulkEC_20cm_2, VWC_30cm_2, Ka_30cm_2, T_30cm_2, BulkEC_30cm_2, VWC_40cm_2, Ka_40cm_2, T_40cm_2, BulkEC_40cm_2, VWC_50cm_2, Ka_50cm_2, T_50cm_2, BulkEC_50cm_2, VWC_10cm_3, Ka_10cm_3, T_10cm_3, BulkEC_10cm_3, VWC_20cm_3, Ka_20cm_3, T_20cm_3, BulkEC_20cm_3, VWC_30cm_3, Ka_30cm_3, T_30cm_3, BulkEC_30cm_3, VWC_40cm_3, Ka_40cm_3, T_40cm_3, BulkEC_40cm_3, VWC_50cm_3, Ka_50cm_3, T_50cm_3, BulkEC_50cm_3, PAR_umol_CS, PAR_umol_CS_sd, VWC_1, EC_1, T_1, VWC_2, EC_2, T_2, VWC_3, EC_3, T_3.

Example plots

Using data saved at 1 min interval we can compute empirical density distributions, showing how frequently different values have been observed.

PAR photon irradiance

One minute averages for sun above the horizon of measurements with LI-190 quantum sensor every 5 seconds. During winter-time some snow could have accumulated on the sensor.

## Picking joint bandwidth of 0.0503

## UV-B irradiance

One minute averages for sun above the horizon of measurements with an sglux UV-B sensor every 5 seconds. During winter-time some snow could have accumulated on the sensor.

## Picking joint bandwidth of 0.0665

## UV-B:PAR photon ratio

One minute averages for sun above the horizon of measurements with an sglux UV-B sensor every 5 seconds. During winter-time some snow could have accumulated on the sensor. In this figure the unexpected density distribution in February is most likely the result of more snow accumulation on the UV-A sensor than on the PAR sensor, demonstrating that all radiation data collected at this station during winter months must be considered suspect.

## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous x-axis

Soil temperature profile

Here we fit a quantile regression for soil temperature on soil depth for the 5%, 50% and 95% percentiles. This highlights the range of variation at different depths on different months of the year. The data values, not plotted, are medians from three sensors.

Here we show hourly temperatures at different depths over a few days in 2020, showing the daily warming and cooling of the mostly bare soil. The data values shown are medians from three sensors.