- Time interval for acquisition of measurements: 5 s.
- Time interval for logging of summaries: 1 min, 1 h, 1 d.
- Solar radiation measurement: currently seven types of sensors.
- Soil measurements as a depth profile.
- For research at the experimental field of the Viikki campus it provides on-site 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.**

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.

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.

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 |

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 |

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) |

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 |

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.

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

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`

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.