Methodology:

IMAU iWS 18 has sensors for air and surface temperature, air pressure and humidity, as well as an acoustic snow height sensor, a propeller-vane anemometer measuring wind direction and speed, and a radiometer for measuring downward and upward shortwave and longwave radiative fluxes. The Incoming and Reflected SW and LW fluxes provided are corrected values. A bulk-algorithm-based SEB model (described in Kuipers Munneke et al., 2009) has been used to derive surface sensible and latent heat fluxes and the ground heat flux. The energy available for melt is also derived from this model, given as the SEB residual when the surface temperature, Tsfc, is above freezing point. Compared to direct eddy correlation observations of turbulent fluxes (e.g., by using a 3-D ultrasonic anemometer), the bulk method that we apply to the AWS observations yields similar results with a root-mean-square difference of typically 3-4 W m-2 at Antarctic sites experiencing frequent air flow (e.g. Van den Broeke et al., 2005). With the wind sensor being at a height of 2-3 m, the bulk method captures most of the turbulent eddies while at the same time not severely violating the assumption of constant flux in the layer between the surface and the instrument height. For further details see Elvidge et al. (2020).

Data collection:

The iWS consists of a custom-built weather station unit, assembled at the Institute of Marine and Atmospheric research Utrecht (IMAU). The unit consists of a 3D-printed plastic housing with an array of miniature solar panels on top. In the housing, there are sensors for air temperature, surface air pressure, relative humidity, as well as a GPS, an acoustic snow height sensor, an ARGOS communication antenna, and three Lithium batteries that fuel the unit when solar radiation is absent. The unit is complemented by a propeller-vane Young anemometer measuring wind direction and speed. Additionally, all radiation fluxes are measured with a Kipp and Zonen CNR4 radiometer.

Due to malfunctioning, no subsurface temperature measurements are available.

Data quality:

A number of corrections and adjustments to the raw data were applied for this dataset. For further details see Elvidge et al. (2020).

1) The temperature sensor is not artificially ventilated. This leads to a positive bias in temperature measurements under conditions of high insolation and low wind speed. Simultaneous observations from a thin-wire thermocouple provided a correction procedure, detailed in Smeets et al. (2018).

2) The relative humidity sensor is placed within the unit and experiences elevated temperature under sunny conditions. Internally-observed temperature of the sensor allows the calculation of a dew point, which gives a relative humidity for the observed air temperature. Next, relative humidity is corrected for observations over ice rather than over water. And finally, a well-known underestimation of RH-observations at low temperatures is corrected for by fitting a polynomial through the upper percentile of data and define that polynomial as a RH of 100%. Relative humidity can sometimes greatly exceed 100%, especially in very cold conditions. It is unknown if this is a sensor characteristic or real supersaturation. If required, RH can be capped at 100% without a large error in the corresponding absolute, specific humidity.

3) Longwave radiation observations are corrected for the internal instrument temperature, and for window heating under high insolation conditions.

4) Acoustic snow height observations are corrected for the dependency of sound propagation on air temperature.

5) A few small data 30- to 60-minute data gaps were filled by linear interpolation.