PROMICE automatic weather station data

The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Currently the PROMICE automatic weather station network includes 25 instrumented sites in Greenland. Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate is important for reliable present and future assessment of changes to the Greenland ice sheet. Here we present the PROMICE vision, methodology, and each link in the production chain for obtaining and sharing quality-checked data. In this paper we mainly focus on the 5 critical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic webbased database of known data quality issues is associated with the data products at (https://github.com/GEUS-PROMICE/ PROMICE-AWS-data-issues/). As part of the living data option, the datasets presented and described here are available at DOI: 10.22008/promice/data/aws, https://doi.org/10.22008/promice/data/aws (Fausto and van As, 2019).

The aim of this paper is to describe the PROMICE AWS dataset in detail. We discuss the measurement with insights into 5 post-processing and sensor calibration. The dataset is freely available at www.promice.org (DOI:10.22008/promice/data/aws).
We start with a description on how to construct the AWSs, followed by a technical description of the AWS instruments, the data production chain, examples of typical station measurements, and finally a summary and outlook.
2 The AWS design 2.1 The tripod 10 The AWS tripod is constructed from 32 mm (1.25 ) and 44 mm (1.75 ) radius aluminium tubes with 3 mm braided stainless steel wires forming a free-standing tetrahedral structure that connects legs and mast in a stable tripod ( Figure 2). Most sensors are attached to the 1.7 m long horizontal boom, which is 2.7 m above surface (Figure 2). Weighing c. 50 kg, the battery box, is hanging under the mast to increase the mass of the AWS and to lower its center of gravity for better stability (Table 1). The tripod can easily be folded to fit in small helicopters. The tripod can also be tilted during maintenance visit for e.g. sensor 15 replacement. Maintenance visits typically take 2-4 hours, which include sensor replacement due for factory recalibration, redrilling installed sensors in ice, and occasional repairs. Because the tripod is standing freely on the ice surface, it sinks with the melting surface, which results in sonic ranger measurements on the AWS do not capture ice melt. Therefore each PROMICE AWS on ice is accompanied by a separate sonic ranger stake assembly constructed from 32 mm aluminum tubing, typically drilled 7 m into the ice that does not float with on the ice (Figure 2). 20

Instrumentation and data transmission
The PROMICE AWS measures 1) the meteorological parameters required for calculating the surface energy budget, 2) snow and ice ablation/accumulation, 3) sub-surface temperature at 8 depths (Thermistor string, Figure 2), and 4) position by GPS.
The next section provides details on the frequency and accuracy of measurements taken by each sensor. Further sensor details are provided in the Appendix. 25 Measurements are taken every 10 minutes and stored in the data logger locally. The AWSs transmit hourly averages based on 10-minute measurements during the period with ample solar power, between day-of-year 100 and 300 (10 April and 26 October in non-leap years). Exceptions are parameters with low variability (GPS position, station tilt, surface height, etc.) that are transmitted less frequently (every 6 hours) in order to reduce transmission cost. In winter, between day-of-year 300 and 30 100, the stations only transmit daily averages of all parameters to limit power consumption by the satellite modem. Trans-3 https://doi.org /10.5194/essd-2021-80 Open Access Earth System Science Data Discussions Preprint. Discussion started: 19 March 2021 c Author(s) 2021. CC BY 4.0 License. mission is done through the Iridium satellite network that has coverage even at the northernmost latitudes. The Iridium short burst service transmits up to 340 bytes per message. The program running on the data logger ensures a correctly transferred data string from the logger to the transmitter if an Iridium satellite is in view. In case of no successful transmission through the satellite the logger program will try again. Depending on the availability of the Iridium service the logger program can also queue the message for delivery at a later time with better satellite connection. This relatively low power operation mode 5 ensures unnecessary transmission attempts with a low rate of message loss. Moreover, the logger program encode the data in a binary format before transmission, which reduces the size of the message and by that transmission costs with about 2/3.
To ensure reliable and accurate measurements, instruments in the field are swapped following an instrument maintenance schedule based on information from manufacturers and from experience, for instance with battery life and performance, when 10 charging batteries without a charge regulator. The maintenance schedule is a guideline; not always does a field crew return to an AWS in time for a scheduled sensor swap. For example, we only visit the AWSs in the north-eastern part of Greenland (KPC, 1) every 3-4 years as their remoteness weighs heavily on the logistics budget. Thankfully, the most remote PROMICE AWSs experience less melt, lower accumulation and weaker storms than some other places, reducing the need for maintenance visits. PROMICE AWS data are processed by the production chain algorithm with some manual expert quality checking twice a year (typically in January and after the summer), and in real time with automated quality check in the PROMICE database. For our production chain algorithm, we make use of the raw data recorded every 10 minutes, retrieved from the data logger during 20 maintenance visits (Figure 3). For the period since the last station visit we use the transmitted data for the PROMICE data products. Beside the direct AWS measurements, we also calculate certain variables based on these measurements, for instance tilt-corrected solar radiation and turbulent heat fluxes. In the following, we describe each variable in the PROMICE AWS dataset, and how it is measured or derived. We refer to the manufacturer specific instrument information, accuracy, and power consumption (see Table 2 and sensor specific tables in Appendix). We use simple thresholds on 10-min data to remove spikes 25 and inconsistent or bad measurements (see section "Post processing" below for more information). Available transmitted data are used for filling out data gaps.

Measured variables: description and uncertainty
For most measured variables, the data logger converts readings in voltage to physical values using simple scaling relations with calibration coefficients specific for each instrument. Only when identical sensors can have different calibration coefficients, 30 namely the radiometer and pressure transducer, the conversion from voltage is done in post-processing; the advantage being 4 https://doi.org /10.5194/essd-2021-80  that a sensor swap does not require a data logger program change in the field. Below, we mention all scaling relations needed to manually convert logger data to physical measurements.

Air pressure
Barometric pressure (unit: hPa) is measured in the fiberglass-reinforced polyester logger enclosure (Figure 2, number 9). The logger enclosure is generally located 1.5 m above the ice surface. The barometer manufacturer reports a measurement accuracy 5 of ±2 hPa within the -40 to +60°C temperature range (Table 2, see also Appendix for more information).

Air temperature
Air temperature (unit:°C) is measured inside a fan-aspirated radiation shield ( Figure 2, number 6). The sensor is located approximately 2.6 m above the ice surface, i.e. as high as possible underneath the sensor boom. The measurement height varies when a winter snow cover is present. The temperature sensor is a PT100 probe that changes its electrical resistance 10 with temperature and has an accuracy of ±0.1°C (Table 2, see also Appendix for more information). Also a secondary air temperature reading (°C) is made in the aspirated shield from the Hygroclip temperature/humidity sensor described in the following, also with a manufacturer stated accuracy of ±0.1°C, but we consider the Hygroclip temperature to be less accurate than the PT100, given the need for more frequent sensor re-calibrations.

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Relative humidity (RH) (unit: %) is measured alongside the PT100 in the aspirated radiation shield using a HC2A-S3 (or HC2) "Hygroclip" (Figure 2, number 6). The sensor measures relative humidity with ±0.8 % accuracy. Relative humidity is measured relative to water. For temperatures below freezing, relative humidity is recalculated relative to ice in post-processing (see section 3.3). To distinguish between the two relative humidities in the PROMICE data products, the prior humidity (unadjusted below freezing) is called "relative humidity with respect to water", while the latter simply is "relative humidity". The conversion 20 of relative humidity relative to ice is after Goff and Gratch (1946). Every 1-2 years, the Hygroclip is replaced by a sensor re-calibrated in a closed chamber at room temperature with constant relative humidities of 10, 35, 80 %.

Wind speed and direction
Wind speed and direction (Units: m s −1 and • ) measurement height is approximately 3.1 m above the ice surface, and like the other measurements has a reduced measurement height in case a winter snow layer is present (Figure 2, number 4). An AC sine 25 wave voltage signal is produced by the rotation of the four-bladed propeller, and the pulse count converts to wind speed using a multiplier. According to the manufacturer the sensor can measure wind speeds between 0 and 100 m s −1 , with an accuracy of ±0.3 m s −1 or 1% if the measured value is higher than 30 m s −1 .
Wind direction is measured through changes in the vane angle by a precision potentiometer housed in a sealed chamber on the instrument. The output voltage is directly proportional to vane angle wind direction is measured between 0 -360°with an accuracy of ±3°. Every three years the sensor is replaced and tested for drift and functionality with an "anemometer drive" rotating the propeller at a known rate. The instrument's orientation is logged and reset to "Geographic north " during each maintenance visit to keep wind direction data accurate within ±15°(although much larger station rotations have been encountered). Measurement height is at the sensor boom level of 2.7 m over the ice surface ( Figure 2, number 1). Shortwave radiation is measured by the pyranometers within plastic meniscus domes, allowing minimal water droplet adhesion. The manufacturer reports that sensor uncertainty is 10%. This sensor uncertainty has in practice been found to be ca. 5% for daily totals in Antarctica ((van den Broeke et al., 2004)). The radiometers are recalibrated at Kipp & Zonen every three years. The radiometer 10 is one of the few variables stored in the data logger in voltage units, because every radiometer has a different set of calibration coefficients, whereas all logger programs running on PROMICE AWSs are identical, for practical reasons. In post-processing, sensor readings SR_raw are converted into a physical measurement SR_m following:

Upward and downward shortwave radiation
where C SR V (W m −2 ) −1 is a sensor calibration coefficient and SR_m is either the converted downward and upward shortwave

Upward and downward longwave radiation
Longwave radiation (units: W m −2 ) is also measured by the CNR1/CNR4 radiometer mounted at approximately 2.7 m over the ice surface ( Figure 2, number 1). The radiometer contains a pair of up-and downfacing pyrgeometers, with a spectral range 20 of 4.5 to 42 µm. As for shortwave radiation, longwave radiation is stored in voltage units (LR_raw) in the data logger, and transformed to physical units (LR_m) in post-processing following: where C LR V (W m −2 ) −1 is the sensor calibration coefficient. T rad is the sensor temperature measured in the radiometer casing in°C and T 0 = 273.15°C.

Surface height
The height of the sensor boom (units: m) is measured by a sonic ranger attached to the boom itself attached approximately 0.1 m below the boom (2, number 5a), while the height of the stake assembly is measured about 0.1 m below an aluminum boom connecting stakes drilled into ice (2, number 5b). The sensor outputs a distance (H_raw) that requires an air temperature correction in post-processing. The temperature adjustment is performed following: After temperature correction, the measurement uncertainty of the SR50A sonic ranger reported by the manufacturer (Campbell Scientific) is ± 1 cm or ± 0.4% of the measured distance. The uncertainty of sonic ranger readings in PROMICE was investigated utilizing data from a wintertime accumulation-free period of more than 2 month at the location SCO_U. The associated 5 standard deviations for the two sensors were found to be 1.7 cm and 0.6 cm after spike removal, amounting to 0.7 % and 0.6 % of the measured distance, respectively ((Fausto et al., 2012)). In addition to the sensor uncertainties, occasional problems with the stake assembly occurred, primarily in terms of stability during storms when melted out several meters. Also, an unknown amount of melt-in of the stake assembly can occur, but we speculate this only happens 1) when surface melt since installation has been considerable, increasing the height of and thus pressure applied by the stake assembly, and 2) when the stake bottoms 10 are not plugged with caps, as was only the case until 2010.
The PROMICE AWSs are also equipped with a pressure transducer assembly (PTA) that measures surface height change due to ice ablation (2, number 7). The assembly was first constructed and implemented in Greenland in 2001 by Bøggild et al. (2004), but was further developed within PROMICE ( (Fausto et al., 2012)). The PTA consists of an antifreeze-mixture-filled hose with a pressure transducer attached at the bottom. Drilling the hose typically more than 10 m into the ice, the pressure signal 15 registered by the transducer will be that of the vertical liquid column over the sensor, where the upper level is a bladder fixed on the tripod in a shielded box. This allows in-/outflow antifreeze due to compression while keeping a steady level at roughly 1.5 m above the ice surface depending on the AWS. 2 illustrates the free-standing AWS tripod that floats on the ice surface and moves down with the ablating surface, while the hose itself melts out of the ice, which in turn will reduce the hydrostatic pressure from the vertical liquid column over the pressure transducer at the bottom of the hose. The measured reduction in 20 pressure at the bottom of the hose translates directly into ice ablation. As for the radiometer, every pressure transducer has a different calibration coefficient, which is why measurements are stored in the data logger in voltage units and transformed to a physical measurement in post-processing. Measurement height (H m ), or in fact depth relative to the PTA bladder, is calculated as follows:

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where C P T A is the calibration coefficient. The constants ρ w and ρ af are the densities of water and the 50/50 antifreeze solution, respectively.

Sub-surface temperature
Subsurface temperatures (unit:°C) are measured by a 10 m thermistor (temperature-dependent resistor) string ( Figure 2, number 11). The string measures at 1, 2, 3, 4, 5, 6, 7 and 10 m depth, although depths vary due to the surface ablation and 30 accumulation. The string is constructed at GEUS (see Appendix for more information).

Station tilt
The inclinometer is installed on the sensor boom ( Figure 2, number 2) and is aligned with the radiometer to allow for tilt correction of shortwave radiation measurements. The inclinometer measures the tilt (unit:°) across (left-right) and along (up-down) the sensor boom, which translates into tilt-to-east and tilt-to-north when the sensor boom is perfectly oriented north-south. The tilt sensor readings in voltage units (T ilt raw )are converted into tilt in degrees following: where all constants were determined at GEUS (Table 2). Ice ablation causes the AWS tripod to melt downward; this changing (slippery) surface often results in AWS tilt changes of more than several degrees.

AWS position
We use a single frequency GPS receiver to measure the position (units:°N/°W) and the elevation (unit: m above sea level) of 10 each station to quantify ice dynamics ( Figure 2, number 9). The GPS antenna, as well as the receiver which is contained in the Iridium modem, is placed inside the data logger enclosure. It is a single frequency GPS, which is built into Iridium 9602-LP modem. The receiver type is a NEO-6Q, 1575.42 MHz (L1), 16-channel, with a C/A code. The accuracy is reported to be within 2.5 m. In the PROMICE AWS setup, the GPS receiver is powered up for 5 minutes preceding each Iridium transmission (hourly in summer and daily in winter), during which it attempts to acquire location data every 20 seconds. The return (out 15 of a maximum of 15) that reports the lowest horizontal dilution of precision is written to memory. So far, NUK_U, NUK_L, MIT and QAS_L have been repositioned during maintenance visits over distances larger than several tens of meters. The main reason for this is to reduce the influence of location change on the AWS variables measured, but stations have also been relocated to move them away from a region with opening crevasses. Table 3 shows the horizontal and vertical displacement due to glacier flow and AWS relocation during maintenance visits.

Post processing
In this section, we describe and quantify the filtering process, how we correct measurements, and how we calculate derived variables in the dataset. The hourly, daily, and monthly averaging procedures are also described. Table 4 provides filtering information used in the processing chain. We remove unrealistic spikes from the data by using upper 25 and lower thresholds for each measurement. Measurements outside these (generous) threshold limits, which could happen for a number of known and unknown reasons, are considered erroneous and set to -999. Known reasons will be discussed in section 4.2 (Living data section). Derived variables are also set to -999 when one or more of the listed "core" AWS measurements that serve as input fall outside the threshold limits.

Specific humidity
The specific humidity q (unit: kg/kg) is calculated from relative humidity with respect to water/ice above/below freezing (RH) using the following equation: where = 0.622 is the ratio between the specific gas constants for dry air and water vapor, p is air pressure in Pa and es ice/water is saturation water vapor pressure over ice in Pa (below freezing) or water (above freezing) calculated after Goff and Gratch (1946). 10

Surface temperature
The surface temperature T s (unit:°C) is derived using the measured downward and upward longwave irradiance (LR in and LR out , repectively): where ice sheet surface emissivity = 0.97.

Turbulent energy fluxes
The sensible (SHF) and latent (LHF) heat fluxes (unit: W m −2 ) are estimated using vertical gradients in wind speed, potential temperature, and specific humidity between the measured boom height and the surface described by Van As et al. (2005); Van As (2011). According to the Monin-Obukhov similarity theory, SHF and LHF can be approximated as: Here ρ is the density of air and C p = 1005 JK −1 kg −1 its specific heat capacity at constant pressure. L s = 2.8310 −6 Jkg −1 and L v = 2.5010 −6 Jkg −1 are the latent heat values of sublimation and evaporation, respectively, while κ = 0.4 is the von Karman constant. When estimating turbulent heat fluxes, we need the measurement heights (z u , z T , z q , Table 2) of wind speed 25 (u), temperature (T), and specific humidity (q) as well as the surface roughness lengths for momentum z 0 , for heat, z 0,T and moisture z 0,q . We use z 0 = 0.001 m and z 0,T = z 0,q are calculated using the formulation from Smeets and Van den Broeke (2008a, b) for rough surfaces. The stability correction functions ψ u,T,q Eq. 12 in Holtslag and De Bruin (1988) for stable atmospheric conditions, while we follow Paulson (1970) for unstable conditions. The surface temperature (T s ) is calculated  (8)) and the surface specific humidity is assumed to be at saturation (q s = q sat ).
Several sources of uncertainty apply to the calculation of SHF and LHF. The aerodynamic surface roughness length z 0 is known to vary with surface type (Brock et al., 2006) and through time ( that SHF records by one-level approaches often cover longer time periods. Fausto et al. (2016b, a) found they had to use an unrealistically high z 0 to get agreement between SEB closure and observed ablation rates during extreme sensible and latent heat-driven melt events.

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Tilt correction of downward shortwave radiation and Cloud Cover Tilt correction of solar radiation is performed following Van As (2011). Downward shortwave radiation (SR in ) consists of a diffuse and direct beam part. It is only the direct beam part of SR in that requires tilt correction. We have for a horizontal radiation sensor that the direct beam equals SR in reduced by its diffuse fraction (f dif ). For the tilted radiation sensor, SR in is calculated from the measured value, SR in,m and a correction factor C following: with where SZA is the solar zenith angle, d is the sun declination (the angle of the sun above the plane formed by the earth's equator), w is the hour angle (the angle between the sun's current position in the sky and its position at solar noon), lat and lon are the site's latitude and longitude in radians, and lastly θ sensor and φ sensor are the radiometer's tilt angle and direction, respectively. The calculation procedures for d and w and SZA are detailed in NOAA (2020). We estimate f dif spanning from 0.2 for clear skies to 1 for overcast conditions, while assuming a linear dependency on the cloud cover fraction (Harrison 30 et al., 2008). We approximate the cloud cover fraction from its dependence of the near-surface air temperature (T air ) on LR in (Van As et al., 2005). For this purpose, we calculate a theoretical downward longwave radiation flux corresponding to clear sky conditions using the equation from Swinbank (1963): and to overcast conditions assuming the black-body radiation: The cloud cover (limited to the [0:1] range) is then calculated as: Albedo Surface broadband solar reflectivity in the 0.3 to 2.5 µm wavelength range, a.k.a., albedo (unitless) is calculated from 10 minute found a 5% uncertainty on pyranometer measurements, while the manufactorer, Kipp & Zonen, estimate a more conservative value of 10% uncertainty. We conservatively assume 10 % uncertainty in the calculated albedo.

Ice surface height
The pressure transducer assembly (PTA, Fig 2, sensor 7) setup is influenced by variations in air pressure. The air pressure contributions to the measured PTA signal H M are eliminated using the following equation: where P A (Unit: hPa) is air pressure, P C (Unit: hPa) is the known pressure given by the manufacturer to which the sensor 25 was calibrated, g = 9.82 m s −2 is the gravitational acceleration, and ρ l = 1090 kg m −3 is the antifreeze mixture density at 0°C. Changes in H L are equal to ice ablation. Fausto et al. (2012Fausto et al. ( , 2016a compared PTA time series to hose measurements manually performed in field and recorded distances from sonic rangers to quantify instrument inaccuracies, which were found to be accurate within 0.04 m.

Averaging
The time reported in our data products specifies the hour/day/month during which the measurements are taken, as opposed to other products that list the exact timestamp of the end of the averaging period. Hourly averages are calculated from 10-min values if at least one value is available (10-

Measurement success rate
To illustrate the PROMICE AWS data coverage, we determined the "success rate" in terms of available daily averages for all measured variables that are required for estimating the surface energy budget: air pressure, air temperature, humidity, wind  Table 5. The data are organised in ASCII files, organized following Table 5, with 46 columns in the hourly datafiles, 45 columns in the daily datafiles, and 24 columns in the monthly datafiles. The datafiles can be accessed through "Download Data" on the PROMICE webpage https://www.promice.org (DOI: 10.22008/promice/data/aws).

Data examples
To create a quick insight into the data product, we show examples of data from AWSs in two contrasting locations: TAS in southeast Greenland near Tasiilaq and UPE in northwest Greenland near Upernavik (Figure 1).

Wind speed
Time series spanning the years 2012 through 2014 of weekly median wind-speeds and maximum 10-min wind speed within 25 that week for TAS_L and UPE_U are displayed in Figure 5. Median wind speeds are lower at TAS_L than at UPE_U, whereas the opposite is true for maximum wind speeds, because TAS_L is located in a region well-known for its Piteraq storms.

Air temperature
The daily average air temperature for the two stations near Upernavik is shown in Figure 6. The temperature is higher at the lower station, UPE_L, than at the upper station, UPE_U due to an elevation difference of more than 700 m. The tendency of the temperature to have a higher variability during winter months than during summer is also evident from these time series.

Living data and continuing improvements
PROMICE will continue to update and make available the data products as AWS data comes in. It is likely that there are as-yet-unknown issues in the existing data we are releasing as part of this dataset, and new issues may arise in as-yet-to-be collected data. Also, some issues are known but hard to identify and some issues are systematic, which can be corrected for more generally. Below, we list known dataset issues in three categories: 1) Issues that are hard to identify 2) issues we in some 15 way can correct for systematically, and 3) Errors caused by humans, animals, and anything due to instrument failure.
Here we list dataset issues we have encountered over the years following the above three categories: 1. Hard to identify (a) Often a high inclinometer variability, presumably caused by AWS shaking, or instrument failure.
(d) Sonic ranger membrane not robust enough to always survive the period between maintenance visits (instrument failure).
(e) Instruments buried in snow during winter and/or spring.

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(f) Tripod collapse due to compacting snow.
(g) AWS falling over in extreme winds or crevassed terrain.
(h) Bent sensor boom due to compacting snow, impacting alignment radiometer and inclinometer.
(i) Leaks in or overfilling of the pressure transducer assembly. (c) Various instrument failures. 10 The most recent data files will in most cases comprise of transmitted data, which will be updated after the next maintenance visit. Data download from the logger will improve data quality and coverage. During strong winds the AWSs can topple or sensors break down. AWSs can also be covered by winter-accumulated snow, in which cases will reduce the data quality for many variables. We can with our height measurements on both the station and stake assembly monitor when certain instruments are covered in snow. At present, AWS covered in snow has only happened at three locations, namely QAS_U, QAS_M and

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MIT. Data recorded after and during these events are often identified by the automatic processing routine and will be clearly identifiable for the data user as erroneous data. A maintenance visit either in spring or summer will often result in a station being moved, leveled, and/or rotated, in which case variables such as surface height will undergo an easily recognizable shift.
Identified dataset issues that we plan to correct for or implement in future data products: 20 1. Shading by instruments and station frame impacting albedo.
2. Instrumental monitoring of AWS orientation, which could influence the correction of the shortwave radiation and wind direction.
3. Instrumental monitoring of rain.

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While we do our best to clean the data appropriately and address known issues (see above), we recognize that correcting issues is more complicated than simply documenting them, and that some corrections may not be possible, or may be subjective and a function of different use cases. We therefore introduce a user-contributable dynamic web-based database of known data quality issues at (https://github.com/GEUS-PROMICE/PROMICE-AWS-data-issues/). The current implementation uses GitHub "issues", although a future version may use a different database backend that the DOI would resolve. Each issue is tagged with station(s), sensor(s), and year(s) where the issue occurs. Users who are working with a station, sensor, or timeframe of data are encouraged to search the issue database and see if there are any known relevant data issues. If users discover a data issue that is not currently documented, they can add it to the database. A PROMICE team-member will review and tag any issues as verified, and then suggest a fix. Future versions of the product will implement these fixes if possible, and the issues will be closed but remain accessible.  Figure 4). All PROMICE AWS data products are available at https://doi.org/10.22008/promice/data/aws.
In addition to advancing science, the PROMICE AWS network is now poised to contribute to operational products. With 20 recent advances in the quality and transparency of the PROMICE data delivery pipeline described here, as well as the increasing prevalence of machine-to-machine transfer protocols among data users, the entire PROMICE station data archive -including near real-time observations -is now readily available to ingest in weather forecast and climate reanalysis applications. With the original AWS stations quickly approaching its fifteenth anniversary, the PROMICE data record is crossing the halfway mark of a thirty-year climatological reference period. With the launch of the PROMICE AWS data issues on GitHub (https: 25 //github.com/GEUS-PROMICE/PROMICE-AWS-data-issues/), we hope to continue to support the growing PROMICE user community into the next decade.

Thermometer and Hygrometer
Rotronic MP102H with Pt100 (±0.1 K) and HC2-S3 (or HC2) probe (±0.1 K, ± 0.8% rh, at 23°C ± 5 K), housed in a RS12T aspirated shield. Accuracy and factory measurement error of Rotronic probes: The Rotronic system uses ventilated weather and radiation shields RS12T with a 12 VDC fan. Due to the white housing of the radiation shield the influence of thermal radiation on the measurements of temperature and humidity is reduced to a minimum. The shield also offers optimum protection in 5 stormy weather, even against horizontally driven rain and snow. The fan is supplied by a separate cable.

Radiometer
Kipp & Zonen CNR1 and CNR4. CNR4 is a four-component net radiometer for accurate and reliable measurements. There are four separate signal outputs and the integrated temperature sensors can be used to calculate the net radiation. The CNR4 combines two pyranometers for solar radiation with two pyrgeometers for infrared measurements. The upper pyrgeometer has a silicon meniscus dome so that water rolls off and the field of view is 180°. The design is lightweight and the white sun shield 5 reduces solar heating of the instrument body. Although similar to CNR4, the older CNR1 has a slightly different instrument body and measurement range (see Tables below), but perform with similar accuracy. We do not flag the products with respect to which instrument type we used for that each station setup. We therefore assume the same accuracy for both CNR1 and CNR4.

Sonic rangers
The accuracy of the SR50A sonic ranger given by the manufacturer (Campbell Scientific) is: ± 1 cm or ± 0.4% of the measuring height after temperature correction.

Pressure transducer
The PROMICE AWSs are equipped with an Ørum & Jensen NT1400/NT1700 pressure transducer assembly that allow us to 5 monitor ice surface height change due to ablation. The pressure transducer sensor has an accuracy of 2.5 cm given by the manufacturer (Ørum & Jensen Elektronik A/S).