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Guidance on the use of measured data

Data recorded by the automatic measuring stations in the avalanche warning networks (IMIS) are made available every half-hour. The SLF is unable to check or verify their accuracy. The measured data are therefore unchecked raw data.

Errors can arise from the following factors:

- The sites for the IMIS stations are selected according to avalanche-related technical deliberations. The stations are situated in the vicinity of avalanche starting zones and are generally not connected to the electricity network. In view of the solar energy supply, neither heated nor cooled sensors can be used. This impairs the data quality in a number of ways. In some cases, the quality does not comply with the customary meteorological standards. Anemometers can become iced-up, for example, and indicate excessively low values (cf. Fig. 1) and, especially when there is no wind, excessively high air temperatures can be measured.

- In the event of a sensor defect, an immediate repair may be impossible in some circumstances, depending on the weather, because many IMIS stations are accessible only by helicopter. In such cases, an error message is not recorded.

Die IFKIS-Stationen stehen auf vielen Berggipfeln und liefern den Lawinenkommissionen und der Lawinenwarnung unverzichtbare Daten zu jeder Tages- und Nachtzeit. Weil sie nicht ans Stromnetz angeschlossen sind, können die Sensoren nicht geheizt werden. Bei extremen Ereignissen können sie mit Raureif überzogen werden, wie hier am Pizzo Tremorgio im Tessin (Foto: E. Salinetti, 8.12.2008).

Abb. 1: IMIS wind station Pizzo Tremorgio (TI) covered with rime. The rime or ice does not always disappear due to meteorological conditions (sun, heat) and it can take days or weeks until someone can free the station from ice or rime (photo: E. Salinetti, 08.12.2008).


The main purpose of the automatic measuring stations is to collect data quickly from avalanche starting zones for those who are responsible for public safety in settlements and on the roads. These individuals are trained to interpret the data and are expected to be able to cope with any erroneous values. Anyone using these data, when planning backcountry tours for instance, must take into account the issue of data quality. The SLF cannot accept any liability for problems arising during backcountry touring or off-piste activities that are attributable to flawed measured data.

Snow depth (HS) and new fallen snow (HN)

The chart tracking snow depths over time allows conclusions to be drawn about snowfall, settling and melting. In case of snowfall, the modelled new fallen snow depth (6h) can serve as a useful aid in interpreting the data. In the event of a major snowfall, for example, the snow cover can settle significantly, its depth either remaining constant or decreasing, even while snow is still falling (example). In other words, the amount of fresh snow cannot be determined simply by measuring snow depth. Such measure of fresh snow depth is modelled by the snowpack simulation program and may not accurately reflect real conditions.

Problems can arise from the following factors in connection with snow depth measurement:

Problem Influence on measured data
Wind, snow transport

example 1

example 2

The snow depth curve rises or falls very sharply. If it does, the modelled fresh snow values are no longer accurate.
Effect of temperature on the ultrasound sensor

example

On sunny days the snow depth curve regularly dips marginally around midday. This response is caused by the temperature correction performed by the ultrasound sensor, which is inaccurate (too great) because the temperature sensor is not cooled.
Avalanches Individual snow stations can be struck by avalanches. In such cases the snow depth curve rises abruptly. If this occurs, the snow depth and modelled fresh snow values are no longer accurate.
Measuring errors

example

The ultrasound sensor is prone to inexplicable measuring errors.


Mean wind speed (VW), peak gust (VW_max) and wind direction (DW)

Wind speed measurements are a key aid when estimating the formation of snowdrift accumulations. The wind charts recorded by the automated stations plot mean wind speed (vectorial average over 30 minutes), peak gusts (highest wind speed over 30 minutes) and wind direction (averaged wind direction over 30 minutes).

Problems can arise from the following factors in connection with wind measurement:

Problem Influence on measured data
Deposition of hoarfrost

The sensors of most wind stations are not heated and can therefore become coated with hoarfrost and fail. If the hoarfrost persists for a long period, the energy supply can fail in some circumstances (after about 2 weeks) and measurements then cease completely.

The anemometer does not rotate, indicates a constant value for the duration of the hoarfrost, or measures lower than real wind speeds. Reduced wind speeds are practically impossible to detect; the best approach is to compare the measured values with those of neighbouring stations.


Air temperature (TA) and snow surface temperature (TSS)

Air and snow temperatures are key factors in assessing avalanche danger, particularly in springtime when wet snow avalanches are most likely. The pattern traced by the two temperature curves also permits conclusions to be drawn about the extent of cloud cover. When the air temperature and snow surface temperature curves trace a similar pattern, the weather is overcast; when they diverge, the skies are clear (example).

Problems can arise from the following factors in connection with air temperature measurement:

Problem Influence on measured data
Sunshine Since the sensor is not cooled, it heats up on exposure to the sun and indicates excessive values.