Quality control of weighing precipitation gauge measurements

Weighing precipitation gauges are increasingly being used for ground-based precipitation monitoring because of their greater accuracy compared to tipping bucket gauges, especially in cases of high precipitation intensity and when measuring solid precipitation. Another advantage highlighted by manufacturers of weighing gauges is their lower maintenance requirements. For automatic precipitation measurement GeoSphere Austria currently uses tipping bucket gauges and weighing gauges, the latter with two orifice sizes of 400 cm² and 500 cm². Each of the gauges is equipped with a precipitation detection instrument.

Despite their good performance characteristics, weighing precipitation gauges are sometimes subject to errors, which can be divided into two groups. The first group includes, for example, data outside the measurement range or other errors related to high- or low-amplitude noise, such as incorrect precipitation measurements caused by decanting and/or refilling of the bucket, as well as temperature- or wind-induced errors due to inappropriate noise filtering by the gauge software (spurious precipitation). The other group of errors is related to missing precipitation recordings, mainly caused by internal problems with the gauge-software – this is the opposite case to spurious precipitation measurements, where the internal filter is too restrictive and thus discards the weight gain during a precipitation event.

During quality control process, the data should be corrected for both types of errors wherever possible. The quality control and correction of high-amplitude noise or spurious precipitation values can be performed using appropriate algorithms that are part of the standard check routines (out-of-range tests, internal consistency checks, etc.). Correcting the second type of error (missing precipitation data) is more difficult because the algorithms underlying the data generation by the gauge are unknown (black-box) and there is no way to recover the lost data.

In cases where the internal software filtering is too aggressive, raw bucket-level data could be used to provide an estimate of the missing precipitation amount. In an attempt to account for this source of error at the gauge level, we have developed an automated procedure that combines the change in bucket weight recorded by weighing gauge with measurements from an independent instrument (precipitation monitor) based on 1-min data to filter out mechanical noise and estimate the amount of precipitation that is not recorded by the gauge software. The idea behind this algorithm is that it should be an additional decision-making support for quality control of daily precipitation data in order to obtain precipitation information lost due to software-related issues with the precipitation gauge.