Weighing lysimeters yield the most precise and realistic measures for evapotranspiration (ET) and precipitation (P), which are of great importance for many questions regarding soil and atmospheric sciences. An increase or a decrease of the system mass (lysimeter plus seepage) indicates P or ET. These real mass changes of the lysimeter system have to be separated from measurement noise (e.g., caused by wind). A promising approach to filter noisy lysimeter data is (i) to introduce a smoothing routine, like a moving average with a certain averaging window, w, and then (ii) to apply a certain threshold value, δ, accounting for measurement accuracy, separating significant from insignificant weight changes. Thus, two filter parameters are used, namely w and δ. In particular, the time-variable noise due to wind as well as strong signals due to heavy precipitation pose challenges for such noise-reduction algorithms. If w is too small, data noise might be interpreted as real system changes. If w is too wide, small weight changes in short time intervals might be disregarded. The same applies to too small or too large values for δ. Application of constant w and δ leads either to unnecessary losses of accuracy or to faulty data due to noise. The aim of this paper is to solve this problem with a new filter routine that is appropriate for any event, ranging from smooth evaporation to strong wind and heavy precipitation. Therefore, the new routine uses adaptive w and δ in dependence on signal strength and noise (AWAT – adaptive window and adaptive threshold filter). The AWAT filter, a moving-average filter and the Savitzky–Golay filter with constant w and δ were applied to real lysimeter data comprising the above-mentioned events. The AWAT filter was the only filter that could handle the data of all events very well. A sensitivity study shows that the magnitude of the maximum threshold value has practically no influence on the results; thus only the maximum window width must be predefined by the user.