The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
Gerd Wessolek - One of the best experts on this subject based on the ideXlab platform.
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Technical note: Improving the AWAT Filter with interpolation schemes for advanced processing of high resolution data
2016Co-Authors: Andre Peters, Thomas Nehls, Gerd WessolekAbstract:Abstract. Weighing lysimeters with appropriate data Filtering yield the most precise and unbiased information for precipitation (P) and evapotranspiration (ET). A recently introduced Filter scheme for such data is the AWAT (Adaptive Window and Adaptive Threshold) Filter [Peters, A., Nehls, T., Schonsky, H., and Wessolek, G.: Separating precipitation and evapotranspiration from noise – a new Filter Routine for high-resolution lysimeter data, Hydrol. Earth Syst. Sci., 18, 1189–1198, doi:10.5194/hess-18-1189-2014, 2014]. The Filter applies an adaptive threshold to separate significant from insignificant mass changes, guaranteeing that P and ET are not overestimated, and uses a step interpolation between the significant mass changes. In this contribution we show that the step interpolation scheme, which reflects the resolution of the measuring system, can lead to unrealistic prediction of P and ET, especially if they are required in high temporal resolution. We introduce linear and spline interpolation schemes to overcome these problems. To guarantee that medium to strong precipitation events abruptly following low or zero fluxes are not smoothed in an unfavourable way, a simple heuristic selection criterion is used, which attributes such precipitations to the step interpolation. The three interpolation schemes (step, linear and spline) are tested and compared using a data set from a grass-reference lysimeter with one minute resolution, ranging from 1 January to 5 August 2014. The selected output resolutions for P and ET prediction are one day, one hour and 10 minutes. As expected, the step scheme yielded reasonable flux rates only for a resolution of one day, whereas the other two schemes are well able to yield reasonable results for any resolution. The spline scheme returned slightly better results than the linear scheme concerning the differences between Filtered values and raw data. Moreover, this scheme allows continuous differentiability of Filtered data so that any output resolution for the fluxes is sound. Since computational burden is not problematic for any of the interpolation schemes, we suggest to use always the spline scheme.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high-resolution lysimeter data
Hydrology and Earth System Sciences, 2014Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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separating precipitation and evapotranspiration from noise a new Filter Routine for high resolution lysimeter data
Hydrology and Earth System Sciences, 2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high resolution lysimeter data
2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract:Abstract. 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) indicate P or ET. These real mass changes of the lysimeter system have to be separated from measurement noise (e.g. caused by wind). The typical way 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. Especially the time variable noise due to wind and 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 lead either to unnecessary losses of accuracy or to faulty data due to noise. The aim of this paper is to solve that problem with a new Filter Routine, which 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 which 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, so that only the maximum window width must be predefined by the user.
Andre Peters - One of the best experts on this subject based on the ideXlab platform.
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Towards an unbiased Filter Routine to determine precipitation and evapotranspiration from high precision lysimeter measurements
Journal of Hydrology, 2017Co-Authors: Andre Peters, Jannis Groh, Frederik Schrader, Wolfgang Durner, Harry Vereecken, Thomas PützAbstract:Weighing lysimeters are considered to be the best means for a precise measurement of water fluxes at the interface between the soil-plant system and the atmosphere. Any decrease of the net mass of the lysimeter can be interpreted as evapotranspiration (ET), any increase as precipitation (P). However, the measured raw data need to be Filtered to separate real mass changes from noise. Such Filter Routines typically apply two steps: (i) a low pass Filter, like moving average, which smooths noisy data, and (ii) a threshold Filter that separates significant from insignificant mass changes. Recent developments of these Filters have identified and solved some problems regarding bias in the data processing. A remaining problem is that each change in flow direction is accompanied with a systematic flow underestimation due to the threshold scheme. In this contribution, we analyze this systematic effect and show that the absolute underestimation is independent of the magnitude of a flux event. Thus, for small events, like dew or rime formation, the relative error is high and can reach the same magnitude as the flux itself. We develop a heuristic solution to the problem by introducing a so-called “snap Routine”. The Routine is calibrated and tested with synthetic flux data and applied to real measurements obtained with a precision lysimeter for a 10-month period. The heuristic snap Routine effectively overcomes these problems and yields an almost unbiased representation of the real signal.
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Technical note: Improving the AWAT Filter with interpolation schemes for advanced processing of high resolution data
2016Co-Authors: Andre Peters, Thomas Nehls, Gerd WessolekAbstract:Abstract. Weighing lysimeters with appropriate data Filtering yield the most precise and unbiased information for precipitation (P) and evapotranspiration (ET). A recently introduced Filter scheme for such data is the AWAT (Adaptive Window and Adaptive Threshold) Filter [Peters, A., Nehls, T., Schonsky, H., and Wessolek, G.: Separating precipitation and evapotranspiration from noise – a new Filter Routine for high-resolution lysimeter data, Hydrol. Earth Syst. Sci., 18, 1189–1198, doi:10.5194/hess-18-1189-2014, 2014]. The Filter applies an adaptive threshold to separate significant from insignificant mass changes, guaranteeing that P and ET are not overestimated, and uses a step interpolation between the significant mass changes. In this contribution we show that the step interpolation scheme, which reflects the resolution of the measuring system, can lead to unrealistic prediction of P and ET, especially if they are required in high temporal resolution. We introduce linear and spline interpolation schemes to overcome these problems. To guarantee that medium to strong precipitation events abruptly following low or zero fluxes are not smoothed in an unfavourable way, a simple heuristic selection criterion is used, which attributes such precipitations to the step interpolation. The three interpolation schemes (step, linear and spline) are tested and compared using a data set from a grass-reference lysimeter with one minute resolution, ranging from 1 January to 5 August 2014. The selected output resolutions for P and ET prediction are one day, one hour and 10 minutes. As expected, the step scheme yielded reasonable flux rates only for a resolution of one day, whereas the other two schemes are well able to yield reasonable results for any resolution. The spline scheme returned slightly better results than the linear scheme concerning the differences between Filtered values and raw data. Moreover, this scheme allows continuous differentiability of Filtered data so that any output resolution for the fluxes is sound. Since computational burden is not problematic for any of the interpolation schemes, we suggest to use always the spline scheme.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high-resolution lysimeter data
Hydrology and Earth System Sciences, 2014Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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separating precipitation and evapotranspiration from noise a new Filter Routine for high resolution lysimeter data
Hydrology and Earth System Sciences, 2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high resolution lysimeter data
2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract:Abstract. 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) indicate P or ET. These real mass changes of the lysimeter system have to be separated from measurement noise (e.g. caused by wind). The typical way 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. Especially the time variable noise due to wind and 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 lead either to unnecessary losses of accuracy or to faulty data due to noise. The aim of this paper is to solve that problem with a new Filter Routine, which 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 which 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, so that only the maximum window width must be predefined by the user.
Thomas Nehls - One of the best experts on this subject based on the ideXlab platform.
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Technical note: Improving the AWAT Filter with interpolation schemes for advanced processing of high resolution data
2016Co-Authors: Andre Peters, Thomas Nehls, Gerd WessolekAbstract:Abstract. Weighing lysimeters with appropriate data Filtering yield the most precise and unbiased information for precipitation (P) and evapotranspiration (ET). A recently introduced Filter scheme for such data is the AWAT (Adaptive Window and Adaptive Threshold) Filter [Peters, A., Nehls, T., Schonsky, H., and Wessolek, G.: Separating precipitation and evapotranspiration from noise – a new Filter Routine for high-resolution lysimeter data, Hydrol. Earth Syst. Sci., 18, 1189–1198, doi:10.5194/hess-18-1189-2014, 2014]. The Filter applies an adaptive threshold to separate significant from insignificant mass changes, guaranteeing that P and ET are not overestimated, and uses a step interpolation between the significant mass changes. In this contribution we show that the step interpolation scheme, which reflects the resolution of the measuring system, can lead to unrealistic prediction of P and ET, especially if they are required in high temporal resolution. We introduce linear and spline interpolation schemes to overcome these problems. To guarantee that medium to strong precipitation events abruptly following low or zero fluxes are not smoothed in an unfavourable way, a simple heuristic selection criterion is used, which attributes such precipitations to the step interpolation. The three interpolation schemes (step, linear and spline) are tested and compared using a data set from a grass-reference lysimeter with one minute resolution, ranging from 1 January to 5 August 2014. The selected output resolutions for P and ET prediction are one day, one hour and 10 minutes. As expected, the step scheme yielded reasonable flux rates only for a resolution of one day, whereas the other two schemes are well able to yield reasonable results for any resolution. The spline scheme returned slightly better results than the linear scheme concerning the differences between Filtered values and raw data. Moreover, this scheme allows continuous differentiability of Filtered data so that any output resolution for the fluxes is sound. Since computational burden is not problematic for any of the interpolation schemes, we suggest to use always the spline scheme.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high-resolution lysimeter data
Hydrology and Earth System Sciences, 2014Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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separating precipitation and evapotranspiration from noise a new Filter Routine for high resolution lysimeter data
Hydrology and Earth System Sciences, 2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high resolution lysimeter data
2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract:Abstract. 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) indicate P or ET. These real mass changes of the lysimeter system have to be separated from measurement noise (e.g. caused by wind). The typical way 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. Especially the time variable noise due to wind and 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 lead either to unnecessary losses of accuracy or to faulty data due to noise. The aim of this paper is to solve that problem with a new Filter Routine, which 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 which 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, so that only the maximum window width must be predefined by the user.
Horst Schonsky - One of the best experts on this subject based on the ideXlab platform.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high-resolution lysimeter data
Hydrology and Earth System Sciences, 2014Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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separating precipitation and evapotranspiration from noise a new Filter Routine for high resolution lysimeter data
Hydrology and Earth System Sciences, 2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract: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.
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Separating precipitation and evapotranspiration from noise – a new Filter Routine for high resolution lysimeter data
2013Co-Authors: Andre Peters, Thomas Nehls, Horst Schonsky, Gerd WessolekAbstract:Abstract. 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) indicate P or ET. These real mass changes of the lysimeter system have to be separated from measurement noise (e.g. caused by wind). The typical way 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. Especially the time variable noise due to wind and 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 lead either to unnecessary losses of accuracy or to faulty data due to noise. The aim of this paper is to solve that problem with a new Filter Routine, which 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 which 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, so that only the maximum window width must be predefined by the user.
Habib Khalfalah - One of the best experts on this subject based on the ideXlab platform.
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A modified pendant drop method for transient and dynamic interfacial tension measurement
Colloids and Surfaces A: Physicochemical and Engineering Aspects, 1996Co-Authors: Youssef Touhami, Graham H. Neale, Vladimir Hornof, Habib KhalfalahAbstract:Abstract An apparatus has been constructed based upon the drop shape method for the determination of surface and interficial tensions. The system, built around a commercial pendant drop instrument, incorporates computer-based video image analysis. A user interface is designed and built around the core subprograms. A Filter Routine using a global threshold is used along with an edge-tracing algorithm for the extraction of the drop profile. The tension is calculated using the Jennings and Pallas algorithm. The time needed to digitize a single frame and to extract the drop profile coordinates is about 0.5 s. The instrument is capable of measuring surface tension of water of 72.00 ± 0.07 mN m −1 . The method is suitable for measuring transient as well as dynamic interfacial tensions. The relaxation of surfactant adsorption layers at the oil/water interfaces can also be inferred from the data obtained.