Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement
<p>The intensity and phase of precipitation at the ground surface can have important implications not only for meteorological and hydrological situations but also in terms of hazards and risks. In the field, Thies disdrometers are sometimes used to monitor the quantity and nature of precipitat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-09-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/13/4683/2020/amt-13-4683-2020.pdf |
| _version_ | 1818245253106237440 |
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| author | M. Fehlmann M. Rohrer M. Rohrer A. von Lerber M. Stoffel M. Stoffel M. Stoffel |
| author_facet | M. Fehlmann M. Rohrer M. Rohrer A. von Lerber M. Stoffel M. Stoffel M. Stoffel |
| author_sort | M. Fehlmann |
| collection | DOAJ |
| description | <p>The intensity and phase of precipitation at the ground surface can have important implications not only for meteorological and hydrological situations but also in terms of hazards and risks. In the field, Thies disdrometers are sometimes used to monitor the quantity and nature of precipitation with high temporal resolution and very low maintenance and thus provide valuable information for the management of meteorological and hydrological risks. Here, we evaluate the Thies disdrometer with respect to precipitation detection, as well as the estimation of precipitation intensity and phase at a pre-alpine site in Switzerland (1060 m a.s.l.), using a weighing precipitation gauge (OTT pluviometer) and a two-dimensional video disdrometer (2DVD) as a reference. We show that the Thies disdrometer is well suited to detect even light precipitation, reaching a hit rate of around 95 %. However, the instrument tends to systematically underestimate rainfall intensities by 16.5 %, which can be related to a systematic underestimation of the number of raindrops with diameters between 0.5 and 3.5 mm. During snowfall episodes, a similar underestimation is observed in the particle size distribution (PSD), which is, however, not reflected in intensity estimates, probably due to a compensation by snow density assumptions. To improve intensity estimates, we test PSD adjustments (to the 2DVD) and direct adjustments of the resulting intensity estimates (to the OTT pluviometer), the latter of which are able to successfully reduce the systematic deviations during rainfall in the validation period. For snowfall, the combination of the 2DVD and the OTT pluviometer seems promising as it allows for improvement of snow density estimates, which poses a challenge to all optical precipitation measurements. Finally, we show that the Thies disdrometer and the 2DVD agree well insofar as the distinction between rain and snowfall is concerned, such that an important prerequisite for the proposed correction methods is fulfilled. Uncertainties mainly persist during mixed-phase precipitation or low precipitation intensities, where the assignment of precipitation phase is technically challenging, but less relevant for practical applications. We conclude that the Thies disdrometer is suitable not only to estimate precipitation intensity but also to distinguish between rain and snowfall. The Thies disdrometer therefore seems promising for the improvement of precipitation monitoring and the nowcasting of discharge in pre-alpine areas, where considerable uncertainties with respect to these quantities are still posing a challenge to decision-making.</p> |
| first_indexed | 2024-12-12T14:29:58Z |
| format | Article |
| id | doaj.art-24b8450fd1fe465ea95ece20ae4b157d |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-12T14:29:58Z |
| publishDate | 2020-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-24b8450fd1fe465ea95ece20ae4b157d2022-12-22T00:21:34ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482020-09-01134683469810.5194/amt-13-4683-2020Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvementM. Fehlmann0M. Rohrer1M. Rohrer2A. von Lerber3M. Stoffel4M. Stoffel5M. Stoffel6Climate Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, Geneva, SwitzerlandClimate Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, Geneva, SwitzerlandMeteodat GmbH, Zurich, SwitzerlandFinnish Meteorological Institute (FMI), Helsinki, FinlandClimate Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, Geneva, SwitzerlandDepartment of Earth Sciences, University of Geneva, Geneva, SwitzerlandDepartment F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland<p>The intensity and phase of precipitation at the ground surface can have important implications not only for meteorological and hydrological situations but also in terms of hazards and risks. In the field, Thies disdrometers are sometimes used to monitor the quantity and nature of precipitation with high temporal resolution and very low maintenance and thus provide valuable information for the management of meteorological and hydrological risks. Here, we evaluate the Thies disdrometer with respect to precipitation detection, as well as the estimation of precipitation intensity and phase at a pre-alpine site in Switzerland (1060 m a.s.l.), using a weighing precipitation gauge (OTT pluviometer) and a two-dimensional video disdrometer (2DVD) as a reference. We show that the Thies disdrometer is well suited to detect even light precipitation, reaching a hit rate of around 95 %. However, the instrument tends to systematically underestimate rainfall intensities by 16.5 %, which can be related to a systematic underestimation of the number of raindrops with diameters between 0.5 and 3.5 mm. During snowfall episodes, a similar underestimation is observed in the particle size distribution (PSD), which is, however, not reflected in intensity estimates, probably due to a compensation by snow density assumptions. To improve intensity estimates, we test PSD adjustments (to the 2DVD) and direct adjustments of the resulting intensity estimates (to the OTT pluviometer), the latter of which are able to successfully reduce the systematic deviations during rainfall in the validation period. For snowfall, the combination of the 2DVD and the OTT pluviometer seems promising as it allows for improvement of snow density estimates, which poses a challenge to all optical precipitation measurements. Finally, we show that the Thies disdrometer and the 2DVD agree well insofar as the distinction between rain and snowfall is concerned, such that an important prerequisite for the proposed correction methods is fulfilled. Uncertainties mainly persist during mixed-phase precipitation or low precipitation intensities, where the assignment of precipitation phase is technically challenging, but less relevant for practical applications. We conclude that the Thies disdrometer is suitable not only to estimate precipitation intensity but also to distinguish between rain and snowfall. The Thies disdrometer therefore seems promising for the improvement of precipitation monitoring and the nowcasting of discharge in pre-alpine areas, where considerable uncertainties with respect to these quantities are still posing a challenge to decision-making.</p>https://amt.copernicus.org/articles/13/4683/2020/amt-13-4683-2020.pdf |
| spellingShingle | M. Fehlmann M. Rohrer M. Rohrer A. von Lerber M. Stoffel M. Stoffel M. Stoffel Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement Atmospheric Measurement Techniques |
| title | Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement |
| title_full | Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement |
| title_fullStr | Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement |
| title_full_unstemmed | Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement |
| title_short | Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement |
| title_sort | automated precipitation monitoring with the thies disdrometer biases and ways for improvement |
| url | https://amt.copernicus.org/articles/13/4683/2020/amt-13-4683-2020.pdf |
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