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| Titel |
Evaporation from weighing precipitation gauges: impacts on automated gauge measurements and quality assurance methods |
| VerfasserIn |
R. D. Leeper, J. Kochendorfer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 6 ; Nr. 8, no. 6 (2015-06-04), S.2291-2300 |
| Datensatznummer |
250116421
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| Publikation (Nr.) |
 copernicus.org/amt-8-2291-2015.pdf |
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| Zusammenfassung |
| Evaporation from a precipitation gauge can cause errors in the amount of
measured precipitation. For automated weighing-bucket gauges, the World
Meteorological Organization (WMO) suggests the use of evaporative
suppressants and frequent observations to limit these biases. However, the
use of evaporation suppressants is not always feasible due to environmental
hazards and the added cost of maintenance, transport, and disposal of the
gauge additive. In addition, research has suggested that evaporation prior
to precipitation may affect precipitation measurements from auto-recording
gauges operating at sub-hourly frequencies. For further evaluation, a field
campaign was conducted to monitor evaporation and its impacts on the quality
of precipitation measurements from gauges used at U.S. Climate Reference
Network (USCRN) stations. Two Geonor gauges were collocated, with one gauge
using an evaporative suppressant (referred to as Geonor-NonEvap) and the
other with no suppressant (referred to as Geonor-Evap) to evaluate
evaporative losses and evaporation biases on precipitation measurements.
From June to August, evaporative losses from the Geonor-Evap gauge exceeded
accumulated precipitation, with an average loss of 0.12 mm h−1. The
impact of evaporation on precipitation measurements was sensitive to the
choice of calculation method. In general, the pairwise method that utilized
a longer time series to smooth out sensor noise was more sensitive to gauge
evaporation (−4.6% bias with respect to control) than the
weighted-average method that calculated depth change over a smaller window
(<+1% bias). These results indicate that while climate and
gauge design affect gauge evaporation rates, computational methods also
influence the magnitude of evaporation biases on precipitation measurements.
This study can be used to advance quality insurance (QA) techniques used in other automated
networks to mitigate the impact of evaporation biases on precipitation
measurements. |
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