Error sources of precipitation measurements using electronic weight systems

Sevruk, Boris; Chvíla, B.

doi:10.1016/j.atmosres.2004.10.026

Cited by 18 publications

(8 citation statements)

References 3 publications

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“…Sevruk et al, 2009;Ren and Li, 2007;Sevruk and Chvíla, 2005;Legates and DeLiberty, 1993). Other uncertainties related to precipitation in high-elevation areas might be attributed to missing snow monitoring equipment (i.e.…”

Section: Uncertainties In the Verification Resultsmentioning

confidence: 99%

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Zambrano‐Bigiarini

Nauditt²,

Birkel

et al. 2017

Hydrol. Earth Syst. Sci.

224

153

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Abstract. Accurate representation of the real spatiotemporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.This study attempts to exhaustively evaluate -for the first time -the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25 • grid if necessary). The modified Kling-Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.Results revealed that most SRE products performed better for the humid South (36.4-43.7 • S) and Central Chile (32.18-36.4 • S), in particular at low-and mid-elevation zones (0-1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM-JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all timescales, which can be clearly attributed to its bias-correction procedure using 217 stations from Chile. Good results were also obtained by the research products CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1, while CMORPH, PERSIANN-CDR, and the real-time PERSIANN-CCS-Adj were less skillful in representing observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resource assessments. We finally conclude that despite improvements of most SRE products, a site-specific assessment is still needed before any use in catchment-scale hydrological studies.

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Section: Uncertainties In the Verification Resultsmentioning

confidence: 99%

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Zambrano‐Bigiarini

Nauditt²,

Birkel

et al. 2017

Hydrol. Earth Syst. Sci.

224

153

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“…Weighing style gauge/load cell technology replaced the tipping bucket in 2003 (6.3 in/16 cm OTT Pluvio All‐Weather Precipitation Accumulation Gauge, AWPAG) with a sensor accuracy of at least 0.25 mm for individual precipitation events (supporting information Figure S2). Electronic weighing gauges, including the OTT Pluvio AWPAG, have shown reduced precipitation bias compared to heated tipping buckets [ Rasmussen et al ., ; Sevruk and Chvíla , ]. Field comparisons of heated tipping buckets and electronic weighing systems for snowfall have measured 24% less precipitation for tipping gauges primarily due to evaporative losses related to gauge heating [ Savina et al ., ].…”

Section: Methodsmentioning

confidence: 99%

Tundra water budget and implications of precipitation underestimation

Liljedahl

Hinzman

Kane

et al. 2017

Water Resources Research

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Difficulties in obtaining accurate precipitation measurements have limited meaningful hydrologic assessment for over a century due to performance challenges of conventional snowfall and rainfall gauges in windy environments. Here, we compare snowfall observations and bias adjusted snowfall to end‐of‐winter snow accumulation measurements on the ground for 16 years (1999–2014) and assess the implication of precipitation underestimation on the water balance for a low‐gradient tundra wetland near Utqiagvik (formerly Barrow), Alaska (2007–2009). In agreement with other studies, and not accounting for sublimation, conventional snowfall gauges captured 23–56% of end‐of‐winter snow accumulation. Once snowfall and rainfall are bias adjusted, long‐term annual precipitation estimates more than double (from 123 to 274 mm), highlighting the risk of studies using conventional or unadjusted precipitation that dramatically under‐represent water balance components. Applying conventional precipitation information to the water balance analysis produced consistent storage deficits (79 to 152 mm) that were all larger than the largest actual deficit (75 mm), which was observed in the unusually low rainfall summer of 2007. Year‐to‐year variability in adjusted rainfall (±33 mm) was larger than evapotranspiration (±13 mm). Measured interannual variability in partitioning of snow into runoff (29% in 2008 to 68% in 2009) in years with similar end‐of‐winter snow accumulation (180 and 164 mm, respectively) highlights the importance of the previous summer's rainfall (25 and 60 mm, respectively) on spring runoff production. Incorrect representation of precipitation can therefore have major implications for Arctic water budget descriptions that in turn can alter estimates of carbon and energy fluxes.

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“…Precipitation gauges are accompanied by errors induced due to wind effects (systematic wind field deformation above the gauge orifice) (Wolff et al, 2013;Colli, 2014) and the inhomogeneity introduced by the design of the measuring device (Sevruk and Nespor, 1994). Further errors include wetting losses (Sevruk, 1974a) and evaporation losses (Sevruk, 1974b;Leeper and Kochendorfer, 2015) inside the collection funnel, sampling errors due to weighing and tipping mechanisms (Sevruk and Chvíla, 2005) and in-and out-splashing effects due to device location, as well as random observational and instrumental errors. These errors and limitations have been recognised by the WMO (1984;; 2010 and, specifically for snow, 1998) and were highlighted in comparison studies carried out over the past decades (e.g.…”

Section: Sources Of Error For Precipitation Gaugesmentioning

confidence: 99%

“…Depending on site setup, precipitation data can be automatically sampled at 10 s resolution and integrated over 1 min. With it, averaging fluctuations or noise caused by the vibrations of the weighing gauge mechanisms/wind pumping (Sevruk and Chvíla, 2005; WMO, 2010) are automatically removed. Nevertheless, high-resolution data output by weighing gauges offers further the possibility of investigating rain intensity of short intensive rain showers.…”

Section: Spatial and Temporal Sampling Designmentioning

confidence: 99%

Standardized precipitation measurements within ICOS: rain, snowfall and snow depth: a review

Dengel¹,

Graf²,

Grünwald³

et al. 2018

International Agrophysics

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Precipitation is one of the most important abiotic variables related to plant growth. Using standardised measurements improves the comparability and quality of precipitation data as well as all other data within the Integrated Carbon Observation System network. Despite the spatial and temporal variation of some types of precipitation, a single point measurement satisfies the requirement as an ancillary variable for eddy covariance measurements. Here the term precipitation includes: rain, snowfall (liquid water equivalent) and snow depth, with the latter two being of interest only where occurring. Weighing gauges defined as Integrated Carbon Observation System standard with the capacity of continuously measuring liquid and solid precipitation are installed free-standing, away from obstacles obstructing rain or snowfall. In order to minimise wind-induced errors, gauges are shielded either naturally or artificially to reduce the adverse effect of wind speed on the measurements. Following standardised methods strengthens the compatibility and comparability of data with other standardised environmental observation networks while opening the possibility for synthesis studies of different precipitation measurement methodologies and types including a wide range of ecosystems and geolocations across Europe.

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Error sources of precipitation measurements using electronic weight systems

Cited by 18 publications

References 3 publications

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Tundra water budget and implications of precipitation underestimation

Standardized precipitation measurements within ICOS: rain, snowfall and snow depth: a review

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