Measurements of wind-induced loss of solid precipitation: description of a Norwegian field study

Wolff, Mareile; Isaksen, Ketil; Brækkan, Ragnar; Alfnes, Eli; Petersen‐Øverleir, Asgeir; Ruud, Erik

doi:10.2166/nh.2012.166

Cited by 25 publications

(22 citation statements)

References 3 publications

(3 reference statements)

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“…The sigmoid transfer function (Wolff et al, 2013) was also tested with these data, but like in Kochendorfer et al (2017), the more complex sigmoid function produced similar biases and RMSE, so the simpler Eq. (3) was used.…”

Section: Transfer Function Modelsmentioning

confidence: 99%

“…The magnitude of this effect varies with J. Kochendorfer et al: Analysis of single-Alter-shielded and unshielded measurements wind speed; wind shielding; the shape of the precipitation gauge; and the predominant size, phase, and fall velocity of the hydrometeors (Colli et al, 2015;Folland, 1988;Groisman et al, 1991;Theriault et al, 2012;Wolff et al, 2013). Because all these factors affect the amount of undercatch, it is difficult to accurately describe and adjust the resultant errors for all gauges, in all places, in all types of weather.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, studies of the magnitude and importance of such measurement errors have been performed using both analytical (Colli et al, 2015(Colli et al, , 2016Nespor and Sevruk, 1999;Theriault et al, 2012) and observational approaches (e.g. Chen et al, 2015;Ma et al, 2015;Rasmussen et al, 2012;Wolff et al, 2013Wolff et al, , 2015. The ultimate goal of all this research is to facilitate the creation of accurate and consistent precipitation records spanning different climates and different measurement networks, including measurements by different gauges and shields (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE

Kochendorfer

Nitu

Wolff

et al. 2017

Hydrol. Earth Syst. Sci.

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119

216

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Abstract. Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant inaccuracies. Solid precipitation is particularly difficult to measure accurately, and wintertime precipitation measurement biases between different observing networks or different regions can exceed 100 %. Using precipitation gauge results from the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, crystal habit, and wind were quantified. The methods used to calculate gauge catch efficiency and correct known biases are described. Adjustments, in the form of "transfer functions" that describe catch efficiency as a function of air temperature and wind speed, were derived using measurements from eight separate WMO-SPICE sites for both unshielded and single-Alter-shielded precipitationweighing gauges. For the unshielded gauges, the average undercatch for all eight sites was 0.50 mm h −1 (34 %), and for the single-Alter-shielded gauges it was 0.35 mm h −1 (24 %). After adjustment, the mean bias for both the unshielded and single-Alter measurements was within 0.03 mm h −1 (2 %) of zero. The use of multiple sites to derive such adjustments makes these results unique and more broadly applicable to other sites with various climatic conditions. In addition, errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites were estimated.

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Section: Transfer Function Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE

Kochendorfer

Nitu

Wolff

et al. 2017

Hydrol. Earth Syst. Sci.

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119

216

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“…Further information about the test site, including an evaluation of the homogeneity and a list of instruments, can be found in Wolff et al (2010Wolff et al ( , 2013.…”

Section: Site Descriptionmentioning

confidence: 99%

Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study

Wolff

Isaksen

Petersen‐Øverleir³

et al. 2015

Hydrol. Earth Syst. Sci.

159

174

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Abstract. Precipitation measurements exhibit large coldseason biases due to under-catch in windy conditions. These uncertainties affect water balance calculations, snowpack monitoring and calibration of remote sensing algorithms and land surface models. More accurate data would improve the ability to predict future changes in water resources and mountain hazards in snow-dominated regions.In 2010, a comprehensive test site for precipitation measurements was established on a mountain plateau in southern Norway. Automatic precipitation gauge data are compared with data from a precipitation gauge in a Double Fence Intercomparison Reference (DFIR) wind shield construction which serves as the reference. A large number of other sensors are provided supporting data for relevant meteorological parameters.In this paper, data from three winters are used to study and determine the wind-induced under-catch of solid precipitation. Qualitative analyses and Bayesian statistics are used to evaluate and objectively choose the model that best describes the data. A continuous adjustment function and its uncertainty are derived for measurements of all types of winter precipitation (from rain to dry snow). A regression analysis does not reveal any significant misspecifications for the adjustment function, but shows that the chosen model does not describe the regression noise optimally. The adjustment function is operationally usable because it is based only on data available at standard automatic weather stations.The results show a non-linear relationship between undercatch and wind speed during winter precipitation events and there is a clear temperature dependency, mainly reflecting the precipitation type. The results allow, for the first time, derivation of an adjustment function based on measurements above 7 m s −1 . This extended validity of the adjustment function shows a stabilization of the wind-induced precipitation loss for higher wind speeds.

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“…This may indicate that the variance 1/τ should vary with the amount of precipitation. Precipitation observations are both uncertain and have biases due to wind exposure, especially for snow (Wolff et al, 2013). Therefore, in a more realistic model, properties of the location and measurement situations should also be included.…”

Section: Annual Precipitation In Southern Norwaymentioning

confidence: 99%

Spatial models with explanatory variables in the dependence structure

2014

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Please cite this article as: Ingebrigtsen, R., Lindgren, F., Steinsland, I., Spatial models with explanatory variables in the dependence structure. Spatial Statistics (2013), http://dx.doi.org/10. 1016/j.spasta.2013.06.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractGeostatistical models have traditionally been stationary. However, physical knowledge about underlying spatial processes often require models with nonstationary dependence structures. Thus, there has been an interest in the literature to provide flexible models and computationally efficient methods for non-stationary phenomena. In this work, we demonstrate that the stochastic partial differential equation (SPDE) approach to spatial modelling provides a flexible class of non-stationary models where explanatory variables easily can be included in the dependence structure. In addition, the SPDE approach enables computationally efficient Bayesian inference with integrated nested Laplace approximations (INLA) available through the R-package r-inla. We illustrate the suggested modelling framework with a case study of annual precipitation in southern Norway, and compare a non-stationary model with dependence structure governed by elevation to a stationary model. Further, we use a simulation study to explore the annual precipitation models. We investigate identifiability of model parameters and whether the deviance information criterion (DIC) is able to distinguish datasets from the non-stationary and stationary models.

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Measurements of wind-induced loss of solid precipitation: description of a Norwegian field study

Cited by 25 publications

References 3 publications

Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE

Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE

Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study

Spatial models with explanatory variables in the dependence structure

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