How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed

Rasmussen, Roy; Baker, Bruce L.; Kochendorfer, John; Meyers, Tilden P.; Landolt, Scott; Fischer, A.; Black, Jenny; Thériault, Julie M.; Kucera, Paul A.; Gochis, David; Smith, Charles W.; Nitu, Rodica; Hall, Mark E.; Ikeda, Kyoko; Gutmann, E. D.

doi:10.1175/bams-d-11-00052.1

Cited by 611 publications

(587 citation statements)

References 18 publications

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“…The root-mean-square error (RMSE) and mean absolute error for the 2014/2015 CRP-estimated SWE is 8.8 and 7.5 mm respectively. These error results are comparable to Rasmussen et al (2012), who found an RMSE of 5.1 mm between SWE estimated from snow depth and from a CRP. The 2014/2015 CRP-estimated SWE errors are considerably lower compared to other large-scale SWE measurement methods such as remote sensing.…”

Section: Estimating Swe From Moderated Neutron Intensity Above Snowpacksupporting

confidence: 87%

“…However, the CRP was installed within a laboratory, and Desilets et al (2010) provided limited details of their study and did not include the relationship they utilised for deriving SWE from measured moderated neutron counts. Using a CRP to monitor SWE was also tested at the Marshall Field Site, Colorado, USA (Rasmussen et al, 2012). Again, limited details were given on the methods of the study and the empirical relationship used to predict SWE from moderated neutron intensity.…”

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confidence: 99%

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Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

Sigouin

Bing

2016

The Cryosphere

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Abstract. Measuring snow water equivalent (SWE) is important for many hydrological purposes such as modelling and flood forecasting. Measurements of SWE are also crucial for agricultural production in areas where snowmelt runoff dominates spring soil water recharge. Typical methods for measuring SWE include point measurements (snow tubes) and large-scale measurements (remote sensing). We explored the potential of using the cosmic-ray soil moisture probe (CRP) to measure average SWE at a spatial scale between those provided by snow tubes and remote sensing. The CRP measures above-ground moderated neutron intensity within a radius of approximately 300 m. Using snow tubes, surveys were performed over two winters (2013/2014 and 2014/2015) in an area surrounding a CRP in an agricultural field in Saskatoon, Saskatchewan, Canada. The raw moderated neutron intensity counts were corrected for atmospheric pressure, water vapour, and temporal variability of incoming cosmic-ray flux. The mean SWE from manually measured snow surveys was adjusted for differences in soil water storage before snowfall between both winters because the CRP reading appeared to be affected by soil water below the snowpack. The SWE from the snow surveys was negatively correlated with the CRP-measured moderated neutron intensity, giving Pearson correlation coefficients of −0.90 (2013/2014) and −0. 87 (2014/2015). A linear regression performed on the manually measured SWE and moderated neutron intensity counts for 2013/2014 yielded an r 2 of 0.81. Linear regression lines from the 2013/2014 and 2014/2015 manually measured SWE and moderated neutron counts were similar; thus differences in antecedent soil water storage did not appear to affect the slope of the SWE vs. neutron relationship.The regression equation obtained from 2013/2014 was used to model SWE using the moderated neutron intensity data for 2014/2015. The CRP-estimated SWE for 2014/2015 was similar to that of the snow survey, with an root-mean-square error of 8.8 mm. The CRP-estimated SWE also compared well to estimates made using snow depths at meteorological sites near (< 10 km) the CRP. Overall, the empirical equation presented provides acceptable estimates of average SWE using moderated neutron intensity measurements. Using a CRP to monitor SWE is attractive because it delivers a continuous reading, can be installed in remote locations, requires minimal labour, and provides a landscape-scale measurement footprint.

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Section: Estimating Swe From Moderated Neutron Intensity Above Snowpacksupporting

confidence: 87%

mentioning

confidence: 99%

Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

Sigouin

Bing

2016

The Cryosphere

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“…Di Baldassarre and Montanari (2009) suggest that forcing uncertainty has attracted less attention because it is "often considered negligible" relative to parametric and structural uncertainties. Nevertheless, forcing uncertainty merits more attention in some cases, such as in snow-affected watersheds where meteorological and energy balance measurements are scarce (Bales et al, 2006;Schmucki et al, 2014) and prone to errors due to environmental or instrumental factors (Huwald et al, 2009;Lundquist et al, 2015;Rasmussen et al, 2012). Forcing uncertainty is enhanced in complex terrain where meteorological variables exhibit high spatial variability Flint and Childs, 1987;Herrero and Polo, 2012;Lundquist and Cayan, 2007).…”

Section: S Raleigh Et Al: Physical Model Sensitivity To Forcing mentioning

confidence: 99%

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Raleigh

Lundquist

Clark

2015

Hydrol. Earth Syst. Sci.

162

128

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Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snowaffected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

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“…This research utilizes remotely sensed satellite precipitation estimates and reanalysis data to supplement in situ measurements. It is important to note that both in situ station data and satellite precipitation estimates have difficulty detecting the snow component of precipitation (Rasmussen et al 2012).…”

Section: Datamentioning

confidence: 99%

Multi-annual variations in winter westerly disturbance activity affecting the Himalaya

et al. 2014

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How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed

Cited by 611 publications

References 18 publications

Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Multi-annual variations in winter westerly disturbance activity affecting the Himalaya

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