In search of operational snow model structures for the future – comparing four snow models for 17 catchments in Norway

Abstract In order to use the best suited snow models to investigate snow conditions at ungauged sites and for a changed climate, we have tested four snow models for 17 catchments in Norway. The Crocus and seNorge models are gridded whereas the Distance Distribution Dynamics (DDD) model with its two versions, DDD_CX and DDD_EB, is catchment based. Crocus and DDD_EB use energy balance for estimating snowmelt and SeNorge and DDD_CX use temperature-index methods. SeNorge has calibrated the temperature-index against observed snowmelt, whereas DDD_CX has calibrated the temperature-index against runoff. The models use gridded temperature and precipitation at 1 h resolution for the period 2013–2016. Crocus needs additional forcing from a numerical weather prediction model, whereas DDD_EB calculates the energy-balance elements by using proxy models forced by temperature and precipitation. The threshold temperature for solid and liquid precipitation is common for all the models and equal to 0.5 °C. No corrections of precipitation or temperature are allowed. The snow simulations are validated against observed snow water equivalent (SWE) and against satellite derived snow covered area (SCA). SeNorge and DDD_EB perform best with respect to both SWE and SCA suggesting model structures suited for describing snow conditions at ungauged sites and for a changed climate.

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Present temperature, precipitation and rain-on-snow climate in Svalbard

10.5194/egusphere-egu2020-21647 ◽

2020 ◽

Author(s):

Siiri Wickström ◽

Marius O. Jonassen ◽

John Cassano ◽

Timo Vihma ◽

Jørn Kristiansen

Keyword(s):

Weather Prediction ◽

Open Water ◽

Low Pressure ◽

Numerical Weather Prediction Model ◽

The Arctic ◽

Pressure Center ◽

Climate Conditions ◽

Temperature And Precipitation ◽

Pressure Centre ◽

Local Variability

Potentially high-impact warm and wet winter conditions have become increasingly common in recent decades in the arctic archipelago of Svalbard. In this study, we document present 2m temperature, precipitation and rain-on-snow (ROS) climate conditions in Svalbard and relate them to different atmospheric circulation (AC) types. For this purpose, we utilise a set of observations together with output from the high resolution numerical weather prediction model AROME-Arctic. We find that 2m median temperatures vary the most across AC types in winter and spring, and the least in summer. Southerly and southwesterly flow is associated with 10th percentile 2m temperatures above freezing in all seasons. In terms of precipitation, we find the highest amounts and intensities with onshore flow over open water. Sea ice appears to play a strong role in the local variability in both 2m temperature and precipitation. ROS is a frequent phenomenon in the study period, in particular below 250 m ASL. In winter, ROS only occurs with AC types from the southerly sector or during the passage of a low pressure centre or trough. Most of these events occur during southwesterly flow, with a low pressure center west of Svalbard.&#160;

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Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

The Cryosphere ◽

10.5194/tc-6-1323-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1323-1337 ◽

Cited By ~ 54

Author(s):

T. M. Saloranta

Keyword(s):

Snow Water Equivalent ◽

Statistical Evaluation ◽

Model Performance ◽

Spatiotemporal Variability ◽

Spatiotemporal Resolution ◽

Model Code ◽

Temperature And Precipitation ◽

Rugged Topography ◽

Snow Model ◽

Snow Conditions

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

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A virtual network for estimating daily new snow water equivalent and snow depth in the Swiss Alps

Annals of Glaciology ◽

10.3189/172756410791386427 ◽

2010 ◽

Vol 51 (54) ◽

pp. 32-38 ◽

Cited By ~ 6

Author(s):

Luca Egli ◽

Tobias Jonas ◽

Jean-Marie Bettems

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Weather Prediction ◽

Practical Importance ◽

Cost Effective ◽

Numerical Weather Prediction Model ◽

Swiss Alps ◽

Virtual Network ◽

Systematic Bias ◽

Snow Water

AbstractDaily new snow water equivalent (HNW) and snow depth (HS) are of significant practical importance in cryospheric sciences such as snow hydrology and avalanche formation. In this study we present a virtual network (VN) for estimating HNW and HS on a regular mesh over Switzerland with a grid size of 7 km. The method is based on the HNW output data of the numerical weather prediction model COSMO-7, driving an external accumulation/melting routine. The verification of the VN shows that, on average, HNW can be estimated with a mean systematic bias close to 0 and an averaged absolute accuracy of 4.01 mm. The results are equivalent to the performance observed when comparing different automatic HNW point estimations with manual reference measurements. However, at the local scale, HS derived by the VN may significantly deviate from corresponding point measurements. We argue that the VN presented here may introduce promising cost-effective options as input for spatially distributed snow hydrological and avalanche risk management applications in the Swiss Alps.

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Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

10.5194/tc-2021-74 ◽

2021 ◽

Author(s):

Hans Lievens ◽

Isis Brangers ◽

Hans-Peter Marshall ◽

Tobias Jonas ◽

Marc Olefs ◽

...

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Weather Prediction ◽

Slight Reduction ◽

European Alps ◽

Mountainous Regions ◽

Seasonal Snow ◽

Spatio Temporal ◽

Snow Conditions ◽

Snow Mass

Abstract. Seasonal snow in mountain regions is an essential water resource. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) from regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We demonstrate the ability of the Sentinel-1 mission to monitor weekly snow depth at sub-kilometer (100 m, 300 m and 1 km) resolutions over the European Alps, for 2017–2019. Sentinel-1 backscatter observations, especially for the cross-polarization channel, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in a change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. For dry snow conditions, the 1 km Sentinel-1 retrievals have a spatio-temporal correlation (R) of 0.87 and mean absolute error (MAE) of 0.17 m compared to in situ measurements across 743 sites in the European Alps. A slight reduction in performance is observed for the retrievals at 300 m (R = 0.85 and MAE = 0.18 m) and 100 m (R = 0.79 and MAE = 0.21 m). The results demonstrate the ability of Sentinel-1 to provide regional snow estimates at an unprecedented resolution in mountainous regions, where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resources management, flood forecasting and numerical weather prediction.

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Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

The Cryosphere Discussions ◽

10.5194/tcd-6-1337-2012 ◽

2012 ◽

Vol 6 (2) ◽

pp. 1337-1366 ◽

Cited By ~ 5

Author(s):

T. M. Saloranta

Keyword(s):

Snow Water Equivalent ◽

Statistical Evaluation ◽

Model Performance ◽

Model Fit ◽

Spatiotemporal Variability ◽

Spatiotemporal Resolution ◽

Model Code ◽

Temperature And Precipitation ◽

Snow Model ◽

Snow Conditions

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1×1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates among others snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a~thorough spatiotemporal statistical evaluation of the model performance in 1957–2011 is made using the two major sets of extensive in-situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the distribution of model fit for SWE has a clear dependency on elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet still process-based method to construct snow maps of high spatiotemporal resolution. It is especially well-suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

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A Methodology for Optimizing Numerical Weather Prediction Models

10.5194/egusphere-egu2020-4149 ◽

2020 ◽

Author(s):

Rafaella - Eleni Sotiropoulou ◽

Ioannis Stergiou ◽

Efthimios Tagaris

Keyword(s):

Numerical Weather Prediction ◽

Land Surface ◽

Prediction Models ◽

Carbon Sink ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Numerical Weather ◽

Temperature And Precipitation ◽

Statistical Measures ◽

Numerical Weather Prediction Models

Optimizing the performance of numerical weather prediction models is a very complicated process due to the numerous parameterization choices provided to the user. In addition, improving the predictability of one model&#8217;s variable (e.g., temperature) does not necessarily imply the improvement of another (e.g., precipitation). In this work the Technique of Preference by Similarity to the Ideal Solution (TOPSIS) is suggested as a method to optimize the performance of a numerical weather prediction model. TOPSIS provides the ability of using multiple statistical measures as ranking criteria for multiple forecasting variables. The Weather Research and Forecasting model (WRF) is used here for application of TOPSIS in order to optimize the model&#8217;s performance by the combined assessment of temperature and precipitation over Europe. Six ensembles optimize model&#8217;s physics performance (i.e., microphysics, planetary boundary layer, cumulus scheme, Long&#8211;and Short&#8211; wave and Land Surface schemes). The best performing option for each ensemble is selected by using multiple statistical criteria as input for the TOPSIS method, based on the integration of entropy weights. The method adopted here illustrates the importance of an integrated evaluation of weather prediction models&#8217; performance and suggests a pathway for its improvement.Acknowledgments LIFE CLIMATREE project &#8220;A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas&#8221; (LIFE14 CCM/GR/000635).

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