Considering snow depositions and transport mechanisms in meteorological analysis of predicting snow loads on buildings

The meteorological conditions determine snow evolutions on and around buildings. Those climatic impacts have been independently considered in the current design codes of buildings, namely the wind pressure and the snow load. But snow load estimates are only based on precipitation assessments without considering the joint effect of wind and snow precipitation which lead to wind dominated deposition and snow transport. Thus, the statisticsbased data provided by those building codes seem practically inadequate or at least incomplete. Using the data provided by upper-air and ground meteorological stations in northern China, including snowfall, wind velocity, and temperature, as indicators, we propose a series of statistical relationships covering aspects of snow precipitation and snow drifting. For those meteorological indicators that can affect building snow evolutions, more detailed analyses on their spatial and temporal changes have been achieved. The regionalization of snow precipitation features in northern China has also been proposed based on the clustering analysis on meteorological joint effects. The result shows that the snowfall in most northern regions of China is generally not heavy and in the form of a uniform deposition. The wind dominated depositions around buildings are more likely to appear in the regions around the Bohai Sea.

Radar measurements of blowing snow off a mountain ridge

Josué Etienne Gehring, Wolf Hendrik Huwald, Michael Lehning, Benjamin Andreas Walter

Modelling and forecasting wind-driven redistribution of snow in mountainous regions with its implications on avalanche danger, mountain hydrology or flood hazard is still a challenging task often lacking in essential details. Measurements of drifting and blowing snow for improving process understanding and model validation are typically limited to point measurements at meteorological stations, providing no information on the spatial variability of horizontal mass fluxes or even the vertically integrated mass flux. We present a promising application of a compact and low-cost radar system for measuring and characterizing larger-scale (hundreds of metres) snow redistribution processes, specifically blowing snow off a mountain ridge. These measurements provide valuable information of blowing snow velocities, frequency of occurrence, travel distances and turbulence characteristics. Three blowing snow events are investigated, two in the absence of precipitation and one with concurrent precipitation. Blowing snow velocities measured with the radar are validated by comparison against wind velocities measured with a 3D ultra-sonic anemometer. A minimal blowing snow travel distance of 60–120m is reached 10–20% of the time during a snow storm, depending on the strength of the storm event. The relative frequency of transport distances decreases exponentially above the minimal travel distance, with a maximum measured distance of 280 m. In a first-order approximation, the travel distance increases linearly with the wind velocity, allowing for an estimate of a threshold wind velocity for snow particle entrainment and transport of 7.5–8.8m/s, most likely depending on the prevailing snow cover properties. Turbulence statistics did not allow a conclusion to be drawn on whether low-level, low-turbulence jets or highly turbulent gusts are more effective in transporting blowing snow over longer distances, but highly turbulent flows are more likely to bring particles to greater heights and thus influence cloud processes. Drone-based photogrammetry measurements of the spatial snow height distribution revealed that increased snow accumulation in the lee of the ridge is the result of the measured local blowing snow conditions.

2020

Simulation of atmospheric flows over complex terrain for wind power potential assessment

When assessing the economic viability of a wind farm, the estimation of the on-site wind power potential is perhaps the most important step. The most common way of evaluating the wind power potential of an area of interest consists of making on-site measurements for a period of one year. In order to take account of the inter-annual variation of wind speed, the one year of data are normally correlated with data recorded at a reference site where long-term data (typically > 10 years) are available. A correlation analysis is formulated for the concurrent data sets at the reference and prediction sites. This correlation is then used to transform the long-term wind speed at the reference site to the long-term wind speed that would have been expected at the prediction site had long-term measurements been made at this site. An alternative approach is also used, which consists of establishing site-to-site relationships using a numerical model to simulate meteorological situations which are typical for the area of interest. These relationships are then used to transpose the known long-term wind statistics of the reference site to the prediction site. Such an approach is applied in this work to the region of Chasseral & Mt-Crosin. The wind data available for a period of 16 years at Chasseral are transposed to the Mt-Crosin site where they are then compared to the data measured at the location of the installed wind farm. Over complex terrain, the linearised models traditionally used for wind power potential assessment fail to reproduce accurate wind fields. Therefore, to be applied to mountainous terrain such as that found in Switzerland, the approach relying on numerical simulation requires the development and validation of a numerical tool capable of simulating wind fields over complex topography. As the numerical model would have to deal with relatively steep slopes requiring a fine horizontal (50-100 m) and vertical resolution (∼5-10 m in the lowest levels), a fluid dynamics model was used which solves the complete set of Navier-Stokes equations with κ-ε turbulence closure. The standard version of the model used (CFX4) is modified in a novel way to extend its field of application so that atmospheric phenomena could be simulated which are typical of the meso-scale. The modified version solves the flow equations with the anelastic approximation (deep Boussinesq) and assuming a background rotation of the wind field (with the high altitude wind field following the geostrophic approximation). In the first part of this work, the numerical model is validated. The results obtained in this phase show that for meteorological situations for which the wind at the ground is coupled to the high altitude wind, the numerical model is able to satisfactorily reproduce: the flow in the surface layer, reproducing the effects associated with the ground roughness, roughness change, or heat flux through the ground; the flow in the Ekman layer together with the interaction between the free flow thermal stability conditions and the boundary layer; the linear and non-linear effects associated with the perturbation induced by a mountain in a stably stratified flow. In the second stage of this work, an extension of the standard Measure-Correlate-Predict method is presented to calculate the wind speed distribution at the prediction site, from transposition relationships and from the wind statistics at the reference site. The validity of the underlying assumptions is confirmed using concurrent data sets that were collected at both the reference and prediction sites. To evaluate the accuracy that can be achieved with the transposition assumptions, a back-prediction is performed using the transposition relationships obtained using the observations. Different types of transposition relationships have been investigated. Finally, the transposition methodology is applied to calculate the wind speed conditions at Mt-Crosin from the Chassera1 data, using the transposition relationships calculated by the numerical model for a range of meteorological situations typical for the area considered. The Mt-Crosin to Chassera1 sector wind speed ratios calculated by the numerical model tend to slightly underestimate those observed. The mean wind speeds obtained from the transposition are underestimated by 7% to 18% at the three measuring mast locations on Mt-Crosin. The yearly energy output that can be produced by a wind turbine in these conditions is underestimated by 8% to 36%. For a further period, the actual energy production of the three installed wind turbines has been compared with the model prediction at hub height, which showed that the transposition results underestimate the actual yearly production by 22% to 24%. From the transposition of the long-term data at Chassera1 (16 years), with the relationships obtained by the numerical model, a wind power potential of between 470 MWh/year (Côte Est) and 596 MWh/year (Côte Nord) is predicted using the characteristics of a Vestas-V44 wind turbine. From the work presented here, it appears that for well-exposed sites such as those located along the Jura Crest, the methods developed are able to give a wind power potential prediction with a similar accuracy as a one year measurement campaign performed on site.

EPFL1998

Improving Alpine Flood Prediction through Hydrological Process Characterization and Uncertainty Analysis

Cara Christine Tobin

Among the many challenges of Alpine flood prediction is describing complex, meteo-hydrological processes in a simplified, robust manner that can be easily integrated into operational forecasting. In this dissertation, improved methods to characterize these processes are developed and integrated into the hydrological modeling component of an operational flood forecasting system used in the Swiss Alps. Detailed studies are conducted to improve hydrological model inputs, processes and outputs. Improvements, detailed in four chapters of this thesis, address the overarching goal of this work – the reduction of flood forecasting uncertainty. The accuracy of flood predictions in Alpine areas is contingent upon adequate interpolation of meteorological forcings, which has significant impacts on discharge volumes and flood peaks. This thesis demonstrates an improvement in the interpolation of temperature and precipitation inputs using a robust variogram which considers anisotropy and using a geostatistical interpolation method to distribute inputs in space and time. Results show that using elevation as the external drift factor better describes orographically-induced precipitation and temperature gradients. Also, the consideration of anisotropy is integral in detailing spatial patterns of precipitation induced by storm advection. Hydrological flood forecasting in mountainous areas also requires accurate partitioning between rain and snowfall to properly estimate the extent of runoff contributing areas. Partitioning is improved in this work by using a new method to integrate Limited Area Model output. Unlike standard hydrological procedures in inferring snowfall limit estimates based on dry, ground temperature measurements, Limited Area Model output considers the vertical, humid, atmospheric structure in its snowfall limit calculations. In effect, this method provides good estimates of runoff contributing areas in the spring as evidenced by validation on discharge measurements and satellite images of snow coverage. Accurately describing snowmelt processes on a sub-daily scale is also of critical importance in Alpine flood forecasting. However, the complex topography of the study region has limited observations available for validation. This thesis presents the development of a new physically-based snowmelt method applicable to regions with limited data. This method uses only daily minimum and maximum temperatures to mimic the effects of radiation. A comparative analysis of snowmelt methods is validated with snow lysimeter data and with a unique, distributed meteorological dataset collected by a wireless weather station network. Results demonstrate that the new method is competitive with more complex snowmelt methods as shown by accurately reproducing diurnal snowmelt cycles. Conveying limits of certainty on flood prediction outputs to users is critical because of epistemic and aleatory errors inherent to environmental modeling. Due to the presence of these errors, the GLUE methodology and multi-criteria performance ideas have been adapted with a fit-for-purpose uncertainty estimation technique in the final part of this thesis. With this method, hydrological model parameters are constrained based on hydrograph behavior, with a particular focus on flood peak response. A key component of the technique is a visualization tool which shows acceptable ensembles of discharge with respect to individual and combined criteria. By integrating the aforementioned input and process improvements into the hydrological model, calibration achieves model outputs that capture observed river discharge. Also, the uncertainty associated with hydrological modeling output error is reduced. Findings of this thesis are applicable to operational flood forecasting in general and have proven utility in improving hydrological model predictions in mountainous regions. Due to the novelty of the developments in terms of new methods or the use of tools and data sources previously unexploited in flood forecasting, further testing of the improvements is recommended. Future research in quantifying the chain of uncertainty produced by combining probabilistic forecast inputs with the hydrological output ensembles is also critical when the improved flood forecasting model becomes fully operational.

EPFL2012