scholarly journals Seasonal evolution of a ski slope under natural and artificial snow: detailed observations and modelling

2016 ◽  
Author(s):  
Pierre Spandre ◽  
Hugues François ◽  
Emmanuel Thibert ◽  
Samuel Morin ◽  
Emmanuelle George-Marcelpoil
Keyword(s):  
Water Mass ◽  
Differential Gps ◽  
Seasonal Evolution ◽  
Ski Resorts ◽  
Ski Industry ◽  
Physically Based ◽  
Snow Conditions ◽  
Projected Changes ◽  
Pm 10 ◽  
The Impact

Abstract. The production of Machine Made (MM) snow is now generalized in ski resorts and represents the most common adaptation method to mitigate the impacts of both the natural variability and projected changes of the climate on the snow conditions to guarantee suitable conditions for skiing. Most investigations of the impact of snow conditions on the economy of the ski industry under past, present or projected climate focus on the production of MM snow. So far, none of them accounted for the efficiency of the snowmaking process i.e. the actual MM snow mass that can be recovered from a given water mass used for snowmaking. The present study consisted in observations and interpolation on a 0.5 × 0.5 m grid of snow conditions (depth and mass) using a Differential GPS method and snow density coring, after single sessions of production (prior to MM snow spreading by grooming machines) and on the ski slope as opened to skiers, on a beginner trail in Les Deux Alpes ski resort (French Alps). A detailed physically based snowpack model accounting for grooming and snowmaking was used to address the seasonal evolution of the snowpack and compared to the observations. Our results show that approximately 30 % of the water mass can be recovered as MM snow within 10 m from the center of a MM snow pile after the production and 50 % within 20 m. The observations and simulations on the ski slope were relatively consistent with 60 % (±10 %) of the water mass used for snowmaking within the edge of the ski slope. We also addressed the losses due to thermodynamic effects resulting in less than 10 % of the total water mass in the present case. The main uncertainty pertains to the surface of observations: the surface of the ski slope opened to skiers changed along the season and objective uncertainties exist, in particular from man-made decisions. These results suggest that even in the ideal conditions for production a significant fraction of the water used for snowmaking can not be found as MM snow within the edge of the ski slope with most of the lost fraction of water due to site dependent characteristics (e.g. meteorological conditions, topography, human decisions).

scholarly journals Determination of snowmaking efficiency on a ski slope from observations and modelling of snowmaking events and seasonal snow accumulation

2017 ◽  
Vol 11 (2) ◽  
pp. 891-909 ◽  
Author(s):  
Pierre Spandre ◽  
Hugues François ◽  
Emmanuel Thibert ◽  
Samuel Morin ◽  
Emmanuelle George-Marcelpoil
Keyword(s):  
Water Mass ◽  
Snow Accumulation ◽  
Differential Gps ◽  
Current Case ◽  
Seasonal Snow ◽  
Ski Resorts ◽  
Physically Based ◽  
Snow Conditions ◽  
The Impact

Abstract. The production of Machine Made (MM) snow is now generalized in ski resorts and represents the most common method of adaptation for mitigating the impact of a lack of snow on skiing. Most investigations of correlations between snow conditions and the ski industry's economy focus on the production of MM snow though not one of these has taken into account the efficiency of the snowmaking process. The present study consists of observations of snow conditions (depth and mass) using a Differential GPS method and snow density coring, following snowmaking events and seasonal snow accumulation in Les Deux Alpes ski resort (French Alps). A detailed physically based snowpack model accounting for grooming and snowmaking was used to compute the seasonal evolution of the snowpack and compared to the observations. Our results show that approximately 30 % of the water mass can be recovered as MM snow within 10 m from the center of a MM snow pile after production and 50 % within 20 m. Observations and simulations on the ski slope were relatively consistent with 60 % (±10 %) of the water mass used for snowmaking within the limits of the ski slope. Losses due to thermodynamic effects were estimated in the current case example to be less than 10 % of the total water mass. These results suggest that even in ideal conditions for production a significant fraction of the water used for snowmaking can not be found as MM snow within the limits of the ski slope with most of the missing fraction of water. This is due to site dependent characteristics (e.g. meteorological conditions, topography).

scholarly journals Adaptive capacity of ski resorts in Western Norway to projected changes in snow conditions

2020 ◽  
pp. 1-16
Author(s):  
Halvor Dannevig ◽  
Ida M. Gildestad ◽  
Robert Steiger ◽  
Daniel Scott
Keyword(s):  
Adaptive Capacity ◽  
Ski Resorts ◽  
Western Norway ◽  
Snow Conditions ◽  
Projected Changes

scholarly journals Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

2021 ◽  
Author(s):  
Christian Acal ◽  
Ana M. Aguilera ◽  
Annalina Sarra ◽  
Adelia Evangelista ◽  
Tonio Di Battista ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Functional Data ◽  
Central Italy ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Area Of Interest ◽  
Pm 10 ◽  
The Impact ◽  
Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

Toward a Robust Canopy Hydrology Scheme with Precipitation Subgrid Variability

2007 ◽  
Vol 8 (3) ◽  
pp. 439-446 ◽  
Author(s):  
Dagang Wang ◽  
Guiling Wang
Keyword(s):  
Land Surface ◽  
Surface Characteristics ◽  
Hydrological Processes ◽  
Canopy Interception ◽  
Time Step ◽  
Covered Area ◽  
Surface Models ◽  
Physically Based ◽  
Interception Loss ◽  
The Impact

Abstract Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.

The future of ski resorts in the Swiss Alps: using DMDU to identify tipping points

2021 ◽  
Author(s):  
Saeid Ashraf Vaghefi ◽  
Veruska Muccione ◽  
Kees C.H. van Ginkel ◽  
Marjolijn Haasnoot
Keyword(s):  
Snow Cover ◽  
Climate Models ◽  
Swiss Alps ◽  
Tipping Points ◽  
Annual Variability ◽  
Ski Resorts ◽  
The Future ◽  
Total Profit ◽  
Climate Uncertainty ◽  
Snow Conditions

<p>The future of ski resorts in the Swiss Alps is highly uncertain. Being dependent on snow cover conditions, winter sport tourism is highly susceptible to changes in temperature and precipitation. With the observed warming of the European Alps being well above global average warming, snow cover in Switzerland is projected to shrink at a rapid pace. Climate uncertainty originates from greenhouse gas emission trajectories (RCPs) and differences between climate models. Beyond climate uncertainty, the snow conditions are strongly subject to intra-annual variability. Series of unfavorable years have already led to the financial collapse of several low-altitude ski resorts. Such abrupt collapses with a large impact on the regional economy can be referred to as climate change induced socio-economic tipping points. To some degree, tipping points may be avoided by adaptation measures such as artificial snowmaking, although these measures are also subject to physical and economical constraints. In this study, we use a variety of exploratory modeling techniques to identify tipping points in a coupled physical-economic model applied to six representative ski resorts in the Swiss Alps. New high-resolution climate projections (CH2018) are used to represent climate uncertainty. To improve the coverage of the uncertainty space and accounting for the intra-annual variability of the climate models, a resampling technique was used to produce new climate realizations. A snow process model is used to simulate daily snow-cover in each of the ski resorts. The likelihood of survival of each resort is evaluated from the number of days with good snow conditions for skiing compared to the minimum thresholds obtained from the literature. Economically, the good snow days are translated into the total profit of ski resorts per season of operation. Multiple unfavorable years of total profit may lead to a tipping point. We use scenario discovery to identify the conditions under which these tipping points occur, and reflect on their implications for the future of snow tourism in the Swiss Alps.</p>

A Comparative Study of Data and Physically Based Gas Turbine Modeling for Long-Term Monitoring Scenarios: Part II — Emission Prediction Utilizing Different Levels of Design Information

2018 ◽  
Author(s):  
Moritz Lipperheide ◽  
Thomas Bexten ◽  
Manfred Wirsum ◽  
Martin Gassner ◽  
Stefano Bernero
Keyword(s):  
Gas Turbine ◽  
A Priori ◽  
Model Development ◽  
Fine Tuning ◽  
Plant Operator ◽  
Engine Model ◽  
Physically Based ◽  
Design Type ◽  
The Impact

Reliable engine and emission models allow for an online monitoring of commercial gas turbine operation and help the plant operator and the original equipment manufacturer (OEM) to ensure emission compliance of the aging engine. However, model development and validation require fine-tuning on the particular engines, which may differ in a fleet of a single design type by production, assembly and aging status. For this purpose, Artificial Neural Networks (ANN) offer a good and fast alternative to traditional physically-based engine modeling, because the model creation and adaption is merely an automatized process in commercially available software environments. However, ANN performance depends strongly on the availability of suitable data and a-priori data processing. The present work investigates the impact of specific engine information from the OEM’s design tools on ANN performance. As an alternative to a strictly data-based benchmark approach, engine characteristics were incorporated into ANNs by a pre-processing of the raw measurements with a simplified engine model. The resulting ‘virtual’ measurements, i.e. hot gas temperatures, then served as inputs to ANN training and application during long-term gas turbine operation. When processed input parameters were used for ANNs, overall long-term NOx prediction improved by 55%, and CO prediction by 16% in terms of RMSE, yielding comparable overall RMSE values to the physically-based model.

scholarly journals Quality of outdoor and indoor environment during the cruise of the tall ship STS Fryderyk Chopin

2018 ◽  
Vol 49 ◽  
pp. 00024
Author(s):  
Szymon Firląg
Keyword(s):  
Relative Humidity ◽  
Ventilation System ◽  
Pm 2.5 ◽  
The North ◽  
University Of Technology ◽  
Open Sea ◽  
The North Sea ◽  
Pm 10 ◽  
The Impact

The aim of this paper is to present the results of measurements, on the quality of internal and external environment, carried out during the cruise of the tall ship STS Fryderyk Chopin. The cruise took place between 16th and 30th September 2017 as part of the scientific seminar of the Warsaw University of Technology (WUT) on the Wave, addressed to students of the WTU. After leaving the port of Edinburgh, crossing the North Sea, the Danish straits, stops in Copenhagen and Kołobrzeg, the tall ship reached Szczecin after two weeks. The measurements carried out on the deck included the temperature and relative humidity of the indoor air in three cabins and the men’s bathroom. In two cabins, the CO2 concentration was measured additionally. The outdoor temperature, relative humidity and concentration of PM 1.0, PM 2.5 and PM 10 were also measured. The obtained results allowed to assess the quality of the internal environment in accordance with the standards and to analyze the effectiveness of the mechanical ventilation system. Measurements of particulate matter have shown significant differences between outdoor air quality in the open sea and in ports or near major shipping routes. It turned out that the impact of emissions from passing ships using diesel engines is clearly visible.

scholarly journals Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith observations under snow conditions

2017 ◽  
Vol 10 (1) ◽  
pp. 155-165 ◽  
Author(s):  
Wengang Zhang ◽  
Guirong Xu ◽  
Yuanyuan Liu ◽  
Guopao Yan ◽  
Dejun Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Microwave Radiometer ◽  
Retrieval Algorithm ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Vapor Density ◽  
Heavy Snowfall ◽  
Snow Conditions ◽  
The Impact ◽  
Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith observations. The MWR retrievals were averaged in a ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith observations than in zenith observations, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith observations. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith observations to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith observations, respectively. The discrepancies between MWR retrievals and RAOB profiles by altitude present the same situation. Cases studies show that the impact of snow on accuracies of MWR retrievals is more serious in heavy snowfall than in light snowfall, but off-zenith observation can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall mainly due to the retrieval algorithm, which does not consider the effect of snow, and the accumulated snow on the top of the radome increases the signal noise of MWR measurements. As the snowfall drops away by gravity on the sides of the radome, the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

scholarly journals Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds

2017 ◽  
Author(s):  
David J. Peres ◽  
Antonino Cancelliere ◽  
Roberto Greco ◽  
Thom A. Bogaard
Keyword(s):  
Early Warning ◽  
Continuous Series ◽  
Uncertainty Sources ◽  
Uncertain Knowledge ◽  
Physically Based ◽  
Negative Impacts ◽  
Set Up ◽  
The Impact ◽  
Triggering Rainfall ◽  
Landslide Triggering

Abstract. Uncertainty in rainfall datasets and landslide inventories is known to have negative impacts on the assessment of landslide–triggering thresholds. In this paper, we perform a quantitative analysis of the impacts that the uncertain knowledge of landslide initiation instants have on the assessment of landslide intensity–duration early warning thresholds. The analysis is based on an ideal synthetic database of rainfall and landslide data, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model. This dataset is then perturbed according to hypothetical reporting scenarios, that allow to simulate possible errors in landslide triggering instants, as derived from historical archives. The impact of these errors is analysed by combining different criteria to single-out rainfall events from a continuous series and different temporal aggregations of rainfall (hourly and daily). The analysis shows that the impacts of the above uncertainty sources can be significant. Errors influence thresholds in a way that they are generally underestimated. Potentially, the amount of the underestimation can be enough to induce an excessive number of false positives, hence limiting possible landslide mitigation benefits. Moreover, the uncertain knowledge of triggering rainfall, limits the possibility to set up links between thresholds and physio-geographical factors.

scholarly journals The Impact of Initial Snow Conditions on the Numerical Weather Simulation of a Northern Rockies Atmospheric River

2021 ◽  
Vol 22 (1) ◽  
pp. 155-167
Author(s):  
William Rudisill ◽  
Alejandro Flores ◽  
James McNamara
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Hydrologic Model ◽  
Radiative Properties ◽  
Numerical Weather ◽  
Atmospheric River ◽  
Surface Snow ◽  
Snow Conditions ◽  
The Impact

AbstractSnow’s thermal and radiative properties strongly impact the land surface energy balance and thus the atmosphere above it. Land surface snow information is poorly known in mountainous regions. Few studies have examined the impact of initial land surface snow conditions in high-resolution, convection-permitting numerical weather prediction models during the midlatitude cool season. The extent to which land surface snow influences atmospheric energy transport and subsequent surface meteorological states is tested using a high-resolution (1 km) configuration of the Weather Research and Forecasting (WRF) Model, for both calm conditions and weather characteristic of a warm late March atmospheric river. A set of synthetic but realistic snow states are used as initial conditions for the model runs and the resulting differences are compared. We find that the presence (absence) of snow decreases (increases) 2-m air temperatures by as much as 4 K during both periods, and that the atmosphere responds to snow perturbations through advection of moist static energy from neighboring regions. Snow mass and snow-covered area are both important variables that influence 2-m air temperature. Finally, the meteorological states produced from the WRF experiments are used to force an offline hydrologic model, demonstrating that snowmelt rates can increase/decrease by factor of 2 depending on the initial snow conditions used in the parent weather model. We propose that more realistic representations of land surface snow properties in mesoscale models may be a source of hydrometeorological predictability

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