Abstract
When the vehicle is only affected by road conditions and its own speed, in the low temperature rain and snow weather. In order to clarify the order of the importance of each influencing ...factor at the exit section of the tunnel on the driving stability, this article uses the analytic hierarchy process to sort them. The results show that the order of importance is: Friction coefficient
μ
s
> driving speed
ν
(m/s)> circular curve radius
R
(m), longitudinal slope
i
(%). According to the sorting results, targeted safeguard measures can be provided for the tunnel design, operation and use process.
Snow variability in the Swiss Alps 1864–2009 Scherrer, Simon C.; Wüthrich, Christian; Croci‐Maspoli, Mischa ...
International journal of climatology,
December 2013, Volume:
33, Issue:
15
Journal Article
Peer reviewed
Open access
ABSTRACT
We present a climate analysis of nine unique Swiss Alpine new snow series that have been newly digitized. The stations cover different altitudes (450–1860 m asl) and all time series cover ...more than 100 years (one from 1864 to 2009). In addition, data from 71 stations for the last 50–80 years for new snow and snow depth are analysed to get a more complete picture of the Swiss Alpine snow variability. Important snow climate indicators such as new snow sums (NSS), maximum new snow (MAXNS) and days with snowfall (DWSF) are calculated and variability and trends analysed. Series of days with snow pack (DWSP) ≥ 1 cm are reconstructed with useful quality for six stations using the daily new snow, local temperature and precipitation data. Our results reveal large decadal variability with phases of low and high values for NSS, DWSF and DWSP. For most stations NSS, DWSF and DWSP show the lowest values recorded and unprecedented negative trends in the late 1980s and 1990s. For MAXNS, however, no clear trends and smaller decadal variability are found but very large MAXNS values (>60 cm) are missing since the year 2000. The fraction of NSS and DWSP in different seasons (autumn, winter and spring) has changed only slightly over the ∼150 year record. Some decreases most likely attributable to temperature changes in the last 50 years are found for spring, especially for NSS at low stations. Both the NSS and DWSP snow indicators show a trend reversal in most recent years (since 2000), especially at low and medium altitudes. This is consistent with the recent ‘plateauing’ (i.e. slight relative decrease) of mean winter temperature in Switzerland and illustrates how important decadal variability is in understanding the trends in key snow indicators.
Climatically driven changes in snow characteristics (snowfall, snowpack, and snowmelt) will affect hydrologic and ecological systems in Alaska over the coming century, yet there exist no projections ...of downscaled future snow pack metrics for the state of Alaska. We updated historical and projected snow day fraction (PSF, the fraction of days with precipitation falling as snow) from McAfee et al. We developed modeled snowfall equivalent (SFE) derived from the product of snow-day fraction (PSF) and existing gridded precipitation for Alaska from Scenarios Network for Alaska and Arctic Planning (SNAP). We validated the assumption that modeled SFE approximates historical decadally averaged snow water equivalent (SWE) observations from snowcourse and Snow Telemetry (SNOTEL) sites. We present analyses of future downscaled PSF and two new products, October–March SFE and ratio of snow fall equivalent to precipitation (SFE:P) based on bias-corrected statistically downscaled projections of Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Model (GCM) temperature and precipitation for the state of Alaska. We analyzed mid-century (2040–2069) and late-century (2070–2099) changes in PSF, SFE, and SFE:P relative to historical (1970–1999) mean temperature and present results for Alaska climate divisions and 12-digit Hydrologic Unit Code (HUC12) watersheds. Overall, estimated historical the SFE is reasonably well related to the observed SWE, with correlations over 0.75 in all decades, and correlations exceeding 0.9 in the 1960s and 1970s. In absolute terms, SFE is generally biased low compared to the observed SWE. PSF and SFE:P decrease universally across Alaska under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 emissions scenarios, with the smallest changes for RCP 4.5 in 2040–2069 and the largest for RCP 8.5 in 2070–2099. The timing and magnitude of maximum decreases in PSF vary considerably with regional average temperature, with the largest changes in months at the beginning and end of the snow season. Mean SFE changes vary widely among climate divisions, ranging from decreases between −17 and −58% for late twenty-first century in southeast, southcentral, west coast and southwest Alaska to increases up to 21% on the North Slope. SFE increases most at highest elevations and latitudes and decreases most in coastal southern Alaska. SFE:P ratios indicate a broad switch from snow-dominated to transitional annual hydrology across most of southern Alaska by mid-century, and from transitional to rain-dominated watersheds in low elevation parts of southeast Alaska by the late twenty-first century.
One hundred and sixty–nine years of weather station data were analyzed to quantify the changing nature of the winter season precipitation phase in the downtown area of Toronto (Canada). The ...precipitation variables examined were rainfall, snowfall water equivalent, total precipitation, rain days, snow days, and precipitation days. From these precipitation variables, three precipitation phase metrics were constructed for further analysis: the fraction of total precipitation that fell as snow, the fraction of precipitation days that recorded snow, and finally, the precipitation phase index (PPI) derived from comparing the rainfall to the snowfall water equivalent. Snowfall and snow days were decreasing at the most significant rate over this time period, and although rain days were increasing, total precipitation and precipitation days were also decreasing at a statistically significant rate. All three precipitation phase metrics suggest that winters are becoming less snowy in Toronto’s urban center. We also looked at trends and changes in average winter season temperatures to explore correlations between warming temperatures and changes in the winter season precipitation phase. Of the three precipitation phase metrics considered, the ratio of snow days to precipitation days recorded the strongest time series trend and the strongest correlation with warming temperatures.
Nearest neighbour model for prediction of weather in terms of snow day/no snow day for consecutive three days in advance (lead time up to 72 h) was tested in two different modes of prediction for two ...different stations; Dhundi in Himachal Pradesh and Stage-II in Jammu and Kashmir (J&K), in the Pir Panjal range of NW Himalaya, with two different types of data. The data of station Stage-II are incomplete with less data of 12 winters (winter 1991–92 to winter 2003–04, missing data of 1994–95) and those of station Dhundi are complete with more data of 15 winters (winter 1989–90 to winter 2003–04). The model performance was tested with incomplete and complete data respectively, in two different modes. First, in mode I prediction of weather is made based on the probability of snowfall calculated from nearest days/nearest situations. Secondly, in mode II the prediction was made considering the previous day's probability of snowfall also, along with the probability of snowfall calculated from nearest days/situations, i.e. while forecasting for day-2 (lead time 48 h), probability of snowfall for day-1 (lead time 24 h) is also taken into account. The model performance is found to be better for mode II compared to mode I for all three days except for day-1 forecast with incomplete data. The model performance is better for Stage-II compared to Dhundi in both the modes. Significant difference in the model performance for day-1 and day-2 forecasts is found between those with incomplete data compared to those with complete data. The model results are briefly discussed here.
Physical description: A small sleigh made for a dummy. A seating board sawn into shape attaches to the Laws on two pairs of feet. Metal elms in front of them shining. Transverse joining the Feet
a ...metal rod in the middle with a loop for a drawstring. Seat board painted green.
fyysinen kuvaus: Pieni, nukelle tehty kelkka. Muotoon sahattu istuinlauta kiinnittyy jalaksiin kahdella jalkaparilla. Metalliset jalakset edessä loivasti kaarevat. Jalaksia edessä yhdistävässä poikittaisessa
metallitangossa keskellä silmukka vetonarua varten. Istuinlauta maalattu vihreäksi.
This study examines long-term (1991–2092) precipitation trends in the Upper Ganga Basin (UGB) across multiple elevations, using historical data (1991–2022) and future Coupled Model Intercomparison ...Project Phase 6 (CMIP6) climate model datasets (2022–2092). By identifying the top five best-fit models from 29 climate model datasets, this study assesses historical patterns and project future changes in the Near-Future-Term (NFT) (2023–2056) and Far-Future-Term (FFT) (2061–2092) under moderate and extreme scenarios. Based on the evaluation of 29 CMIP6 climate models precipitation products, five models namely: BCC-CSM2-MR, CMCC-CM2-SR5, INM-CM5–0, KIOST-ESM, and NESM3 were selected as the best models describing precipitation over UGB and future precipitation of these models were bias corrected using linear scaling bias correction approach. Also it was observed that in the UGB region usually the climate models are much more reliable in capturing the precipitation pattern in lower and mid elevation regions as compared to higher-elevation regions. Further analysis of best five climate models' future precipitation indicates a substantial increase in wet spell occurrences in the south-western part of UGB, while the north-western and central regions are expected to see a rise in dry spell counts, although dry spell increase is insignificant. The mid-elevation range from 2200 to 4050 m which historically receives the most precipitation and is expected to continue experiencing the highest volume of precipitation in the future. The lower elevation range (287–1500 m) has anticipated the most substantial increase in future precipitation. Analysis suggests a future rise in low precipitation events (2.5 mm/day to 10 mm/day) at high elevations (4050 m to 7399 m) and an increase in events exceeding 10 mm/day in mid to low elevations (287 m to 4050 m). Although an increase in low precipitation events is expected across the entire UGB, it is particularly noteworthy that the region between 287 m to 4050 m is likely to experience a greater prevalence of these events. Historically, high elevations witnessed most of the years with over 20 days of annual snowfall. However, future scenarios indicate a prominent shift, predominantly anticipating <20 days per year of snowfall, signifying a significant change in snowfall patterns within the UGB. An analysis of snowfall patterns within the UGB indicates that historically, in the elevation range of 1500 m to 2200 m, some years have experienced 3–7 days of snowfall per year, however, under the SSP245 (NFT) scenario, this is projected to decrease to 2–5 days per year. Conversely, in scenarios SSP245 (FFT), SSP585 (NFT), and SSP585 (FFT), it is anticipated that there will be no snowfall in this elevation range in the future.
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•Evaluation of CMIP6 climate models precipitation in western Himalayan region.•Long-term changes in precipitation using newly developed precipitation and CMIP6 climate model datasets.•Changes in extreme precipitation and snowfall by constructing multiple elevation bands.
ABSTRACT
In this study we analysed the spatial distribution of the long‐term average and interannual variability of the number of snow days (NSD) and the number of precipitation days (NPD) in winter ...(DJFM) in the Spanish Pyrenees, using data from 38 meteorological stations for the period 1981–2010. The interannual variability of the NSD and the NPD in winter was related to the frequency of weather types over the Iberian Peninsula. Data from six stations were also used to analyse a longer time period (1961–2013) to confirm the consistency of the results obtained during the main study period (1981–2010).
The results indicated that the NPD is only influenced by the distance to sea whereas the NSD is determined by elevation and distance to the sea. A high frequency of west (W), northwest (NW) and cyclonic (C) weather systems led to a high NPD in winter across the entire study area, whereas the frequency of north (N) weather types was only correlated with the NPD at the most westerly stations. For the NSD there was a gradient from the Western Pyrenees to eastern areas, mainly explained by the frequency of N weather types in the former area, and high frequencies of NW and W weather types associated with the latter. For most stations there was no significant trend found in the NPD or the NSD for the 1981–2010 period. However, a slight decrease was found for stations strongly correlated with NW weather types, and a slight increase was found for stations strongly correlated with the C weather type, which was related to a decreasing (increasing) frequency of NW (C) weather types during the same period. Analysis of the 1961–2013 and 1971–2000 time slices using a smaller subset of stations revealed a similar relationship between weather types and the NSD. This indicates that the 1981–2010 period is sufficiently representative to describe the relationship of the NSD and the NPD to weather type frequency. However, the study period chosen can markedly influence the trends observed, as the results showed a statistically significant decrease in the NSD for the 1971–2000 period, but no significant trends for the 1961–2013 and 1980–2010 periods.
The number of days with a snow depth above a certain threshold is the key factor for winter tourism in an Alpine country like Switzerland. An investigation of 34 long‐term stations between 200 and ...1800 m asl (above sea level) going back for at least the last 60 years (1948–2007) shows an unprecedented series of low snow winters in the last 20 years. The signal is uniform despite high regional differences. A shift detection analysis revealed a significant step‐like decrease in snow days at the end of the 1980's with no clear trend since then. This abrupt change resulted in a loss of 20% to 60% of the total snow days. The stepwise increase of the mean winter temperature at the end of the 1980's and its close correlation with the snow day anomalies corroborate the sensitivity of the mid‐latitude winter to the climate change induced temperature increase.
Recent research on the effects of school cancellations because of snow or storms confirms what school authorities in Canada and the United States have understood for some time: missed school days ...have a detrimental effect upon student learning. Disrupted instructional time and student learning have been analyzed in Massachusetts and in policy studies conducted in the Canadian province of Nova Scotia. One 2012 study in Massachusetts showed a strong relationship between student absences and achievement, but little or no impact attributable to inclement-weather school closures. Yet on balance, most research studies link school-day cancellations with declining student test scores. This research note assesses the impact of storm closings in Nova Scotia between the school years 2008-2009 and 2017-2018. There, the number of snow days is normally double that of Massachusetts and reported rates of student absenteeism are higher. This study assesses the 'accumulative effect' of missing whole school days, planned and unplanned, on student mathematics scores and high-school completion, and it proposes a some policy responses. Some consideration is given also to the profound impact of COVID-19 school disruptions and remote learning experiments on the changing policy landscapes in both Nova Scotia and Massachusetts.
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