Factors Influencing Changes of the Initial Stable Water Isotopes Composition in the Seasonal Snowpack of the South of Western Siberia, Russia

Stable water isotopes in snowpack and snowfalls are widely used for understanding hydrological processes occurring in the seasonally snow-covered territories. The present study examines the main factors influencing changes of the initial stable water isotopes composition in the seasonal snow cover of the south of Western Siberia. Studies of the isotopic composition of snow precipitation and snow cover, as well as experiments with them, were carried out during two cold seasons of 2019–2021, and laser spectroscopy PICARRO L2130-i (WS-CRDS) was used for the determination of water isotope composition (δ18O and δD). The main changes in the isotopic composition of the snow cover layers in the studied region are associated with the existence of a vertical temperature gradient between the layers and with the penetration of soil moisture into the bottom layers in the absence of soil freezing. During the winter period, the sublimation from the top layer of snow is observed only at the moments of a sharp increase in the daily air temperature. At the end of winter, the contrast between day and night air temperatures determines the direction of the shift in the isotopic composition of the top layer of snow relative to the initial snow precipitation.

Using and Evaluating the Efficiency of GRIMs Model in Making Some Simulations of Weather Variables in Three Countries in Middle East: A Snowfall Case Study

Predicting weather by numerical models have been used extensively in research works for Middle East, mostly for dust storms, rain showers, and flash floods with a less deal of interest on snow precipitation. In this study, the Global/Regional Integrated Model System (GRIMs) that was developed in South Korea was used to predict a rare snowfall event occurred in three countries in Middle East (Syria, Jordan and Iraq) located between (25-65 oE; 12-42 oN) in year 2008. The main aim of this study was to test GRIMs efficiency, which would be used for the first time in Middle East, to make predictions of weather parameters such as pressure, temperature, and relative humidity especially in the selected area. In addition, the study would investigate the conditions that caused the snowfall event. GRIMs model was installed, compiled, and run on a Linux platform by using NCEP-NCAR reanalysis dataset as initial conditions on 0.5 × 0.5 grid resolution to make simulations for three days at intervals of three hours. The output of the model was evaluated by making comparisons with actual data obtained from the GFS Agency dataset and the model showed its efficiency. The snowfall event was synoptically discussed in details. It was found that the snowfall event was a result of fast succession systems of a strong cold high pressure and then a deep warm low pressure. The high instability in the region had led to form large cumuliform clouds with snow precipitation as a rare event in very long period.

The Relationship between Clouds Containing Multiple Layers 7.5–30 m Thick and Surface Weather Conditions

Previous studies have identified finely laminated, or layered, features within Arctic clouds. This study focuses on quasi-horizontal layers that are 7.5 to 30 m thick, within clouds from 0 to 5 km altitude. No pre-selection for any particular cloud types was made prior to the identification of laminations. We capitalize on the 4-year measurement record available from Eureka, Nunavut (79.6∘ N, 85.6∘ W), using the Canadian Network for the Detection of Atmospheric Composition Change (CANDAC) Rayleigh–Mie–Raman Lidar (CRL; 1 min, 7.5 m resolution). Laminated features are identified on 18% of all days, from 2016–2019. Their presence is conclusively excluded on 12% of days. March, April, and May have a higher measurement cadence and show laminations on 41% of days. Individual months show laminations on up to 50% of days. Our results suggest that laminations are not rare phenomena at Eureka. To determine laminations’ likely contribution to Arctic weather and climate, local weather reports were obtained from the nearby Environment and Climate Change Canada (ECCC) weather station. Days with laminated clouds are strongly correlated with precipitating snow (r = 0.63), while days with non-laminated clouds (r = −0.40) and clear sky days (r = −0.43) are moderately anti-correlated with snow precipitation.

Tipping Bucket Rain Gauge Data Processing System: A Review

The tipping bucket system consists of funnel which collects the water of the rain in a container which is like a seesaw type module which tips side by side and collects the water. When the level of the water decreases below a preset level, the lever changes its side, causing the collected water to dump in a vessel and electrical signal is sent. By this system the high, medium or heavy rainfall character can be obtained. The rainfall character is calculated by the rainfall in 1 hour and corresponding number of pulses clicking in a period of 10 minutes. Various types of tipping bucket systems are reviewed by using rainfall and snow precipitation, using internet enabling, using rain drop imaging and artificial intelligence and also using wireless sensor network and GSM data transmission. Tipping Bucket is the most useful parameter for measuring the rainfall. In this way the rainfall is measured using the Tipping Bucket Rain Gauge System.

Train Performance Analysis Using Heterogeneous Statistical Models

This study investigated the effect of a harsh winter climate on the performance of high-speed passenger trains in northern Sweden. Novel approaches based on heterogeneous statistical models were introduced to analyse the train performance to take time-varying risks of train delays into consideration. Specifically, the stratified Cox model and heterogeneous Markov chain model were used to model primary delays and arrival delays, respectively. Our results showed that weather variables including temperature, humidity, snow depth, and ice/snow precipitation have a significant impact on train performance.

Snowfall and snow cover evolution in the Eastern Pre-Pyrenees (NE Iberian Peninsula)

Snow cover has significant impacts on geoecological dynamics as well as on socio-economical systems. An accurate quantification of snow precipitation patterns in mountain regions is needed to better understand the spatio-temporal implications of snow cover. The objective of this work is to characterize the patterns of solid precipitation and snow cover in two high Mediterranean massifs. To this purpose, we analyse instrumental data series of snowfall and snow depth of Port del Comte (2316 m a.s.l.) and Cadí-Nord (2134 m). Both stations are situated in the eastern Pre-Pyrenees and include 14 consecutive snow seasons from November to May, allowing to (i) explore the dependence of the main drivers of snowpack: temperature and snowfall; (ii) find out the most frequent circulation weather types associated with high intensity snowfall events, and finally (iii) investigate the role of the North Atlantic Oceanic (NAO) teleconnection pattern explaining snow cover evolution during the winter season. Data show that snowfall is controlled by similar weather types in both stations that resulted in similar snowfall averages: 205 cm and 258 cm at Port del Comte and Cadí-Nord, respectively. Nevertheless, local factors interfere with the amount of snow depth recorded, which is moderately different between stations. Whereas Cadí-Nord records a seasonal mean of 66 cm, Port del Comte records a smaller quantity of 25 cm with a high interannual and seasonal variability. In fact, snowfall recurrence, snow amount or duration in the ground is considerably variable among years (CV20). In these stations, snow cover duration is determined by the precipitation in the form of snow falling during the previous months. Snowfalls in moderate to severe episodes (15 cm in 24 h) are mainly driven by Atlantic flows, mostly from NW. In addition, NAO pattern is negatively correlated with snowfall in November and December months (R-0.50), showing a weaker and not statistically significant correlation during the rest of the winter season.

Impacts of atmospheric rivers on the hydrometeorology of the Euphrates-Tigris Basin in the snowmelt season

<p>Atmospheric rivers (ARs) are important components of the global water cycle as they are responsible for over 90% of the poleward moisture transport at middle to high latitudes. ARs travelling thousands of kilometers over arid North Africa could interact with the highlands of the Mesopotamia and thus affect the hydrometeorology and water resources of the Euphrates-Tigris Basin. Here, we use a state-of-the-art AR tracking database, and reanalysis and observational datasets to investigate the climatology (1979-2017) and influences of these ARs in snowmelt season (March-April). The Red Sea and northeast Africa are found to be the major source regions of these ARs, which are typically associated with the eastern Mediterranean trough positioned over the Balkan Peninsula and a blocking anticyclone over the Near East-Caspian region, triggering southwesterly air flow towards the highlands of the Euphrates-Tigris Basin. AR days exhibit enhanced precipitation over the crescent-shaped orography of the Euphrates-Tigris Basin. Mean AR days indicate wetter (up to +2 mm day<sup>-1</sup>) and warmer (up to +1.5<sup>o</sup>C) conditions than all-day climatology. On AR days, while snowpack tends to decrease (up to 30%) in the Zagros Mountains, it can show decreases or increases in the Taurus Mountains depending largely on elevation. A further analysis with the aid of observations and reanalysis for the three extreme AR events indicates that ARs coinciding with large scale sensible heat transport can have notable impacts on the surface hydrometeorological conditions such as snowmelt, rain-on-snow precipitation and increasing daily discharges of the Euphrates and Tigris rivers. These results suggest that ARs can have notable impacts on the hydrometeorology and water resources of the basin, particularly of lowland Mesopotamia, a region that is famous with great floods in the ancient narratives.</p>

Assessment of the size of very small glaciers in Khibiny mountains

The presence of very small glaciation in the Khibiny mountains was discovered by V.F. Perov in 1958. Since then and until the beginning of the 21st study of glaciers in this area has not been carried out. In general, warming is recorded on the Kola Peninsula. The average annual increase in temperature is 2.3°C ± 1°C during the last 50 years, which correlates with the data of other researchers. The glaciers in the Khibiny mountains should have significantly reduced their size in this connection. In other Arctic regions degradation of glaciation is noted. Some researchers predicted to disappear of Khibiny glaciation. In order to assess the change in glacier areas, we carried out a comprehensive study of small glaciers in the Khibiny mountains. We studied dynamics of glaciers over the past 15 years using field observations and the use of GIS and remote sensing methods. In our study, we used aerial photographs of 1958, digital models built on the basis of field measurements, and satellite images for years different in meteorological parameters. Our research shows, despite the slight degradation of glaciation, this tendency is modest and glaciers’ area remains relatively stable. Our research shows that glaciers shrank significantly in the early 2000s and showed a tendency towards complete disappearance. Their area has decreased by more than two times compared to the figures obtained in 1958. This fact may be caused by the increase in snow precipitation through in recent years. After 2007, there has been an increase in snow precipitation. In subsequent years, starting in 2008, the glaciers began to regain their size. Over the past 15 years, the average area of two glaciers (No. 3 and No. 4) exceeds the size of 1958. Glaciers No. 1 and No. 2 have shrinkage.