Comparison of snow cover characteristics according to weather stations and ERA 5-Land reanalysis in the Perm region

2021 ◽  
Vol 2 ◽  
pp. 95-110
Author(s):  
A.D., Kryuchkov ◽  
◽  
N.A Kalinin ◽  
Keyword(s):  
Snow Cover ◽  
Interannual Variability ◽  
Seasonal Variability ◽  
Snow Depth ◽  
Reanalysis Data ◽  
Error Distribution ◽  
Current Version ◽  
Weather Stations ◽  
Perm Region

Comparison of snow cover characteristics according to weather stations and ERA 5-Land reanalysis in the Perm region / Kryuchkov A.D., Kalinin N.A. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 95-110. The consistency of information on the snow depth contained in the ERA 5-Land reanalysis with data of weather stations of the Perm region is analyzed. The study is performed for the period from October 1990 to May 2020. It is shown that the interannual variability of the snow cover is generally successfully reflected by the current version of the reanalysis. Data on the snow availability are more accurately reproduced during the period of formation of the snow cover than during its melt. The performed calculations demonstrate a systematic overestimation of the snow depth in the ERA 5-Land reanalysis relative to the actual observations and a predominantly meridional error distribution on the territory of the Perm region. The maximum values in the seasonal variability of the snow cover occur earlier in the reanalysis than in the actual observations. Keywords: snow cover, reanalysis, weather stations, seasonal variability, interannual variability

scholarly journals SNOW COWER DYNAMIC ON THE TERRITORY OF THE PERM REGION IN THE 1988-2018 PERIOD

2019 ◽  
Vol 29 (2) ◽  
pp. 243-251
Author(s):  
A.D. Kryuchkov ◽  
O.V. Istomina
Keyword(s):  
Spatial Structure ◽  
Snow Cover ◽  
Temporal Variability ◽  
Snow Depth ◽  
Entire Region ◽  
Statistical Parameters ◽  
Period Data ◽  
Spatio Temporal ◽  
Perm Region

The article examines the features of snow cover occurrence in the Perm region for the 30 year period. Data about the onset, destruction, duration of steady snow cover and snow depth are given. Statistical parameters for single stations and the entire region are calculated. Spatio-temporal variability of basic snow cover characteristics is analyzed. It is shown that the dates of onset and destruction of steady snow cover have shifted to later terms during 1988-2018 period. It is determined that the increase in the number of days with a stable snow cover was observed after a long reduction in recent years. The features of the spatial structure of snow cover distribution in the region are revealed. It is established that the snow depth decreased until the end of the 00-ies of the XXI century, in recent years there has been a tendency to increase in values.

scholarly journals Variations and changes in snow depth at meteorological stations Barentsburg and Hornsund (Spitsbergen)

2017 ◽  
Vol 58 (75pt1) ◽  
pp. 11-20 ◽  
Author(s):  
Marzena Osuch ◽  
Tomasz Wawrzyniak
Keyword(s):  
Snow Cover ◽  
Interannual Variability ◽  
Trend Analysis ◽  
Air Temperature ◽  
Snow Depth ◽  
Moving Average ◽  
Onset Date ◽  
The Novel ◽  
Temperature And Precipitation ◽  
Snow Cover Duration

ABSTRACTIn this study, seasonality and interannual variability of snow depth at two stations (Hornsund and Barentsburg) located in western Spitsbergen are investigated. For this purpose, the novel Moving Average over Shifting Horizon method combined with trend estimation was used. The Hornsund and Barentsburg stations exhibit similar snow depth trends during early autumn and late spring when statistically significant decreases were estimated at both stations (for August 1984–July 2016). In the remaining period, there are differences in outcomes between stations. The results for Barentsburg from October till the end of May are characterised by the lack of a trend while at Hornsund decreases of snow depth were estimated. The largest changes occur in May when the snow depth was at its maximum. Differences in the estimated tendencies were explained with the help of a trend analysis for air temperature and precipitation. An analysis of maximum snow depth, snow onset date, snow disappearance date and snow-cover duration is included. The results of the assessment depend on the location, with a lack of statistically significant changes in Barentsburg, and later snow onset date, shorter duration and decrease of maximum depth in Hornsund.

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

Interannual variability versus seasonal variability in the Tropical Atlantic

1984 ◽  
Vol 11 (8) ◽  
pp. 787-790 ◽  
Author(s):  
Joël Picaut ◽  
Jacques Servain ◽  
Antonio J. Busalacchi ◽  
Marc Seva
Keyword(s):  
Interannual Variability ◽  
Seasonal Variability ◽  
Tropical Atlantic

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

scholarly journals Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

2006 ◽  
Vol 44 ◽  
pp. 281-287 ◽  
Author(s):  
Shotaro Uto ◽  
Haruhito Shimoda ◽  
Shuki Ushio
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Snow Depth ◽  
East Antarctica ◽  
Ice Thickness ◽  
First Year ◽  
Total Thickness ◽  
Sea Ice Thickness ◽  
Landfast Ice ◽  
Northeasterly Wind

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Depth ◽  
Spatial Scales ◽  
Swiss Alps ◽  
Balance Model ◽  
Near Surface ◽  
Surface Rock ◽  
Depth Data ◽  
Rock Temperature

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

scholarly journals Validation of MODIS snow cover images over Austria

2006 ◽  
Vol 3 (4) ◽  
pp. 1569-1601 ◽  
Author(s):  
J. Parajka ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Classification Accuracy ◽  
Snow Depth ◽  
Seasonal Pattern ◽  
Acquisition Time ◽  
Spatial And Temporal Variability ◽  
Temporal Shift ◽  
Early Afternoon ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon).

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