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2022 ◽  
Vol 16 (1) ◽  
pp. 61-85
Author(s):  
Emma K. Fiedler ◽  
Matthew J. Martin ◽  
Ed Blockley ◽  
Davi Mignac ◽  
Nicolas Fournier ◽  
...  

Abstract. The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.


2022 ◽  
Vol 16 (1) ◽  
pp. 87-101
Author(s):  
Julien Meloche ◽  
Alexandre Langlois ◽  
Nick Rutter ◽  
Alain Royer ◽  
Josh King ◽  
...  

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.


2022 ◽  
pp. 1-45
Author(s):  
Xiang Han ◽  
Haikun Zhao ◽  
Philip J. Klotzbach ◽  
Liguang Wu ◽  
Graciela B. Raga ◽  
...  

Abstract This study finds an enhanced relationship in recent years between January–March eastern Tibetan Plateau snow depth (TPSD) and the frequency of rapidly intensifying tropical cyclones (RITCs) over the western Northern Pacific (WNP) during the following peak TC season (July–November). The correlation between TPSD and RITCs is significant during 2000–2014 but was insignificant from 1979–1999. During 2000–2014, when TPSD increases, there is an enhanced low-level anomalous anticyclone over the subtropical eastern North Pacific mainly due to the combined effect of advection and dynamics of the climatological prevailing westerly jet. Northeasterly wind anomalies are observed on the flank of the anticyclonic circulation anomaly, favoring anomalously cool sea surface temperature (SST). These anomalies lead to an anomalous pattern similar to the Pacific meridional mode (PMM), via a wind-evaporation feedback and cold advection. A Gill-type Rossby response to the PMM-like negative phase results in an anticyclonic circulation anomaly over the WNP, suppressing RITCs during 2000–2014. A nearly opposite circulation anomaly occurred when TPSD was lower during 2000–2014. There is a weak relationship between TPSD and RITCs, due to the lack of a link between TPSD and the PMM-like pattern from 1979–1999. Decadal changes in the relationship between TPSD and RITCs are mainly due to the meridional displacement of the prevailing westerly jet which may be in response to decadal-to-multi-decadal variability of SST anomalies. These changes then result in changes in the relationship between January–March TPSD and the PMM-like pattern.


Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Douglas M. Hultstrand ◽  
Steven R. Fassnacht ◽  
John D. Stednick ◽  
Christopher A. Hiemstra

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.


2021 ◽  
Vol 9 ◽  
Author(s):  
Philipp Anhaus ◽  
Christian Katlein ◽  
Marcel Nicolaus ◽  
Stefanie Arndt ◽  
Arttu Jutila ◽  
...  

Radiation transmitted through sea ice and snow has an important impact on the energy partitioning at the atmosphere-ice-ocean interface. Snow depth and ice thickness are crucial in determining its temporal and spatial variations. Under-ice surveys using autonomous robotic vehicles to measure transmitted radiation often lack coincident snow depth and ice thickness measurements so that direct relationships cannot be investigated. Snow and ice imprint distinct features on the spectral shape of transmitted radiation. Here, we use those features to retrieve snow depth. Transmitted radiance was measured underneath landfast level first-year ice using a remotely operated vehicle in the Lincoln Sea in spring 2018. Colocated measurements of snow depth and ice thickness were acquired. Constant ice thickness, clear water conditions, and low in-ice biomass allowed us to separate the spectral features of snow. We successfully retrieved snow depth using two inverse methods based on under-ice optical spectra with 1) normalized difference indices and 2) an idealized two-layer radiative transfer model including spectral snow and sea ice extinction coefficients. The retrieved extinction coefficients were in agreement with previous studies. We then applied the methods to continuous time series of transmittance and snow depth from the landfast first-year ice and from drifting, melt-pond covered multiyear ice in the Central Arctic in autumn 2018. Both methods allow snow depth retrieval accuracies of approximately 5 cm. Our results show that atmospheric variations and absolute light levels have an influence on the snow depth retrieval.


2021 ◽  
Author(s):  
Fabiana Castino ◽  
Bodo Wichura ◽  
Harald Schellander ◽  
Michael Winkler

<p>The characterization of the snow cover by snow water equivalent (SWE) is fundamental in several environmental applications, e.g., monitoring mountain water resources or defining structural design standards. However, SWE observations are usually rare compared to other snow measurements as snow depth (HS). Therefore, model-based methods have been proposed in past studies for estimating SWE, in particular for short timescales (e.g., daily). In this study, we compare two different approaches for SWE-data modelling. The first approach, based on empirical regression models (ERMs), provides the regional parametrization of the bulk snow density, which can be used to estimate SWE values from HS. In particular, we investigate the performances of four different schemes based on previously developed ERMs of bulk snow density depending on HS, date, elevation, and location. Secondly, we apply the semi-empirical multi-layer Δsnow model, which estimates SWE solely based on snow depth observations. The open source Δsnow model has been recently used for deriving a snow load map for Austria, resulting in an improved Austrian standard. A large dataset of HS and SWE observations collected by the National Weather Service in Germany (DWD) is used for calibrating and validating the models. This dataset consists of daily HS and three-times-a-week SWE observations from in total ~1000 stations operated by DWD over the period from 1950 to 2020. A leave-one-out cross validation is applied to evaluate the performance of the different model approaches. It is based on 185 time series of HS and SWE observations that are representative of the diversity of the regional snow climatology of Germany. Cross validation reveals for all ERMs: 90% of the modelled SWE time series have a root mean square error (RMSE) and a bias lower than 45 kg/m² and 2 kg/m², respectively. The Δsnow model shows the best performance with 90% of the modelled SWE time series having an RMSE lower than 30 kg/m² and bias similar to the ERMs. This comparative study provides new insights on the reliability of model-based methods for estimating SWE values. The results show that the Δsnow model and, to a lower degree, the developed ERMs can provide satisfactory performances even on short timescales. This suggest that these models can be used as reliable alternative to more complex thermodynamic snow models, even more if long-term meteorological observations aside HS are scarce.</p>


2021 ◽  
Author(s):  
Fabiana Castino ◽  
Bodo Wichura

<p>The current European standard for snow loads on structures relies on characteristic values (i.e., snow loads with an annual probability of exceedance of 0.02 and referred to as the 50-year mean return levels) derived for Germany in 2005 using about 350 snow water-equivalent (SWE) time series from ground stations operated by the German National Weather Service (DWD) [<em>DIN EN 1991-1-3/NA:2019-04</em>, 2019]. Here we present a methodology for generating a new ground snow-loads map for Germany, which aims at improving the relative coarse spatial resolution and reducing uncertainties and inconsistencies at national borders of the actual standard. Our methodology is based on (1) high-quality and homogeneous snow-cover time series, including both daily snow-depth (from about 6000 stations in Germany and in neighbouring countries) and three-weekly water-equivalent observation (from about 10<sup>3</sup> German stations) over the period from 1950 to 2020, (2) an integrated model combining an empirical regression model for snow bulk density and the semi-empirical multi-level ΔSNOW model for generating accurate daily SWE values from 6000 snow-depth time series [<em>Castino et al.</em>, 2022], (3) the spatial interpolation of both daily snow-depth and modelled-SWE time series using a universal-kriging method to generate high spatial-resolution (~1km<sup>2</sup>) rasterised daily snow loads over the period from 1950 to 2020, and (4) the extreme value analysis of the rasterized daily snow loads for estimating the characteristic values at high spatial resolution for the entire German territory. The uncertainties of the obtained characteristic snow-load values will be estimated using a leave-one-out cross validation based on a selection of observed-SWE time series representative of the diversity of the regional snow climatology in Germany. Finally, the characteristic values of the snow-load map generated with this methodology will be compared with the current German standard.   </p> <p> </p> <p><strong>References</strong></p> <p>Castino, F., H. Schellander, B. Wichura, and M. Winkler (2022), SWE modelling: comparison between different approaches applied to Germany, abstract submitted to D-A-CH MeteorologieTagung - 21-25.03.2022, Leipzig.</p> <p>DIN EN 1991-1-3/NA:2019-04 (2019), Nationaler Anhang - National festgelegte Parameter - Eurocode 1: Einwirkungen auf Tragwerke - Teil 1-3: Allgemeine Einwirkungen - Schneelasten, edited, p. 22, Deutsches Institut für Normung e.V., Beuth-Verlag, Berlin.</p>


2021 ◽  
Vol 2 (4) ◽  
pp. 1245-1261
Author(s):  
Martin Wegmann ◽  
Yvan Orsolini ◽  
Antje Weisheimer ◽  
Bart van den Hurk ◽  
Gerrit Lohmann

Abstract. As the leading climate mode of wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Eurasian cryosphere as a possible predictor for the wintertime NAO. However, missing correlation between snow cover and wintertime NAO in climate model experiments and strong non-stationarity of this link in reanalysis data are questioning the causality of this relationship. Here we use the large ensemble of Atmospheric Seasonal Forecasts of the 20th Century (ASF-20C) with the European Centre for Medium-Range Weather Forecasts model, focusing on the winter season. Besides the main 110-year ensemble of 51 members, we investigate a second, perturbed ensemble of 21 members where initial (November) land conditions over the Northern Hemisphere are swapped from neighboring years. The Eurasian snow–NAO linkage is examined in terms of a longitudinal snow depth dipole across Eurasia. Subsampling the perturbed forecast ensemble and contrasting members with high and low initial snow dipole conditions, we found that their composite difference indicates more negative NAO states in the following winter (DJF) after positive west-to-east snow depth gradients at the beginning of November. Surface and atmospheric forecast anomalies through the troposphere and stratosphere associated with the anomalous positive snow dipole consist of colder early winter surface temperatures over eastern Eurasia, an enhanced Ural ridge and increased vertical energy fluxes into the stratosphere, with a subsequent negative NAO-like signature in the troposphere. We thus confirm the existence of a causal connection between autumn snow patterns and subsequent winter circulation in the ASF-20C forecasting system.


2022 ◽  
Vol 269 ◽  
pp. 112840
Author(s):  
Haili Li ◽  
Chang-Qing Ke ◽  
Qinghui Zhu ◽  
Mengmeng Li ◽  
Xiaoyi Shen

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3599
Author(s):  
Hongju Chen ◽  
Jianping Yang ◽  
Yongjian Ding ◽  
Qingshan He ◽  
Qin Ji

In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data.


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