scholarly journals EVALUATION OF A GLOBAL SNOW DEPTH ANALYSIS BASED ON OPTIMAL INTERPOLATION

2019 ◽  
Vol 9 (4) ◽  
pp. 831-836
Author(s):  
Cezar Kongoli ◽  
◽  
Tomas Smith ◽  
Keyword(s):  
Snow Depth ◽  
Optimal Interpolation ◽  
Depth Analysis

scholarly journals Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2

2019 ◽  
Vol 11 (24) ◽  
pp. 3049 ◽  
Author(s):  
Cezar Kongoli ◽  
Jeffrey Key ◽  
Thomas M. Smith
Keyword(s):  
North America ◽  
Snow Depth ◽  
Weather Prediction ◽  
Weighted Average ◽  
Optimal Interpolation ◽  
Horizontal Distance ◽  
Two Dimensional ◽  
In Situ Data ◽  
The Difference

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.

scholarly journals Estimation of Daily Spatial Snow Water Equivalent from Historical Snow Maps and Limited In-Situ Measurements

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Sami A. Malek ◽  
Roger C. Bales ◽  
Steven D. Glaser
Keyword(s):  
Snow Depth ◽  
Spatial Interpolation ◽  
Snow Water Equivalent ◽  
High Spatial Resolution ◽  
Optimal Interpolation ◽  
Novel Approach ◽  
Tuolumne River ◽  
Snow Water ◽  
Iot Devices

We present a scheme aimed at estimating daily spatial snow water equivalent (SWE) maps in real time and at high spatial resolution from scarce in-situ SWE measurements from Internet of Things (IoT) devices at actual sensor locations and historical SWE maps. The method consists of finding a background SWE field, followed by an update step using ensemble optimal interpolation to estimate the residuals. This novel approach allowed for areas with parsimonious sensors to have accurate estimates of spatial SWE without explicitly discovering and specifying the spatial-interpolation features. The scheme is evaluated across the Tuolumne River basin on a 50 m grid using an existing LiDAR-based product as the historical dataset. Results show a minimum RMSE of 30% at 50 m resolutions. Compared with the operational SNODAS product, reduction in error is up to 80% with historical LiDAR-measured snow depth as input data.

scholarly journals Spatiotemporal Variation of Snow Depth in the Northern Hemisphere from 1992 to 2016

2020 ◽  
Vol 12 (17) ◽  
pp. 2728
Author(s):  
Xiongxin Xiao ◽  
Tingjun Zhang ◽  
Xinyue Zhong ◽  
Xiaodong Li
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Annual Average ◽  
Depth Analysis ◽  
Variation Trends

A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by the lack of data products and/or valid snow retrieval algorithms. The overall objective of this study is to investigate the variation characteristics of snow depth across the Northern Hemisphere from 1992 to 2016. We developed long-term Northern Hemisphere daily snow depth (NHSnow) datasets from passive microwave remote sensing data using the support vector regression (SVR) snow depth retrieval algorithm. NHSnow is evaluated, along with GlobSnow and ERA-Interim/Land, for its accuracy across the Northern Hemisphere against meteorological station snow depth measurements. The results show that NHSnow performs comparably well with a relatively high accuracy for snow depth with a bias of −0.6 cm, mean absolute error of 16 cm, and root mean square error of 20 cm when benchmarked against the station snow depth measurements. The analysis results show that annual average snow depth decreased by 0.06 cm per year from 1992 to 2016. In the three seasons (autumn, winter, and spring), the areas with a significant decreasing trend of seasonal maximum snow depth are larger than those with a significant increasing trend. Additionally, snow cover days decreased at the rate of 0.99 day per year during 1992–2016. This study presents that the variation trends of snow cover days are, in part, not consistent with the variation trends of the annual average snow depth, of which approximately 20% of the snow cover areas show the completely opposite variation trends for these two indexes over the study period. This study provides a new perspective in snow depth variation analysis, and shows that rapid changes in snow depth have been occurring since the beginning of the 21st century, accompanied by dramatic climate warming.

scholarly journals Uncertainty in predicting the Eurasian snow: Intercomparison of land surface models coupled to a regional climate model

2019 ◽  
Author(s):  
Da-Eun Kim ◽  
Seon Ki Park
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Regional Climate ◽  
Analysis Data ◽  
Land Surface Models ◽  
Community Land Model ◽  
Eurasian Snow ◽  
Depth Analysis ◽  
Surface Models

Abstract. Variability of large and synoptic scale circulations in Asia is strongly affected by the winter and spring Eurasian snow. Therefore, an accurate prediction of the Eurasian snow is of the utmost importance in predicting the climate and weather phenomena in Asia. Most global/regional models are coupled with several land surface models (LSMs) in which the land surface process parameters are calculated under their own physical principles and parameterization schemes. In this study, using the Weather Research and Forecasting (WRF) model, we make intercomparision of LSMs in terms of simulating the Eurasian snow. Simulations are carried out from 1 June 2009 to 31 August 2010, including a spin-up time of 6 months, by employing four different LSMs – the Unified Noah LSM, the Noah LSM with multiparameterization options (Noah-MP), the Rapid Update Cycle (RUC) LSM, and the Community Land Model version 4 (CLM4). The NCEP Final (FNL) Operational Global Analysis data are used as initial and boundary conditions. The LSM results are evaluated using the Canadian Meteorological Centre Daily Snow Depth Analysis Data, the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra Snow Cover Monthly L3 Global 0.05Deg Climte Modeling Grid (CMG) Version 6, and the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo Product. Although all the LSMs represent reasonable results, the Noah-MP represents the most accurate predictions in all three variables (snow depth, fractional snow cover, and albedo), in terms of not only quantitative aspects but also spatial correlation patterns. Our results indicate that prediction of the Eurasian snow cover is sensitive to the choice of LSMs coupled to the global/regional climate models, and hence the future climate projections.

scholarly journals Impacts of Snow Initialization on Subseasonal Forecasts of Surface Air Temperature for the Cold Season

2013 ◽  
Vol 26 (6) ◽  
pp. 1956-1972 ◽  
Author(s):  
Jee-Hoon Jeong ◽  
Hans W. Linderholm ◽  
Sung-Ho Woo ◽  
Chris Folland ◽  
Baek-Min Kim ◽  
...  
Keyword(s):  
Air Temperature ◽  
Snow Depth ◽  
Surface Air Temperature ◽  
Systematic Bias ◽  
Seasonal Predictability ◽  
Potential Predictability ◽  
Sea Ice Extent ◽  
Albedo Feedback ◽  
Snow Albedo ◽  
Depth Analysis

Abstract The present study examines the impacts of snow initialization on surface air temperature by a number of ensemble seasonal predictability experiments using the NCAR Community Atmosphere Model version 3 (CAM3) AGCM with and without snow initialization. The study attempts to isolate snow signals on surface air temperature. In this preliminary study, any effects of variations in sea ice extent are ignored and do not explicitly identify possible impacts on atmospheric circulation. The Canadian Meteorological Center (CMC) daily snow depth analysis was used in defining initial snow states, where anomaly rescaling was applied in order to account for the systematic bias of the CAM3 snow depth with respect to the CMC analysis. Two suites of seasonal (3 months long) ensemble hindcasts starting at each month in the colder part of the year (September–April) with and without the snow initialization were performed for 12 recent years (1999–2010), and the predictability skill of surface air temperature was estimated. Results show that considerable potential predictability increases up to 2 months ahead can be attained using snow initialization. Relatively large increases are found over East Asia, western Russia, and western Canada in the later part of this period. It is suggested that the predictability increases are sensitive to the strength of snow–albedo feedback determined by given local climate conditions; large gains tend to exist over the regions of strong snow–albedo feedback. Implications of these results for seasonal predictability over the extratropical Northern Hemisphere and future direction for this research are discussed.

TEM/AEM characterization of fine-grained clay minerals in very-low-grade rocks: Evaluation of contamination by empa involving celadonite family minerals

1996 ◽  
Vol 54 ◽  
pp. 694-695
Author(s):  
Gejing Li ◽  
D. R. Peacor ◽  
D. S. Coombs ◽  
Y. Kawachi
Keyword(s):  
Electron Microscopy ◽  
Volcanic Rocks ◽  
Analytical Electron Microscopy ◽  
Metamorphic Rocks ◽  
New Mineral ◽  
Chemical Compositions ◽  
Low Grade ◽  
Fine Grained ◽  
Depth Analysis ◽  
Mineral Aggregates

Recent advances in transmission electron microscopy (TEM) and analytical electron microscopy (AEM) have led to many new insights into the structural and chemical characteristics of very finegrained, optically homogeneous mineral aggregates in sedimentary and very low-grade metamorphic rocks. Chemical compositions obtained by electron microprobe analysis (EMPA) on such materials have been shown by TEM/AEM to result from beam overlap on contaminant phases on a scale below resolution of EMPA, which in turn can lead to errors in interpretation and determination of formation conditions. Here we present an in-depth analysis of the relation between AEM and EMPA data, which leads also to the definition of new mineral phases, and demonstrate the resolution power of AEM relative to EMPA in investigations of very fine-grained mineral aggregates in sedimentary and very low-grade metamorphic rocks.Celadonite, having end-member composition KMgFe3+Si4O10(OH)2, and with minor substitution of Fe2+ for Mg and Al for Fe3+ on octahedral sites, is a fine-grained mica widespread in volcanic rocks and volcaniclastic sediments which have undergone low-temperature alteration in the oceanic crust and in burial metamorphic sequences.

Doping effects on the geometric and electronic structure of tin clusters

2019 ◽  
Vol 21 (44) ◽  
pp. 24478-24488 ◽  
Author(s):  
Martin Gleditzsch ◽  
Marc Jäger ◽  
Lukáš F. Pašteka ◽  
Armin Shayeghi ◽  
Rolf Schäfer
Keyword(s):  
Density Functional Theory ◽  
Electronic Structure ◽  
Density Functional ◽  
Beam Deflection ◽  
Functional Theory ◽  
Doping Effects ◽  
Depth Analysis ◽  
Trajectory Simulations

In depth analysis of doping effects on the geometric and electronic structure of tin clusters via electric beam deflection, numerical trajectory simulations and density functional theory.

Delay Discounting in Impulsive Behavior

2019 ◽  
Vol 24 (4) ◽  
pp. 312-321 ◽  
Author(s):  
Diana Moreira ◽  
Fernando Barbosa
Keyword(s):  
Delay Discounting ◽  
Human Subjects ◽  
Empirical Studies ◽  
Cochrane Collaboration ◽  
Initial Assessment ◽  
Impulsive Behavior ◽  
Manual Search ◽  
Study Results ◽  
Depth Analysis ◽  
The Future

Abstract. Delay discounting (DD) is the process of devaluing results that happen in the future. With this review, we intend to identify specificities in the processes of DD in impulsive behavior. Studies were retrieved from multiple literature databases, through rigorous criteria (we included systematic reviews and empirical studies with adult human subjects), following the procedures of the Cochrane Collaboration initiative. Of the 174 documents obtained, 19 were considered eligible for inclusion and were retained for in-depth analysis. In addition, 13 studies from the manual search were included. Thus, a total of 32 studies were selected for review. The objectives/hypotheses, results, and the main conclusion(s) were extracted from each study. Results show that people with pronounced traits of impulsivity discount rewards more markedly, that is, they prefer immediate rewards, though of less value, or postponed losses, even though they worsen in the future. Taken together, the existing data suggest the importance of inserting DD as a tool for initial assessment in conjunction with measures of addiction and stress level, as well as the consideration of new therapies.

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