scholarly journals A spatio-temporal analysis of trends in Northern Hemisphere snow-dominated area and duration, 1971–2014

2017 ◽  
Vol 58 (75pt1) ◽  
pp. 21-35 ◽  
Author(s):  
Michael I. Allchin ◽  
Stephen J. Déry
Keyword(s):  
Northern Hemisphere ◽  
Binary Classification ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
Energy Budgets ◽  
Rutgers University ◽  
Snow Covered Area ◽  
Covered Area ◽  
Spatio Temporal

ABSTRACTSeasonal snow-cover modulates water and energy budgets across large areas of the Northern Hemisphere. Previous research, based on satellite imagery interpreted and curated by the Rutgers University Snow Laboratory, has identified significant negative and positive trends in annual snow-covered duration and area at hemispheric and continental scales between 1971 and 2014. This study uses the same dataset to generate more detailed descriptions of spatial variations in these trends, maps intraannual variations in sign, statistical significance and strength, and quantifies associations with latitude and elevation. It also considers the limitations and uncertainties associated with a binary classification of this type, and the implications for trend magnitudes of adopting alternatives to the conventional assumption of 100% (0%) actual fractional snow-covered area in ‘snow-covered’ (‘snow-free’) spatial units at different stages of the snow-season. This prompts adoption of alternative terminology, referring to ‘snow-dominated’ area and duration. In response to questions about the dataset's veracity raised by some prior studies, it discusses climatological factors of potential relevance in explaining spatio-temporal trend patterns, and considers how biases might possibly have been introduced as a result of extraneous influences.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1502
Author(s):  
Ben Wilkes ◽  
Igor Vatolkin ◽  
Heinrich Müller
Keyword(s):  
Visual Analysis ◽  
Binary Classification ◽  
Statistical Significance ◽  
Image Features ◽  
Support Vector ◽  
Audio Signals ◽  
Text And Image ◽  
The Impact ◽  
Music Track

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.

scholarly journals Spatio-temporal Analysis of Maximum and Minimum Temperature in Bangladesh

2015 ◽  
Vol 7 (2) ◽  
pp. 73-77 ◽  
Author(s):  
MN Uddin ◽  
MSA Mondal ◽  
NMR Nasher
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
Temperature Data ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Maximum Temperatures ◽  
Kendall Method

The analysis of annual mean maximum and annual mean minimum temperature data are studied in GIS environment, obtained from 34 meteorological stations scattered throughout the Bangladesh from 1948 to 2013. IDW method was used for the spatial distribution of temperature over the study area, using ArcGIS 10.2 software. Possible trends in the spatially distributed temperature data were examined, using the non-parametric Mann-Kendall method with statistical significance, and the magnitudes of available trends were determined using Sen’s method in ArcMap depiction. The findings of the study show positive trends in annual mean maximum temperatures with 90%, 95%, 99% and 99.9% significance levels.DOI: http://dx.doi.org/10.3329/jesnr.v7i2.22210 J. Environ. Sci. & Natural Resources, 7(2): 73-77 2014

scholarly journals Representing Variability in Subgrid Snow Cover and Snow Depth in a Global Land Model: Offline Validation

2014 ◽  
Vol 27 (9) ◽  
pp. 3318-3330 ◽  
Author(s):  
T. Nitta ◽  
K. Yoshimura ◽  
K. Takata ◽  
R. O’ishi ◽  
T. Sueyoshi ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Surface Model ◽  
Snow Cover Fraction ◽  
Snow Covered Area ◽  
Covered Area ◽  
Global Land

Abstract Subgrid snow cover is one of the key parameters in global land models since snow cover has large impacts on the surface energy and moisture budgets, and hence the surface temperature. In this study, the Subgrid Snow Distribution (SSNOWD) snow cover parameterization was incorporated into the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. SSNOWD assumes that the subgrid snow water equivalent (SWE) distribution follows a lognormal distribution function, and its parameters are physically derived from geoclimatic information. Two 29-yr global offline simulations, with and without SSNOWD, were performed while forced with the Japanese 25-yr Reanalysis (JRA-25) dataset combined with an observed precipitation dataset. The simulated spatial patterns of mean monthly snow cover fraction were compared with satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The snow cover fraction was improved by the inclusion of SSNOWD, particularly for the accumulation season and/or regions with relatively small amounts of snowfall; snow cover fraction was typically underestimated in the simulation without SSNOWD. In the Northern Hemisphere, the daily snow-covered area was validated using Interactive Multisensor Snow and Ice Mapping System (IMS) snow analysis datasets. In the simulation with SSNOWD, snow-covered area largely agreed with the IMS snow analysis and the seasonal cycle in the Northern Hemisphere was improved. This was because SSNOWD formulates the snow cover fraction differently for the accumulation season and ablation season, and represents the hysteresis of the snow cover fraction between different seasons. The effects of including SSNOWD on hydrological properties and snow mass were also examined.

scholarly journals Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

2021 ◽  
Author(s):  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Junzhe Bao ◽  
Yongli Yang ◽  
Huili Zhu ◽  
...  
Keyword(s):  
United States ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
The United States ◽  
Incidence Rates ◽  
Influenza Like Illness ◽  
Temporal Cluster ◽  
Control And Prevention ◽  
Spatio Temporal ◽  
And Control

Abstract Introduction: About 8% of Americans get influenza during an average season from the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning of influenza and the prediction of public health. Methods In this study, we analyzed the characteristics of Influenza-like Illness (ILI) by Geographic Information System and SARIMA model, respectively. Spatio-temporal cluster analysis detected 23 clusters of ILI during the study period. Results The highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. The Local spatial autocorrelation analysis revealed the High-High cluster was mainly located in Louisiana and Mississippi. This means that if the influenza incidence is high in Louisiana and Mississippi, the neighboring states will also have higher influenza incidence rates. The regression model SARIMA(1, 0, 0)(1, 1, 0)52 with statistical significance was obtained to forecast the ILI incidence of Mississippi. Conclusions The study showed, the ILI incidence will begin to increase in the 45th week 2020 and peak in the 6th week 2021. To conclude, notable epidemiological differences were observed across states, indicating that some states should pay more attention to prevent and control respiratory infectious diseases.

scholarly journals Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement

2012 ◽  
Vol 24 (7) ◽  
pp. 1371-1381 ◽  
Author(s):  
Mahesh B. Nagarajan ◽  
Markus B. Huber ◽  
Thomas Schlossbauer ◽  
Gerda Leinsinger ◽  
Andrzej Krol ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Breast Mri ◽  
Temporal Analysis ◽  
Lesion Segmentation ◽  
Spatio Temporal

scholarly journals Estimating relationships between snow water equivalent, snow covered area, and topography to extend the Airborne Snow Observatory dataset

2017 ◽  
Author(s):  
Dominik Schneider ◽  
Noah P. Molotch ◽  
Jeffrey S. Deems
Keyword(s):  
Resource Management ◽  
Water Resource Management ◽  
Satellite Images ◽  
Snow Water Equivalent ◽  
Spatial And Temporal Patterns ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Spatio Temporal ◽  
To Come

Abstract. A new spatio-temporal dataset from the ongoing Airborne Snow Observatory (ASO) provides an unprecedented look at the spatial and temporal patterns of snow water equivalent (SWE) over multiple years in the Tuolumne Basin, California, USA. We found that fractional snow covered area (fSCA) significantly improves our ability to model the distribution of SWE based on relationships between SWE, fSCA, and topography. Further, the broad availability of satellite images of fSCA facilitates the transfer of these relationship to different years with minimal degradation in performance (r2 = 0.85, % MAE = 33 %, % Bias = 1 %) compared with models fit on the same day, by considering variations in SWE depth as expressed by differences in fSCA between years. The crux of this proposition is in selecting the model to transfer. We offer a method with which to select a model from another year based on the similarity in SWE distribution at existing snow pillows in the area. Comparison of the best transferred predictions and the selected predictions results in a mild decrease in r2 (0.02) and moderate increases in % MAE (15 %) and % Bias (10 %). The results motivate further refinement in the technique used to select the best model because if these dates can be identified then SWE can be modeled at accuracy levels equivalent to models generated from ASO data collected on the day of interest. Lastly, we found that models from ASO observations of anomalously low snowpacks in 2015 still transferred to other years, although the same cannot be said for the reverse. This research provides a first attempt at extending the value of ASO beyond the observations and we expect ASO will continue to provide insights for improving water resource management for years to come.

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