scholarly journals The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research

2021 ◽  
Vol 13 (10) ◽  
pp. 4711-4726
Author(s):  
Xiaohua Hao ◽  
Guanghui Huang ◽  
Tao Che ◽  
Wenzheng Ji ◽  
Xingliang Sun ◽  
...  
Keyword(s):  
Snow Cover ◽  
Regional Climate ◽  
Commission Error ◽  
Cloud Detection ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Snow Cover Extent ◽  
Kappa Value ◽  
Academy Of Sciences

Abstract. A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

scholarly journals Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

2013 ◽  
Vol 17 (10) ◽  
pp. 3921-3936 ◽  
Author(s):  
M. Ménégoz ◽  
H. Gallée ◽  
H. W. Jacobi
Keyword(s):  
Snow Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
The Tibetan Plateau ◽  
Monsoon Period ◽  
Medium Range Weather Forecast ◽  
Snow Cover Extent ◽  
The Himalaya

Abstract. We applied a Regional Climate Model (RCM) to simulate precipitation and snow cover over the Himalaya, between March 2000 and December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis of the European Centre of Medium range Weather Forecast (ECMWF) used as lateral boundaries. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its eastern part. However, our simulation provides a first estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are not very reliable. During the two years of simulation, our model resembles the snow cover extent and duration quite accurately in these areas. Both snow accumulation and snow cover duration differ widely along the Himalaya: snowfall can occur during the whole year in western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan Plateau, a much more marked dry season occurs from October to March. Snow cover does not have a pronounced seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the extra-monsoon period.

Validity of participants’ self-reported diagnosis for a work absence due to a mental health problem compared with physician-certified diagnosis for the same work absence among 709 Canadian workers

2020 ◽  
pp. oemed-2020-106658
Author(s):  
Mahée Gilbert-Ouimet ◽  
Xavier Trudel ◽  
Karine Aubé ◽  
Ruth Ndjaboue ◽  
Caroline S Duchaine ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Problem ◽  
Sensitivity And Specificity ◽  
Health Problem ◽  
High Specificity ◽  
Work Absence ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Kappa Value ◽  
Total Agreement

ObjectivesThis study assesses the validity of a self-reported mental health problem (MHP) diagnosis as the reason for a work absence of 5 days or more compared with a physician-certified MHP diagnosis related to the same work absence. The potential modifying effect of absence duration on validity is also examined.MethodsA total of 709 participants (1031 sickness absence episodes) were selected and interviewed. Total per cent agreement, Cohen’s kappa, sensitivity and specificity values were calculated using the physician-certified MHP diagnosis related to a given work absence as the reference standard. Stratified analyses of total agreement, sensitivity and specificity values were also examined by duration of work absence (5–20 workdays,>20 workdays).ResultsTotal agreement value for self-reported MHP was 90%. Cohen’s kappa value was substantial (0.74). Sensitivity was 77% and specificity was 95%. Absences of more than 20 workdays had a better sensitivity than absences of shorter duration. A high specificity was observed for both short and longer absence episodes.ConclusionThis study showed high specificity and good sensitivity of self-reported MHP diagnosis compared with physician-certified MHP diagnosis for the same work absence. Absences of longer durations had a better sensitivity.

Links between spring snow cover and long-term alpine vegetation change

2020 ◽  
Author(s):  
Shengwei Zong ◽  
Christian Rixen
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Plant Species ◽  
Climate Warming ◽  
Vegetation Change ◽  
Alpine Vegetation ◽  
Alpine Ecosystems ◽  
Snow Cover Extent ◽  
The Relationship

<p><span>Snow is an important environmental factor determining distributions of plant species in alpine ecosystems. During the past decades, climate warming has resulted in significant reduction of snow cover extent globally, which led to remarkable alpine vegetation change. Alpine vegetation change is often caused by the combined effects of increasing air temperature and snow cover change, yet the relationship between snow cover and vegetation change is currently not fully understood. To detect changes in both snow cover and alpine vegetation, a relatively fine spatial scales over long temporal spans is necessary. In this study in alpine tundra of the Changbai Mountains, Northeast China, we (1) quantified spatiotemporal changes of spring snow cover area (SCA) during half a century by using multi-source remote sensing datasets; (2) detected long-term vegetation greening and browning trends at pixel level using Landsat archives of 30 m resolution, and (3) analyzed the relationship between spring SCA change and vegetation change. Results showed that spring SCA has decreased significantly during the last 50 years in line with climate warming. Changes in vegetation greening and browning trend were related to distributional range dynamics of a dominant indigenous evergreen shrub <em>Rhododendron aureum</em>, which extended at the leading edge and retracted at the trailing edge. Changes in <em>R. aureum</em> distribution were probably related to spring snow cover changes. Areas with decreasing <em>R. aureum</em> cover were often located in snow patches where probably herbs and grasses encroached from low elevations and adjacent communities. Our study highlights that spring SCA derived from multi-source remote sensing imagery can be used as a proxy to explore relationship between snow cover and vegetation change in alpine ecosystems. Alpine indigenous plant species may migrate upward following the reduction of snow-dominated environments in the context of climate warming and could be threatened by encroaching plants within snow bed habitats.</span></p>

scholarly journals Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

SLEEP ◽  
2020 ◽  
Vol 43 (11) ◽  
Author(s):  
Maurice Abou Jaoude ◽  
Haoqi Sun ◽  
Kyle R Pellerin ◽  
Milena Pavlova ◽  
Rani A Sarkis ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Cohen’S Kappa ◽  
Scalp Eeg ◽  
Cohen's Kappa ◽  
Eeg Recordings

Abstract Study Objectives Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods Using a clinical dataset of polysomnograms from 6,431 patients (MGH–PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm’s inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH–PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24–72 h) scalp EEG recordings from 112 patients (scalpEEG dataset). Results The algorithm achieved a Cohen’s kappa of 0.74 on the MGH–PSG holdout testing set and cross-validated Cohen’s kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen’s kappa ~ 0.75 ± 0.11). The algorithm’s performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities. Conclusion We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China

2014 ◽  
Vol 15 (5) ◽  
pp. 2067-2084 ◽  
Author(s):  
Xue-Jun Zhang ◽  
Qiuhong Tang ◽  
Ming Pan ◽  
Yin Tang
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Snow Cover ◽  
Land Surface ◽  
Hydrologic Model ◽  
Reliable Estimate ◽  
Time Step ◽  
Snow Cover Extent ◽  
Monthly Streamflow

Abstract A long-term consistent and comprehensive dataset of land surface hydrologic fluxes and states will greatly benefit the analysis of land surface variables, their changes and interactions, and the assessment of land–atmosphere parameterizations for climate models. While some offline model studies can provide balanced water and energy budgets at land surface, few of them have presented an evaluation of the long-term interaction of water balance components over China. Here, a consistent and comprehensive land surface hydrologic fluxes and states dataset for China using the Variable Infiltration Capacity (VIC) hydrologic model driven by long-term gridded observation-based meteorological forcings is developed. The hydrologic dataset covers China with a 0.25° spatial resolution and a 3-hourly time step for 1952–2012. In the dataset, the simulated streamflow matches well with the observed monthly streamflow at the large river basins in China. Given the water balance scheme in the VIC model, the overall success at runoff simulations suggests that the long-term mean evapotranspiration is also realistically estimated. The simulated soil moisture generally reproduces the seasonal variation of the observed soil moisture at the ground stations where long-term observations are available. The modeled snow cover patterns and monthly dynamics bear an overall resemblance to the Northern Hemisphere snow cover extent data from the National Snow and Ice Data Center. Compared with global product of a similar nature, the dataset can provide a more reliable estimate of land surface variables over China. The dataset, which will be publicly available via the Internet, may be useful for hydroclimatological studies in China.

scholarly journals POS1401 ASSESSMENT OF INTERREADER RELIABILITY IN SCORING PATIENTS WITH HAND OSTEOARTHRITIS AND PSORIATIC ARTHRITIS BY FLUORESCENCE OPTICAL IMAGING

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 983.2-983
Author(s):  
B. Drude ◽  
Ø. Maugesten ◽  
S. G. Werner ◽  
G. R. Burmester ◽  
J. Berger ◽  
...  
Keyword(s):  
Psoriatic Arthritis ◽  
Optical Imaging ◽  
Phase 1 ◽  
Hand Osteoarthritis ◽  
Scoring Method ◽  
Cohen’S Kappa ◽  
Fluorescence Optical Imaging ◽  
Cohen's Kappa ◽  
Kappa Value ◽  
Pip Joint

Background:Fluorescence Optical Imaging (FOI) utilises the fluorophore indocyanine green (ICG) to reflect enhanced microcirculation in hand and finger joints due to inflammation.Objectives:We wanted to assess the interreader reliability of FOI enhancement in patients with hand osteoarthritis (OA) and psoriatic arthritis (PsA). Furthermore, predefined typical morphologic patterns were included to determine the ability of FOI to discriminate between both diagnoses.Methods:An atlas with example images of grade 0-3 in different joint groups and typical morphologic patterns (‘streaky signals’[1], ‘green/blue nail sign’[2], ‘Werner sign’[3,4], and ‘Bishop’s crozier sign’) of PsA and hand OA was created. Two readers scored all joints in both hands (30 in total) of 20 cases with hand OA and PsA. The cases were randomly mixed and both readers were blinded to diagnosis. Each joint was rated on a semiquantitative scale from 0 to 3 in five different images (PrimaVista Mode (PVM), phase 1, 2 (first and middle image), and 3) during the FOI sequence according to the scoring method FOIAS (fluorescence optical imaging activity score)[1,3]. Interreader reliability on scoring joint enhancement was calculated using linear weighted Cohen’s kappa (κ). Agreement on diagnosis (hand OA vs. PsA) and different morphologic patterns was assessed by calculating (regular) Cohen’s kappa.Results:Overall agreement on scoring joint enhancement (all phases) was substantial (κ = 0.75), with greatest consensus in phase 2 first (κ = 0.75) and lowest agreement in phase 1 (κ = 0.46). Reliability varied in different joint groups (wrist, MCP, (P)IP, DIP), with almost perfect overall agreement on PIP joint affection (κ = 0.81), substantial agreement on wrist (κ = 0.69) and DIP joint affection (κ = 0.63), and moderate agreement on MCP joint affection (κ = 0.49) across all phases. Consensus on morphologic patterns showed overall fair agreement (κ = 0.37) with a similar kappa value on the ability to discriminate between both diagnoses (κ = 0.3).Conclusion:Joint enhancement in FOI can be reliably assessed using a predefined scoring method. The ability of FOI to differentiate between hand OA and PsA seems to be limited. Clearer definition and more training might be needed to better agree on morphologic patterns in FOI.References:[1] Glimm AM, Werner SG, Burmester GR, et al. Ann Rheum Dis. 2016 Mar;75(3):566-570[2] Wiemann O, Werner SG, Langer HE, et al. J Dtsch Dermatol Ges. 2019 Feb;17(2):138-148[3] Werner SG, Langer HE, Ohrndorf S, et al. Ann Rheum Dis. 2012 Apr;71(4):504-510[4] Zeidler H 2019. Fluoreszenzoptische Bildgebung. In: Zeidler H, Michel BA. Differenzialdiagnose rheumatischer Erkrankungen 5. Aufl. Springer, Heidelberg, S. 88-89Disclosure of Interests:Benedict Drude: None declared, Øystein Maugesten: None declared, Stephanie Gabriele Werner: None declared, Gerd Rüdiger Burmester: None declared, Jörn Berger Employee of: Xiralite GmbH, Ida K. Haugen: None declared, Sarah Ohrndorf: None declared

scholarly journals A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring

2014 ◽  
Vol 7 (2) ◽  
pp. 669-691 ◽  
Author(s):  
T. W. Estilow ◽  
A. H. Young ◽  
D. A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Environmental Data ◽  
Climatic Data ◽  
Climate Data ◽  
Snow Cover Extent ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. This paper describes the long-term, satellite-based visible snow cover extent NOAA climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data has a spatial resolution of 190.5 km at 60 ° latitude, are updated monthly, and span from 4 October 1966 to present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data are provided in netCDF format and are archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the dataset are presented herein. In general, the CDR provides similar spatial and temporal variability as its widely used predecessor product. Key refinements to the new CDR improve the product's grid accuracy and documentation, and bring metadata into compliance with current standards for climate data records.

scholarly journals A regional climate model hindcast for Siberia – assessing the added value of snow water equivalent using ESA GlobSnow and reanalyses

2012 ◽  
Vol 6 (6) ◽  
pp. 4637-4671
Author(s):  
K. Klehmet ◽  
B. Geyer ◽  
B. Rockel
Keyword(s):  
Regional Climate Model ◽  
Snow Cover ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Snow Accumulation ◽  
Added Value ◽  
Snow Cover Extent ◽  
Snow Water

Abstract. This study analyzes the added value of a regional climate model hindcast of CCLM compared to global reanalyses in providing a reconstruction of recent past snow water equivalent (SWE) for Siberia. Consistent regional climate data in time and space is necessary due to lack of station data in that region. We focus on SWE since it represents an important snow cover parameter in a region where snow has the potential to feed back to the climate of the whole Northern Hemisphere. The simulation was performed in a 50 km grid spacing for the period 1948 to 2010 using NCEP Reanalysis 1 as boundary forcing. Daily observational reference data for the period of 1987–2010 was obtained by the satellite derived SWE product of ESA DUE GlobSnow that enables a large scale assessment. The analyses includes comparisons of the distribution of snow cover extent, example time series of monthly SWE for January and April, regional characteristics of long-term monthly mean, standard deviation and temporal correlation averaged over subregions. SWE of CCLM is compared against the SWE information of NCEP-R1 itself and three more reanalyses (NCEP-R2, NCEP-CFSR, ERA-Interim). We demonstrate a significant added value of the CCLM hindcast during snow accumulation period shown for January for many subregions compared to SWE of NCEP-R1. NCEP-R1 mostly underestimates SWE during whole snow season. CCLM overestimates SWE compared to the satellite-derived product during April – a month representing the beginning of snow melt in southern regions. We illustrate that SWE of the regional hindcast is more consistent in time than ERA-Interim and NCEP-R2 and thus add realistic detail.

scholarly journals The Swiss snow climatology as seen by reanalyses and further gridded datasets

2021 ◽  
Author(s):  
Monika Goeldi ◽  
Stefanie Gubler ◽  
Christian Steger ◽  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Data Sets ◽  
Data Set ◽  
Climate Services ◽  
Snow Cover Extent ◽  
Snow Water

<p>Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.</p><p>In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.</p><p>The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations – e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.</p>

scholarly journals Development and validation of a new MODIS snow-cover-extent product over China

2021 ◽  
Author(s):  
Xiaohua Hao ◽  
Guanghui Huang ◽  
Zhaojun Zheng ◽  
Xingliang Sun ◽  
Wenzheng Ji ◽  
...  
Keyword(s):  
Snow Cover ◽  
China Meteorological Administration ◽  
Commission Errors ◽  
Clear Sky ◽  
Percentage Points ◽  
Snow Cover Extent ◽  
Terra Modis ◽  
Development And Validation ◽  
Academy Of Sciences ◽  
Omission Errors

Abstract. Based on the MOD09GA/MYD09GA 500-m surface reflectance, a new MODIS snow-cover-extent (SCE) product over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets — Terra-MODIS and Aqua-MODIS SCE datasets, and a final daily cloud-gap-filled (CGF) SCE dataset. The formers are generated mainly through optimizing snow-cover discriminating rules over different land-cover types, and the latter is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. Validation against 362 China Meteorological Administration (CMA) stations shows during snow seasons the overall accuracies (OA) of the three datasets are all larger than 93 %, the omission errors (OE) are all constrained within 9 %, and the commission errors (CE) are all constrained within 10 %. Biases ranging from the lowest 0.98 to the medium 1.02, to the largest 1.03 demonstrate on a whole the SCEs given by the new product are neither overestimated nor underestimated significantly. Based on the same ground reference data, we found the new product’s accuracies are clearly higher than those of standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For examples, compared with the CE of 23.78 % that the standard MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 %, versus 89.54 % of the standard MOD10A1F product and 84.36 % of the standard MYD10A1F product. Besides, as expected snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China, and thereby can better serve for hydrological, climatic, environmental, and other related studies there.

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