Operational sub-pixel snow mapping over the Alps with NOAA AVHRR data

AbstractThis study is part of research activities concentrating on the real-time application of the U.S. National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor for snow-cover analysis of the European Alps. For mapping snow cover in heterogeneous terrain, we implement the widely used linear spectral mixture algorithm to estimate snow cover at sub-pixel scale. Principal component analysis, including the reflective part of AVHRR channel 3, is used to estimate fractions of “snow” and “not snow” within a pixel, using linear mixture modeling. The combination of these features leads to a fast, simple solution for operational and near-real-time processing. The presented algorithm is applied on the European Alps on 17 January 2003 and successfully maps snow at sub-pixel scale. The detailed snow-cover information makes it easy to recognize the complex topography of the Alps, more so than with either a classic binary map or a Moderate Resolution Imaging Spectroradiometer (MODIS) snow product. The sub-pixel algorithm reasonably identifies snow-cover fractions in regions and at altitudes where neither the classic binary map nor the MODIS algorithm detects any snow. Differences concerning the snow distribution are found in forested areas as well as in the lowest-elevation zones. The algorithm substantially improves snow mapping over complex topography for operational and near-realtime applications.

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Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis

Remote Sensing ◽

10.3390/rs10111757 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1757 ◽

Cited By ~ 10

Author(s):

Sarah Asam ◽

Mattia Callegari ◽

Michael Matiu ◽

Giuseppe Fiore ◽

Ludovica De Gregorio ◽

...

Keyword(s):

Snow Cover ◽

Regional Scale ◽

Temporal Trends ◽

Earth Observation ◽

European Alps ◽

Climate Variables ◽

South Tyrol ◽

Mean Temperature ◽

The Alps ◽

The Relationship

Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables.

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Contrasting seasonal changes in temperature, precipitation and snow cover simulated over the European Alps during the twentieth century

10.5194/egusphere-egu21-11097 ◽

2021 ◽

Author(s):

Martin Ménégoz ◽

Julien Beaumet ◽

Hubert Gallée ◽

Xavier Fettweis ◽

Samuel Morin ◽

...

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Climate Model ◽

Decadal Variability ◽

Regional Climate ◽

Horizontal Resolution ◽

Glacier Mass Balance ◽

European Alps ◽

Climate Trends ◽

The Alps

<p>The evolution of temperature, precipitation and snow cover in the European Alps have been simulated with the regional climate model MAR applied with a 7 kilometre horizontal resolution and driven by the ERA-20C (1902-2010) and the ERA5 reanalyses (1981-2018). A comparison with observational datasets, including French and Swiss local meteorological stations, in-situ glacier mass balance measurements and reanalysis product demonstrates high model skill for snow cover duration and snow water equivalent (SWE) as well as for the climatology and the inter-annual variability of both temperature and precipitation. The relatively high resolution allows to estimate the meteorological variables up to 3000m.a.s.l. The vertical gradient of precipitation simulated by MAR over the European Alps reaches 33% km-1 (1.21 mmd-1.km-1) in summer and 38%km-1 (1.15mmd mmd-1.km-1) in winter, on average over 1971&#8211;2008 and shows a large spatial variability. This study evidences seasonal and altitudinal contrasts of climate trends over the Alps. A significant (pvalue< 0.05) increase in mean winter precipitation is simulated in the northwestern Alps over 1903&#8211;2010, with changes typically reaching 20% to 40% per century, a signal strongly modulated by multi-decadal variability during the second part of the century. A general drying is found in summer over the same period, exceeding 20% to 30% per century in the western plains and 40% to 50% per century in the southern plains surrounding the Alps but remaining smaller (<10%) and not significant above 1500ma.s.l. Over 1903&#8211;2010, the maximum of daily precipitation (Rx1day) shows a general and significant increase at the annual timescale and also during the four seasons, reaching local values between 20% and 40% per century over large parts of the Alps and the Apennines. Trends of Rx1day are significant (pvalue<0.05) only when considering long time series, typically 50 to 80 years depending on the area considered. Some of these trends are nonetheless significant when computed over 1970&#8211;2010, suggesting a recent acceleration of the increase in extreme precipitation. Rx1day increase occurs where the annual correlation between temperature and intense precipitation is high. The highest warming rates in MAR are found at low elevations (< 1000 m a.s.l) in winter, whereas they are found at high elevations (> 2000 m a.s.l) in summer. In spring, warming trends show a maximum at intermediate elevations (1500 m to 1800 m). Our results suggest that higher warming at these elevations is mostly linked with the snow-albedo feedback in spring and summer.</p>

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Operational monitoring of snow cover in the Swiss Alps using real-time NOAA-AVHRR data

IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293) ◽

10.1109/igarss.1999.774538 ◽

2003 ◽

Author(s):

S. Voigt ◽

M. Koch ◽

M.F. Baumgartner

Keyword(s):

Snow Cover ◽

Real Time ◽

Swiss Alps ◽

Operational Monitoring ◽

Noaa Avhrr ◽

Avhrr Data

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Short-term numerical avalanche forecast used operationally at Météo-France over the Alps and Pyrenees

Annals of Glaciology ◽

10.1017/s0260305500015093 ◽

1998 ◽

Vol 26 ◽

pp. 357-366 ◽

Cited By ~ 7

Author(s):

Yves Durand ◽

Gérald Giraud ◽

Laurent Mérindol

Keyword(s):

Snow Cover ◽

Real Time ◽

Numerical Models ◽

Meteorological Conditions ◽

Short Term ◽

The Past ◽

The Alps ◽

Hazard Estimation ◽

Automatic Tool

Avalanche-hazard estimation for the present and the following days is one of the main tasks of the avalanche forecaster. For 4 years, some have used the results of a series of automatic numerical models in the Alpine massifs of France. Thee programs describe in real time the main meteorological conditions(SAFRAN),the evolution of the snow cover(Crocus)and the resulting avalanche risks[MÉPRA)at different elevations, slopes and aspects of the massifs considered.This paper presents the latest evolution of this automatic tool. With the new version it is now possible to provide 1 day forecasts of the state of the snow cover over the massifs of both the Alps and Pyrenees, including the main characteristics of the snowpack and an assessment of the corresponding avalanche hazards.To achieve this result, the main changes were with SAFRAN. Two combined methods are used within the same package: adaptations of larger-scale meteorological forecasts and use of observations of analogous weather situations from the past. These two approaches are complementary especially for evaluating precipitation where the second solution has important fine-scale information while the first exhibits important local biases.Validation of this new application was done carefully and proved the quality of the method, now used in real-time by local forecasters. We present some validation results, concerned both with forecasted precipitation fields at the scale of the massif and forecasted avalanche risks deduced fromMÉPRA.

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Hydrological and meteorological aspects of floods in the Alps: an overview

Hydrology and Earth System Sciences ◽

10.5194/hess-7-785-2003 ◽

2003 ◽

Vol 7 (6) ◽

pp. 785-798 ◽

Cited By ~ 27

Author(s):

Baldassare Bacchi ◽

Roberto Ranzi

Keyword(s):

Field Experiments ◽

Flood Hazard ◽

Spatial Scales ◽

European Alps ◽

Hydrological Models ◽

Bavarian Alps ◽

Research Activities ◽

The Alps ◽

Major Floods ◽

Distributed Hydrological Models

Abstract. This introductory paper presents and summarises recent research on meteorological and hydrological aspects of floods in the Alps. The research activities were part of the international research project RAPHAEL (Runoff and Atmospheric Processes for flood HAzard forEcasting and controL) together with experiments within the Special Observing Period-SOP conducted in autumn 1999 for the Mesoscale Alpine Programme —MAP. The investigations were based on both field experiments and numerical simulations, using meteorological and hydrological models, of ten major floods that occurred in the past decade in the European Alps. The two basins investigated were the Ticino (6599 km2) at the Lago Maggiore outlet on the southern side of the Alps and the Ammer catchment (709 km2) in the Bavarian Alps. These catchments and their sub-catchments cover an appropriate range of spatial scales with which to investigate and test in an operational context the potential of both mesoscale meteorological and distributed hydrological models for flood forecasting. From the data analyses and model simulations described in this Special Issue, the major sources of uncertainties for flood forecasts in mid-size mountain basins are outlined and the accuracy flood forecasts is assessed. Keywords: floods, mountain hydrology, meteorological models, Alps

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Short-term numerical avalanche forecast used operationally at Météo-France over the Alps and Pyrenees

Annals of Glaciology ◽

10.3189/1998aog26-1-357-366 ◽

1998 ◽

Vol 26 ◽

pp. 357-366 ◽

Cited By ~ 3

Author(s):

Yves Durand ◽

Gérald Giraud ◽

Laurent Mérindol

Keyword(s):

Snow Cover ◽

Real Time ◽

Numerical Models ◽

Meteorological Conditions ◽

Short Term ◽

The Past ◽

The Alps ◽

Hazard Estimation ◽

Automatic Tool

Avalanche-hazard estimation for the present and the following days is one of the main tasks of the avalanche forecaster. For 4 years, some have used the results of a series of automatic numerical models in the Alpine massifs of France. Thee programs describe in real time the main meteorological conditions (SAFRAN), the evolution of the snow cover (Crocus) and the resulting avalanche risks [MÉPRA) at different elevations, slopes and aspects of the massifs considered.This paper presents the latest evolution of this automatic tool. With the new version it is now possible to provide 1 day forecasts of the state of the snow cover over the massifs of both the Alps and Pyrenees, including the main characteristics of the snowpack and an assessment of the corresponding avalanche hazards.To achieve this result, the main changes were with SAFRAN. Two combined methods are used within the same package: adaptations of larger-scale meteorological forecasts and use of observations of analogous weather situations from the past. These two approaches are complementary especially for evaluating precipitation where the second solution has important fine-scale information while the first exhibits important local biases.Validation of this new application was done carefully and proved the quality of the method, now used in real-time by local forecasters. We present some validation results, concerned both with forecasted precipitation fields at the scale of the massif and forecasted avalanche risks deduced from MÉPRA.

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Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽

10.3390/metabo11050265 ◽

2021 ◽

Vol 11 (5) ◽

pp. 265

Author(s):

Ruchi Sharma ◽

Wenzhe Zang ◽

Menglian Zhou ◽

Nicole Schafer ◽

Lesa A. Begley ◽

...

Keyword(s):

Gas Chromatography ◽

Real Time ◽

Rapid Assessment ◽

Principal Component ◽

Corticosteroid Treatment ◽

Breath Analysis ◽

Blood Eosinophil ◽

Exhaled Breath ◽

Respiratory Illnesses ◽

Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

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An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Sensors ◽

10.3390/s21082869 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2869

Author(s):

Jiaen Wu ◽

Kiran Kuruvithadam ◽

Alessandro Schaer ◽

Richie Stoneham ◽

George Chatzipirpiridis ◽

...

Keyword(s):

Real Time ◽

Inertial Sensor ◽

Neurological Diseases ◽

Principal Component ◽

Sampling Frequency ◽

Powerful Analytical Tool ◽

Optical Motion ◽

Analysis System ◽

Optical Motion Capture ◽

Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

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