ARTICLE | doi:10.20944/preprints201910.0341.v1
Online: 29 October 2019 (15:37:11 CET)
Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting and hazard mapping. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel- 1 SAR data download. Our avalanche detection algorithm has an average probability of detection of 67.2 % with a false alarm rate averaging 45.9, with maximum POD's over 85 % and minimum FAR's of 24.9 % compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 x 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3 % were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79 % with high POD's in cases of medium to large wet snow avalanches. For the first time, we can present a dataset of spatiotemporal avalanche activity over several winters from a large region. This unique dataset allows for research into the relationship between avalanche activity and triggering meteorological factors, mapping of avalanche prone areas and near-real time avalanche activity monitoring to assist public avalanche forecasting. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.
Subject: Keywords: Snow avalanches; mathematical models; snow entrainment; Voellmy and Grigorian friction laws; hydraulic models; runout distance; analytic solutions
Online: 6 February 2020 (09:11:48 CET)
This note first summarizes the history of the manuscript "On a Continuum Model for Avalanche Flow and Its Simplified Variants" by Grigorian and Ostroumov―published in the Special Issue on snow avalanche dynamics of Geosciences―since the early 1990s and explains the guiding principles in editing it for publication. The changes are then detailed and some explanatory notes given for the benefit of readers who are not familiar with the early Russian work on snow avalanche dynamics. Finally, the editor's personal views as to why he still considers this paper of relevance for avalanche dynamics research today are presented in brief essays on key aspects of the paper, namely the role of simple and complex models in avalanche research and mitigation work, the status and possible applications of Grigorian's stress-limited friction law, and non-monotonicity of the dynamics of the Grigorian–Ostroumov model in the friction coefficient. A comparison of the erosion model proposed by those authors with two other models suggests to enhance it with an additional equation for the balance of tangential momentum across the shock front. A preliminary analysis indicates that continuous scouring entrainment is possible only in a restricted parameter range and that there is a second erosion regime with delayed entrainment.
ARTICLE | doi:10.20944/preprints202007.0381.v1
Subject: Earth Sciences, Environmental Sciences Keywords: snow; snow cover area; fractional snow cover; Sentine-2
Online: 17 July 2020 (14:05:06 CEST)
Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolution at global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrain using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectances after masking cloud and snow-free areas. The NDSI-FSC function is calibrated using Pléiades very high resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5×tanh(a×NDSI+b)+0.5 where a = 2.65 and b = -1.42 yielding a root mean square error of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level.
ARTICLE | doi:10.20944/preprints202011.0545.v1
Subject: Earth Sciences, Atmospheric Science Keywords: ephemeral snow; snowpack; seasonal snow; United States
Online: 20 November 2020 (12:33:03 CET)
Snowpack seasonality in the conterminous United States (U.S.) is explored using a daily,14 km horizontal resolution gridded snow water equivalent and snow depth reanalysis product. I2calculated seasonal snowpacks using two established methods: (1) the classic Sturm approach that3requires 60 days of snow cover with a peak depth >50 cm and (2) the snow seasonality metric (SSM)4that only requires 60 days of continuous snow cover. The latter approach yields continuous values5from -1 to +1, where -1 (+1) indicates an ephemeral (seasonal) snowpack. Both approaches identify6seasonal snowpacks in western mountains and the northernmost central and eastern U.S. By relaxing7the depth constraint and providing continuous values, the SSM identifies greater areas of seasonal8snowpacks compared to the Sturm method, particularly in the upper Midwest, New England, and the9Intermountain West. Ephemeral snowpacks are identified throughout lower elevation regions of the10western U.S. and across a broad swath centered near 35°N spanning the lee of the Rocky Mountains11to the Atlantic coast. Because it lacks a depth constraint, the SSM approach is sensitive to interannual12variability, indicating it may inform the location of shallow but long-duration snowpacks at risk13of transitioning to becoming ephemeral with climatic change. A case study in Oregon during an14extreme snow drought year highlights seasonal to ephemeral snowpack transitions.
Subject: Earth Sciences, Atmospheric Science Keywords: snow; snowmelt; snow cover; sublimation; Indus; High Mountain Asia
Online: 23 September 2021 (10:24:05 CEST)
The Indus basin is considered as the one with the highest dependence on snowmelt runoff in High Mountain Asia. The recent High Mountain Asia snow reanalysis enables to go beyond previous studies by evaluating both snowmelt and snow sublimation at the basin scale. Over 2000-2016, basin-average snowmelt is 101 11 Gt.a-1 (121 ± 13 mm.a-1), which represents about 25-30% of basin-average annual precipitation. Snow sublimation accounts for 11% of the mean annual snow ablation, but with a large spatial variability across the basin.
ARTICLE | doi:10.20944/preprints202208.0258.v1
Online: 15 August 2022 (11:26:56 CEST)
The optical signals detected on multiple satellite platforms over snow surfaces are determined by the optical properties of snow surface and atmosphere. The solution of both direct and inverse problems of an atmosphere – underlying snow system requires simple relationships between top-of-atmosphere (TOA) reflectance R and microphysical/optical characteristics of both snow and atmosphere. The task of this paper is to present a simple analytical relationship between the value of R as detected on a satellite with atmosphere/snow properties. Such a relationship can be established using a numerical solution of integro - differential radiative transfer equation (RTE) (Liou, 2022). However, this path is quite complicated and time consuming. The analytical solutions of RTE are needed for the solution of various applied atmospheric and snow optics problems (Cachorro et al., 2022; Mei et al., 2020, 2022; Kokhanovsky, 2021). This is the main driver of this work. To simplify the problem under study we consider the case of Antarctica, where both snow and atmosphere are almost free of pollutants. This work is focused on the simulation of the moderate spectral resolution TOA measurements (1nm or so) and the spectral range 400-1000nm.
ARTICLE | doi:10.20944/preprints201906.0250.v1
Subject: Earth Sciences, Atmospheric Science Keywords: energy balance; snowmelt; snow hydrology; snow-vegetation interaction; shortwave radiation; longwave radiation
Online: 25 June 2019 (08:59:58 CEST)
Radiation is the major driver of snowmelt, and hence its estimation is critically important. Net radiation reaching the forest floor is influenced by vegetation density. Previous studies in mid-latitude conifer forests have confirmed that net radiation decreases and then subsequently increases with increasing vegetation density, for clear sky conditions. This leads to existence of a net radiation minimum at an intermediate vegetation density. With increasing cloud cover, the minimum radiation shifts toward lower densities, sometimes resulting in a monotonically increasing radiation with vegetation density. The net radiation trend, however, is expected to change across sites, affecting the magnitude and timing of individual radiation components. This research explores the variability of net radiation on snow-covered forest floor for different vegetation densities along a latitudinal gradient. We especially investigate how the magnitude of minimum/maximum radiation and the corresponding vegetation density change with the site geographical location. To evaluate these, the net radiation is evaluated using the Forest Radiation Model at six different locations in predominantly white spruce (Picea glauca) canopy cover across North America, ranging from 45 to 66°N latitudes. Results show that the variation of net radiation with vegetation density considerably varies with latitude. In higher latitude forests, the magnitude of net radiation is generally smaller, and the minimum radiation is exhibited at relatively sparser vegetation densities, under clear sky conditions. For interspersed cloudy sky conditions, net radiation non-monotonically varies with latitude across the sites, depending on the seasonal sky cloudiness and air temperature. Latitudinal sensitivity of net radiation is lower on north-facing hillslopes than on south-facing sites.
ARTICLE | doi:10.20944/preprints201705.0029.v3
Subject: Earth Sciences, Atmospheric Science Keywords: snow depth; snow cover; soil moisture; snowmelt; seasonal prediction; land-atmosphere feedbacks
Online: 23 August 2017 (08:05:47 CEST)
Subseasonal-to-seasonal (S2S) weather forecasting has improved in recent years, thanks partly to better representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is probed via two causal pathways. Through the first pathway, snow depth anomalies in month 1 cause snow depth anomalies in month 2, which then cause temperature anomalies in month 2. This pathway represents the snow–albedo feedback, as well as cooling due to insulation, emissivity and heat loss. It is active from fall to summer, and its effect peaks in March/April in the midlatitudes and in May/June at high latitudes. A complementary second pathway, where snow depth anomalies in month 1 cause soil moisture anomalies in month 2, which then cause temperature anomalies in month 2 through soil moisture–temperature feedbacks, is only active in spring and summer. Its effect peaks later in the warm season than the effect of the first pathway. Geographically, snow depth mediates north of, and soil moisture south of, the areas with the highest temperature predictability from snow depth. These results indicate that the two pathways describe complementary physical mechanisms. The first pathway embodies month-to-month persistence of snow depth, and the second pathway represents melting of snow from one month to the next.
ARTICLE | doi:10.20944/preprints202001.0300.v1
Subject: Earth Sciences, Other Keywords: snow; synthetic aperture radar; Sentinel-1; spatial variability; spectral scaling; topography; wet snow
Online: 26 January 2020 (01:42:48 CET)
This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150-250 m respectively in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100 -1,000 m range with a break at (180-360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications.
ARTICLE | doi:10.20944/preprints201701.0119.v1
Online: 26 January 2017 (08:07:51 CET)
Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.
ARTICLE | doi:10.20944/preprints201911.0391.v1
Subject: Earth Sciences, Environmental Sciences Keywords: snow characteristics; optical remote sensing; snow albedo; PROMICE; Sentinel 3; OLCI; atmospheric correction; Arctic aerosol
Online: 30 November 2019 (11:23:46 CET)
We present a simplified atmospheric correction algorithm for the snow/ice albedo retrieval using single view satellite measurements. The validation of the technique is performed using Ocean and Land Colour Instrument (OLCI) on board Copernicus Sentinel - 3 satellite and ground spectral or broadband albedo measurements from locations on the Greenland ice sheet and in the French Alps. Through comparison with independent ground observations, the technique is shown to perform accurately in a range of conditions from a 2100 m elevation mid-latitude location in the French Alps to a network of 15 locations across a 2390 m elevation range in seven regions across the Greenland ice sheet. Retrieved broadband albedo is accurate within 5% over a wide (0.5) broadband albedo range of the (N = 4,155) Greenland observations and with no apparent bias.
ARTICLE | doi:10.20944/preprints202207.0403.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Climate Projection; Downscaling; Drought; Runoff; Snow; Wildfire
Online: 26 July 2022 (10:42:21 CEST)
Snowpack loss in midlatitude mountains is ubiquitously projected by Earth system models, though the magnitudes, persistence and time horizons of decline vary. Using daily downscaled hydroclimate and snow projections we examine changes in snow seasonality across the U.S. Pacific Southwest region during a simulated severe 20-year dry spell in the 21st century (2051–2070) developed as part of the 4th California Climate Change Assessment to provide a "stress test" for water resources. Across California’s mountains, substantial declines (30–100% loss) in median peak annual snow water equivalent accompany changes in snow seasonality throughout the region compared to the historic period. We find 80% of historic seasonal snowpacks transition to ephemeral conditions. Subsetting empirical-statistical wildfire projections for California by snow seasonality transition regions indicates a two-to-four-fold increase in burned area, consistent with recent observations of high elevation wildfires following extended drought conditions. By analyzing six of the major California snow-fed river systems we demonstrate snowpack reductions and seasonality transitions result in concomitant declines in annual runoff (47-58% of historic values). The negative impacts to statewide water supply reliability by the projected dry spell will likely be magnified by changes in snowpack seasonality and increased wildfire activity.
ARTICLE | doi:10.20944/preprints202109.0388.v1
Online: 22 September 2021 (15:25:12 CEST)
We have proposed a simple algorithm to retrieve the total ozone column and snow properties (spectral albedo and effective light absorption path) using the high spatial resolution single – view MSI/S-2 measurements over Antarctica.
ARTICLE | doi:10.20944/preprints202011.0243.v1
Subject: Earth Sciences, Atmospheric Science Keywords: permanent snow line; Quaternary; Cirque; Glaciers; Binalod
Online: 6 November 2020 (13:13:07 CET)
Accordingly, the present study is aimed at investigating quaternary climate changes in Binalod Heights. To identify glacial effects, Morphic indices, field evidence and effects, climatic evidence, and (laboratory) experimental analysis were employed. Determining the permanent snow line in the region was conducted using the Right Method and 65 cirques which are considered as much enriched feeding resources for the formation an ice cover in the region. The expansion of settlements in the region are lower than the permanent snow life is more accumulated than above the border of the permanent snow line. This issue indicates that refrigeration cells do not have the ability to create civil nuclear. Regarding quaternary climate changes and the gradual warming of the climate, the initial core of the City of Mashhad ranges from the center of the Kashf rood River to northeastern heights of Binalod. In addition, the existence of glacial cirques in heights as an important factor in feeding refrigerating conditions has been effective on the expansion of urbanization of Mashhad in the past time. Our new geomorphological mapping and landsystem reconstructions provide an important insight into the response of temperate Binalod glaciers to rapidly-warming climate
ARTICLE | doi:10.20944/preprints202009.0761.v1
Subject: Earth Sciences, Atmospheric Science Keywords: black carbon; snow; albedo; glaciers; trajectories, Vallunaraju
Online: 30 September 2020 (17:53:58 CEST)
The role of Black Carbon (BC) as a contributor to glacial retreat is of particular interest to the scientific community and decision makers, due to its impact on snow albedo and glacier melt. In this study, a thermal-optical instrument (LAHM) was used to measure effective Black Carbon (eBC) in a series of surface snow samples collected from the Vallunaraju glacier, Cordillera Blanca, between April 2019 and May 2020. The time series obtained indicates a marked seasonal variability of eBC with maximum concentrations during the dry season and dramatic decrease during the wet season. The concentrations detected ranged between a minimum of 3.73 ng/g and 4.23 ng/g during the wet season and a maximum of 214.13 ng/g and 181.60 ng/g during the dry season, in the accumulation and ablation zone. Using SNICAR model, the reduction of albedo was estimated at 6.36% and 6.60% during the dry season and 0.68% and 0.95% during the wet season, which represents an average radiative forcing of 4.52 ± 1.84 W/m2 and 4.69 ± 1.59 W/m2 in the accumulation zone, and 0.49 ± 0.27 W/m2 and 0.68 ± 0.43 W/m2 in the ablation zone. The melting of snow due to the eBC translates into 80.18 ± 37.30 kg/m2 and 83.16 ± 32.75 kg/m2 during the dry season, and 7.91 ± 4.29 kg/m2 and 10.85 ± 6.62 kg/m2 during the wet season, in the accumulation and ablation zones, respectively. Finally, the HYSPLIT trajectory assessment shows that aerosols predominate in the Amazon rainforest, especially when forest fires are most abundant according to VIIRS images.
TECHNICAL NOTE | doi:10.20944/preprints202009.0529.v1
Subject: Earth Sciences, Environmental Sciences Keywords: snow; albedo; remote sensing; OLCI; Sentinel-3
Online: 23 September 2020 (03:45:37 CEST)
This document describes the theoretical basis of the algorithms used to determine properties of snow and ice from the measurements of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites within the Pre-operational Sentinel-3 snow and ice products (SICE) project: http://snow.geus.dk/. The code used for the SICE retrieval and its documentation can be found at https://github.com/GEUS-SICE/pySICE. The algorithms were developed after the work from Kokhanovsky et al. (2018, 2019, 2020).
ARTICLE | doi:10.20944/preprints201809.0444.v1
Subject: Earth Sciences, Atmospheric Science Keywords: snow-darkening, light-absorbing aerosols, dust and black carbon, elevated-heat- pump effect, snow cover–monsoon relationship, Blanford hypothesis
Online: 22 September 2018 (23:18:38 CEST)
The impact of snow darkening by deposition of light absorbing aerosols (LAAs) on snow cover over the Himalaya-Tibetan-Plateau (HTP) and influence on the Asian monsoon are investigated using the NASA Goddard Earth Observing System Model Version 5 (GEOS-5). We find that during April-May-June, deposition of LAAs on snow leads to a reduction in surface albedo, initiating a sequence of feedback processes, starting with increased surface solar radiation, rapid snowmelt in HTP and warming of the surface and upper troposphere, followed by enhanced low-level southwesterlies and increased dust loading over the Himalayas-Indo-Gangetic Plain. The warming is amplified by increased dust aerosol heating, and subsequently amplified by latent heating from enhanced precipitation over the Himalaya foothills and northern India, via the Elevated Heat Pump (EHP) effect during June-July-August. The reduced snow cover in the HTP anchors the enhanced heating over the Tibetan Plateau and its southern slopes, in conjunction with an enhancement of the Tibetan Anticyclone, and the development of an anomalous Rossby wavetrain over East Asia, leading to weakening of the subtropical westerly jet, and northward displacement and intensification of the Mei-Yu rainbelt. Our results suggest that atmosphere-land heating by LAAs, particularly desert dust play a fundamental role in physical processes underpinning the snow-monsoon relationship proposed by Blandford more than a century ago.
ARTICLE | doi:10.20944/preprints201903.0148.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Blizzards; blowing snow; climatology; self-organizing maps; synoptic typing
Online: 14 March 2019 (07:03:28 CET)
Stretching along the border of North Dakota and Minnesota, The Red River Valley (RRV) of the North has the highest frequency of reported blizzards within the contiguous United States. Despite the numerous impacts these events have, few systematic studies exist discussing the meteorological properties of blizzards. As a result, forecasting these events and lesser blowing snow events is an ongoing forecast challenge. This study presents a climatology of atmospheric patterns associated with RRV blizzards for the winter seasons of 1979-1980 to 2017-2018. Patterns were identified using subjective and objective techniques using meteorological fields from the North American Regional Reanalysis (NARR). The RRV experiences on average, 2.6 events per year. Blizzard frequency is bimodal with peaks occurring in December and March. The events can largely be typed into four meteorological categories dependent on the forcing that drives the blizzard: Alberta Clippers, Arctic Fronts, Colorado Lows, and Hybrids. Objective classification of these blizzards using a competitive neural network known as the Self-Organizing Map (SOM) demonstrates that gross segregation of the events can be achieved with a small (8-class) map. This implies that objective analysis techniques can be used to identify these events in weather and climate model output that may aid future forecasting and risk assessment projects.
COMMUNICATION | doi:10.20944/preprints201805.0333.v1
Subject: Earth Sciences, Atmospheric Science Keywords: atmospheric river; avalanche; debris flow; flooding; snow level; typhoon
Online: 24 May 2018 (05:53:39 CEST)
On 5-7 April 2018 a landfalling atmospheric river resulted in widespread heavy precipitation in the Sierra Nevada of California and Nevada. Observed snow levels during this event were among the highest snow levels recorded since observations began in 2002 and exceeded 2.75 km for 31 hours in the northern Sierra Nevada and 3.75 km for 12 hours in the southern Sierra Nevada. The anomalously high snow levels and over 80 mm of precipitation caused flooding, debris flows, and wet snow avalanches in the upper elevations of the Sierra Nevada. The origin of this atmospheric river was super typhoon Jelawat, whose moisture remnants were entrained and maintained by an extratropical cyclone in the northeast Pacific. This event was notable due to its April occurrence, as six other typhoon remnants that caused heavy precipitation with high snow levels (mean = 2.92 km) in the northern Sierra Nevada all occurred during October.
ARTICLE | doi:10.20944/preprints201810.0264.v1
Subject: Earth Sciences, Other Keywords: snow; semi-arid climate; data assimilation; particle filter; SWE; MERRA-2
Online: 12 October 2018 (11:17:16 CEST)
The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.
ARTICLE | doi:10.20944/preprints202104.0486.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Land-surface modelling system; hydrology; carbon; surface energy balance; open water; snow
Online: 19 April 2021 (13:23:53 CEST)
The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand which would facilitates modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, e.g. Copernicus, is presented . This paper introduces recent examples of novel ECLand developments on (i) vegetation, (ii) snow, (iii) soil, (iv) open water/lake (v) river/inundation, and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations, and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both, process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation prediction framework.
REVIEW | doi:10.20944/preprints201811.0217.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Climate; land-atmosphere interaction; clouds; diurnal cycle; snow cover; Prairies; land-use; hydrometeorology
Online: 8 November 2018 (14:13:53 CET)
Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land-atmosphere-cloud coupling. The key reason is that trained observers made hourly estimates of opaque cloud fraction that obscures the sun, moon or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality that they can be calibrated against Baseline Surface Radiation Network data to give the climatology of the daily short-wave, longwave and total cloud forcing (SWCF, LWCF and CF). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing reverses sign from negative in the warm season to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10°C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature and the pressure height of the lifting condensation level are all tightly coupled to opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between cloud forcing and the warm season imbalance of the diurnal cycle; which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forci, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of mixing ratio which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season and its dependence on reflective cloud.
ARTICLE | doi:10.20944/preprints202207.0290.v1
Subject: Earth Sciences, Environmental Sciences Keywords: forecast; Earth Observation; time series; Snow Line Elevation; Alps; mountains; environmental modeling; machine learning
Online: 19 July 2022 (14:20:12 CEST)
Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.
ARTICLE | doi:10.20944/preprints202106.0544.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Snowfall Retrieval; Snow Water Equivalent; Cloud Liquid Water; Emissivity; Brightness Temperature; Passive Microwave; GPM
Online: 22 June 2021 (14:22:16 CEST)
Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤ 100 kg m-2) and low values of cloud liquid water path (LWP 100–150 g m-2), the scattering of light snowfall (intensities ≤ 0.5 mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m-2 and the LWP is greater than 100–150 g m-2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m-2 and LWP is lower than 100–150 g m-2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.
ARTICLE | doi:10.20944/preprints202106.0120.v1
Subject: Earth Sciences, Atmospheric Science Keywords: IMERG; Stage IV; Infrared; Passive microwave; Snow; Ice; Precipitation; GPM; Wet-bulb temperature; AMSR-2
Online: 3 June 2021 (14:59:21 CEST)
Various products of the Integrated Multisatellite Retrievals for GPM (IMERG) and passive mi-crowave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the CONUS and using Stage-IV product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and their combination. PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow- and ice-covered surfaces (1) PMW products (except AMSR-2) show a higher correlation coefficient than IR, (2) IR and PMW precipitation products tend to overestimate precipitation, but at colder temperatures (e.g., Tw<-10oC) PMW products tend to underestimate and IR product continues to show large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation oc-currence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.
ARTICLE | doi:10.20944/preprints201910.0090.v1
Subject: Biology, Ecology Keywords: alpine forest gap; freeze-thaw cycle; fungi to bacteria ratio; snow cover depth; total phenol
Online: 8 October 2019 (10:57:01 CEST)
Alpine forest gaps can distribute snowfall, solar radiation and rainfall, thus inducing a heterogeneous hydrothermal microenvironment between the inside and outside areas of forest gaps. Additionally, the characteristics of the heterogeneous microenvironment could vary greatly across the gap location properties during winter and the growing season. To determine the response of total phenol loss (TPL) from the litter to alpine forest gap disturbance during decomposition, we conducted a field litterbag experiment within a representative fir (Abies faxoniana Rehd.) forest based on the gap location properties. The TPL and abundances of fungi and bacteria from two typical shrub species (willow, Salix paraplesia Schneid., and bamboo, Fargesia nitida (Mitford) Keng f.) were measured during the following periods over two years: snow formation (SF), snow cover (SC) snow melting (ST), the early growing season (EG) and the later growing season (LG). At the end of the study, we found that the snow cover depth, frequencies of the freeze-thaw cycle and the fungal copy g-1 to bacterial copy g-1 ratio had significant effects on the litter TPL. The abundances of fungi and bacteria decreased from the gap center to the closed canopy during the two SF, SC, ST and LG periods and reversed during the two EG periods. The TPL closely followed the same trend as the microbial abundance during the first year of incubation. In addition, both species had larger TPLs in the gap center during the first winter, first year and entire two years. These findings suggest that alpine forest gap formation accelerates litter TPL and plays a dual role during specific critical periods by distributing abiotic and biotic factors directly and indirectly. In conclusion, reduced snow cover depth and duration during winter warming under current climate change scenarios or as gaps vanish may slow litter TPL in alpine biomes.
ARTICLE | doi:10.20944/preprints201906.0162.v1
Subject: Earth Sciences, Atmospheric Science Keywords: snow characteristics; optical remote sensing; sow grain size; specific surface area; albedo; Sentinel 3, OLCI
Online: 17 June 2019 (10:48:48 CEST)
The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing (http://step.esa.int/main/toolboxes/snap/). In this work we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020nm, we derive important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on the spatial grid of 300m. The algorithm also incorporates cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long – term snow property records essential for climate monitoring and data assimilation studies - especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.
ARTICLE | doi:10.20944/preprints201702.0080.v1
Subject: Earth Sciences, Environmental Sciences Keywords: ROS; snow; rain; flood; WRF; numerical weather forecast; energy balance; discharge estimation; early alert system
Online: 22 February 2017 (04:26:49 CET)
From June 18 to 19, 2013, the Ésera river in the Pyrenees, Northern Spain, caused widespread damage due to flooding as a result of torrential rains and sustained snowmelt. We estimate the contribution of snow melt to total discharge applying a snow energy balance to the catchment. Precipitation is derived from sparse local measurements and the WRF-ARW model over three nested domains, down to a grid cell size of 2 km. Temperature profiles, precipitation and precipitation gradient are well simulated, although with a possible displacement regarding the observations. Snowpack melting was correctly reproduced and verified in three instrumented sites, and according to satellite images. We found that the hydrological simulations agree well with measured discharge. Snowmelt represented 33% of total runoff during the main flood event and 23% at peak flow. The snow energy balance model indicates that most of the energy for snow melt during the day of maximum precipitation came from turbulent fluxes. This approach forecast correctly peak flow and discharge during normal conditions at least 24h in advance and could give an early warning of the extreme event 2.5 days before.
Subject: Earth Sciences, Environmental Sciences Keywords: loess, Holocene, ruin soil, archaeological sediment, vesicular layer, aeolian dust, biocrusts, clast pavements, climate change, snow
Online: 18 March 2019 (09:34:47 CET)
Loess was deposited in the Negev during the Pleistocene, but such sediments seem to be missing for the Holocene. This could be due to erosion unless structures such as ruins offered protection. We studied soils developed on archaeological hilltop ruins in the Negev and the Petra region and compared them with local soils, paleosols, geological outcrops, and current dust. The ruin soils in both regions were found to consist of similarly complex mixtures of local and remote sediment sources. They differ from sediments deposited during current dust storms. This seems due to fixation processes: average accretion rates are estimated to ~0.14 mm/a, suggesting that only ~3% of the current dust that can be trapped with dry marble dust collectors is stored in the soils. Vegetation, biocrusts, and/or clast pavements associated with vesicular layers seem to act as sediment-fixing agents. As well, climate might play a role: rain, and in particular one snowstorm in the Petra region brought a high amount of sediment that was more similar to the ruin soils. Wet deposition and snow might catalyze dust deposition and enhance fixation by fostering vegetation and crust formation. Frequent snow during the Pleistocene might be one explanation of enhanced loess deposition.
CONCEPT PAPER | doi:10.20944/preprints201612.0043.v1
Subject: Earth Sciences, Atmospheric Science Keywords: climate change; climatic water balance; irrigation; natural snow cover; the DAS indicator project; Saxony-Anhalt; soil moisture content
Online: 7 December 2016 (11:30:40 CET)
Implementation of the German Climate change Strategy in the Federal State of Saxony-Anhalt is discussed in this paper. It shares the requirement and importance of sustainable development. An overview of strategy, The DAS Indicator System is provided with results of a portion of work being done for the ministry of agriculture by Deutscher Wetterdienst (DWD). Applicability of the indicator system is also shown by evaluation of results for specific indicators from 1961-2015.
ARTICLE | doi:10.20944/preprints201908.0059.v1
Subject: Biology, Forestry Keywords: deciduous forest; female; forest bathing; forest therapy; Positive and Negative Affect Schedule; Profile of Mood States; Restorative Outcome Scale; restoration; Shinrin-Yoku; snow covered forest; Subjective Vitality Scale; winter
Online: 5 August 2019 (08:56:32 CEST)
Forest recreation can be successfully conducted for the purpose of psychological relaxation, as has been proven in previous scientific studies. During the winter in many countries, when snow cover occurs frequently, forest recreation (walking, relaxation, photography, etc.) is common. Nevertheless, whether forest therapy conducted in a forest environment with a snow cover will also have a positive effect on psychological indicators remains unknown. Furthermore, male subjects frequently participate in forest therapy experiments, whereas females are rarely involved. Thus, in this study, the effectuality of forest recreation during winter and with snow cover was tested on 32 young females. For these reasons, the experiment involved 15-minute periods of relaxation in a forest environment or in an urban environment, in addition to a pre-test under indoor conditions. Four psychological questionnaires (POMS, PANAS, ROS, SVS) were administered to participants before and after interventions. Results showed that participants’ levels of negative mood, as measured by different aspects of the POMS questionnaire (tension-anxiety, anger-hostility, depression-dejection, confusion, fatigue), decreased after exposure to the forest environment. In contrast, both tension-anxiety and anger-hostility increased in the urban environment. The indicator of negative affect from the PANAS questionnaire also increased after exposure to the urban environment, whereas the indicator of positive affect based on PANAS was higher in the forest environment than in the urban environment. Restorativeness and subjective vitality exhibited higher values after exposure to the forest environment in comparison to those from the control and pre-test. The changes in these indicators demonstrates that forest recreation in the snow during winter can significantly increase psychological relaxation in young females, as well as showing that recreation can be successfully conducted under these winter conditions.