1,721,065 research outputs found
How to reduce sampling errors in spaceborne cloud radar-based snowfall estimates
Full text linkSnowfall is an important climate change indicator affecting surface albedo, glaciers, sea ice, freshwater storage, cloud lifetime, and ecosystems. Precise snowfall measurements at high latitudes are particularly important for the estimation of the mass balance of ice sheets; however, the snowfall is difficult to quantify with in situ measurements in those locations. In this context, spaceborne radar and radiometer atmospheric missions can help in the assessment of snowfall at high latitudes. The decommissioned NASA CloudSat mission provided invaluable information about global snowfall climatology from 2006 to 2023. The CloudSat-based estimates of global snowfall are considered the reference for global snowfall estimates, but these data suffer from poor sampling and the inability to see shallow or retrieve heavy precipitation, which limits their use, for example, as input to surface mass balance models of the major ice sheets. WIVERN (WInd VElocity Radar Nephoscope), one of the ESA Earth Explorer 11 selected missions, is equipped with a conical scanning 94 GHz Doppler radar and a passive 94 GHz radiometer, with the main objective of measuring global in-cloud horizontal winds, but also quantifying cloud ice water content and precipitation rate. Its conically scanning system, with a 42° incidence angle, is expected to reduce the radar blind zone near the surface (especially over the ocean) and allows the mission to have a swath width of 800 km and 70 times more sampled points than a fixed-looking instrument. The proposed radar measurements tackle the current uncertainties in snowfall estimates, highly improving the sampling frequency and accuracy of snowfall measurements. The uncertainty in snowfall measurements arises from various factors, including the diurnal cycle, uncertainty in the Z–S relationship, and the sampling error. This study quantifies each of these contributors individually and demonstrates the improved sampling capabilities of the WIVERN conically scanning geometry for some specific regions (Antarctica, Greenland) by computing the sampling error at different spatial and temporal scales via simulations of WIVERN vs. CloudSat orbits and scanning geometry, based on the snowfall rates produced by ERA5 reanalysis.
Results show that a WIVERN-like conically scanning system significantly reduces the uncertainty in polar snowfall estimates if compared to a CloudSat-like near-nadir fixed viewing geometry. While CloudSat generates acceptable errors at the annual zonal scales, WIVERN can produce estimates within the climatological variability for latitude–longitude domain larger than 0.5° × 0.5° already at the monthly timescale, making it a valuable product for regional climate model evaluation and as an input to surface mass balance models of the major ice sheets and glaciers
Indentifying glacial features with sentinel-2 data
No full textThe Tibetan Plateau is a vast elevated plateau in Central and East Asia. It contains thousands of glaciers and other geographical features. Through this area rivers like the Brahmaputra is flowing and making a basin providing about millions of people a home. The last years global warming has been a focus of public and scientific debate. Not knowing what to expect and what the changes are result in ruling uncertainties which are of major concern because it could cause serious implications for water resources. During this study the area located in the Upper Brahmaputra in the South-East of the Tibetan Plateau called the Yiong Zangbu catchment will be investigated. To understand what glacial changed have occurred in the past few years data from different years were collected, processed and compared. The used datasets are ASTER GDEM, HydroSHEDS, GLIMS glacier mask and Sentinel-2 . Sentinel-2 data has been processed and different images are made like true colour and false colour images. Next to that a combination of datasets are used to see whether there is an accuracy issue and to understand what kind of features can be found on which height and what spectral reflectance belongs to it. After processing all the data of two following years differences can be seen. There are lakes that are frozen out within three months. Also glaciers which expanded downwards the hills. This can be a result of strong winters. On the higher parts there was more precipitation, which is helpful while remaining the current glaciers. <br/
Greenland Ice Sheet Memory for Cloud Radiation determines its impact on the Surface Mass Balance
No full textAs of yet, there is no consensus on the role of the cloud radiative effect (CRE) on the Greenland Ice Sheet (GrIS). This study focuses on the seasonal and temporal variability of the CRE, to better understand the response of the firn. To do so, we combine satellite observations, climate-model output, and a snow model. We separate short-term and long-term impacts. The results show a positive CRE for all seasons, with an annual short-term CRE of 24.7 , which is largest in fall. The long-term response of the GrIS to the CRE is positive and dominant in summer ablation areas, decreasing the albedo and enhancing melt-water runoff. This long-term effect stresses the influence of the firn conditions on its response to CRE, and highlights the need to include a snow model to study GrIS cloud radiation. The (lack off) long-term component of the CRE explains the conflict in previous studies.<br/
Stability of the floating ice shelf of the Petermann glacier and its response to a changing environment
No full textNearly all major glaciers in Greenland have reduced in size over the last two decades. An increase in the amount of ice transported from the Greenland ice sheet to the oceans is predicted following an increase in Arctic air and ocean temperatures. One of the last glaciers with a floating ice shelf and draining a substantial area of the Greenland ice sheet is the Petermann glacier in North West Greenland. With two major calving events in 2010 and 2012 the extent of its floating ice shelf was reduced to only half of that prior to 2010 and since 2016 new fractures indicate a new calving event is predicted to reduce the length of the glacier by ~14 km. Multiple studies have indicated that after the major calving event of 2012 the glacier accelerated and a new increase in the velocity, possibly linked to the next calving event, has already been observed. With every part of the glacier’s ice shelf that is lost the resistive force that holds the glacier back is reduced and the amount of ice drained to the ocean increases. Losing its entire ice shelf could lead to a significant increase in the contribution of the Petermann glacier to global sea level rise as the Petermann fjord extends inlands below sea level for nearly a hundred kilometers. This study uses ice thickness and surface elevation data combined with velocity data from different sources to analyze the current and future stability of the Petermann glacier. Ice thickness and the velocity data is used as input in a fracture model in order to investigate the different contributions of stress, thinning and an increase in the availability of surface water to the depth crevasses can reach. The areas on the glacier that show locations where crevasses penetrate deep into the ice indicate that the glacier is vulnerable to fracturing in those spots. Connected weak spots might indicate further potential for future calving events. The results derived from the thickness data and the subsequent melt rates show that near the grounding line the glacier is experiencing significantly larger melt rates than near the calving front. The high melt rates are concentrated in space and caused three large basal channels to form, which run downstream parallel to the flow direction. The location of the western channel corresponds to the location of fractures that initiated during the same time the channel deepened, indicating a relationship between an increase in melt rate and fracturing. This relation is also observed in the results from the fracture model, where there is enough water and the ice shelf thinness fractures are capable of penetrating deep in the glacier ice. The results also show that when the average melt rate between 2011 and 2017 continues to prevail the floating ice shelf of the Petermann might be gone within the next decad
Optimizing Support Vector Machines with ISBA-A-gs Land Surface Variables as a Surrogate Model to Simulate ASCAT Derived Parameters
No full textThe TU-Wien developed a soil moisture retrieval algorithm that uses the incidence angle dependence of backscatter to obtain soil moisture estimates (Wagner et al., 1999). The core of this algorithm is a second order Taylor expansion with which the backscatter is normalized at a reference angle. Studies have shown that the first and second order derivative within this Taylor expansion, known as slope and curvature, are somehow related to the wet biomass and structure of vegetation. The general approach to forward model satellite observations with land surface variables in a data assimilation framework is through a radiative transfer model (Albergel et al., 2017). However, this requires plenty of assumptions about the vegetation canopy (such as stem height, shape, size, orientation etc.) and is therefore relatively inefficient for understanding the impact of soil moisture and vegetation dynamics on backscatter on a large scale. This study investigates the possibility of using support vector machines as a surrogate model instead of a radiative transfer model to link the TU-Wien normalized backscatter and slope to land surface variables soil moisture and leaf area index. The land surface variables are simulations from the CO2-responsive ISBA-A-gs land surface model. Support vector machines have the advantage of providing implicit kernel functions, which make them very useful for non-linear problems. The ISBA-A-gs data is provided by Météo-France. In total, 1324 support vector machines have been optimized through a cross validated grid search. The optimized hyperparameters were shown to have spatial consistency and look promising as an initial approach to forward modelling backscatter and slope. The SVM performances are further investigated through corresponding land cover types of grid points and the land surface variables.Geoscience and Remote Sensin
Breakpoint detection through neural nets: A feasibility study
No full textA variety of statistical methods are available to detect sudden changes, or breakpoints, in time series when used as multi-temporal change detection technique. However, these methods are unreliable in the presence of noise. Neural nets might detect breakpoints better. These deep learning models are able to generalize and optimize well, even in the presence of noise. This research tests the feasibility of different neural net architectures to detect breakpoints in generic linear time series. Two relatively simple neural nets are proposed, combined with four different descriptions of breakpoint, and trained on syntheticdata. The neural nets are tested on two datasets: On a separate synthetic dataset and on Australian rainuse-efficieny (RUE) time series, a surrogate for dryland ecosystem functioning. Some of the neural nets built performed exceptionally well on synthetic data, outperforming a benchmark statistical method withmargin. The direct translation to RUE time series was less successful. The results shows great promise for the use of neural nets in change detection. A generalist change detection approach by use of neural nets is likely not optimal. Current developments in deep learning, as well as choosing the right user-case, showgreat promise to unlock the full potential of neural nets in time series analysis.Geoscience and Remote Sensin
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Automated Building Damage Classification using Remotely Sensed Data: Case study: Hurricane Damage on St. Maarten
No full textIn the second half of the 20th and beginning of the 21st century the amount of natural disasters has increased rapidly. Due to this rise in occurrences, more people are affected. An important indicator for people affected is the amount of damage to buildings. To gather this information aid workers now have to go into the field to gather data on the amount of destruction. In response to the possible dangers these people encounter in the field, remote sensing and analysis techniques have been developed for automated damage detection. However, due to various limitations on the implementation, these techniques are not yet widely adopted in emergency response and humanitarian aid.This work compares two methods and two data sources for the detection of building damage. The methods are evaluated on their accuracy and implementability within humanitarian aid in disaster situations. The main methods considered are equalisation of histograms of pre-event and post-event imagery, followed by Univariate Image Differencing; and a convolutional neural network on features withdrawn from post-event imagery, using OpenStreetMap data. Remotely sensed data sources considered are synthetic aperture radar and very high resolution optical imagery. All results are analysed and compared to current standards in damage detection. From the results it can be concluded that more research is required for a practical implementation of deep learning techniques. The constraint posed by the requirement of large datasets, make these methods impracticable without sufficient preparation and resources. More simpler methods, like Univariate Image Differencing, can be validated on smaller ground-truth datasets, and are therefore easier in implementation when resources are limited. The possible accuracy increase of deep learning methods does, at this moment, not outweigh the ease of an elementary differencing approach.Geomatic
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