06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Motivation, structure and goals of the collaborative research centre 1667 : advancing technologies of very low-altitude satellites - ATLAS(2025) Fasoulas, Stefanos; Pagan, Adam S.; Traub, Constantin; Annighöfer, Björn; Barz, Stefanie; Beck, Andrea; Cunis, Torbjørn; Dekorsy, Thomas; Essig, Stephanie; Fichter, Walter; Flemisch, Bernd; Herdrich, Georg; Hobiger, Thomas; Kallfass, Ingmar; Kästner, Johannes; Klinkner, Sabine; Lamanna, Grazia; Loehle, Stefan; Pfeiffer, Marcel; Poser, Rico; Roth, Johannes; Saliba, Michael; Schneider, Martin; Sneeuw, Nico; Wagner, GerdThe Collaborative Research Centre (CRC) 1667 “Advancing Technologies of Very Low Altitude Satellites-ATLAS” was established in April 2024 with the scientific goal of addressing the fundamental challenges of making satellite operations in Very Low Earth Orbits (VLEO) sustainable. These orbits are beneficial for satellite services that have become indispensable to our modern society. Moreover, access to VLEO offers the opportunity to operate satellites without exposure or contribution to the increasing contamination of traditional orbits with space debris. Seventeen highly interlinked research projects have been selected to investigate and advance accurate numerical and experimental methods for gas-surface interactions, novel concepts utilising the residual atmosphere and minimising the satellite sizes, and mission-related challenges of a selected scenario. In addition, support projects cover topics related to public outreach and academic exchange and assist in achieving the strategic goal of positioning the University of Stuttgart as a key contributor to this internationally very important research area. In summary, the CRC ATLAS aims to constitute a research-oriented profile-building measure at the University of Stuttgart with a strong international reputation.Item Open Access A probabilistic approach to characterizing drought using satellite gravimetry(2024) Saemian, Peyman; Tourian, Mohammad J.; Elmi, Omid; Sneeuw, Nico; AghaKouchak, AmirIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.Item Open Access A Kalman filter approach for estimating daily discharge using space‐based discharge estimates(2025) Ke, Siqi; Tourian, Mohammad J.; Sneeuw, Nico; Frasson, Renato Prata de Moraes; Paiva, Rodrigo C. D.; Durand, Michael; Gleason, Colin; Elmi, Omid; Malaterre, Pierre‐Olivier; David, CédricThe SWOT satellite mission is the first to conduct a global survey of the Earth's surface waters, measuring water surface height, river width, and water surface slope, based on which river discharge is estimated. At mid‐latitudes, the repeat orbit design of SWOT only allows a sampling of twice per repeat cycle, which is considered too low for most hydrological applications. To address the spatiotemporal limitations of SWOT, we develop a method that assimilates SWOT observations across continuous reaches within a single‐branch river network to obtain daily discharge estimates. Our model‐free assimilation method provides a linear dynamic system that includes a process model based on a physically based spatiotemporal discharge correlation model and observation equations utilizing SWOT products. We solve this dynamic system using a simple Kalman filter in the time domain, assimilating SWOT observations and incorporating the physically based prior to estimate daily discharge. Since SWOT discharge products were not yet available during the period of this research, we used synthetic SWOT data sets, introducing random and systematic errors through Monte Carlo simulation. The validation of the estimated discharge against true discharge over all test cases leads to a median correlation as high as 0.95, a median NSE for residuals (mean‐removed discharge) as high as 0.81, and a median relative bias as low as 5%, respectively. These promising results suggest that daily discharge for continuous reaches in a river network can be obtained through our data assimilation framework.Item Open Access A novel spatial filter to reduce north-south striping noise in GRACE spherical harmonic coefficients(2022) Yi, Shuang; Sneeuw, NicoPrevalent north-south striping (NSS) noise in the spherical harmonic coefficient products of the satellite missions gravity recovery and climate experiment greatly impedes the interpretation of signals. The overwhelming NSS noise always leads to excessive smoothing of the data, allowing a large room for improvement in the spatial resolution if this particular NSS noise can be mitigated beforehand. Here, we put forward a new spatial filter that can effectively remove NSS noise while remaining orthogonal to physical signals. This new approach overcomes the limitations of the previous method proposed by Swenson and Wahr (2006), where signal distortion was large and high-order coefficients were uncorrectable. The filter is based on autocorrelation in the longitude direction and cross-correlation in the latitude direction. The NSS-type noise identified by our method is mainly located in coefficients of spherical harmonic order larger than about 20 and degree beyond 30, spatially between latitudes ± 60°. After removing the dominating NSS noise with our method, a weaker filter than before is added to handle the residual noise. Thereby, the spatial resolution can be increased and the amplitude damping can be reduced. Our method can coincidentally reduce outliers in time series without significant trend bias, which underpins its effectiveness and reliability.