Preprints
Filtering by Subject: Computer Sciences
Technical note: A procedure to clean, decompose and aggregate time series
Published: 2022-11-18
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Physical Sciences and Mathematics, Statistics and Probability
Errors, gaps and outliers complicate and sometimes invalidate the analysis of time series. While most fields have developed their own strategy to clean the raw data, no generic procedure has been promoted to standardize the pre-processing. This lack of harmonization makes the inter-comparison of studies difficult, and leads to screening methods that can be arbitrary or case-specific. This study [...]
CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2
Published: 2022-09-24
Subjects: Computer Sciences, Earth Sciences, Physical Sciences and Mathematics
Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the [...]
Autonomous Passage Planning for a Polar Vessel
Published: 2022-08-31
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Other Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Sustainability
We introduce a method for long-distance maritime route planning in polar regions, taking into account complex changing environmental conditions. The method allows the construction of optimised routes, describing the three main stages of the process: discrete modelling of the environmental conditions using a non-uniform mesh, the construction of mesh-optimal paths, and path smoothing. In order to [...]
Space-time landslide hazard modeling via Ensemble Neural Networks
Published: 2022-06-02
Subjects: Applied Statistics, Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics, Statistics and Probability
For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the [...]
Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAIHub
Published: 2022-04-20
Subjects: Computer Sciences, Earth Sciences, Engineering, Environmental Sciences
Most deep learning application studies have limited accessibility and reproducibility for researchers and students in many domains, especially in earth and climate sciences. In order to provide a step towards improving accessibility to deep learning models in such disciplines, this study presents a community-driven framework and repository, EarthAIHub, that is powered by TensorFlow.js, where deep [...]
A computational framework for time dependent deformation in viscoelastic magmatic systems
Published: 2022-04-02
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Mathematics
Time-dependent ground deformation is a key observable in active magmatic systems, but is challenging to characterize. Here we present a numerical framework for modeling transient deformation and stress around a subsurface, spheroidal pressurized magma reservoir within a viscoelastic half-space with variable material coefficients, utilizing a high-order finite-element method and explicit [...]
Practical Reproducibility in Geography and Geosciences
Published: 2022-03-28
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Higher Education
Reproducible research is often perceived as a technological challenge, but it is rooted in the challenge to improve scholarly communication in an age of digitisation. When computers become involved and researchers want to allow other scientists to inspect, understand, evaluate, and build upon their work, they need to create a research compendium that includes the code, data, computing [...]
Automated machine learning to evaluate the information content of tropospheric trace gas columns for fine particle estimates over India: a modeling testbed
Published: 2022-03-21
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Environmental Engineering, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology
India is largely devoid of high-quality and reliable on-the-ground measurements of fine particulate matter (PM2.5). Ground-level PM2.5 concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using [...]
Forecasting Marine Heatwaves using Machine Learning
Published: 2022-02-05
Subjects: Computer Sciences, Earth Sciences, Oceanography and Atmospheric Sciences and Meteorology
Recently, severe warm-water episodes have occurred frequently against a background trend of global ocean warming. Sea Surface Temperature anomalies have an impact on the integrity of marine ecosystems which is an important part of the Earth’s climate system. The drastic effects of Marine Heatwaves on aquatic life have been on a steady incline in the recent years, damaging aquatic ecosystems [...]
WaterBench: A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting
Published: 2022-01-01
Subjects: Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Hydrology
This study proposes a comprehensive benchmark dataset for streamflow forecasting, WaterBench, that follows FAIR data principles that is prepared with a focus on convenience for utilizing in data-driven and machine learning studies, and provides benchmark performance for state-of-art deep learning architectures on the dataset for comparative analysis. By aggregating the datasets of streamflow, [...]
Towards Progressive Geospatial Information Processing on Web Systems: A Case Study for Watershed Analysis in Iowa
Published: 2021-12-26
Subjects: Computer Sciences, Earth Sciences, Environmental Engineering, Environmental Sciences
Geographic Information Systems (GIS) are available as stand-alone desktop applications as well as web platforms for vector- and raster-based geospatial data processing and visualization. While each approach offers certain advantages, limitations exist that motivate the development of hybrid systems that will increase the productivity of users for performing interactive data analytics using [...]
Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on Graphics Processing Units (GPUs)
Published: 2021-11-11
Subjects: Atmospheric Sciences, Computer Sciences, Meteorology, Numerical Analysis and Scientific Computing, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become numerically rather costly. In this study, we assessed the potential of exploiting graphics [...]
Airborne laser scanning proxies of canopy light transmission in forests
Published: 2021-10-29
Subjects: Biodiversity, Biogeochemistry, Computer Sciences, Earth Sciences, Ecology and Evolutionary Biology, Environmental Sciences, Forest Sciences, Life Sciences, Physical Sciences and Mathematics, Plant Sciences, Software Engineering, Statistics and Probability
Reliable estimates of canopy light transmission are critical to understanding the structure and function of vegetation communities but are difficult and costly to attain by traditional field inventory methods. Airborne laser scanning (ALS) data uniquely provide multi-angular vertically resolved representation of canopy geometry across large geographic areas. While previous studies have proposed [...]
Bridging knowledge gaps with hybrid machine-learning forest ecosystem models (ML-FEMs): inferential simulation of past understory light regimes
Published: 2021-10-29
Subjects: Artificial Intelligence and Robotics, Biodiversity, Biogeochemistry, Computer Sciences, Earth Sciences, Ecology and Evolutionary Biology, Forest Sciences, Life Sciences, Physical Sciences and Mathematics, Plant Sciences
Soil moisture is a key limiting factor of plant productivity in boreal and montane regions, producing additional climate feedbacks through evaporation, regeneration, mortality, and respiration. Understory solar irradiation – the primary driver of surface temperature and evaporative demand – remains poorly represented in vegetation models due to a lack of 3-D canopy geometry. Existing models are [...]
Near-term forecasts of stream temperature using process-guided deep learning and data assimilation
Published: 2021-08-14
Subjects: Computer Sciences, Hydrology, Statistics and Probability, Water Resource Management
Near-term forecasts of environmental outcomes can inform real-time decision making. Data assimilation modeling techniques can be used for forecasts to leverage real-time data streams, where the difference between model predictions and observations can be used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided deep learning and data [...]