Derived Data Products Research Articles

The thesan project: public data release of radiation-hydrodynamic simulations matching reionization-era JWST observations

ABSTRACT Cosmological simulations serve as invaluable tools for understanding the Universe. However, the technical complexity and substantial computational resources required to generate such simulations often limit their accessibility within the broader research community. Notable exceptions exist, but most are not suited for simultaneously studying the physics of galaxy formation and cosmic reionization during the first billion years of cosmic history. This is especially relevant now that a fleet of advanced observatories (e.g. James Webb Space Telescope, Nancy Grace Roman Space Telescope, SPHEREx, ELT, SKA) will soon provide an holistic picture of this defining epoch. To bridge this gap, we publicly release all simulation outputs and post-processing products generated within the thesan simulation project at www.thesan-project.com. This project focuses on the z ≥ 5.5 Universe, combining a radiation-hydrodynamics solver (arepo-rt), a well-tested galaxy formation model (IllustrisTNG) and cosmic dust physics to provide a comprehensive view of the Epoch of Reionization. The thesan suite includes 16 distinct simulations, each varying in volume, resolution, and underlying physical models. This paper outlines the unique features of these new simulations, the production and detailed format of the wide range of derived data products, and the process for data retrieval. Finally, as a case study, we compare our simulation data with a number of recent observations from the James Webb Space Telescope, affirming the accuracy and applicability of thesan. The examples also serve as prototypes for how to utilize the released data set to perform comparisons between predictions and observations.

Boundary Pass Underwater Listening Station—Data management and processing

The Boundary Pass Underwater Listening Station, sponsored by Transport Canada, Port of Vancouver and JASCO Applied Sciences, has continuously operated two compact tetrahedral hydrophone arrays beneath the busy shipping lanes leading to Vancouver, Canada since June 2020. The arrays are deployed 300 m apart on the seabed at 190 m depth and are cabled 2.6km to shore. A high sample rate of 512 kHz on all eight channels, with 24-bit samples, produces almost 400 TB/yr of raw acoustic data. Automated real-time data analysis calculates ambient noise statistics, detects marine mammal sounds, measures underwater radiated noise of individual ships, and creates spatial noise maps of the hulls of some vessels passing closely overhead. The derived data products are stored in an easy-to-maintain online database of just a few TB/yr. Results in graphic, tabular and compressed audio formats are made available to scientists and project managers through an intuitive web interface. The project follows international standards for ship noise and ambient noise calculations, so the results can be compared with measurements made elsewhere.

Forward looking sonar applications for unmanned vehicles

Unmanned vehicles have the potential to greatly expand our ability to observe dynamic ocean environments. Whereas distributed acoustic sensors can monitor discrete measurement points in time and space, autonomous maritime platforms provide increased flexibility. Acoustic principles are a key component of this functionality and forward looking sonar (FLS) systems can acquire acoustically derived data products while enabling better informed autonomy behaviors based on enhanced situational and environmental awareness. This paper explores applications of forward look sonar technology when integrated with underwater vehicles. Utility of both interferometric and two-dimensional acoustic data products are examined to illustrate the impact on technologies associated with navigational drift mitigation and ocean observation. Results from sonar platform integration and in-water acquisition for development of an underwater terrain-aided navigation (UTAN) particle filter, an acoustic enabled simultaneous localization and mapping (SLAM) algorithm, obstacle avoidance techniques, and seabed observations are presented.

Phyllosilicate decomposition on Bennu due to prolonged surface exposure

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission to carbonaceous asteroid (101955) Bennu performed detailed mapping with a suite of instruments to characterize the composition and geology of its surface. Here we use data from the OSIRIS-REx Thermal Emission Spectrometer (OTES) instrument to investigate the relationship of OTES-derived spectral indices to other derived data products from OTES, the OSIRIS-REx Camera Suite (OCAMS), and the OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS) at global and local scales. We quantitatively confirm that high values of the OTES silicate stretching slope (from ∼10 to ∼12 μm) in midday spectra that are indicative of thin and/or patchy dust cover are strongly associated with low thermal inertia (high porosity), low albedo boulders on Bennu. These high porosity boulders have brecciated textures with embedded clasts that likely originated on Bennu's parent body or during its disruption. The high porosity of these boulders is a key factor in the local production of the dust or its entrapment, as some large, brecciated boulders with a lower porosity have little evidence of dust. A second OTES spectral parameter, the silicate bending band depth near 22.7 μm applied to early evening spectra, is not correlated to thermal inertia, but is weakly to strongly correlated to albedo, OVIRS-derived 1.05 μm and 2.74 μm band depths, OVIRS-derived hydrogen abundance, and modeled nanophase troilite abundance. In several regions on Bennu there is a strong spatial relationship between these parameters, whereby areas with shallower silicate bending bands also have shallower 1.05 μm and 2.74 μm bands and lower albedo with higher nanophase troilite abundances. These correlations, combined with analysis of the silicate bending band in laboratory experiments of space weathered and mildly heated carbonaceous chondrites, suggests that decreased silicate bending band depths signify decomposition of phyllosilicates, likely Fe-bearing, due to space weathering or mild heating (<600 °C) via solar radiation during Bennu's time in near-Earth space. There is a strong association of larger silicate bending band depths in areas dominated by small rocks and unresolved material and in areas with small (≤ 25 m) craters identified as the spectrally reddest on Bennu, suggesting that this material has been more recently exposed due to impact and/or mass wasting processes. The shallowest silicate bending depths are associated with larger rocks and boulders that appear to have the longest surface exposure history, although there is band depth variation among them suggesting either initial composition variation that resulted in different responses to space weathering or heating, or varied exposure history of individual boulders themselves. We predict that any grains returned from Bennu with a significant surface exposure history will be characterized by shallower 22.7 μm, 1.05 μm and 2.7 μm band depths and increased sulfide (troilite) abundance, as well as textural and chemical evidence for phyllosilicate dehydration.

The CARMENES search for exoplanets around M dwarfs

The large amounts of astrophysical data being provided by existing and future instrumentation require efficient and fast analysis tools. Transfer learning is a new technique promising higher accuracy in the derived data products, with information from one domain being transferred to improve the accuracy of a neural network model in another domain. In this work, we demonstrate the feasibility of applying the deep transfer learning (DTL) approach to high-resolution spectra in the framework of photospheric stellar parameter determination. To this end, we used 14 stars of the CARMENES survey sample with interferometric angular diameters to calculate the effective temperature, as well as six M dwarfs that are common proper motion companions to FGK-type primaries with known metallicity. After training a deep learning (DL) neural network model on synthetic PHOENIX-ACES spectra, we used the internal feature representations together with those 14+6 stars with independent parameter measurements as a new input for the transfer process. We compare the derived stellar parameters of a small sample of M dwarfs kept out of the training phase with results from other methods in the literature. Assuming that temperatures from bolometric luminosities and interferometric radii and metallicities from FGK+M binaries are sufficiently accurate, DTL provides a higher accuracy than our previous state-of-the-art DL method (mean absolute differences improve by 20 K for temperature and 0.2 dex for metallicity from DL to DTL when compared with reference values from interferometry and FGK+M binaries). Furthermore, the machine learning (internal) precision of DTL also improves as uncertainties are five times smaller on average. These results indicate that DTL is a robust tool for obtaining M-dwarf stellar parameters comparable to those obtained from independent estimations for well-known stars.

Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem

ESA’s Swarm mission is a constellation probing both Earth’s interior and geospace, delivering magnetic and plasma measurements which are used to generate many derived data products. From empirical magnetic field models of the core, crust, ionosphere, and magnetosphere, to multi-point estimates of ionospheric currents and in-situ plasma properties, these are challenging to navigate, process, and visualize. The VirES for Swarm platform (https://vires.services) has been built to tackle this problem, providing tools to increase usability of Swarm data products. The VirES (Virtual environments for Earth Scientists) platform provides both a graphical web interface and an API to access and visualise Swarm data and models. This is extended with a cloud-hosted development environment powered by JupyterHub (the “Virtual Research Environment/VRE”). VirES provides two API’s: the full VirES API for which a dedicated Python client is provided, viresclient, and the more interoperable Heliophysics API (HAPI). The VRE is furnished with a bespoke Python environment containing thematic libraries supporting science with Swarm. This service aims to ease the pathway for scientists writing computer code to analyze Swarm data products, increase opportunities for collaboration, and leverage cloud technologies. Beyond simply providing data and model access to Python users, it is extremely helpful to provide higher-level analysis and visualization tools, and ready-to-use code recipes that people can explore and extend. Critically for space physics, this involves crossover with many other datasets and so it is highly valuable to embed such tools within the wider data and software ecosystems. Through Swarm DISC (Data, Innovation, and Science Cluster), we are tackling this through cookbooks and Python libraries. Cookbooks are built and presented using Jupyter technologies, and tested to work within the VRE. A new library we are building is SwarmPAL, which includes tools for time-frequency analysis and inversion of magnetic field measurements for electric current systems, among others, while relying on the VirES server to provide data portability and other utilities. This paper reviews the current state of these tools and services for Swarm, particularly in the context of the Python in Heliophysics Community, and the wider heliophysics and geospace data environment.

COMPTEL data analysis using GammaLib and ctools

More than 20 yr after the end of NASA’s Compton Gamma-Ray Observatory mission, the data collected by its Imaging Compton Telescope (COMPTEL) still provide the most comprehensive and deepest view of our Universe in MeV gamma rays. While most of the COMPTEL data are archived at NASA’s High Energy Astrophysics Science Archive Research Center (HEASARC), the absence of any publicly available software for their analysis means the data cannot benefit from the scientific advances made in the field of gamma-ray astronomy at higher energies. To make this unique treasure again accessible for science, we developed open source software that enables a comprehensive and modern analysis of the archived COMPTEL telescope data. Our software is based on a dedicated plug-in to the GammaLib library, a community-developed toolbox for the analysis of astronomical gamma-ray data. We implemented high-level scripts for building science analysis workflows in ctools, a community-developed gamma-ray astronomy science analysis software framework. We describe the implementation of our software and provide the underlying algorithms. Using data from the HEASARC archive, we demonstrate that our software reproduces derived data products that were obtained in the past using the proprietary COMPTEL software. We furthermore demonstrate that our software reproduces COMPTEL science results published in the literature. This brings the COMPTEL telescope data back into life, allowing them to benefit from recent advances in gamma-ray astronomy, and gives the community a means to unveil its still hidden treasures.

Galaxy And Mass Assembly (GAMA): Data Release 4 and the z &amp;lt; 0.1 total and z &amp;lt; 0.08 morphological galaxy stellar mass functions

ABSTRACT In Galaxy And Mass Assembly Data Release 4 (GAMA DR4), we make available our full spectroscopic redshift sample. This includes 248 682 galaxy spectra, and, in combination with earlier surveys, results in 330 542 redshifts across five sky regions covering ∼250 deg2. The redshift density, is the highest available over such a sustained area, has exceptionally high completeness (95 per cent to rKiDS = 19.65 mag), and is well-suited for the study of galaxy mergers, galaxy groups, and the low redshift (z &amp;lt; 0.25) galaxy population. DR4 includes 32 value-added tables or Data Management Units (DMUs) that provide a number of measured and derived data products including GALEX, ESO KiDS, ESO VIKING, WISE, and HerschelSpace Observatory imaging. Within this release, we provide visual morphologies for 15 330 galaxies to z &amp;lt; 0.08, photometric redshift estimates for all 18 million objects to rKiDS ∼ 25 mag, and stellar velocity dispersions for 111 830 galaxies. We conclude by deriving the total galaxy stellar mass function (GSMF) and its sub-division by morphological class (elliptical, compact-bulge and disc, diffuse-bulge and disc, and disc only). This extends our previous measurement of the total GSMF down to 106.75 M$_{\odot } \, h_{70}^{-2}$ and we find a total stellar mass density of ρ* = (2.97 ± 0.04) × 108 M$_{\odot } \, h_{70}$ Mpc−3 or $\Omega _*=(2.17 \pm 0.03) \times 10^{-3} \, h_{70}^{-1}$. We conclude that at z &amp;lt; 0.1, the Universe has converted 4.9 ± 0.1 per cent of the baryonic mass implied by big bang Nucleosynthesis into stars that are gravitationally bound within the galaxy population.

Quantification of DAS VSP Quality: SNR vs. Log-Based Metrics.

The initial quantification of data quality is an important step in seismic data acquisition design, including the choice of sensing strategy. The signal-to-noise ratio (SNR) often drives the choice of distributed acoustic sensing (DAS) parameters in vertical seismic profiling (VSP). We compare this established approach for data quality assessment with metrics comparing DAS data products to available well logs. First, we create kinematic and dynamic data products derived from original seismic data, such as the interval velocity and amplitude of P-wave arrivals. Next, we quantify the quality of derived data products using well log data by calculating various statistical metrics. Using a large dataset of 220 different VSP experiments with a fixed source location and various DAS acquisition parameters, such as gauge length (GL), conveyance type, and lead-in length, we analyzed the statistical distribution of various metrics. The results indicate the decoupling between seismic-based and log-based metrics as well as between the quality of dynamic and kinematic data-products for the same record. Therefore, we propose using fit-for-purpose metrics to optimize the acquisition cost. In particular, for ray-based tomographic processing, it is sufficient to use traveltime-based metrics. On the other hand, for advanced dynamic analysis, amplitude-based metrics define the quality of final processing products. Hence, it is crucial to use fit-for-purpose metrics to optimize DAS VSP acquisition.

A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

This paper presents the first complete approach to achieving environmental intelligence support in the management of vegetation within electrical power transmission corridors. Contrary to the related studies that focused on the mapping of power lines, together with encroaching vegetation risk assessment, we realised predictive analytics with vegetation growth simulation. This was achieved by following the JDL/DFIG data fusion model for complementary feature extraction from Light Detection and Ranging (LiDAR) derived data products and auxiliary thematic maps that feed an ensemble regression model. The results indicate that improved vegetation growth prediction accuracy is obtained by segmenting training samples according to their contextual similarities that relate to their ecological niches. Furthermore, efficient situation assessment was then performed using a rasterised parametrically defined funnel-shaped volumetric filter. In this way, RMSE≈1 m was measured when considering tree growth simulation, while a 0.37 m error was estimated in encroaching vegetation detection, demonstrating significant improvements over the field observations.

INSAT-3D and -3DR relative radiometric performance over bright target

The Geostationary Earth Orbit (GEO) satellite INSAT-3D and -3DR derived data products plays a vital role in helping to develop long-term trends of many climate variables and to improve our understanding of earth's environmental processes and dynamics occurring on the land, in the oceans and in the atmosphere. In order to provide high quality, consistent and well calibrated measurements, the radiometric calibration of the sensor must be consistent, stable and traceable to its calibration standards. Due to absence of an onboard calibration mechanism for the visible (VIS) and Short Wave Infra-Red (SWIR) bands, accuracy of Top-of-atmospheric radiance of these bands data depend on vicarious calibration methods to provide post launch calibration coefficients to compensate for the degradation responsivity. In this study, a methodology has been implemented to perform degradation assessment and seasonal variation pattern for the visible and SWIR band of INSAT-3D and -3DR satellite based on clear-sky observations. The current methodology relies on clear sky apparent reflectance which are derived from level-1 images over pseudo-invariant desert target. In this study, authors are focused to investigate visible and SWIR band performance based on clear-sky observations over desert target and the instrument long-term drift traced by a polynomial fitting function. A trending algorithm is proposed here, which consists of a polynomial function (for the description of instrument degradation performance) with two sine terms for quashing the impact of the seasonal variations of the solar zenith angle and atmospheric constituents, is applied to fit the time series of apparent reflectance.

PHANGS–ALMA: Arcsecond CO(2–1) Imaging of Nearby Star-forming Galaxies

Abstract We present PHANGS–ALMA, the first survey to map CO J = 2 → 1 line emission at ∼1″ ∼100 pc spatial resolution from a representative sample of 90 nearby (d ≲ 20 Mpc) galaxies that lie on or near the z = 0 “main sequence” of star-forming galaxies. CO line emission traces the bulk distribution of molecular gas, which is the cold, star-forming phase of the interstellar medium. At the resolution achieved by PHANGS–ALMA, each beam reaches the size of a typical individual giant molecular cloud, so that these data can be used to measure the demographics, life cycle, and physical state of molecular clouds across the population of galaxies where the majority of stars form at z = 0. This paper describes the scientific motivation and background for the survey, sample selection, global properties of the targets, Atacama Large Millimeter/submillimeter Array (ALMA) observations, and characteristics of the delivered data and derived data products. As the ALMA sample serves as the parent sample for parallel surveys with MUSE on the Very Large Telescope, the Hubble Space Telescope, AstroSat, the Very Large Array, and other facilities, we include a detailed discussion of the sample selection. We detail the estimation of galaxy mass, size, star formation rate, CO luminosity, and other properties, compare estimates using different systems and provide best-estimate integrated measurements for each target. We also report the design and execution of the ALMA observations, which combine a Cycle 5 Large Program, a series of smaller programs, and archival observations. Finally, we present the first 1″ resolution atlas of CO emission from nearby galaxies and describe the properties and contents of the first PHANGS–ALMA public data release.

The OH (3-1) nightglow volume emission rate retrieved from OSIRIS measurements: 2001 to 2015

Abstract. The OH airglow has been used to investigate the chemistry and dynamics of the mesosphere and the lower thermosphere (MLT) for a long time. The infrared imager (IRI) aboard the Odin satellite has been recording the night-time 1.53 µm OH (3-1) emission for more than 15 years (2001–2015), and we have recently processed the complete data set. The newly derived data products contain the volume emission rate profiles and the Gaussian-approximated layer height, thickness, peak intensity and zenith intensity, and their corresponding error estimates. In this study, we describe the retrieval steps for these data products. We also provide data screening recommendations. The monthly zonal averages depict the well-known annual oscillation and semi-annual oscillation signatures, which demonstrate the fidelity of the data set (https://doi.org/10.5281/zenodo.4746506, Li et al., 2021). The uniqueness of this Odin IRI OH long-term data set makes it valuable for studying various topics, for instance, the sudden stratospheric warming events in the polar regions and solar cycle influences on the MLT.

Evaluation of Phase One Scan Station for Analogue Aerial Image Digitisation

Historical aerial photographs represent a special cultural asset for preserving information about land cover and land use change in the twentieth century with a high spatial and temporal resolution. A current topic is the digitisation of historical images to make them accessible to a wider range of users and to preserve them from age deterioration. For a photogrammetric evaluation, a high geometric stability and accuracy during the digitization process is required. In this work, the resolving power and geometric quality of a Phase One iXM-MV150F high-performance camera was investigated, which is used at the Landesamt für Geoinformation und Landentwicklung Baden-Württemberg in the project ‘Digitaler Luftbildatlas Baden-Württemberg’ for the digitisation of historical aerial photographs. The resolving power of the system was empirically measured and analysed. The required modulation transfer function was determined using Siemens stars. With this method, the significant influence of the focus setting and deviations of the plane-parallel alignment could be determined. Using a digitised aerial survey of the Vaihingen/Enz test field, the impact of the above-mentioned effects and the influence of the geometry of the scanning camera on the quality of the derived data products was shown in comparison to a photogrammetric scanner. The comparison showed that dedicated photogrammetric scanners still achieve a higher accuracy, even if a high-quality optical system is used for the digitising stand with the document camera. Further investigations are justified to improve the accuracy and stability of digitising the aerial image with a document camera.

Grass modelling in data-limited areas by incorporating MODIS data products

• MODIS data products are incorporated into data assimilations of grass modelling. • Multiple scenarios of data availability are assumed for data-limited areas. • Different methods of data assimilation are compared based on data usage. Process-based grass models (PBGMs) are widely used for predicting grass growth under potential climate change and different management practices. However, accurate predictions using PBGMs heavily rely on field observations for data assimilation. In data-limited areas, performing robust and reliable estimates of grass growth remains a challenge. In this paper, we incorporated satellite-based MODIS data products, including leaf area index, gross primary production and evapotranspiration, as an additional supplement to field observations. Popular data assimilation methods, including Bayesian calibration and the updating method ensemble Kalman filter, were applied to assimilate satellite derived information into the BASic GRAssland model (BASGRA). A range of different combinations of data assimilating methods and data availability were tested across four grassland sites in Norway, Finland and Canada to assess the corresponding accuracy and make recommendations regarding suitable approaches to incorporate MODIS data. The results demonstrated that optimizing the model parameters that are specific for grass species and cultivar should be targeted prior to updating model state variables. The MODIS derived data products were capable of constraining model’s simulations on phenological development and biomass accumulation by parameter optimization with its performance exceeding model outputs driven by default parameters. By integrating even a small number of field measurements into the parameter calibration, the model’s predictive accuracy was further improved - especially at sites with obvious biases in the input MODIS data. Overall, this comparative study has provided flexible solutions with the potential to strengthen the capacity of PBGMs for grass growth estimation in practical applications.

HELP: the Herschel Extragalactic Legacy Project

ABSTRACT We present the Herschel Extragalactic Legacy Project (HELP). This project collates, curates, homogenizes, and creates derived data products for most of the premium multiwavelength extragalactic data sets. The sky boundaries for the first data release cover 1270 deg2 defined by the Herschel SPIRE extragalactic survey fields; notably the Herschel Multi-tiered Extragalactic Survey (HerMES) and the Herschel Atlas survey (H-ATLAS). Here, we describe the motivation and principal elements in the design of the project. Guiding principles are transparent or ‘open’ methodologies with care for reproducibility and identification of provenance. A key element of the design focuses around the homogenization of calibration, meta data, and the provision of information required to define the selection of the data for statistical analysis. We apply probabilistic methods that extract information directly from the images at long wavelengths, exploiting the prior information available at shorter wavelengths and providing full posterior distributions rather than maximum-likelihood estimates and associated uncertainties as in traditional catalogues. With this project definition paper, we provide full access to the first data release of HELP; Data Release 1 (DR1), including a monolithic map of the largest SPIRE extragalactic field at 385 deg2 and 18 million measurements of PACS and SPIRE fluxes. We also provide tools to access and analyse the full HELP data base. This new data set includes far-infrared photometry, photometric redshifts, and derived physical properties estimated from modelling the spectral energy distributions over the full HELP sky. All the software and data presented is publicly available.

Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products

AbstractVarious methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

Assessing data change in scientific datasets

SummaryScientific datasets are growing rapidly and becoming critical to next‐generation scientific discoveries. The validity of scientific results relies on the quality of data used and data are often subject to change, for example, due to observation additions, quality assessments, or processing software updates. The effects of data change are not well understood and difficult to predict. Datasets are often repeatedly updated and recomputing derived data products quickly becomes time consuming and resource intensive and may in some cases not even be necessary, thus delaying scientific advance. Despite its importance, there is a lack of systematic approaches for best comparing data versions to quantify the changes, and ad‐hoc or manual processes are commonly used. In this article, we propose a novel hierarchical approach for analyzing data changes, including real‐time (online) and offline analyses. We employ a variety of fast‐to‐compute numerical analyses, graphical data change representations, and more resource‐intensive recomputations of a subset of the data product. We illustrate the application of our approach using three scientific diverse use cases, namely, satellite, cosmological, and x‐ray data. The results show that a variety of data change metrics should be employed to enable a comprehensive representation and qualitative evaluation of data changes.

Combining citizen science and weather radar data to study large‐scale bird movements

The study of large‐scale animal mass movements requires suitable large‐scale sampling methods. Weather radar (WR) has been known to register biological targets since the 1960s. Arranged in large networks, radar is suitable to study regional to continent‐wide dynamics of aerofauna and to respond to increasing human–wildlife conflicts in the air. Tools for the spatiotemporal validation of faunistic interpretations of WR measurements in situ are only sparsely available. Citizen science (CS) bird observation repositories established in the past 20 years have accumulated millions of entries of species‐specific information well beyond their time of existence across vast areas. Together with other CS data sources, these databases can relieve the taxonomic shortcomings of WR and thus extend and refine the use of WR data. CS and WR data combined can efficiently provide large amounts of species‐specific data in three dimensions in a short time at low or no cost. Species‐specific data are particularly relevant to tackle loss of biodiversity, one of the greatest challenges in today's world. In this forum paper, we present features and qualities of ornithological CS and WR data, and their potential to provide unprecedented insights into regional to continent‐wide aerial movements of birds. We aim to discuss specific fields of applications where maximum information yield is to be expected, which is otherwise inaccessible, and in which way combined approaches would support biological research and derived data products and services for stakeholders, e.g. in aviation and the general public as beneficiaries.

Quality estimation of magnetotelluric impedance tensors using neural networks

While much of the workflow for geophysical data processing and analysis is automated, final quality assurance typically requires the decisions of skilled human analysts and interpreters. The quality and reliability of geophysical inverse models depends directly on the effectiveness of input data. The influence of spurious data on derived data products used in the inversion has been reduced by this effectiveness. Failure to identify and mitigate bias in data products can lead to costly errors. By applying supervised machine learning (ML), a neural network can be trained to recognize features in data that a human domain expert would identify as characteristic of poor data quality. In this study, we use magnetotelluric (MT) data as an example of a geophysical data set appropriate for such a training exercise. While MT data are used to estimate the resistivity structure of the subsurface, the concepts we discuss are universal to seismic, potential fields, and other geophysical data sets. We train a neural network, pyMAGIQ (Python-based magnetotelluric impedance qualifier), with multiple hidden layers and demonstrate that it successfully generates the nonlinear mapping function required to assess the quality of MT data. The training set is a large database of frequency-domain MT impedance tensors from the National Science Foundation-funded EarthScope MT project. A human-assigned quality index is associated with each impedance. We apply pyMAGIQ to unrated MT data from the United States and Canada and confirm that the ML-assigned quality factors are consistent with those assigned by trained human operators. We also apply sensitivity analysis to the trained neural network. This reveals that the human- and ML-assigned data quality index depends on the magnitude of the confidence limits on (1) the phases and (2) the continuity of the apparent resistivities and phases with respect to frequency.