UXarray is a community-developed Python package that extends the widely used Xarray ecosystem with native support for horizontally unstructured meshes, eliminating the need for costly, problematic regridding prior to visualization and analysis. Designed to meet the growing demands of kilometer-scale climate and weather models, UXarray aims to become a preeminent tool for the analysis, visualization, and postprocessing of Earth system data on irregular grids. It has been used in practice across a wide range of high-resolution atmospheric and ocean models, including MPAS, CAM-SE, E3SM, FESOM2, IFS, and ICON.Recently, UXarray played a key role in the 2025 WCRP Digital Earth – Global Hackathon (DEGH), where over 600 researchers, spanning four continents, collaborated to explore km-scale outputs, contributed from 11 different modeling centers from around the world. The use of UXarray was essential to fulfilling hackathon objectives, such as promoting global collaboration, sharing best-practice in process-based analysis of km-scale simulations, developing practical km-scale analysis workflows, and facilitating model intercomparison.This presentation will highlight UXarray’s current capabilities—including visualization tools and foundational analysis operators—share insights from the DEGH experience, outline future development plans, and highlight ways that the community can engage to shape the package moving forward.

Project Pythia is the educational arm of the Pangeo community, and provides a growing collection of community driven and developed training resources that help geoscientists navigate the Pangeo ecosystem, and the myriad complex technologies essential for today’s Big Data science challenges. Project Pythia began in 2020 with the support of a U.S. NSF EarthCube award. Much of the initial effort focused on Pythia Foundations: a collection of Jupyter Notebooks that covered essential topics such as Python language basics; managing projects with GitHub; authoring and using “binderized” Jupyter Notebooks; and many of Pangeo’s core packages such as Xarray, Pandas, and Matplotlib. Building upon Foundations, the Pythia community turned its attention toward creating Pythia Cookbooks: exemplar collections of recipes for transforming raw ingredients (publicly available, cloud-hosted data) into scientifically useful results. Built from Jupyter Notebooks, Cookbooks are explicitly tied to reproducible computational environments and supported by a rich infrastructure enabling collaborative authoring and automated health-checking – essential tools in the struggle against the widespread notebook obsolescence problem. Open-access, cloud-based Cookbooks are a democratizing force for growing the capacity of current and future geoscientists to practice open science within the rapidly evolving open science ecosystem. In this talk we outline our vision of a sustainable, inclusive open geoscience community enabled by Cookbooks. With further support from the NSF, the Pythia community will accelerate the development and broad buy-in of these resources, demonstrating highly scalable versions of common analysis workflows on high-value datasets across the geosciences. Infrastructure will be deployed for performant data-proximate Cookbook authoring, testing, and use, on both commercial and public cloud platforms. Content and community will expand through annual workshops, outreach, and classroom use, with recruitment targeting under-served communities. Priorities will be guided by an independent steering board; sustainability will be achieved by nurturing a vibrant, inclusive community backed by automation that lowers barriers to participation.

The arrival of kilometer-scale climate and global weather models presents substantial challenges for the analysis and visualization of the resulting data, not only because of their tremendous size but also because of the employment of unstructured grids upon which the governing equations of state are solved. Few Open Source analysis and visualization software tools exist that are capable of operating directly on unstructured grid data. Those that do exist are not comprehensive in the capabilities they offer, do not scale adequately, or both. Recognizing this gap in much-needed capability, Project Raijin - funded by an NSF EarthCube award - and the DOE SEATS project, launched a collaborative effort to develop an open source Python package called UXarray. UXarray extends the widely used Xarray package, providing support for operating directly (without regridding) on unstructured grid model outputs found in the Earth System Sciences, such as CAM-SE, MPAS, SCRIP, UGRID, and in the future, ICON. Much like Xarray, UXarray provides fundamental analysis and visualization operators, upon which more specialized, domain-specific capabilities can be layered. This talk will present an overview of the current capabilities of UXarray, provide a roadmap for near term future development, and will describe how the Pangeo community can contribute to this on-going effort.

The Geoscience Community Analysis Toolkit (GeoCAT) team develops and maintains data analysis and visualization tools on structured and unstructured grids for the geosciences community in the Scientific Python Ecosystem (SPE). In response to dealing with increasing geoscientific data sizes, GeoCAT prioritizes scalability, ensuring its implementations are scalable from personal laptops to HPC clusters. Another major goal of the GeoCAT team is to ensure community involvement throughout the whole project lifecycle, which is realized through an open development mindset by encouraging users and contributors to get involved in decision-making. With this model, we not only have our project stack open-sourced but also ensure most of the project assets that are directly related to the software development lifecycle are publicly accessible.

Global soil moisture mapping at high spatial and temporal resolution is important for various meteorological, hydrological, and agricultural applications. Recent research shows that the land surface reflection in the forward direction of Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution land surface information, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of the GNSS-Reflectometry (GNSS-R) technique, resulting in a nonlinear relationship between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture using bistatic reflectance observations acquired from the recently launched Cyclone GNSS (CYGNSS) mission. Specifically, several land surface parameters are obtained from remote sensing products and integrated with Soil Moisture Active Passive (SMAP) enhanced soil moisture retrievals to facilitate daily quasi-global CYGNSS soil moisture mapping at 9 km. Based on cross-validation against SMAP data, the ML algorithm is shown to be suitable for retrieving soil moisture from CYGNSS. Median values of unbiased root-mean-square-difference for the quasi-global coverage or regions with vegetation water content less than 5 kg/m(2) are 0.0395 cm3/cm(3 )and 0.0320 cm(3)/cm(3), respectively. Likewise, via independent evaluation against more than 100 in-situ sites, the algorithm is shown to have an unbiased root-mean-square-error of 0.0543 cm(3)/cm(3). CYGNSS-based retrievals contain similar spatial variability as SMAP across different seasons. Moreover, through a robust triple collocation technique, the accuracy of CYGNSS soil moisture is relatively high over moderately vegetated regions with correlations ranging from 0.4 to 0.8. Based on these validation results, we argue that derived CYGNSS soil moisture estimates can supplement current global soil moisture databases and provide more frequent retrievals at 9 km.

A multilayer module is incorporated into the Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) for determining the reflections and propagation of electric fields within a series of multilayer dielectric slabs. This module can be used in conjunction with other SCoBi components to simulate complex, bistatic simulation schemes that include features such as surface roughness, vegetation, antenna effects, and multilayer soil moisture interactions on reflected signals. This paper introduces the physics underlying the multilayer module and utilizes it to perform a simulation study of the response of SoOp-R measurements with respect to subsurface soil moisture parameters. For a frequency range of 100–2400 MHz, it is seen that the SoOp-R response to a single dielectric slab is mostly frequency insensitive; however, the SoOp-R response to multilayer dielectric slabs will vary between frequencies. The relationship between SoOp-R reflectivity and the contributing depth is visualized, and the results show that SoOp-R measurements can display sensitivity to soil moisture below the penetration depth. By simulation of simple soil moisture profiles with different wetting and drying gradients, the dielectric contrast between layers is shown to be the greatest contributing factor to subsurface soil moisture sensitivity. Overall, it is observed that different frequencies can sense different areas of a soil moisture profile, and this behavior can enable subsurface soil moisture data products from SoOp-R observations.

Signals of opportunity (SoOp) has the potential to offer cost-effective global remote sensing for land applications. Because of the complexity of SoOp scattering over land, comprehensive bistatic scattering models and simulators can help demonstrate its feasibility. To investigate this potential, we have developed a generalized, fully polarimetric forward model: the SoOp Coherent Bistatic Scattering (SCoBi) model. We have also developed a simulator framework, employing the SCoBi model to create an analysis environment for a community of researchers, scientists, and users with little or no electromagnetic background. We aim to enable studying and analyzing new SoOp methods with varying configurations; determining the optimal cases for specific missions; and generating, visualizing, and analyzing test data with the help of SCoBi.

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) receives the forward scattered L-band GNSS signals between ±37° latitudes. The received signals over land are previously shown to be highly sensitive to surface soil moisture (SM). Assuming coherent reflections over land, the CYGNSS bistatic radars can provide a spatial resolution of around 7 × 0.5 km and a revisit time of 1-2 days. SM retrieval at such a high spatio-temporal resolution could help advance hydrometeorology and agriculture applications. This study examines case scenarios for determining the relations of CYGNSS-deliverables and available SM data as well as specifying the requirements for CYGNSS-derived SM retrieval. Preliminary results demonstrate moderate correlation between CYGNSS measurements and SMAP SM. However, the results also show that accurate derivation of high spatio-temporal SM products from CYGNSS measurements is a challenging problem due to the heterogeneous land covers, varying topography, and surface roughness.

This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 ∘ × 0.0833 ∘ (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm 3 /cm 3 and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018.

Grant # 80NSSC18K1329 from the National Aero- nautics and Space Administration (NASA) Earth and Space Science Fellowship Program

Global navigation satellite system reflectometry (GNSS-R) has the potential to offer a cost-effective solution for global land observations. In this study, we aim to understand GNSS-R sensitivity to changing land geophysical parameters. For this objective, we performed simulations of a ground-based receiver using a recently developed coherent bistatic vegetation scattering model (SCoBi-Veg) to detect GNSS-R signatures under varying soil moisture (SM), vegetation water content (VWC), and surface roughness during a full corn growing season. We modeled different corn growth stages by using in situ measurement data. We analyzed the simulated reflectivity and received power values based on the aforementioned variable input parameters. This study demonstrates that specular reflections dominate the diffusely scattered contribution in case of moderate roughness, regardless of the corn field row structure or the polarization. Significant correlations between VWC and cross-polarized reflectivity values are also shown. Furthermore, the study quantifies the effects of SM and surface roughness on GNSS-R deliverables.

Conventional microwave remote sensing has been performed with mono-static active radars for decades. However, SoOp (Signal of Opportunity) has been gaining a great interest among researchers in recent years because it removes costs for a transmitter antenna by reception of existing direct and/or reflected signals. Although SoOp has produced encouraging results for the remote sensing of ocean surface roughness and wind vectors, the concept is still emerging and requires exhaustive analysis in order to be applied on land observations such as retrieval of biomass, soil moisture, surface topography, and snow depth. Bistatic analytical models and simulators can fulfill the need for analysis. They create environments that enable computation, validation, and examination of methods for future missions, which are difficult to perform in the real world experiments. Being motivated by this phenomenon, we have developed a generalized coherent forward model of bistatic scattering from vegetation cover for SoOp applications with the name SCoBi-Veg (SoOp Coherent Bistatic Scattering Model for Vegetated Terrains), which is currently under review by IEEE Transactions on Geoscience and Remote Sensing [1] [2]. We have also developed a simulator that employs SCoBi-Veg model, for the sake of creating a medium for a community of researchers, scientists, and users with little-or-no electromagnetic background to study new methods with varying configurations, to analyze such methods, to determine the optimal cases for specific missions, to generate, visualize, and analyze test data. In fact, SCoBi is a framework that implements only the simulator for vegetated terrains (SCoBi-Veg) for now. The simulator is being open-sourced in the Matlab/Octave development environment. It takes many inputs for vegetation, antennas, ground, and preferences. It generates received field and power, reflectivity, and/or NBRCS (normalized bistatic radar cross-section) for direct, coherent (specular), and incoherent (diffuse) contributions. This paper describes the ongoing open sourcing and the capabilities of the SCoBi simulator.

SCoBi-Veg stands for Signals of opportunity Coherent Bistatic scattering model for Vegetated terrains. It simulates polarimetric reflectometry of vegetation canopy over a flat ground using a Monte Carlo scheme. The model is aimed at assessing the value of navigation and communication satellite Signals of Opportunity in a range of frequencies from P- to S-bands for remote sensing of a number of geophysical land parameters such as soil moisture and biomass. A fully polarimetric expression for bistatic scattering from a vegetation canopy is first formulated for a general case and is then specialized to the practical case of ground-based/low-altitude platforms with passive receivers overlooking vegetation using the signals transmitted from large distances. Using analytical wave theory in conjunction with distorted Born approximation, the transmit and receive antenna effects (i.e., polarization crosstalk/mismatch, orientation, and altitude) are explicitly accounted for. The forward model developed here enables the understanding of the effect of different geophysical parameters and system configurations on the coherent and incoherent components of the reflected signatures. It can thus help developing robust inverse algorithm for extraction of soil moisture and biomass. The model is applied to P-band signals of geostationary communication satellites to describe polarimetric reflections from tree canopies as observed from down-looking platforms at various altitudes. The relative contributions of diffuse and specular scattering on total reflected power and reflectivity are quantified for various observing scenarios.

BACKGROUND:Pain is a multidimensional condition of multiple origins. Determining both intensity and underlying cause are critical for effective management. Utilization of painkillers does not follow any guidelines relying on biomarkers, which effectively eliminates objective treatment. The aim of this study was to evaluate the use of serum cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) as pain biomarkers. This work could significantly advance the diagnosis and treatment of pain.METHODS:We assessed the potential utility of serum COX-2 and iNOS as objective measures of pain in a sample of American patients. Pain was scaled between level 0-5 in accordance with the level reported by the patients. Blood samples were collected from 102 patients in the emergency room. Sandwich ELISA was used to determine the COX-2 and iNOS levels in the blood serum while statistical analysis was performed using Pearson product-moment correlation coefficients, Regression and Receiver Operating Characteristics (ROC) analyses. The biomarker results were also compared with self-reports of pain by the patients using conventional pain ratings and patients were asked to report the cause of the pain. Pain levels were clustered into four groups as 0 [self-reported 0], 1 [self-reported as 1], 2 [self-reported as 2 and 3] and 3 [self-reported as 4 and 5]. Co-expression of COX-2 and iNOS could significantly alter pain development and its sensitization. Therefore, iNOS dependent COX-2 levels were employed as categorized level.RESULTS:Self-reported pain levels did not show a correlation with the serum level of COX-2 and iNOS. The lack of correlation is attributed to multiple reasons including patients' intake of painkillers prior to participation, painkiller intake habit, chronic diseases, and subjectivity of self-reported pain. Increased serum COX-2 levels were reported in relation to the subtypes of these health issues. Further, 83% of the patients who reported pain also showed the presence of COX-2 in serum, while only 53% of the patients showed the presence of iNOS in serum. Moderate relation was found between the clustered pain level and categorized COX-2 and iNOS- levels.CONCLUSIONS:The findings support the requirement of further studies to use COX-2 and iNOS as prognostic biomarkers for objective quantification of pain at the clinical level.

Soil moisture (SM) plays a significant role on the Earth's water, energy, and carbon transfers. Thus, global sensing of SM is vital. Conventional active and passive monostatic instruments have been used for SM retrieval for decades. For the sake of increasing spatiotemporal resolutions and decreasing costs, Global Navigation Satellite System Reflectometry (GNSS-R) have been examined recently. However, the high number of dynamic factors that affect GNSS-R observables in land applications make the use of this technique challenging. In this paper, we aim to present our preliminary observations about simulated GNSS-R signatures through a vegetated terrain in order to unveil GNSS-R sensitivity to dynamic land parameters. We exploited our recently developed coherent bistatic vegetation scattering model (SCoBi-Veg) for these simulations. We modelled a full growing season of corn field by using in situ measurement data. We observed received power variations as a function of observation angle and corn growth stage. First findings indicate the dominance of the coherent contribution over incoherent one. Results also demonstrate the effect of the growth stages on the received power.

A coherent bistatic vegetation scattering model, based on a Monte Carlo simulation, is being developed to simulate polarimetric bi-static reflectometry at VHF/UHF-bands (240-270 MHz). The model is aimed to assess the value of geostationary satellite signals of opportunity to enable estimation of the Earth's biomass and root-zone soil moisture. An expression for bistatic scattering from a vegetation canopy is derived for the practical case of a ground-based/low altitude platforms with passive receivers overlooking vegetation. Using analytical wave theory in conjunction with distorted Born approximation (DBA), the transmit and receive antennas effects (i.e., polarization, orientation, height, etc.) are explicitly accounted for. Both the coherency nature of the model (joint phase and amplitude information) and the explicit account of system parameters (antenna, altitude, polarization, etc) enable one to perform various beamforming techniques to evaluate realistic deployment configurations. In this paper, several test scenarios will be presented and the results will be evaluated for feasibility for future biomass and root-zone soil moisture application using geostationary communication satellite signals of opportunity at low frequencies.

Tower and ground controls take significant role as much as arrival and transit traffic management on aeronautics. Both the safety of the vehicles and livings on the airport, and minimization requirement for flight costs with the help of optimizations on ground operations point out the importance of tower and ground controls. Especially the recent improvements on air transport have resulted in a drastic increase in air traffic intensity. As an inherent consequence of this progress, ground traffic volume must be diminished starting from modification on behaviors of air traffic controllers. Optimization of ground movements of vehicles by means of advanced algorithms is taken into consideration in this study. The study aims to compute the shortest paths for vehicles from apron or their current position to especially runway thresholds, taxi endpoints or any position and to report the results to controllers. Although such reports cannot be restrictive, those can be significantly usable decision support alternatives for controllers. In order to implement the system under consideration, Floyd-Warshall shortest path algorithm has been utilized. This node-based algorithm has been chosen since the ground routings in an airport are performed by controllers using virtual node points defined on the taxiways. The system created with the help of this algorithm is named “Floyd-Warshall Ground Route Optimization System” (FLOW-GRO). In the FLOW-GRO system, the minimum taxi-time between each node in the airport is taken as the weight of that edge since the maximum taxi speed on each taxiway segment may vary. Algorithm is fed by a start and an end point as inputs, and returns a list of nodes as an output that represents the shortest way. The application of the FLOW-GRO System has been examined in a well-working Air Traffic Management Simulation System (ATCTRSIM) that has been developed by TUBITAK BILGEM. As an inevitable conclusion of this study may be that, ground traffic controllers can be continuously helped with shortest path suggestions generated by a decision support system. Therefore, controllers may acquire a tendency to create vehicle routes in the shortest possible way in the long run, thanks to the system. A potential future phase of the study may be the evaluation of the routing performance of traffic controllers over the ground movements with the help of the system. In addition, conflict avoidance in a dynamic manner can be examined. Keywords—Shortest path, Floyd Warshall, optimization, ground controllers, FLOW-GRO.

This study focuses on localization of Unmanned Aerial Vehicles (UAV) since permanent navigation has vital significance to support position information and to avoid getting lost. Actually, there exist effective aeronautical navigation systems in use. Inertial Navigation System (INS) and Global Positioning System (GPS) are two representatives of the most common systems utilized in traditional aerial vehicles. However, an alternative supporter system for UAVs should be mentioned since INS and GPS have serious deficiencies for UAVs such as accumulated errors and satellite signal loss, respectively. Such handicaps are coped with integrating these systems or exploiting other localization systems. Terrain Referenced Navigation (TRN) could be a good alternative as a supporter mechanism for these main systems. This study aims to localize a UAV accurately by using only the elevation data of the territory in order to simulate a TRN system. Application of the methodology on a real UAV is also considered for the future. Thus assumptions and limitations are designed regarding the constraints of real systems. In order to represent terrain data, Digital Elevation Model (DEM) with original 30 meter-resolution (Eroglu and Yilmaz 2013 ) and also synthetically generated 10 meter-resolution maps are utilized. The proposed method is based on searching the measured elevation values of the flight within the DEM and makes use of simulation techniques to test the accuracy and the performance. The whole system uses sequences of elevation values with a predefined length (i.e. profile). Mainly, all possible profiles are generated and stored before the flight. We identify, classify and sort profiles to perform search operations in a small subset of the terrain. During the flight, a measured flight profile is searched by the Binary search method (Eroglu 2013 ) within a small neighborhood of corresponding profile set.

Recently, unmanned aerial vehicles (UAVs) have become one of the most popular and promising means for both military and civilian posts and academic research areas. Localization of the UAVs and persistent tracking of a UAV have vital importance to provide a UAV with navigation information and help to cope with getting lost permanently. Indeed, Inertial Navigation System (INS) and Global Positioning System (GPS) seem to be adequate for navigation of UAVs. However, an alternative augmented navigation system for UAVs should be taken into consideration since INS has accumulated errors and GPS always has the possibility of jamming and satellite signal loss. Terrain Referenced Navigation (TRN) could be a good alternative as a decision support system for these main systems. This study aims to detect the location of a lost or GPS-disabled UAV throughout a planned flight by using only the terrain data. In addition, assumptions and limitations are minimized for the sake of simplifying the process to apply this methodology on a real UAV in the future, e.g. flight through all directions with physically possible turn rates is allowed. In order to provide data of the terrain, Digital Elevation Model (DEM) of the flight region with 30m resolution is exploited. The proposed method is based on searching and matching the collected elevation values of the terrain below UAV within the DEM and makes use of simulation techniques to test the accuracy and performance. The whole algorithm utilizes a sequence of elevation values with a predefined length (i.e. profile). Mainly, all possible profiles are generated before the flight and stored in a huge search space. We identify, sort and classify these elevation profiles in order to perform search operations in a small subset of the huge set. During the flight, a sequence of terrain elevations, which is computed with the help of radar and barometric altimeter measurements, is searched within a small neighborhood of corresponding profile set.