- promote cross-network and -discipline engagement
- develop and test new technologies and sensors
- expand quality assurance methods and techniques
- support modeling and satellite data products
Orals: Tue, 5 May, 08:30–10:15 | Room -2.92
High spatiotemporal monitoring of near-surface precipitation phase (N-SPP) is essential for weather forecasting, transportation, agriculture, and hydrology. However, operational capability remains constrained as manual observations are being phased out, in situ surface instruments are sparse and often lack representativeness, and discrepancies persist between aloft phase estimates from radar/satellite and actual conditions at the ground. Recent studies have also reported an “upper-limit” effect in radar-based N-SPP products, further underscoring the need for low-cost, automated, and scalable alternatives.
Urban surveillance cameras are ubiquitous in modern cities and continuously capture near-surface scenes at high temporal resolution. As precipitation particles traverse the camera field of view, the resulting videos inherently preserve rich visual–temporal signatures of hydrometeors, providing a viable basis for visually discriminating precipitation phases. Moreover, leveraging existing camera infrastructures requires no dedicated sensor deployment, making camera sensor networks a cost-effective solution for high-resolution N-SPP monitoring. Nevertheless, reliably distinguishing common N-SPP types (i.e., rain, snow, and graupel) in unconstrained videos remains challenging due to subtle inter-class differences and strong sensitivities to illumination changes, complex backgrounds, camera settings, and wind-driven trajectories.
Our previous study (Wang et al., 2025) demonstrated the feasibility of using urban surveillance cameras for N-SPP monitoring. Guided by meteorological, optical, and imaging principles, we identified discriminative cues from both daytime and nighttime videos and developed an efficient framework that couples MobileNetV2-based transfer learning for spatial feature extraction with GRU-based temporal modeling. Using a self-curated 94-hour surveillance video dataset and benchmarking against 24 baseline methods, the proposed approach achieved the best overall performance, with accuracies of 0.9677 on the dataset and 0.9301 in real-world field validation against manually quality-controlled 2DVD measurements. The model also remained stable under variations in camera settings and across day–night conditions, and exhibited satisfactory wind robustness for wind speeds below 5 m/s.
Building on these results, we further move toward operational, city-scale deployment by discussing key challenges in multi-camera collaborative observing, including camera siting and field-of-view geometric constraints, automated camera screening and tiered selection, and quality-control/anomaly-detection procedures to address occlusion, glare, raindrop adhesion or wiper interference, stream frame loss, and camera-parameter drift. These discussions provide practical guidance for constructing a stable and reliable surveillance camera–based N-SPP monitoring network.
Reference:
Wang, X., Zhao, K., Huang, H., Zhou, A., & Chen, H. (2025). Surveillance camera-based deep learning framework for high-resolution ground hydrometeor phase observation. Atmospheric Measurement Techniques Discussions, 2025, 1-38.
How to cite: Wang, X., Zhao, K., and Yang, Z.: Surveillance Camera Sensor Networks: An Emerging Observing System for Near-Surface Precipitation Phase Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2076, https://doi.org/10.5194/egusphere-egu26-2076, 2026.
The health effects of ultrafine particles (UFP) have come under increased scrutiny. In 2021, the Health Council of the Netherlands published the advisory report “Risks of ultrafine particles in the outside air”, which highlighted that our knowledge of UFP exposure and health effects is limited by a lack of structural measurements of UFP concentrations in the Netherlands. The report therefore recommended
- measuring UFP concentrations structurally in the Dutch National Air Quality Monitoring Network and
- performing structural and validated model calculations to obtain a national overview of UFP exposure.
At the subsequent request of the Ministry of Infrastructure and Water Management, RIVM made an inventory of available data, knowledge and measurement equipment, and developed integrated strategies for incorporating structural UFP measurements into the national network and improving UFP models.
The National Air Quality Monitoring Network currently performs indicative UFP measurements with three TSI EPC 3783 instruments. Diurnal and weekly cycles can already be observed in these indicative data, and they have been used to scale an updated empirical map of average UFP concentrations in the Netherlands as well. On a project base, short-term measurements of UFP are performed using more compact equipment.
Newer counting equipment (TSI CPC 3750-CEN10) has been purchased recently to perform stationary UFP concentration measurements according to the latest technical standard (EN 16976:2024). The locations where the new measurement equipment will be placed are selected with the specific aim to improve the yearly average concentration map and obtain better estimates for the exposure of the Dutch population to UFP. The data will be made available to the public.
This presentation will discuss the progress so far, first experiences with the new equipment and next steps, including steps required for compliance with the new EU Air Quality Directive.
How to cite: Batenburg, A., Wesseling, J., Wever, D., van Ratingen, S., Weijers, E., and Stefess, G.: Expansion of UFP measuring capabilities of the Dutch National Air Quality Monitoring Network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3864, https://doi.org/10.5194/egusphere-egu26-3864, 2026.
From rapid assessment to real-time warning: short-term rockfall monitoring along the Route d’Anniviers (Valais, Switzerland)
The Val d’Anniviers is one of the major valleys of the canton of Valais in Switzerland. Well known for its winter sports opportunities and for the villages of Grimentz and Zinal, the valley is accessible by a vertiginous road exposed to a wide range of gravitational hazards. Though the Route d’Anniviers is protected by multiple galleries, severe events can still impact the road.In March 2024, a series of rockfalls led to heavy accumulation of debris atop a road gallery at the entrance of the valley. A week later, on 29 March, bigger blocks hit the road and perforated the gallery. To avoid complete isolation, an alternative route estimated to be less safe was used from the other side of the valley. In this context, real-time monitoring became essential to support decision-making and rapid response during emergency operations and gallery rehabilitation. A first emergency phase focused on rapid assessment: Geoprevent was mandated by the local authorities to deploy an interferometric radar to monitor and accurately assess the area above the Croisettes sector (near Vissoie) and check whether significant movement was ongoing. Early observations suggested only limited changes. This initial phase helped establish shared situational awareness and informed the next steps toward an operational warning setup. An alarm system was then needed to be installed to increase the safety of workers during the gallery renovation, as well as to contribute to a longer-term mitigation plan. Geoprevent was mandated on 10 April by the local authorities and installed a rockfall Doppler radar on 22 April. This technology enables real-time detection of rockfall activity. The radar was coupled with traffic lights and alarm horns, allowing rapid road closure and immediate on-site warning in case of events. SMS and email alerts complement local signaling, and both radar data and live footage from a pan-tilt-zoom remotely controllable camera are available through Geoprevent’s online data portal for remote supervision and event review. This contribution presents the phased monitoring and warning approach implemented at Vissoie—from rapid assessment to operational warning system —and discusses lessons learned for short-term monitoring, emergency installation, data interpretation, and reliable alerting to insure road and people safety in a complex environment.
How to cite: vincent, S., carrel, M., st.pierre, T., vonwartburg, J., stitelmann, O., and wetter, J.: From rapid assessment to real-time warning: short-term rockfall monitoring along the Route d’Anniviers (Valais, Switzerland), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4030, https://doi.org/10.5194/egusphere-egu26-4030, 2026.
In situ atmospheric particulate matter (PM) monitoring networks play a critical role in advancing understanding of air quality dynamics across local to regional scales by providing continuous, site-resolved observations. In contrast to short-term measurement campaigns, sustained monitoring networks enable the characterization of long-term trends, episodic events, and source-specific variability, while providing essential data for model validation and exposure assessment. These capabilities are especially useful in industrial environments, where emissions are spatially heterogeneous, temporally variable, and chemically complex.
We present the design and implementation of the Environmental Monitoring for Industrial Sites (ÉMIS) network, a ground-based, distributed system developed to monitor complex industrial emission environments. The network is designed to operate under challenging conditions, including high-latitude environments with extreme seasonal variability, limited access to grid power, and constrained connectivity. Each station integrates measurements of particulate matter across multiple size fractions (PM2.5, PM5, and PM10) and volatile organic compounds using low-cost optical sensors, local meteorology, and visual documentation via a conditionally triggered camera. Stations are additionally equipped with a modified Wilson and Cooke (MWAC) bottle sampler to collect long-term, sector-representative samples for chemical characterization.
Stations transmit data using long-range radio (LoRa) at user-defined intervals (e.g., two-minute resolution) to a hub node, which aggregates and relays data via cellular or satellite communication to an online dashboard. Unlike Bluetooth or Wi-Fi, LoRa enables kilometer-scale data transmission without the cost or infrastructure requirements of cellular modems. This architecture provides near–real-time insight into emission dynamics while supporting long-term data continuity. Emphasis on low-cost, modular instrumentation reduces financial and logistical barriers associated with traditional monitoring systems, enabling high-density deployments accessible to researchers, industries and public stakeholders.
The initial field deployment consisted of fifteen (15) stations distributed across a copper smelter in Quebec, Canada, spanning more than 1 km². This case study demonstrates the network’s ability to resolve spatial gradients and localized emission signals, while dealing with complex topography and climate conditions. Analysis reveals persistent PM hotspots associated with heavy machinery traffic, ore handling operations, and slag cooling. Periodic PM spikes linked to train transport, as well as clear relationships between wind speed, wind direction, and plume occurrence were identified.
The multi-level data output from the ÉMIS network supports a wide range of applications, including PM source attribution, evaluation of emission dynamics, integration with receptor and dispersion modeling, and validation of satellite-derived products. The network also serves as a testbed for sensor development, quality assurance refinement, and cross-network harmonization under real-world industrial conditions. This work highlights how adaptable, cost-effective ground-based monitoring networks can expand observational capacity in industrial environments and support advances in atmospheric science, air-quality management, and community-informed decision-making.
How to cite: King, J., Norris, E., and Hayes, P.: Environmental Monitoring for Industrial Sites (ÉMIS): A Distributed LoRa-based Network for Real-Time Particulate Matter Characterization in Complex Environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13103, https://doi.org/10.5194/egusphere-egu26-13103, 2026.
Opportunistic sensing based on microwave satellite downlinks has recently gained attention as a cost‑effective approach for monitoring tropospheric phenomena, exploiting the attenuation experienced by communication signals as they propagate through the atmosphere. While this approach has been successfully applied to meteorological phenomena—particularly rainfall estimation using Ku‑band broadcasting links and, more recently, Ka‑band broadband services—its use for geophysical monitoring remains largely unexplored. Volcanic emissions, in particular, release atmospheric constituents capable of significantly affecting microwave propagation through absorption and scattering, depending on particle size, concentration, and chemical composition. In regions surrounding active volcanoes, the interaction between ash, gases, and microwave signals offers an opportunity to detect eruptive activity using low‑cost ground receivers.
This work investigates the feasibility of using commercial satellite downlink signals to sense volcanic emissions in real time. The analysis considers the general interaction between microwave signals and volcanic constituents and examines how the Ku‑ and Ka‑band frequencies commonly used by GEO satellites provide different levels of sensitivity to these atmospheric components. Since these signals are continuously available over wide areas, they offer an attractive resource for passive and inexpensive monitoring.
A key aspect in assessing the feasibility of such opportunistic sensing is the geometry of the satellite–receiver link, which strongly influences the detectability of volcanic emissions. As matter of fact, the relative position of the satellite, the ground station, and the volcanic plume determines whether the microwave path intersects the cloud and at which altitude and distance from the vent this intersection occurs. In volcanic regions such as Mount Etna (Italy), the simultaneous visibility of multiple GEO satellites at different azimuths and elevations increases the likelihood that at least one downlink path crosses the plume, enabling the detection of its impact on the received signal.
This preliminary study provides thus a first assessment of the geometric and physical conditions under which satellite downlink signals can be exploited for volcanic emission detection.
The results suggest that existing broadcast satellite infrastructure could be leveraged also as a low‑cost, wide‑area monitoring system, complementing conventional geophysical instruments and motivating future experimental validation.
Acknowledgements: This work was supported by the following projects: Space It Up, funded by Italian Space Agency (ASI) and the Italian Ministry of University and Research (MUR) – Contract 2024-5-E.0 - CUP I53D24000060005; FoReLab (Departments of Excellence), funded by MUR.
How to cite: Giannetti, F., Sciortino, E. M., Gherardini, O., Sapienza, F., and Piras, A.: Preliminary Design of a Passive System for Monitoring Volcanic Emissions Exploiting Microwave GEO Satellite Downlinks , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13621, https://doi.org/10.5194/egusphere-egu26-13621, 2026.
Long-term, routine monitoring of volatile organic compounds (VOCs) is needed to provide insight into emission sources and patterns, oxidation processes including photochemistry and radical cycling, and the formation of tropospheric ozone and secondary organic aerosol. However, instrumentation that can provide high quality data both in terms of temporal resolution and molecular speciation has historically been cost-prohibitive, and requires advanced users for field deployment, operation, and data analysis.
While the use of proton transfer reaction mass spectrometry, paired with time-of-flight technology (PTR-TOF-MS), has been utilized for the detection of VOCs in a wide variety of environments and measurement platforms; this technique cannot provide molecular structure information and suffers from detection interferences such as fragmentation, cluster formation, and mixed ionization schemes that complicate data analysis and interpretation. Combining in situ gas chromatography (GC) with PTR-TOF-MS is a remedy, offering isomer level resolution and the ability to easily quantify product ion distributions to account for complex detection schemes. The addition of chromatographic pre-separation not only enhances the accuracy of species typically reported by PTR methods, it can also be used to broaden the scope of VOCs quantified using PTR methods, while maintaining low limits of detection (typically 1 ppt) without losing high time resolution data and negating the need for high mass resolving power.
Here, we present a new instrument package for the online, long-term detection of VOCs consisting of an in situ GC system equipped with an integrated thermal desorption (TD) system coupled to a compact PTR TOF-MS. The entire instrument is contained in a 50 x 65 x 55 cm footprint, weighs < 70 kg, and consumes < 600 W for typical operation. Coupling the GC system with the PTR-TOF allows the automated acquisition of both direct, high-time resolution data (e.g. 1 s) and pseudo-continuous molecular speciation data for isomer separation and improved quantification of the direct-PTR data.
A description of the instrument, including its utilization of a new VUV PTR ion source, will be presented along with > 4 weeks of continuous ambient data acquired in Spring 2025 in Thun, Switzerland. This data demonstrates the stability of the system and the value of continuous VOC detection with regular molecular speciation. This compact, easier to operate and maintain system makes it feasible to deploy this technology in many locations, including those with access limitations, for greater spatial resolution to acquire coinciding datasets to elucidate local, regional, and global VOC trends.
The compact chemical ionization time-of-flight mass spectrometer (CI-TOF-MS) described, here utilizing proton transfer reaction (PTR) ionization, was developed with support from the Beckmann Foundation.
How to cite: Claflin, M., Rohner, U., Yatsyna, V., Lerner, B., Dobrecevich, A., Thornton, J., Canagarantna, M., Lopez-Hilfiker, F., and Jayne, J.: Introducing a compact GC-PTR-TOF-MS: A novel method for the long-term monitoring of volatile organic compounds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20308, https://doi.org/10.5194/egusphere-egu26-20308, 2026.
Satellite scatterometers are active radar instruments that transmit microwave pulses towards the Earth's surface and measure how much of this energy is reflected back, a quantity commonly referred to as backscatter. Operating at C-band, the ERS-1/2 ESCAT (1991–2011) and MetOp-A/B/C ASCAT (2007–present) missions together provide more than 35 years of global land surface backscatter observations. These data are widely used for monitoring soil moisture, vegetation dynamics, cryospheric processes, and land cover change. Their coarse spatial sampling of 12.5 km enables very high daily global coverage, which reaches about 82 % during the ASCAT era.
A characteristic feature of scatterometer measurements is azimuthal anisotropy, meaning that backscatter depends systematically on the viewing direction. This arises because ESCAT and ASCAT use multiple fixed fan-beam antennas that observe the same surface under different azimuth angles as the satellite passes overhead. Oriented surface structures such as vegetation patterns, agricultural rows, sand dunes, or wind-blown snow features (sastrugi), interact differently with the radar signal depending on their orientation relative to the radar look direction. While physically meaningful, this directional dependence introduces additional variability when measurements from different viewing geometries are combined, complicating the interpretation of collocated time series.
We present an updated azimuthal correction scheme for ESCAT and ASCAT backscatter that aims to improve the temporal consistency of long-term land surface monitoring. Earlier approaches relied on a unique static reference polynomial derived from multi-year data and mixed viewing geometries to implement the desired azimuthal bias correction, thereby harmonizing the data. Our updated method applies yearly reference polynomials instead and explicitly distinguishes between ascending and descending satellite orbits. This approach thus accounts for long-term land cover changes and systematic diurnal differences between morning and evening overpasses, which are preserved in the corrected backscatter. In addition, the right-looking satellite swath is used as the reference geometry, reflecting the single-swath configuration of the legacy ERS scatterometers. Consequently, this also supports a consistent alignment of measurements across diverse satellite missions. Global comparison maps and local examples show that the updated correction effectively reduces azimuthal noise, as indicated by a lower estimated standard deviation of the corrected backscatter. At the same time, genuine geophysical signals, such as systematic morning–evening differences likely linked to moisture variability, are preserved.
Azimuthal anisotropy is generally undesirable for most monitoring applications, as quantities such as soil moisture do not depend on viewing direction. However, we show that the correction polynomials themselves can provide useful information in specific environments, including sand dunes, ice sheets, and areas dominated by strong point scatterers. This can, for example, enable studies of the temporal migration of sand dunes or sastrugi. We also show that even individual buildings can affect the full footprint of a 25 km ASCAT pixel, as metal–glass structures can produce strong corner reflections when aligned with the radar look direction. The proposed approach therefore supports robust long-term monitoring of natural processes by reducing azimuthal noise, while the correction parameters themselves provide physically meaningful directional information.
How to cite: Lindorfer, R., Hahn, S., Wagner, W., Harrison, C. T., and Melzer, T.: One Researcher's Noise is Another's Signal: Dynamic Azimuthal Correction of ESCAT/ASCAT Backscatter for Long-Term Land Surface Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20688, https://doi.org/10.5194/egusphere-egu26-20688, 2026.
Slow-moving landslides (SMLs) are governed by a combination of causative factors such as geology, tectonic setting, and climatic regime, along with triggering and conditioning factors including precipitation, complex groundwater dynamics, river toe erosion, seismic activity, and anthropogenic modifications. In this study, we present results from monitoring and analysis of a slow-moving landslide at Chandmari Village, Gangtok, in the eastern Himalaya. The analysis is based on real-world data obtained from an operational landslide early warning system active at the site since 2018. An integrated, multi-instrument dataset combining subsurface deformation measurements, hydro-meteorological observations, and high-resolution surface mapping is used to investigate landslide behaviour.
Slope movement was monitored using in-place inclinometer strings installed to depths of up to 30 m within the deforming slope mass to capture depth-wise displacement profiles. These measurements were analysed alongside continuous rainfall data recorded at 5-minute intervals from a local rain gauge to examine hydrological controls on deformation rates. In addition, Differential GPS (DGPS) surveys were conducted to map the spatial distribution of tension cracks on the slope surface. The combined dataset enabled assessment of both slip-surface depth and the spatial extent of the landslide body, as well as their response to seasonal rainfall.
Preliminary results indicate that periods of sustained rainfall correspond to increased displacement rates at specific depth intervals, suggesting activation of shear zones rather than a single, well-defined slip surface. Prolonged rainfall episodes further indicate the involvement of deeper slip surfaces. DGPS-based mapping reveals well-defined tension cracks at the head of the landslide body, while time-lag analysis between rainfall accumulation and deformation response highlights delayed slope adjustment controlled by groundwater dynamics.
The study demonstrates the temporal coupling between subsurface deformation and seasonal hydrological forcing and illustrates the effectiveness of multi-instrument monitoring for characterizing slow-moving landslides. The insights gained into deformation mechanisms support the development of improved early warning and mitigation strategies for urban hill-slope environments under long-term climatic and anthropogenic stress.
How to cite: Kumar M, N. and Vinodini Ramesh, M.: Integrated Analysis of Inclinometer, Rainfall, and DGPS Tension-Crack Data for a Slow-Moving Landslide: A Case Study from Chandmari Village, Sikkim, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21501, https://doi.org/10.5194/egusphere-egu26-21501, 2026.
The University ‘G. d’Annunzio’ of Chieti Pescara Atmospheric Observatory (Ud’A Trabocchi) aims to investigate atmospheric chemistry, greenhouse gases evolution, atmosphere-soil exchanges and marine meteorology within a rural-marine setting. The facility has a 15 m tall tower with seven sample points: each every every 2 m. State-of-the-art instrumentation for long-term continuous measurements of CO2, CH4, N2O and isotopic ratio of CH4 and CO2 (Picarro); O3, NO, NO2, NOx, CO and SO2 (Thermo); NO2 (CAPS, Teledyne); CO2 flux (LI-COR); Radon (Mi.lan); Aerosol concentration and size distribution (SMPS, TSI); column observation of O3, NO2, SO2, HCHO (PANDORA 2S), Aerosol optical depth (CIMEL); GNSS reciver (Geoguard and Stonex); Meteorological station (Vaisala); Celiometer (Vaisala CL61). Finally, the station includes a UAV system equipped with meteorological sensors and low-cost sensor to detect CO2, CH4, O3 and NOx. Data of the first period of observation will be shown and will be discussed possible opportunity of collaborations and synergic activities.
How to cite: Di Carlo, P., Aruffo, E., Mascitelli, A., and Chiacchiaretta, P.: The University ‘G. d’Annunzio’ of Chieti Pescara Atmospheric Observatory (Ud’A Trabocchi): a new supersite for atmospheric observation and research in central Adriatic coast, Italy., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22380, https://doi.org/10.5194/egusphere-egu26-22380, 2026.
This talk will introduce the WxStation, a new all-sky observing system currently undergoing extensive field testing within academic projects. The device combines professional grade in‑situ sensors with a 180° all‑sky camera to improve situational awareness. The addition of all-sky imagery into meteorological workflows can raise the effective resolution of mesoscale monitoring, particularly for cloud‑driven variability that impacts nowcasting, convective initiation, solar ramps, aviation ceiling/visibility, and wildfire situational awareness. A short image sequence quickly conveys a wealth of information to a trained observer—often faster and more intuitively than numbers alone can—making on-site imagery a powerful complement to conventional variables.
Furthermore, low‑power inference compute within the WxStation can automatically derive real‑time meteorological products (e.g., cloud fraction/type/motion) for assimilation into NWP and for wider analysis/climatological studies. In the long-term, these devices could provide insights equivalent to that of having a trained meteorological observer at every site 24/7/365. Two factors influence this outcome: (1) the deployment of enough devices to collect large datasets of high-quality imagery for labelling and (2) the development of AI interpretation models that are more capable per watt of compute power, specifically for meteorological tasks.
Prototype WxStation devices have been installed in real-world environments, including examples that will be highlighted during the talk. A deployment during the TEAMx intensive observing period (~3 summer months) in the European Alps captured frequent thunderstorms and rapid cloud‑field transitions. Another unit at the University of Leeds Farm (UK) has demonstrated day‑to‑day reliability in an agricultural setting. Some cold-weather testing down to –50 ºC will also be discussed. These deployments informed enclosure hardening, uptime targets, remote management, and early data intercomparisons against co‑located reference instruments.
The goals of this talk are to both engage and collaborate with the ground-based monitoring network community to propel this idea forward into new climates. Ideally, we seek network operators, research observatories, boundary‑layer testbeds, and emergency management–oriented networks to co‑design pilot evaluations in 2026. Pilots would quantify installation/ops burden (PoE power, mounting, maintenance), data pathways and latency, and product skill (cloud fraction/type/motion) versus co‑located references (e.g., ceilometers, trained observers). The aim is publishable evaluation results, a reusable ingest/QA template, and a clear path to broader operational use; including, if possible, exploratory NWP assimilation experiments (OSEs) as a stretch goal.
How to cite: Pickering, B.: Intelligent All-sky Cameras for Dense Mesoscale Observations: From Field Trials to Operational Pilots, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22806, https://doi.org/10.5194/egusphere-egu26-22806, 2026.
Extreme windstorms are among the costliest natural disasters in northwest Europe. Traditional wind measurement methods, while reliable, are limited by cost, installation complexity, and sparse spatial coverage, particularly in cluttered urban areas. Higher resolution approaches are therefore needed to monitor near-surface wind dynamics in complex settings.
Motion tracking in videos of foliated trees has reliably captured fine-scale wind variability, showing strong correlations with anemometer measurements, consistent gust estimates across reference objects, and strong temporal coherence; the present work introduces two major advances: (1) the first application of Visual Anemometry (VA) on videos generated from physics-based simulations of trees, and (2) a focus on defoliated trees, enabling mechanistic isolation of branch and trunk responses. Videos are generated from an elastically articulated body model simulating tree responses under mean wind speeds of 7-40 m s⁻¹ (25-144 km h⁻¹).
Results show that input wind speeds are reflected in kinematic tree responses (R² = 0.8), and then that these tree motions are captured in wind speeds estimated from videos by VA (R² ≈ 0.7). Distal and mid-canopy branches dominate the VA response, whereas the stem and inner branches provide only weak contributions, even though informative motion cues may occur anywhere in the canopy. These VA wind estimates were insensitive to camera orientation, confirming that estimation accuracy remains robust across horizontal viewpoints. In this work, we also explore the method's sensitivity to different camera parameters and assess its transferability across different conditions.
Using simulation-based defoliated trees, this work is a step towards a low-cost, scalable alternative to traditional methods, enabling improved detection of extreme gusts, fine-scale hazard mapping, and risk assessment for urban planning and insurance.
How to cite: Kulkarni, S., Hillier, J. K., Bugby, S. L., Marjoribanks, T. I., Bannister, D., and Higham, J.: Seeing the Winds Better: Simulated Videos of Defoliated Tree Motion Capture Extreme Wind Speeds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-254, https://doi.org/10.5194/egusphere-egu26-254, 2026.
Improving measurements of atmospheric water vapour remains a priority for the atmospheric science community, including for evaluation of satellite retrievals. The Pandonia Global Network (PGN) provides long-term, high-temporal-resolution direct-sun observations of total-column water vapour at many sites worldwide, but the PGN H2O product has not yet been widely assessed against independent ground-based datasets across diverse conditions.
Here we present preliminary intercomparisons of PGN total-column water vapour with coincident AERONET sun-photometer retrievals at co-located sites, with additional context from comparisons to GRUAN-processed radiosonde profiles where available. We examine sensitivities to temporal matching and to factors such as solar zenith angle, season, and humidity regime and report initial network-wide and site-to-site statistics (bias, scatter, correlation).
How to cite: Weaver, D., Zhao, X., Hanisco, T. F., Gupta, P., Cede, A., Tiefengraber, M., Gebetsberger, M., and Sommer, M.: Global evaluation of Pandonia Global Network total column water vapour using co-located AERONET and GRUAN observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5857, https://doi.org/10.5194/egusphere-egu26-5857, 2026.
The island of Tenerife (Canary Islands) exhibits the highest volcanic risk in Spain, owing to its long volcanic history combined with high population density. Consequently, an effective volcanic early-warning system is essential to ensure the safety of both the island’s residents and the millions of tourists who visit each year. Tenerife already hosts one of the most advanced volcano monitoring programs worldwide, integrating permanent instrumental networks with periodic geophysical and geochemical surveys.
Continuous microgravity measurements have proven to be a sensitive and reliable tool for detecting changes in magmatic–hydrothermal systems that may remain undetected by other geophysical or geochemical techniques [1]. However, spring-based gravimeters are affected by instrumental drift, which can compromise the interpretation of long-term observations. Superconducting gravimeters, while offering the highest sensitivity, require complex liquid helium refrigeration systems, significantly limiting their operational deployment. In recent years, absolute quantum gravimeters have emerged as the state-of-the-art technology for microgravity monitoring [2]. Time series acquired at active volcanoes, such as Mount Etna [3], demonstrate that these instruments can detect and quantify subsurface mass redistributions associated with volcanic processes. Within the framework of the GEOFIS-CAN project, the Instituto Volcanológico de Canarias (INVOLCAN) has acquired three Exail AQG-A absolute quantum gravimeters to be deployed on Tenerife.
The volcanic system of Tenerife consists of a central caldera (Las Cañadas), which hosts the Teide–Pico Viejo volcanic complex and is characterized by both basaltic and phonolitic activity, and three radial dorsals dominated by fissural basaltic effusive eruptions. The central complex, as well as the northwestern (NW) and northeastern (NE) dorsals, have experienced eruptions within the last 500 years. The three AQG instruments will be installed at: (1) the NW dorsal (Santiago del Teide), the most volcanically active sector of the island; (2) the boundary between the Las Cañadas caldera and the NE dorsal (Izaña); and (3) the southern margin of the caldera (Vilaflor). This network geometry provides comprehensive coverage of all regions potentially affected by future eruptions. Moreover, it enables not only the detection but also the localization of subsurface mass changes within the volcanic edifice. Specifically, this configuration is sufficient to simultaneously and unambiguously constrain both the location and magnitude of the mass variations within the volcanic edifice.
In this work, we present a sensitivity analysis of the proposed gravimetric network configuration, together with preliminary results from the recorded dataset.
References
[1] de Zeeuw-van Dalfsen, E., & Poland, M. P. (2023). Microgravity as a tool for eruption forecasting. Journal of Volcanology and Geothermal Research, 442, 107910.
[2] Ménoret, V., Vermeulen, P., Le Moigne, N., Bonvalot, S., Bouyer, P., Landragin, A., & Desruelle, B. (2018). Gravity measurements below 10− 9 g with a transportable absolute quantum gravimeter. Scientific reports, 8(1), 12300.
[3] Antoni‐Micollier, L., Carbone, D., Ménoret, V., Lautier‐Gaud, J., King, T., Greco, F., ... & Desruelle, B. (2022). Detecting volcano‐related underground mass changes with a quantum gravimeter. Geophysical Research Letters, 49(13), e2022GL097814.
How to cite: D'Auria, L., Pérez, N. M., Álvarez-Hernández, A., de Armas-Rillo, S., García-Hernández, R., López-Díaz, P., M. van Dorth, D., Ortega-Ramos, V., Bertier, P., Faure, L., Gautier, R., and Richard, J.: The Quantum Gravimeter Network of the Canary Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13828, https://doi.org/10.5194/egusphere-egu26-13828, 2026.
Computing power network (CPN) is designed to utilize multi-dimensional resources to complete computing tasks. However, in practical applications, the CPN architecture has difficulty in coordinating cross-domain heterogeneous resources, making it impossible to achieve the real-time and high scalability requirements of computationally intensive and time-sensitive tasks such as levee piping hazard inspection via remote sensing in emergency scenarios. Based on this, we propose a communication and computation integrated network architecture, referred to as (Com)2INet, that integrates “sensing”, “transmission”, and “computation” phases. In the sensing phase, thermal infrared imagery is utilized to retrieve land surface temperature fields through radiative transfer mechanisms, providing a reliable foundation for visual segmentation of piping hazards. In the transmission phase, we adopt the designed multi-path transmission mechanism to promote the efficient data flow across heterogeneous networks. In the computation phase, the proposed SACM algorithm, which is functionally decomposed and implemented as service chains within the proposed network architecture, dynamically processes the retrieved temperature fields to achieve precise hazard identification. This integrated framework ensures seamless interaction between sensing, communication, and computation, addressing the challenges of real-time hazard detection in emergency scenarios.
How to cite: Chen, J.: Service-Chain-Driven Communication and Computing Integration Networking: A Case Study of Levee Piping Hazard Inspection via Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15440, https://doi.org/10.5194/egusphere-egu26-15440, 2026.
Accurate monitoring of rock movement is essential for understanding rock slope instability and early failure mechanisms. However, conventional monitoring techniques often rely on manual measurements or expensive instrumentation, limiting their practicality for continuous and large-scale deployment. This study presents a low-cost image-based monitoring system that enables fully automatic measurement of rock displacement using a YOLOv8 pose estimation model. The proposed system was evaluated at the Miroir d’Argentine rock wall, a limestone rock flake in the Swiss Alps, Switzerland, using image data acquired from a Futuro BRW comparator monitored by a low-cost digital camera. The model was trained to detect the manual dial indicator and estimate pointer positions, allowing rock displacement to be derived automatically from pictures without manual intervention. To further assess the practical applicability of the proposed system, additional images acquired at different time periods were used for independent validation. The results demonstrate that the proposed approach can reliably estimate rock movement with high accuracy and strong generalization capability under real-world conditions. In addition, the robustness of the method was evaluated under simulated fog and blur degradations. The results show that the system maintains stable performance under light to moderate visual degradation, while performance decreases under severe fog and strong motion blur due to reduced geometric visibility. Overall, the proposed method provides an effective, low-cost, and practical solution for continuous rock movement monitoring and shows strong potential for long-term deployment in challenging alpine environments.
How to cite: Lin, R., Jaboyedoff, M., Derron, M.-H., Laurent, A., and Chalé, A.: A Low-Cost Image-Based Monitoring System for Automatic Rock Displacement Measurement Using YOLO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17517, https://doi.org/10.5194/egusphere-egu26-17517, 2026.
Ground-based environment monitoring networks provide essential data for climate and air quality research. However, traditional monitoring infrastructure in remote areas often requires high upfront and operational costs. Satellite communication technologies offer a solution to reach these remote areas, but existing commercial systems frequently pair expensive data loggers with costly satellite transceivers, thus limiting their deployment potential.
We present a novel satellite-enabled data logger designed to maximize compatibility while minimizing both purchase and operational costs. Our custom-designed Printed Circuit Board (PCB) combines data-logging and satellite transmission into a single unit, leveraging low-cost near-real-time bidirectional communication provided by the Astrocast CubeSat constellation. The integration approach helps reducing the hardware costs and the deployment complexity compared to traditional systems.
The platform is built on an STM32L476 microcontroller (MCU) with 64 MB of internal memory and an integrated real-time clock (RTC), providing low-power operation essential for autonomous field deployments. The board is equipped with an atmospheric pressure sensor, an air temperature and relative humidity sensor, along with multiple communication interfaces for a flexible, sensor-agnostic architecture: UART, I²C, and ADC channels. This design allows seamless integration of different third-party environmental sensors such as atmospheric chemistry, hydrological monitoring or ancillary meteorological measurements, without requiring hardware modifications.
The system pair the custom PCB with an Astronode S module for satellite data transmission, enabling bidirectional data transmission with 2-3 transmission opportunities every day. The design provides extended operational autonomy through low-power management while maintaining regular access to measurement throughout Astrocast's API and web UI.
We demonstrate how this design advances the objectives of ground-based monitoring networks by: (1) reducing deployment barriers to remote monitoring through cost-effective satellite connectivity, (2) supporting flexible sensor integration for cross-disciplinary measurement campaigns, (3) providing a scalable foundation for distributed monitoring networks, and (4) offering a validated, replicable platform for future infrastructure development.
How to cite: Tagliacarne, F., Valentini, R., and Renzi, F.: Design and Implementation of a Low-Cost, Satellite-Enabled Environmental Data Logger, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20334, https://doi.org/10.5194/egusphere-egu26-20334, 2026.
Understanding groundwater dynamics in arid environments is challenging due to sparse monitoring networks and strong anthropogenic influences. This study explores the potential of time-lapse microgravity observations to characterize shallow aquifer storage variability in Al-Ain City, United Arab Emirates. Repeated microgravity measurements were conducted at four monitoring wells over a one-year period between 2018 and 2019, alongside continuous groundwater-level observations, to investigate temporal changes in subsurface water mass. Observed gravity variations exhibit pronounced spatial and temporal contrasts across the study area, reflecting heterogeneity in aquifer response. Groundwater-level fluctuations over the monitoring period indicate notable seasonal and inter-annual variability, which is consistently mirrored in the gravity signal. By jointly analyzing gravity anomalies and water-level changes, variations in groundwater storage were quantified under a range of plausible specific yield values representative of shallow arid aquifers. Estimated storage changes indicate substantial redistribution of groundwater mass over the monitoring period, with corresponding volumetric changes on the order of several tenths to more than one million cubic meters, depending on local aquifer properties. The results demonstrate that microgravity monitoring provides an independent and spatially sensitive means of assessing groundwater storage dynamics, particularly in settings where conventional piezometric coverage is limited. This approach offers valuable insights into aquifer behavior under arid climatic conditions and highlights the broader potential of gravity-based methods for groundwater assessment, resource management, and hydrological characterization in data-scarce regions.
How to cite: Darwish, S.: Tracking Shallow Aquifer Storage Variability Using Time-Lapse Microgravity Measurements in an Arid Environment: Evidence from Al-Ain, UAE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22102, https://doi.org/10.5194/egusphere-egu26-22102, 2026.
The National Center for Atmospheric Research (NCAR), funded by the US National Science Foundation, has been supporting field deployments for atmospheric research since the 1960s, with its Earth Observing Laboratory (EOL) coordinating large-scale programs. EOL’s In-situ Sensing Facility (ISF) was forged out of decades of instrument development, advances in technology and software, and an evolution of services and support in response to the changing landscape of research. Today, ISF has evolved into three measurement systems: (1) Airborne Vertical Atmospheric Profiling System - dropsonde technology, (2) Integrated Sounding System - combined in-situ and remote ground-based profiling instruments, and (3) Integrated Surface Flux System - suite of mesonet, turbulence and energy balance sensors mounted on scalable towers. These requestable observing systems are designed for scalability and flexibility, emphasizing sensor development and integration, data management, and robust deployments to remote or challenging locations. Observations from ISF's measurement systems have guided regional model and forecast development, supported boundary layer meteorology and turbulence studies, and enhanced our understanding of severe weather and convective processes. When configured together ISF forms the foundation of LOTOS (Lower Troposphere Observing System) - a proposed sensor network to sample fundamental state parameters vertically through the boundary layer and horizontally across the surrounding landscape to provide a wealth of data to advance process studies and model algorithm development. We present an overview of our facilities' instrument capabilities and the LOTOS concept.
How to cite: Witte, J., Brown, W., Vömel, H., Roden, C., Hoch, S., and Hock, T.: The In-situ Sensing Facility: Agile Observation Networks for Field Campaign Success, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4108, https://doi.org/10.5194/egusphere-egu26-4108, 2026.
How to cite: Kociok, T., Kociok, K., Seiffer, D., and Stein, K.: A Dual-Mode Expansion Cloud Chamber for Reproducible Laboratory Studies of Laser Propagation in Fog and Clouds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4925, https://doi.org/10.5194/egusphere-egu26-4925, 2026.
Cloud radars are powerful tools for investigating cloud formation, radiative processes, and cloud microphysics. In recent years, polarimetric cloud radars have become increasingly common around the world. Since many cloud property retrieval techniques rely on accurately measured reflectivity, ensuring high-quality calibration is essential. The most widely used calibration approach is one based on disdrometer measurements, but this method carries significant uncertainties, particularly due to the vertical variability of rainfall characteristics. Another conventional method is the one based on point target observations, i.e. using hard targets such as corner and sphere reflectors (Toledo et al.,2020), but it is work intensive and difficult to carry out. Another method is the calibration transfer of a radar to those that are not calibrated yet (Jorquera et al.,2023) for e.g. BASTA radar in CCRES. The main disadvantage of the calibration transfer method is the high time consumption. Since the time needed to ship the reference radar to each location and carrying out the calibration is high, in a year maximum 2 or 3 radars can be calibrated. Another method of calibration is by comparison of observations by ground-based cloud radars and space-borne W-band radars in CloudSat and EarthCARE. EarthCARE (CloudSat) flight cycle of 25 (16) days and the requirement in pure ice nonprecipitating clouds during an overpass, makes this method mainly applicable for long-term calibration monitoring. To address all these limitations and complement in cloud radar calibration methods, A. Myagkov et al. (2020) introduced a self-consistency calibration technique that makes use of the polarization capabilities of W-band cloud radars. In this study, we assess the suitability of the self-consistency calibration method and identify the modifications required to make the approach more user-friendly and practical for operation.For this study, we have used 94 GHz cloud radar data operated at 300 elevation angle during days having rainfall rate less than 20 mm/hr. The methodology of self-consistency method consists of 4 steps. 1) Using Rayleigh Plateau detection method (Unal and van den Brule,2024), retrieve propagational (Kdp) and backscattering (δ) components from differential phase (φ) and, differential attenuation (Adp). 2) Calculate non-attenuated reflectivity Z0. 3) Calculate Kdp and Adp using Z0, δ and the coefficients given in A. Myagkov et al. (2020). 4) Compare the measured and calculated Kdp and Adp, and find the best fit for calibration coefficient.The major findings of this study are summarized as follows:1) A 30° elevation angle and rainfall rate below 20 mm/hr are not the only criteria required for applying the self-consistency method. The values of differential backscatter phase(δ) and Doppler spectrum width also play important roles. 2) The influence of surface temperature on the method has been examined.3)The criteria for selecting suitable cloud radar observations for the self-consistency calibration approach have been clearly identified.4)In addition to the calibration of reflectivity, the retrieval of the one-way attenuation profile is shown to be another significant output of the self-consistency calibration technique.
How to cite: Nandan, R. and Unal, C.: Applicability of self-consistency calibration method for polarimetric cloud radars, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5380, https://doi.org/10.5194/egusphere-egu26-5380, 2026.
MEMS Tiltmeters are an effective tool in high-precision monitoring of ground and infrastructure but their sensitivity to temperature variations remains a challenge. This can be a prohibiting factor for their application in field monitoring of natural processes, despite the fact that tiltmeters are likely one of the very few technologies that can provide information on minute movements in real-time and at relatively low cost. Temperature drift, if not removed effectively, can completely mask small tilts or lead to wrong interpretations of the monitored process. Up until very recently, the tiltmeter response to temperature change was assumed to be instantaneous, but previous work by the authors has shown that there is a delay of varying duration between the two over time. We present a weighted least square (WLS) - based method that takes this dynamic change of time-lags into consideration. The time-series data is divided into a number of time-windows based on time-lag values, wherein windows with identical lags are grouped together. Time windows longer than a specified duration are subdivided based on an optimal window length. We describe a method for selecting the optimal window size applicable to different monitoring scenarios. Finally, an iteratively WLS is applied to produce values that best represent the temperature drift over time within each time window. To examine the impact of varying window sizes on results, a sensitivity analysis is performed using the Monte Carlo method, enabling the calculation of prediction intervals. Our approach provides a reliable framework for the removal of temperature drift from tiltmeter recordings, enabling the use of tiltmeters for the monitoring of subtle ground movements. This can be crucial for applications such as early warning systems for landslides and monitoring of the ground surface response to hydrological processes at depth.
How to cite: Pytharouli, S. and Qiu, C.: Removal of temperature drift from tiltmeter recordings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18236, https://doi.org/10.5194/egusphere-egu26-18236, 2026.
The study of atmospheric composition is a major priority for improving the understanding of future climate models; therefore, it is essential to analyze in detail the variations of certain key variables, particularly water vapor, which strongly influences meteorological phenomena and plays a major role in radiative forcing. During my doctoral project, my research focused on studying the variability of water vapor using radiosoundings, an instrumental method for obtaining precise and high-quality measurements down to the UT/LS zone, by improving the quality of measurements through instrumentation. In addition to internal tests aimed at obtaining higher-quality measurements, setting up observation campaigns is a top priority in order to validate the performance of the sondes under real-world conditions, identify seasonal biases, and thus be able to make various corrections if necessary. Therefore, the TRACIS (Tropospheric Research Campaign on Air Moisture Content by Ipral at SIRTA) comparison campaign, in which I participated and which took place at the SIRTA site (Atmospheric Remote Sensing Research Instrumental Site) [48.71331°N, 2.20901°E] from May 12 to June 12, 2025, made it possible to assess the consistency of the data between the different sondes, while comparing these datasets with auxiliary sources such as ground-based observations and satellite measurements. This multi-source approach notably includes measurements from the IPRAL LiDAR, as well as fields from the ERA5 reanalysis model, thus strengthening the comparison, identifying potential systematic biases, and improving the overall interpretation of the results. Preliminary analyses focus on comparisons between the combined working measurement standard (CWS; mean RH M10/RS41) and other convergent datasets (M20, ERA5, and IPRAL LiDAR), allowing an evaluation of the representativeness and consistency of the different observation sources.
How to cite: Poignard, A., Farah, A., Sarkissian, A., Keckhut, P., Alraddawi, D., Khaykin, S., Dupont, J.-C., and Capo, J.: Comparison of TRACIS campaign data with data from radiosonde, IPRAL Lidar and ERA5, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21164, https://doi.org/10.5194/egusphere-egu26-21164, 2026.
Copernicus Ground-Based Observations for Validation (GBOV) is a project funded by the European Commission and is part of the Copernicus Land Monitoring Service (CLMS). The project has two main purposes: to support yearly validation efforts of core CLMS products (BRF, Albedo, FAPAR, LAI, FCOVER, LST, Soil Moisture, GPP, NPP, LSP and Evapotranspiration), five of which are listed among GCOS Essential Climate Variables (ECV); and to maintain a data portal making these validation products available. GBOV has reached a broad user community, with about 1700 users, including ESA optical MPC.
A large number of ground-based measurement networks disseminate publicly available data such as NEON, ICOS, TERN, BSRN and ISMN. Within GBOV, long-term datasets are needed in order to maximise the number of matchups between ground-based measurements and satellite data.
GBOV produces both ground-based, point-scale observations (the so-called “Reference Measurements”), and Analysis Ready Validation Data (ARVD) which are spatially upscaled to match the resolution of CLMS products (the so-called “Land Products”). Those GBOV products cover more than 150 sites and are made available to the community via a data portal freely accessible on https://gbov.land.copernicus.eu/. Available Land Products variables include Surface Bi-directional Reflectance Factors (BRFs), Surface Albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR), Fraction of Vegetation Cover (FCover), Land Surface Temperature (LST), Surface Soil Moisture (SSM). Gross Primary Production (GPP), Net Primary Production (NPP), Land Surface Phenology (LSP) and Evapotranspiration are new products and are not yet available, their release is planned for the end of 2026.
The networks providing GBOV initial input data are unfortunately not evenly distributed. In an attempt to reduce the thematic and geographical gaps, GBOV is developing its own network as part of a collaboration with existing networks. In GBOV phase 1, six ground stations have been upgraded with additional instrumentation. In GBOV phase 2, two ground stations monitoring vegetation variables have been deployed in 2023, one at the Fuji Hokuroku research station in Japan as part of a collaboration with NIES (National Institute of Environmental Studies) and one in 2024 at the Fontainebleau research station (France) as part of a GBOV/ICOS collaboration. In addition, two LST stations were deployed in Fuji Hokuroku and Litchfield (TERN network Australia) in 2024.
Over the past year, several updates have been implemented in the GBOV database to better respond to CLMS and general user requirements. This includes new procedures for specific land cover types in vegetation products, improved uncertainty estimates for soil moisture, and enhanced processing procedures for LST products. Procedures are being developed for the new products: Gross Primary Production, Net Primary Production, Land Surface Phenology and Evapotranspiration.
This presentation will focus on the current status of GBOV products and highlight recent developments and evolutions.
How to cite: Lerebourg, C., Grousset, R., Nativel, S., Carrière, J.-S., Clerici, M., Gobron, N., Muller, J.-P., Song, R., Dash, J., Paramanik, S., James, F., Ghent, D., Anand, J., Shukla, R., and Perez-Hoyos, A.: Copernicus Ground-Based Observations for Validation service (GBOV): overview and latest updates for EO data Cal/Val, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21819, https://doi.org/10.5194/egusphere-egu26-21819, 2026.
The SPRUCE experiment is the primary component of the Terrestrial Ecosystem Science Scientific Focus Area at ORNL focused on terrestrial ecosystems and the mechanisms that underlie their responses to environmental change. As of December 2025, the SPRUCE experiment is ending its planned decadal timeframe. The manipulation evaluated the response of the existing biological communities to a range of warming levels from ambient to +9°C, provided via large, modified open-top chambers. The ambient and +9°C warming treatments were also conducted at eCO2 (in the range of 800 to 900 ppm). Both direct and indirect effects of these experimental perturbations have been analyzed to develop and refine models needed for full Earth system analyses. The instruments and infrastructure needed for these measurements include meteorological tower-based CO2/H2O sampling and analysis systems, air temperature and relative humidity probes, precipitation gauges, wind speed and direction instruments, solar radiation sensors, subsurface soil moisture and temperature sensors, water level and conductivity sensors, continuous vegetation sap flow and dendrometer sensors, and companion dataloggers to record and report the data. The data collection system deployed of approximately 80 dataloggers, 1100 sensors, and associated instruments. Through the 10 years of observations, we collected substantial amount of data and want to present variety of data product available for analysis and scientific investigation.
How to cite: Krassovski, M., Mayes, M., Velliquette, T., Ruggles, T., Hanson, P., and Riggs, J.: Spruce and Peatland Responses Under Changing Environments (SPRUCE) - 10 years of data collection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1694, https://doi.org/10.5194/egusphere-egu26-1694, 2026.
