Observation Network Research Articles

Broadscale spatial synchrony in a West Nile virus mosquito vector across multiple timescales

Insects often exhibit irruptive population dynamics determined by environmental conditions. We examine if populations of the Culex tarsalis mosquito, a West Nile virus (WNV) vector, fluctuate synchronously over broad spatial extents and multiple timescales and whether climate drives synchrony in Cx. tarsalis, especially at annual timescales, due to the synchronous influence of temperature, precipitation, and/or humidity. We leveraged mosquito collections across 9 National Ecological Observatory Network (NEON) sites distributed in the interior West and Great Plains region USA over a 45-month period, and associated gridMET climate data. We utilized wavelet phasor mean fields and wavelet linear models to quantify spatial synchrony for mosquitoes and climate and to calculate the importance of climate in explaining Cx. tarsalis synchrony. We also tested whether the strength of spatial synchrony may vary directionally across years. We found significant annual synchrony in Cx. tarsalis, and short-term synchrony during a single period in 2018. Mean minimum temperature was a significant predictor of annual Cx. tarsalis spatial synchrony, and we found a marginally significant decrease in annual Cx. tarsalis synchrony. Significant Cx. tarsalis synchrony during 2018 coincided with an anomalous increase in precipitation. This work provides a valuable step toward understanding broadscale synchrony in a WNV vector.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Scientific perspective on rukyatul hilal: A case study of assalam kartasura sukoharjo observatory

In essence, rukyat is looking deeply to see whether the moon has undergone changes to indicate the change of the moon or not. With the advancement of the times, observations no longer only use manual binoculars but are supported by various adequate equipment, making it very easy to carry out observations. . Therefore, it is important for humanity to understand what an observatory is and its uses. This research uses qualitative methods supported by documentation and direct field research at the Assalam observatory in Sukoharjo, Central Java. The results of this research are the contribution of the Assalam Observatory in the implementation of rukyatul hilal in Indonesia. The Assalam Observatory is also the Secretary General of JOPI (Indonesian Planetarium Observatory Network) so its involvement in rukyatul hilal is beyond doubt. So far, the results of observations at the Assalam observatory are at 7-8 degrees every month.

Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4 kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (−0.82 ppm and −0.44 ppm) and precision (0.12 ppm and −0.03 ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

Drivers, changes, and impacts of hydrological extremes in India: A review

AbstractThe frequency of hydrological extremes (droughts and floods) has increased in recent years in India. A dense population, intensive agriculture, and rapidly developing physical infrastructure put India under the risk of hydrological extremes. However, a comprehensive overview of the drivers, changes, and impacts associated with hydrological extremes in India has been lacking. We examine the key processes and the role of climate and human interventions on hydrological extremes in India. We discuss the observed and projected changes in hydrological extremes along with their impacts. While the Indian summer monsoon is the primary driver of the hydrological extremes in India, human interventions (irrigation, reservoir storage, and groundwater pumping) play a crucial role in the changes and variability of hydrological extremes. The relative role of climate change and direct human interventions on changing the intensity and impacts of hydrological extremes need to be considered for future adaptation planning under climate change. The observed increase in the frequency and intensity of hydrological extremes will continue in the future with compound and cascading impacts due to the warming climate. We need to strengthen the observational network, improve hydrological modeling, reduce climate uncertainty, and develop robust early warning systems to adapt and mitigate hydrological extremes in India.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Water and Environmental Change

The Impact of Music Therapy in a Pediatric Oncology Setting: An Italian Observational Network Study.

Music Therapy (MT) is a non-pharmacological, art-based intervention that employs music experiences within a therapeutic alliance to attend to clients' physical, emotional, cognitive, and social requirements. This is the first study aiming at investigating the impact of MT on the psychological facets of children suffering from cancer. The study, combining the AQR and m-YPAS assessment tools, evaluated behavioral, sound-musical, and interactive parameters in pediatric oncology patients undergoing MT sessions during hospitalization. Fifty patients admitted to the Paediatric Oncology and Haematology Unit at Policlinico S. Orsola Hospital in Bologna, Italy, were enrolled, irrespective of their treatment regimen. Data collection occurred on the first day of the MT session between 3 p.m. and 5 p.m., with observations conducted by independent observers. In addition to traditional statistical analysis, network analysis was used to explore the combined interactions of all parameters, effectively discerning the distinctive roles played by each one during therapy sessions and their influence on all others. Network analysis highlighted distinct patterns of interactions among parameters during the various sessions, emphasizing the role of positive emotions and a calm setting, the child's ability to take the initiative in sessions, their sense of agency, and the parent's role in guiding them. Significant differences were recorded at each time point between all variables considered. The results of this innovative study may pave the way for future multicenter studies aimed at further exploring the role of MT in children undergoing both curative and palliative treatments for cancer.

Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer

High-performance motion tracking of electro-hydraulic servo systems plays an important role in developing industrial technology. However, the system itself has parameter uncertainty, uncertain nonlinearity, and measurement noise, significantly decreasing the performance of conventional controllers designed based on nominal models. To improve the tracking performance of electro-hydraulic servo systems, an adaptive dynamic surface control method combining extended state observer and radial basis function neural network is proposed. Firstly, an extended state observer combining neural network output and parameter adaptation technology is designed without the need for speed and pressure feedback to observe the system’s unknown state variables and nonlinear matched disturbance. The design of neural networks is used to approximate mismatched disturbance with significant nonlinearity online. Then, the proposed extended state observer is introduced into the neural adaptive backstepping design, effectively compensating for simultaneous matched and mismatched disturbances, thereby suppressing most of the uncertain nonlinearity of the system. Meanwhile, online update of uncertain parameters further improves the tracking performance of the controller. In addition, the introduction of dynamic surface overcomes the “explosion of complexity” issue in backstepping design. The rigorous stability of the closed-loop system is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed controller is validated through comparative experiments on valve-controlled hydraulic cylinder.

Scalability and Performance Optimization Strategies in Large-Scale System Architectures

This paper aims to break away from traditional thinking paradigms by introducing theories and methods from complexity science to provide innovative and effective solutions for scalability and performance optimization of large-scale system architectures. The paper first constructs a complex network model for large-scale system architecture, which includes nodes (services), edges (interactions), and global attributes, along with dynamic models reflecting key metrics such as system performance and scalability, providing a theoretical foundation for subsequent research. Subsequently, from a complexity perspective, it innovatively proposes scalability strategies, including adaptive scalability strategies for complex network optimization, data-driven complex system regulation, and elastic computing and service orchestration mechanisms. In terms of performance optimization, it applies tools such as complex systems observation theory and network flow theory to explore insights and breakthroughs in complexity related to full-link performance monitoring and diagnostics, asynchronous communication, task scheduling, and software-hardware collaborative optimization. Finally, the paper summarizes the research findings, distilling the core concepts and methodological innovations of scalability and performance optimization strategies from a complexity perspective. It also looks ahead to the prospects of further integrating complexity science and information technology in the future, along with potential research topics in new scenarios and challenges.

The application of a high-density street-level air temperature observation network (HiSAN): Spatial and temporal variations of thermal and wind condition in different climatic condition types

Different patterns of urban development will result in different characteristics of the urban heat island effect (UHI). In order to explore the impacts of urban environment characteristics, wind conditions, and climatic condition types on UHI over different periods, this study adopted Tainan City as the study site, and utilized the surface roughness length and wind prediction, to evaluate the correlation between wind conditions and UHI, as well as the thermal environment affected by different environmental. The UHI characteristics were evaluated by using 100 measurement stations (HiSAN) located in different urban environments. The results revealed the expansion of the daily temperature range was associated with an increase in the eastwest UHI centroid point movement; wind conditions affected north-south shifts of UHI centroid point. The built environments was positively correlate with daytime (0.24) and nighttime (0.68) temperatures. Daytime water bodies notably cool surroundings (correlation -0.52), and green spaces enhance nighttime cooling (correlation -0.64). Wind speed negatively correlates with UHI deviation values during the daytime (-0.17) and nighttime (-0.58). The study highlights the impact of wind speeds and temperature ranges on UHI movement, emphasizing effective ventilation's role in urban cooling and quantifying the importance of water and green space in mitigating UHI.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

Lithium-ion batteries possess one of the best energy-weight ratios, while safety and reliability are critical issues for its continued operation. Due to extreme fast-charging, huge application sizes and variable usage environment, the risk of soft battery faults, including soft internal short circuit (SISC), is increased and may evolve into severe accidents. Therefore, accurate early detection of lithium-ion battery fault is imperative to guarantee the battery performance. Motivated by this fact, we proposed a real time fault detection framework for battery soft faults. Based on the Equivalent Circuit Model (ECM) and coupling thermal model, Extended Kalman Filter (EKF) observer is used for reliable monitoring of battery voltage and surface temperature. Inspired by uncertainty interference during the observation process, we explore an observed-based learning approach that combine physical model-based observer and Bidirectional Long Short-Term Memory neural networks (BiLSTMNN) to empower the observer with the ability to learn the uncertainties, which is efficient in distinguishing soft faults information and uncertainties from residual. Furthermore, the memory characteristics of BiLSTMNN are considered to improve the robustness of the detection system, including comprehensive training data and optimized input features. Finally, through the comparative analysis and the robustness evaluation experiments including various driving cycles and internal short circuits (ISC) faults test verity the good performance of the proposed method.

TP-PROFILE: Monitoring the Thermodynamic Structure of the Troposphere over the Third Pole

Ground-based microwave radiometers (MWRs) operating in the K- and V-bands (20–60 GHz) can help us obtain temperature and humidity profiles in the troposphere. Aside from some soundings from local meteorological observatories, the tropospheric atmosphere over the Tibetan Plateau (TP) has never been continuously observed. As part of the Chinese Second Tibetan Plateau Scientific Expedition and Research Program (STEP), the Tibetan Plateau Atmospheric Profile (TP-PROFILE) project aims to construct a comprehensive MWR troposphere observation network to study the synoptic processes and environmental changes on the TP. This initiative has collected three years of data from the MWR network. This paper introduces the data information, the data quality, and data downloading. Some applications of the data obtained from these MWRs were also demonstrated. Our comparisons of MWR against the nearest radiosonde observation demonstrate that the TP-PROFILE MWR system is adequate for monitoring the thermal and moisture variability of the troposphere over the TP. The continuous temperature and moisture profiles derived from the MWR data provide a unique perspective on the evolution of the thermodynamic structure associated with the heating of the TP. The TP-PROFILE project reveals that the low-temporal resolution instruments are prone to large uncertainties in their vapor estimation in the mountain valleys on the TP.

Determination of Earthquake Depths Using Data of Cross-Sectional and Areal Deep Seismic Studies in Siberia

Abstract ––Information on the distribution of earthquake hypocenters for many seismically active zones of Siberia remains insufficient, which is associated with sparse networks of seismological observations. The paper presents the results of determining the earthquake depths in several seismogenic areas in the Altai-Sayan region, Cisbaikalia, Transbaikalia, and Yakutia using the travel time of longitudinal refracted waves from the Mohorovičič discontinuity (Pn waves) from earthquakes and the recently obtained information about the deep structure of these regions. The depth determination algorithm is tested using data from aftershocks of large earthquakes occurring in Tuva in 2011 and 2012 (ML = 6.7 and 6.8), recorded both by the regional seismological network and by a local group of seismic stations. Different methods are applied to reveal that some aftershock depths have a close match, including those for the main shocks – the Tuva-1 and Tuva-2 earthquakes. Another good match of earthquake depths is obtained using Рn waves with materials from regional and detailed studies of the Baikal branch of the Geophysical Survey of the Russian Academy of Sciences in the Muyakan activation site in the Baikal rift zone. The resulting data complement the information on the hypocenter of the main shock and confirm the change of the Muyakan activation cluster from large depths to shallow ones since the onset of activation in 2014. New information on the earthquake depths using Рn waves is obtained in Yakutia along the border of the largest Eurasian and Okhotsk tectonic plates. It is revealed that they decrease to 6–12 km as compared to higher depths of 20–30 km in adjacent areas. The resulting series of new information on the distribution of earthquake hypocenters using Рn waves is extremely important mostly because it indicates the possibility of identifying and redefining earthquake hypocenters using previous seismological observations in seismically active zones of Siberia.

Continuous Leaf Area Index (LAI) Observation in Forests: Validation, Application, and Improvement of LAI-NOS

The leaf area index (LAI) is one of the core parameters reflecting the growth status of vegetation. The continuous long-term observation of the LAI is key when assessing the dynamic changes in the energy exchange of ecosystems and the vegetation’s response indicators to climate change. The errors brought about by non-standard operations in manual LAI measurements hinder the further research utilization of this parameter. The long-term automatic LAI observation network is helpful in reducing errors from manual measurements. To further test the applicability of automatic LAI observation instruments in forest environments, this study carried out comparative validation research of the LAI-NOS (LAI automatic network observation system) at the Wanglang Mountain Ecological Remote Sensing Comprehensive Observation Station, China, comparing it with the results measured by the LAI-2200 Plant Canopy Analyzer (LI-COR, Lincoln, NE, USA), the LAI-probe handheld instrument, and a fisheye lens digital camera (DHP method). Instead of using the original “smoothest window” method, a new method, the “sunrise–sunset” method, is used to extract daily LAI-NOS LAI, and the corresponding confidence level is used to filter the data. The results of the data analysis indicate the following: LAI-NOS has a high data stability. The automatically acquired daily data between two consecutive days has a small deviation and significant correlations. Single-angle/multi-angle LAI measurement results of the LAI-NOS have good correlations with the LAI-2200 (R2 = 0.512/R2 = 0.652), the LAI-probe (R2 = 0.692/R2 = 0.619), and the DHP method (R2 = 0.501/R2 = 0.394). The daily LAI obtained from the improved method, when compared to the original method, both show the same vegetation growth trend. However, the improved method has a smaller dispersion. This study confirms the stability and accuracy of automatic observation instruments in mountainous forests, demonstrating the distinct advantages of automatic measurement instruments in the long-term ground observation of LAIs.

An open flame and smoke detection dataset for deep learning in remote sensing based fire detection

ABSTRACT The early warning of fires is pivotal for preventing substantial economic losses and ecological damage. However, the enhancement of fire model generalization performance still faces challenges due to limitations in existing fire datasets, such as image quantity and heterogeneity. Taking the advantage of rapid advancements in remote sensing technology and artificial intelligence, we introduce the Flame And Smoke Detection Dataset (FASDD), a pioneering collection comprising over 120,000 heterogeneous images covering various fire scenarios, aiming to promote the evolution of fire detection models. FASDD serves as a challenging benchmark for object detection tasks and features three sub-datasets: FASDD_CV, FASDD_UAV, and FASDD_RS. These sub-datasets comprise cross-domain images from ground-based, airborne, and spaceborne sensors, respectively. Extensive experiments using advanced Swin Transformer models demonstrate satisfactory fire detection performance, with mAP scores of 84.9%, 89.7%, and 74.0% on the respective sub-datasets. Notably, the application case in wildfire localization highlights the proficiency of FASDD_RS models for monitoring and tracking forest fires. Trained on FASDD_RS, FASDD_UAV, and FASDD_CV, deep learning models can be individually deployed on edge devices including satellites, drones, and terrestrial sensors, thus empowering collaborative fire warning within the space-air-ground integrated observation network. Additionally, a standardized procedure for data preparation, data procedure, and quality assurance is established, providing off-the-shelf annotations with four common configurations to facilitate machine learning model training. FASDD stands as a pivotal benchmark, providing valuable resources for training and evaluating deep learning fire detection models, especially Large Vision Models (LVM). It holds the potential to accelerate research progress in urban firefighting or forest fire detection, providing vital security measures for early fire warning and emergency response tasks.

Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology

Abstract. In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice concentration, thickness and velocity, but also its level of damage, internal stress and cohesion. Specifically, we estimate the air drag coefficient and the so-called damage parameter of the MEB model. Mimicking the realistic observation network with different combinations of observations, we demonstrate that various issues can potentially arise in a complex sea-ice model, especially in instances for which the external forcing dominates the model forecast error growth. Even though further investigation will be needed using an operational (a coupled dynamics–thermodynamics) sea-ice model, we show that, with the current observation network, it is possible to improve both the observed and the unobserved model state forecast and parameter accuracy.

DEUNET (Dokuz Eylul University Seismological Observation Network): Seismicity of Izmir and Its Surroundings with A New Local Seismic Network in Western Anatolia

DEUNET (Dokuz Eylul University Seismological Observation Network): Seismicity of Izmir and Its Surroundings with A New Local Seismic Network in Western Anatolia

Determination of the Radioactive Nature of the Sediments of the Bays of the Ebrié Lagoon of Abidjan, Ivory Coast

In Ivory Coast, the lagoons represent the transit point for the majority of continental inputs. They are thus increasingly vulnerable and their ecological balance is rapidly modified under the influence of certain natural or anthropogenic factors. The objective of this study is to evaluate the level of presence of lead 210 in sediments. The sediments were sampled during the campaigns of the National Water Quality Observation Network on the bays spread over thirteen study sites. Analysis by alpha spectrometry was carried out in three stages (mineralization with concentrated acids, self-deposition on a silver disk, counting in an alpha detector with a silicone surface barrier). The highest lead-210 activities were obtained in the bays of Koumassi 1 (228.57 ± 12.21 Bq/kg) and Marcory (198.91 ± 9.46 Bq/kg). However, no seasonal variation in lead-210 contamination of sediments in the bays of Abidjan was observed. We wish to extend this study to other aquatic matrices.

Multidimensional neural representations of social features during movie viewing.

The social world is dynamic and contextually embedded. Yet, most studies utilize simple stimuli that do not capture the complexity of everyday social episodes. To address this, we implemented a movie viewing paradigm and investigated how everyday social episodes are processed in the brain. Participants watched one of two movies during an MRI scan. Neural patterns from brain regions involved in social perception, mentalization, action observation and sensory processing were extracted. Representational similarity analysis results revealed that several labeled social features (including social interaction, mentalization, the actions of others, characters talking about themselves, talking about others and talking about objects) were represented in the superior temporal gyrus (STG) and middle temporal gyrus (MTG). The mentalization feature was also represented throughout the theory of mind network, and characters talking about others engaged the temporoparietal junction (TPJ), suggesting that listeners may spontaneously infer the mental state of those being talked about. In contrast, we did not observe the action representations in the frontoparietal regions of the action observation network. The current findings indicate that STG and MTG serve as key regions for social processing, and that listening to characters talk about others elicits spontaneous mental state inference in TPJ during natural movie viewing.

The Troll Observing Network (TONe): plugging observation holes in Dronning Maud Land, Antarctica

Antarctica and the Southern Ocean play vital roles in the Earth system, exerting significant influence on global atmospheric, cryospheric, and oceanic processes. Understanding the ongoing changes in Antarctica and their broader impact on global dynamics is imperative. Achieving this understanding necessitates dedicated and coordinated observations of environmental parameters in the region. The Troll Observing Network (TONe) is Norway's proactive initiative to the call for sustained, coordinated, complementary, geographically spread, and societally relevant long-term observations from Antarctica. Centered around the Troll Research Station in Dronning Maud Land, a region characterized by limited data availability, TONe constitutes a comprehensive state-of-the-art observatory network, specifically designed for environmental observations for studying and monitoring of the atmosphere, terrestrial, and marine environments. TONe aims to facilitate broad and open access to observational data and shared services to the international research communities as a foundation for acquiring knowledge of societal significance. TONe will, however, be a limited contribution the broader international data gathering effort required to underpin a strengthened understanding of Antarctica in the Earth system. TONE is an important initiative focused on a data sparse region of Antarctica, but has only substantial added value when seen in the context of similar ongoing and future efforts around the continent.