Observation Network Research Articles

Congeneric Rodents Differ in Immune Gene Expression: Implications for Host Competence for Tick-Borne Pathogens.

Mice in the genus Peromyscus are abundant and geographically widespread in North America, serving as reservoirs for zoonotic pathogens, including Borrelia burgdorferi (B. burgdorferi), the causative agent of Lyme disease, transmitted by Ixodes scapularis ticks. While the white-footed mouse (Peromyscus leucopus (P. leucopus)) is the primary reservoir in the United States, the deer mouse (P. maniculatus), an ecologically similar congener, rarely transmits the pathogen to biting ticks. Understanding the factors that allow these similar species to serve as a poor and competent reservoir is critical for understanding tick-borne disease ecology and epidemiology, especially as climate change expands the habitats where ticks can transmit pathogens. Our study investigated immunological differences between these rodent species. Specifically, we compared the expression of six immune genes (i.e., TLR-2, IFN-γ, IL-6, IL-10, GATA-3, TGF-β) broadly involved in bacterial recognition, elimination, and/or pathology mitigation in ear biopsies collected by the National Ecological Observatory Network (NEON) as part of their routine surveillance. A principal components analysis indicated that immune gene expression in both species varied in two dimensions: TLR2, IFN-γ, IL-6, and IL-10 (comprising PC1) and TGF-β and GATA3 (comprising PC2) expression tended to covary within individuals. However, when we analyzed expression differences of each gene singly between species, P. maniculatus expressed more TLR2, IL-6, and IL-10 but less IFN-γ and GATA3 than P. leucopus. This immune profile could partly explain why P. leucopus is a better reservoir for bacterial pathogens such as B. burgdorferi.

Storm Peak Laboratory: A Research and Training Facility for the Atmospheric Sciences

Abstract Storm Peak Laboratory, located on the Steamboat Springs Ski Resort in Colorado on the west summit of Mount Werner at 10,532 ft (3220 m) ASL, is an internationally recognized high-elevation atmospheric research station that has been in use for over 40 years. This article provides a brief history of the Storm Peak Laboratory and the major research themes it has supported and discusses opportunities to leverage mountain observatory measurements to advance our understanding of the atmospheric processes. This facility provides long-term measurements of meteorology, clouds, aerosols, snow hydrology and atmospheric gases, and serves as a “proving ground” for instrument development and testing. Storm Peak Laboratory is part of multiple national and international observational networks. Due to the unique capabilities of Storm Peak Laboratory, there is a long history of targeted field campaigns primarily within the following research areas: mixed phase cloud microphysics, atmospheric chemistry pertaining to the formation, characterization, and hygroscopicity of aerosols; and transport and transformation of atmospheric mercury. Research training has been central to the mission of SPL over the last 40 years. Currently, SPL hosts both undergraduate and graduate level courses in atmospheric science and snow hydrology organized by numerous institutions. Examples of these unique research training opportunities are provided.

Plant diversity across dimensions: Coupling biodiversity measures from the ground and the sky.

Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.

Evaluation of multi-source precipitation products for monitoring drought across China

Accurate precipitation data are crucial for effective drought monitoring, especially in China’s complex and diverse climatic regions. This study evaluates the performance of six multisource precipitation products-ERA5-Land, CMORPH CRT, GSMaP MVK, IMERG Late, and IMERG Final-in detecting drought across China from 2009 to 2019, using ground station observations for validation. By applying various drought and evaluation indices across various timescales, this analysis captures short and long-term climate variations, assessing each product’s accuracy across diverse regions. Spatial and temporal analyses revealed that IMERG Final closely aligns with observed precipitation, particularly in the high-rainfall areas like the Yangtze River Basin, while GSMaP MVK and ERA5 tend to overestimate precipitation in arid and semi-arid regions. Discrepancies are most pronounced in complex terrains such as the Qinghai-Tibet Plateau and southwestern mountains, where sparse observational networks exacerbate errors. Drought indices, including SPEI-3 and SPI-1, were used to measure each product’s effectiveness in detecting drought intensity, frequency, and duration. IMERG Final consistently showed the highest correlation with ground data across all drought levels (Light, Moderate, and Severe), while GSMaP MVK and ERA5 tended to overestimate drought occurrences in certain drought-prone areas. Hotspot analyses of indices such as CDD, PRCPTOT, and R95p further confirmed IMERG Final’s accuracy in identifying drought and wet event patterns, closely reflecting ground measurements, whereas ERA5 and GSMaP MVK occasionally overestimated drought frequencies. In summary, IMERG Final emerged as a relatively accurate and reliable product for drought monitoring, showing strong applicability across China’s diverse climatic regions. These findings aid in data correction, enhances understanding of regional drought variability, and integration strategies to improve water resource management and extreme event monitoring.

Towards a high-quality in situ observation network for oxygenated volatile organic compounds (OVOCs) in Europe: transferring metrological traceability to the field

Abstract. Volatile organic compounds (VOCs) have a large impact on the oxidising capacity of the troposphere and are major precursors of tropospheric ozone and secondary atmospheric aerosols. Accurate measurements and data comparability of VOCs among monitoring networks are essential to assessing the trends of these secondary air pollutants. Metrological traceability of the measurements to the International System of Units (SI traceability) contributes to both measurement consistency and data comparability. Accurate, stable and SI-traceable reference gas mixtures (RGMs) and working standards are needed to achieve SI traceability through an unbroken chain of calibrations of the analytical instruments used to monitor VOCs. However, for many oxygenated VOCs (OVOCs), such RGMs and working standards are not available at an atmospheric amount of substance fraction levels (< 10 nmol mol−1). Here, we present the protocols developed to transfer SI traceability to the field by producing two types of SI-traceable working standards for selected OVOCs. These working standards, based on RGMs diluted dynamically with dry nitrogen and on certified spiked whole-air samples, were then assessed using a thermal desorber–gas chromatograph–flame ionisation detector (TD–GC–FID) and proton transfer reaction–time of flight–mass spectrometer (PTR–ToF–MS) as analytical methods. For that purpose, we calibrated five analytical instruments using in-house calibration standards and treated the new SI-traceable working standards as samples. Due to analytical limitations, the assessment was only possible for acetaldehyde, acetone, methanol and methyl ethyl ketone (MEK). Relative differences between assigned and measured values were used to assess the working standards based on the dilution of RGMs. The relative differences were within the measurement uncertainty for acetone, MEK, methanol and acetaldehyde at an amount of substance fractions around 10 nmol mol−1. For the working standards based on certified spiked whole-air samples in pressurised cylinders, results showed a good agreement among the laboratories (i.e. differences within the measurement expanded uncertainty (U) ranging between 0.5 and 3.3 nmol mol−1) and with the certified amount of substance fraction for acetaldehyde (15.7 nmol mol−1 ± 3.6 (U) nmol mol−1), acetone (17 nmol mol−1 ± 1.5 (U) nmol mol−1) and MEK (12.3 nmol mol−1 ± 2.3 (U) nmol mol−1). Despite the promising results for the working standards based on the dilution of RGMs and on certified spiked whole-air samples filled into pressurised cylinders, the assessment must be considered with care due to the large measurement uncertainty, particularly for methanol. Active collaboration among the metrological, meteorological and atmospheric chemistry monitoring communities is needed to tackle the challenges of OVOC monitoring, such as the lack of stable and SI-traceable calibration standards (i.e. RGMs and working standards). Besides this collaboration, other research applications, such as modelling and remote sensing, may benefit from the transfer of SI traceability to monitoring stations.

Instruction-induced modulation of the visual stream during gesture observation.

Although gesture observation tasks are believed to invariably activate the action-observation network (AON), we investigated whether the activation of different cognitive mechanisms when processing identical stimuli with different explicit instructions modulates AON activations. Accordingly, 24 healthy right-handed individuals observed gestures and they processed both the actor's moved hand (hand laterality judgment task, HT) and the meaning of the actor's gesture (meaning task, MT). The main brain-level result was that the HT (vs MT) differentially activated the left and right precuneus, the left inferior parietal lobe, the left and right superior parietal lobe, the middle frontal gyri bilaterally and the left precentral gyrus. MT (vs HT) differentially activated the left and right calcarine cortex, the fusiform gyrus bilaterally, the left inferior temporal gyrus, the left and right hippocampus and parahippocampal gyri, and the temporal pole bilaterally. Processing the actor's moving hand modulates the dorsal action observation network (while processing gesture meaning modulates the ventral object recognition stream). The present results suggest instruction-induced modulation on the visual stream during gesture observation.

Analysis of co-seismic ionospheric disturbance of the Qinghai Mw 7.4 earthquake on May 21, 2021

ABSTRACT In this study, the total electron content (TEC) was extracted from Global Navigation Satellite System (GNSS) data provided by the Crustal Movement Observation Network of China (CMONOC). The ionospheric disturbances related to the Qinghai earthquake on 21 May 2021 were analysed. Significant coseismic ionospheric disturbances (CIDs) were observed near the earthquake epicentre. To further investigate the propagation characteristics of ionospheric anomalies associated with this earthquake, the epicentral area was divided into eight regions, each corresponding to a 45° sector, to analyse the characteristics of ionospheric disturbances. Firstly, based on the spatial and temporal characteristics of the slant total electron content, different degrees of ionospheric disturbances were identified at various time intervals after the earthquake. Secondly, the earthquake generated two types of coseismic ionospheric disturbances at the epicentre. The first type, with velocities of approximately 1.4 km s− 1, 1.8 km s− 1, and 1.0 km s− 1, were triggered by acoustic waves generated by the ground surface rupture induced by the earthquake. The second type, with velocities of about 0.8 km s− 1, represented another form of ionospheric disturbance induced by acoustic waves from the ground surface rupture. Thirdly, the ionospheric anomaly was most pronounced within the 90°–135° sector relative to the epicentre, corresponding to the southeast direction from the epicentre. Finally, based on the data from 15 GNSS stations at the epicentre, the coseismic deformation displacement of the earthquake was determined, with a maximum displacement of 0.24 m, and the leptokurtic slip characteristics of the earthquake were revealed.

Wind–Wave Misalignment in Irish Waters and Its Impact on Floating Offshore Wind Turbines

This study examined the impact of wind–wave misalignment on floating offshore wind turbines (FOWTs) in Irish waters, analysing average weather and extreme events, including hurricane conditions. Using the ERA5 reanalysis dataset validated against Irish Marine Data Buoy Observation Network measurements, the results showed a satisfactory accuracy with an average wind speed error of −0.54 m/s and a strong correlation coefficient of 0.92. Wind–wave misalignment was found to be inversely correlated with wind speed (correlation coefficient: −0.41), with minimum misalignment occurring approximately seven hours after a change in wind direction. The study revealed that misalignment could exceed 30∘ during hurricanes, contradicting standard assumptions of alignment under extreme conditions. The investigation highlighted that in western coastal areas, average misalignment could reach 57.95∘, while sheltered Irish Sea regions experienced lower values, such as 23.06∘. Numerical simulations confirmed that these misalignment events amplified side-to-side turbine deflections significantly. This research underscores the need to incorporate misalignment effects into industry testing standards and suggests that current methodologies may underestimate fatigue loads by up to 50%. This work emphasizes improved design and testing protocols for FOWTs in complex marine environments and highlights the suitability of ERA5 for climate analysis in Ireland.

Enhanced sensor web services by incorporating IoT interface protocols and spatio-temporal data streams for edge computing-based sensing

ABSTRACT The Geospatial Sensor Web (GSW) integrates heterogeneous aerial and ground sensors via cloud-edge linkages and GIS-based approaches, forming a multi-dimensional observation network. However, existing systems struggle to support edge-side collaborative observation due to fragmented physical standards, incompatible protocols, and limited self-configuration. This study proposes an enhanced Sensor Web, integrating IoT protocols and spatio-temporal models for unified access, collaborative management, and dynamic planning. Validated through the City Sensing Base Station (CSBS), a pilot experiment demonstrated the framework integrates diverse sensing resources across over eight protocols, achieving autonomous alignment of more than five platforms with rapid aerial-ground network formation during emergencies. It also validated autonomous collaboration and coordination of aerial-ground resources, enabling dynamic task allocation and execution across heterogeneous systems. Compared with cloud-based architectures, this approach significantly improves resource accessibility and real-time processing. By extending SensorML and Sensor Observation Service (SOS), the framework bridges the gap between conventional Sensor Webs and edge computing demands. Results confirm its effectiveness in coordinating heterogeneous resources and managing dynamic spatio-temporal data. These findings show how Internet of Things (IoT) protocols advance earth observation, modeling and improve GSW efficiency.

Development and Application of a Cost–Effective Analytical Method for Hydrofluorocarbons Using Preconcentrator–Gas Chromatograph–Mass Spectrometer

The expansion of atmospheric observation networks for hydrofluorocarbons (HFCs), which are closely related to climate change and contribute to global warming, significantly impacts our society and daily life. Their emissions are estimated to increase in the future, which is a major challenge. Observations of HFCs globally were performed using expensive GHG–specific equipment installed at AGAGE and other sites, but many research institutions find it difficult to install such equipment. Therefore, we successfully developed a measurement method for six components of HFCs (HFC–23, HFC–32, HFC–125, HFC–134a, HFC–143a, and HFC–152a) with high atmospheric concentrations in various parts of the world by optimizing measurement parameters such as the sample transfer volume and rate, module cooling temperatures, the injection time, the GC oven temperature program, and the monitored ions of a commercially available preconcentrator–GC–MS. Because this developed measurement method is cost–effective and simpler to operate than those of GHG–specific equipment, it is expected to provide an opportunity for many research institutes to measure HFCs. Furthermore, in addition to HFCs, we confirmed that simultaneous measurements can be performed for 97 volatile organic compounds (VOCs), including hazardous components. This research can contribute to the observation of HFCs in countries and regions where the actual status of emissions is unclear or where no or few atmospheric observations were conducted. The results of those observations can be used to formulate more detailed global warming countermeasures.

Influenza Immunization in Very-Low-Birth-Weight Infants: Epidemiology and Long-Term Outcomes.

Very-low-birth-weight infants (VLBWIs; birth weight < 1500 g) are at an increased risk of complicated influenza infection, which frequently includes pneumonia, encephalitis or even death. Data on influenza immunization and its outcome in VLBWIs are scarce. This study aimed to provide epidemiological data on influenza immunization for German VLBWIs and hypothesized that immunization would protect VLBWIs from infection-mediated neurodevelopmental impairment and preserves lung function at early school age. In this observational population-based German Neonatal Network (GNN) study, infants born between 2009 and 2015 were invited to partake in a 6-year follow-up investigation including lung function and developmental testing. Uni- and multivariate analyses were performed to evaluate the clinical characteristics and outcomes of influenza-immunized VLBWIs compared to non-immunized VLBWIs. Influenza immunization was performed in 871 out of the 3358 VLBWIs (26%) with six-year follow-up. Immunized infants were characterized by a low gestational age and higher rates of morbidity, particularly bronchopulmonary dysplasia. Although early immunization showed no safety signals and had protective effects on the long-term risk of bronchitis (OR: 0.2; CI: 0.1-0.6; p = 0.002), most VLBWIs (88.0%) were unimmunized in their first influenza season. Influenza immunization was not associated with improved lung function (forced expiratory volume in one second and forced vital capacity) or a better neurocognitive outcome (intelligence quotient and strengths and difficulties questionnaire) at early school age. In Germany, only one quarter of 6-year-old VLBWIs were immunized against influenza, particularly those born <28 gestational weeks and/or BPD. Specific influenza immunization guidelines that define evidence-based recommendations are needed for this vulnerable group.

Distributed observers for linear systems with communication noises over Markovian switching topologies

This paper studies the distributed state estimation problem for a linear system that is jointly observable with the consideration of communication noises and Markovian switching topologies. To address this issue, a network of observers is proposed, where each observer only measures partial system output, which may not be sufficient to observe the complete state of the system. Subsequently, all observers within the network collaborate by sharing their estimated states with their neighbours through the network, facilitating a cooperative reconstruction of the entire state at each local observer. To better reflect the communication environment, we consider a situation in which communication links may fail and rebuild over time, and communication noises may occur when observers exchange information through Markovian switching topologies. Parameters of observers are designed properly, under which each observer fulfills the estimation task in the mean square sense. Illustrative examples to demonstrate the effectiveness of the distributed observers are also provided.

Improving Indonesia's Tsunami Early Warning: Part I: Developing Synthetic Tsunami Scenarios and Initial Deployment

Indonesia's Java subduction zone has triggered devastating tsunamis, emphasizing the need for a robust Tsunami Early Warning System, specifically for Southern Java, Bali, and Nusa Tenggara. With only six Ocean Bottom Pressure Gauges (OBPGs) currently monitoring tsunami propagation in the deep sea, optimized future sensor deployment is crucial. This paper, the first in a two-part series, proposes new observation networks to enhance tsunami early warning system. Our methodology involves developing synthetic stochastic-slip earthquake-induced tsunami simulations, delineating tsunami lead times, and applying empirical orthogonal functions (EOF) to determine spatial modal energy. We also assess the reliability of spacing and bathymetry for potential sensor locations. Our analysis reveals potential locations for additional OBPGs across the area. The proposed network consists of 42 additional sensors, demonstrating the potential for earlier warnings. These findings lay the groundwork for the second part of our series, where we will develop advanced forecasting models incorporating deep learning techniques based on the proposed location and further optimize sensor locations with the novel approach of hybrid optimizer and deep learning model. By establishing an improved observation network, this study contributes to more effective tsunami early warning systems in Indonesia, potentially mitigating the impact of future events on coastal communities.

Leader-Following Consensus of Linear Multiagent Systems With Aperiodically Sampled Outputs: A Distributed Impulsive-Observer-Based Approach.

This article studies the leader-following consensus problem for a class of linear multiagent systems over a directed graph with aperiodically sampled outputs. First, a novel distributed impulsive-observer-based consensus protocol is designed. This protocol requires only the output measurements at sporadic time instants for the observer and control gain design. Second, by using time-varying Lyapunov function techniques, sufficient conditions for exponential stability of a class of linear impulsive systems are established; subsequently, these stability results are applied for the distributed impulsive observer design. Different from the existing related works, the impulsive observer gain designed in this work is decoupled from the graph properties. As a result, once the impulsive observer gain is designed for one network topology, it can be directly used for other network topologies, as long as the graph properties and the dynamics of local agents satisfy certain conditions. Furthermore, the resilience of the designed protocol is tested under denial of service (DoS) attacks. It is shown that the protocol is robust with respect to low-frequency DoS attacks occurring in the observer communication network. Finally, two examples illustrating the validity and effectiveness of the proposed protocol are included.

Recommendations for developing, documenting, and distributing data products derived from NEON data

AbstractThe National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

A novel framework for leveraging geological environment big data to assess Sustainable Development Goals

&lt;p&gt;Geological environmental big data (GEBD) offers a significant opportunity for the comprehensive monitoring of the Earth’s shallow structures, dynamic processes, and their interactions among surface spheres, promisingly contributing to the precise assessment of sustainable development goals (SDGs). To systematically harness the potential of GEBD in SDG assessment, an innovative framework has been developed to address current challenges through a four-dimensional observation network, a strategy of integrating GEBD into SDG indicators, and a knowledge-driven association-mining method. This framework potentially provides a series of positive and profound impacts, ranging from technology development and environmental conservation to enhanced public awareness.&lt;/p&gt;

The Marine Biodiversity Observation Network Pole to Pole of the Americas: Building a Community of Practice for Detecting Rapid Changes in Coastal Biodiversity

The Marine Biodiversity Observation Network (MBON) is a global community of practice and network that links individuals and groups in an effort to monitor and understand changes in marine biodiversity. MBON functions within the larger framework of the Group on Earth Observations Biodiversity Observation Networks (GEO BON). These networks support mobilization of data to help nations to achieve the Sustainable Development Goals (SDGs) adopted by the United Nations (UN) in 2015 and to address their own internal, local management needs. Marine biodiversity data are important for allowing countries and local communities to monitor changes that result from local human pressures and climate change. Such data enable informed planning and management of coastal areas and resources.

The 20‐Year Highest Tropical Cyclone‐Generated Waves Associated With the Maximum Energy of Seismic Noises

AbstractThe extreme winds of tropical cyclones generate high waves over the ocean, causing severe damage to offshore facilities and coastal communities. Disaster mitigation requires accurate prediction and forecasting of the highest potential waves. However, the physics of wave development is not fully understood, and the number of mid‐ocean observations during extreme tropical cyclones is extremely insufficient. Therefore, physically and statistically evaluating the highest potential waves is difficult. However, ocean waves excite seismic noise (microseisms). Although source sites of microseisms under specific tropical cyclones have been identified by case studies, the extreme ocean wave magnitude under tropical cyclones have not been systematically analyzed using long‐term historical records. Here, we, for the first time, utilize long‐term microseisms observed by seismic observation networks to associate the historical maximum microseisms events with the highest tropical cyclone‐generated wave events around Japan. We show that Typhoon Wipha in 2013, Typhoon Lan in 2017, and Typhoon Hagibis in 2019 were the maximum microseisms events in the past 20 years based on microseism energy within an 8–10 s period. We associate the events with the highest wave heights and the largest wave‐induced sea surface pressures generated by tropical cyclones. Although ocean wave models have a large uncertainty of tropical cyclone‐generated extreme waves, microseisms observations can endorse the results of an ocean wave model even if lack of direct ocean wave observation under tropical cyclones. Furthermore, rich information of microseisms on wave development and propagation has a potential to proceed understanding of the extreme wave physics.

Control the Uniformity of Hydrometric Data

Objective: The purpose of the study is to study the influence of series of hydrometric observations on the transfer of hydrometric sources along the river in order to convert the results and analyze them. since individual long-term series cannot unambiguously characterize the impact of climate change and economic activity on river flow. Theoretical Framework: The prerequisite for the study is the presence of a logical theory of the formation of water flow along the length of the river in various physical and geographical conditions, as well as the presence of a relationship between charge of water in the river and water levels in it. Method: A graph-analytical research method was used, which makes it possible to clearly determine the deviations of measurement data over time from the main trend. The analysis was carried out based on the materials of long-term observations of river flow on the state hydrometeorological observation network of Ukraine. Results and Discussion: The studies carried out confirm all the theoretical justifications for the theory of water flow in river beds and show a reliable way to check for the presence of distortion in runoff calculations when moving observation sites. Using the proposed method, it is possible to easily determine whether the transfer of gauges is the cause of flow distortion or economic activity, etc. Research Implications: The research results indicate the need to clarify periods with homogeneous data and determine the rate of water flow from them for use in design and forecasting. A thorough analysis of the morphometry and regime of the river will allow us to determine the optimal location of the hydrological post. Originality/Value: Despite the wide popularity of the graphic-analytical method for studying the homogeneity of observation series, it has not been used in the proposed form. The importance of the development is that the proposed method allows you to quickly determine the interruption time of a series of observations and make appropriate adjustments to the water cadastre materials.

Regional mapping of climate variables in Galicia from point samples

Galicia's (NW Spain) diverse climate, driven by various factors, exhibits significant spatial and temporal variability, directly affecting the region's economy and society. Taking this into account, it is of great importance to have climate observation networks that cover most of the territory, and in the case of not having them, to have tools that allow the estimation of climate parameters. The main tools used for the estimation of parameters in unmeasured locations are climate database and the interpolation of data from climate stations, with kriging being one of the most widely used interpolation methods. The evaluation of climatic database is very important to establish how well they estimate the parameters and to ensure that they result in a homogeneous data series. For this purpose, in the present study a comparison was made of point data from the MeteoGalicia weather station network with data extracted from the SIMPA, AEMET and ERA5 databases and with data resulting from interpolation using regression kriging methods. The climatic parameters for which data were used for the study are: mean temperature, precipitation and reference potential evapotranspiration (Eto). To extract the data from SIMPA, ERA5 and AEMET and to interpolate point data from MeteoGalicia, we worked with the statistical programming environment R. After extracting the data or interpolating them, they were compared with the data measured in the MeteoGalicia network of climatological stations using the Nash-Sutcliffe index. The results of this study seek to conclude which method is more effective for estimating each of the parameters studied. In general, the predictions based on interpolation (AEMET and regression kriging) presented the best goodness of fit, while SIMPA also presented good results for some parameters. In general, the parameters with the best estimation results were mean temperature, followed by precipitation and finally potential evapotranspiration.