Ground‐based stations are one of many observing systems contributing to the generation of data to evaluate climate trends and variations locally and globally. Networks of stations are made of various numbers of observing sites, equipped with different typologies and varieties of instruments, differently managed and maintained. Among such networks a limited number of stations are required as a reference, to provide top quality traceable measurements and as the top level of a tiered approach; it is these stations which are here designated as Climatological Reference Stations (CRS). At present, there is no agreed definition of the key instrumental and technical features of a CRS, nor are there defined reference measurement procedures. This leads to the situation of a multitude of approaches among different National Meteorological and Hydrological Services (NMHSs), research institutes and other agencies that reduces the comparability of results in space and in time. The lack of CRSs, moreover, is a major contributor to the huge efforts required to harmonize data and detect biases locally, regionally and globally, as in their absence there is a need to fall back on other stations which are more likely to have such biases. This article reports on the outcomes of the work of a group of experts, nominated by the World Meteorological Organization (WMO) Commission for Climatology (CCI), tasked to promote a standardized and agreed concept about the definitions, specifications and technical characteristics of climate reference data and stations for ground‐based networks, with the addition of a practical example of climatological reference station installed in the framework of an European funded project, with the purpose of comparing and confirming the prescribed characteristics and guidance, with respect to practical field use and available instrumentation. The adoption of a unique definition and prescription on the technical setup, measurement procedures and uncertainty evaluation will substantially progress towards a common approach in detecting climate trends. This will improve data comparability in space and time and allow a more robust understanding of climate evolution locally and globally.

Reliable measurements of surface air temperature are crucial in meteorology and climatology. Thermometers must be shielded to avoid atmospheric influence factors, such as solar radiation and rainfall, compromise their accuracy. In the standard document ISO 17 714, which defines test methods for performance comparison of different screens, potential error sources caused by rainfall are mentioned. Besides the well-known psychrometric effect, an error on the temperature reading related to the difference between air and rain temperature causing heat transfer from water to the thermometer is identified. Despite the high relevance, no studies are available in literature that analyze this effect. The work described in this paper aims at quantifying the influence of the rain on the readings of temperature sensors, with radiation screens of different types. The study was performed by introducing an artificial rainfall generator where the water temperature and rain intensity was controlled and comparing the readings of thermometers under the generated rainfall with respect to a developed reference system not affected by rain. The tests show that the error for especially naturally ventilated thermometers can be unacceptably large, up to −4.0 (0.2) K, whereas for the artificially ventilated air thermometers the errors observed are less significant, smaller than −1.1 (0.2) K. This preliminary study underscores the critical influence of rain temperature and intensity on thermometer readings and highlights the importance of further research to develop standardized testing protocols. The reported methodology can also help to develop future standardized test protocol to qualify the device with regards to the measurement error.

To achieve the EU targets in terms of ecological transition, and to assure people health, wellbeing and comfort in the indoor environment, research in the building sector plays a fundamental role. In the last decades the energy efficiency and indoor environmental quality (IEQ) of buildings have increased significantly, but a gap between the actual operational performances of sensors and the expected measurement results and still exists. The Italian project “MIRABLE - Measurement Infrastructure for Research on heAlthy and zero energy Buildings in novel Living lab Ecosystems”, carried out in cooperation between the Italian National Metrology Institute, INRiM, and the Polytechnic University of Torino, aims to develop a methodology to reduce this gap by creating and validating a measurement infrastructure for monitoring the multi-domain indoor environmental conditions and the occupants’ interaction in a full scale Living Laboratory (denominated “H-IEQ LL”). The final goal of the research is to support the realisation of healthy and smart buildings and to reduce energy use. Among the activities of the MIRABLE project, the development of methodologies for defining a ground-truth for low-grade and wearable sensors for IEQ measurements in ongoing. After a thorough analysis of the sensors available on the market, and of the best practices to monitor the occupants’ comfort and interaction with buildings, a set of sensors were selected and metrologically characterised for the four domains on interest for the H-IEQ LL: thermal conditions, lighting conditions (spectral distribution included), acoustic conditions and indoor air quality. The monitoring of the response of the occupants to these variables, by means of low-cost sensors mounted on fixed monitoring stations in the LL and of wearable sensors, will allow to evaluate their feedbacks and interaction with the building systems in a dynamic mode. An overview of the results obtained from the metrological characterisation of IEQ sensors carried out at INRiM, and the preliminary calibration for some of the measured variables (e.g. temperature, sound pressure, CO₂ level, lighting), are presented in this work. The sensors will be installed in the LL, to create a sensor network for multi-domain monitoring of the physical environment and some wearable sensors will be used to monitor the occupant related quantities in a well-controlled environment. A methodology for the sensors in situ use, data collection and periodic calibration is also under definition.

A robust evaluation of predictive uncertainty is essential for deploying machine learning models in high-risk sectors. While various techniques such as Gaussian Processes and Bayesian Neural Networks have been developed to address model uncertainty, the measurement uncertainty associated with input data, particularly regarding heteroscedasticity and autocorrelation, is often overlooked. This work introduces the Generalized Least Squares Support Vector Machines (GLS-SVM), a kernel-based regression model designed to integrate the full variance-covariance matrix of the response variable into the training process. A GUM-consistent methodology is then developed for evaluating prediction uncertainty including a correction for model bias. The performance of the proposed model is validated using two case studies: a simulated regression problem with heteroscedastic, autocorrelated noise and the calibration of a mass flow controller. Results demonstrate that GLS-SVM significantly outperforms other kernel-based models when dealing with correlated data, providing accurate estimations and physically consistent standard uncertainties. This approach offers a versatile, metrologically-informed framework for data-driven regression tasks where measurement covariance information is available and rigorous uncertainty quantification is required.

The Eddy Covariance (EC) technique is widely used to quantify carbon dioxide (CO2) fluxes between the atmosphere and terrestrial ecosystems, playing a crucial role in climate research and carbon cycle studies. To maximize the impact and the meaningfulness of these measurements, they have to be comparable in time and space. The reliability and comparability of EC data critically depend on ensuring metrological traceability to SI units through national standards or internationally agreed references by means of rigorous calibration practices.This study examines the traceability chain for key EC components (air temperature and pressure, wind components and CO2 concentration in air), emphasizing calibration processes for gas analyzers. Gas analyzers, which measure CO2 amount fractions, are calibrated using traceable gas mixtures, such as Certified Reference Materials, linked to primary national standards, ensuring accuracy and minimizing biases. We assess the impact of the calibration uncertainty on overall flux estimates and propose a methodology for periodic recalibration of the analysers to account for their drift and response to environmental influences.By establishing robust links to national metrology standards, this work enhances the traceability and reliability of EC data across diverse ecosystems and temporal scales. The outcomes provide a foundation for harmonizing EC networks globally, improving confidence in CO2 flux measurements and their role in shaping evidence-based climate policies. This focus on calibration underscores the importance of metrology in advancing the precision and usefulness of environmental measurements.

The growing interest in tuning the conduction properties of single-molecule junctions has drawn attention to studying their interaction with incident electromagnetic fields. The theoretical complexity of this problem necessitates the use of nonequilibrium statistical mechanics combined with quantum electrodynamics, leading to extremely time-consuming simulations. In this work, we propose a computationally efficient algorithm, which combines EE-BESD—an efficient and effective simulator of current–voltage characteristics in dark conditions—with approximated models for light interaction, specifically the Tien-Gordon and Floquet models. We validate EE-BESD-PAT through comparison with ab initio calculations and experimental data from the literature. Our computational model demonstrates good agreement with both experimental and density functional theory calculations, demonstrating that the proposed method is a promising computationally efficient tool without sacrificing accuracy.