Francesca Romana Pennecchi

Graziano Coppa

Manuele Soncini

English

Archivio istituzionale della ricerca - INRIM

12 August 2023ISTITUTO NAZIONALE DI RICERCA METROLOGICARepository IstituzionaleStatistical analysis and clusterization of environmental parameters measurements from co-locatedsensors / Coppa, Graziano; Pennecchi, FRANCESCA ROMANA; Soncini, Manuele. - (2022). (Interventopresentato al convegno TECO-2022 tenutosi a Parigi, Francia nel 10-13 ottobre 2022).OriginalStatistical analysis and clusterization of environmental parameters measurements from co-located sensorsPublisher:PublishedDOI:Terms of use:Publisher copyright(Article begins on next page)This article is made available under terms and conditions as specified in the corresponding bibliographicdescription in the repositoryAvailability:This version is available at: 11696/75099 since: 2022-12-21T12:38:15ZStatistical analysis and clusterization of environmental parameters measurements from co-located sensors Abstract One of the most difficult things in metrology for the environment is the evaluation of measurement uncertainties. Air temperature sensors areinfluenced by many external factors, such as solar radiation – both direct and diffuse – humidity, wind speed and direction. Laboratory calibration isnecessary but not sufficient for a comprehensive understanding of the behaviour of air temperature sensors while subject to the wide variety ofenvironmental conditions they meet in normal operation.This work follows up the one by Coppa et al., (2021), where the evaluation of uncertainties of commercially available air temperature sensors and shieldswas computed by a dedicated experiment, where 7 sensors were co-located for 2 months. In that work, uncertainties were evaluated simply bycomputing standard deviations of temperature differences between each sensor and one taken as a reference.G. Coppa1, M. Soncini2, F.R. Pennecchi11 Istituto Nazionale di Ricerca Metrologica, Torino, IT 2 Università di Torino, Torino, ITMail to g.coppa@inrim.itCo-located sensors on the experimental field site.Sensor array during normal phase of the experiment.Distributions of temperature differences between each sensor and a reference during co-location of the measurement stations. Blue vertical dashed lines represent means.Coppa, Quarello, Steeneveld, Jandric and Merlone, 2021, IntJClim, 41: 3705– 3724    DOI: 10.1002/joc.7044The present work provides a more refined uncertainty evaluation by a statistical method, through machine learning and clusterization, in order toevaluate uncertainties in dependence on the environmental conditions.Temperature differences during the co-location experiment have been run through an unsupervised k-means algorithm that revealed several clusters: inparticular, temperature differences are lower, thus leading to smaller uncertainty, under two very different environmental conditions: the first, atnights with very low winds; the second during high-radiation and high-wind days.Distributions of meteorological variables in the 4 clusters identified by the k-means algorithm.Solar radiationRelative humidity Wind speedTemperature differencesRepresentation of 4 clusters in a simplified 3D space of the meteorological parameters.Numerosity of the meteorological observations in the 4 clusters identified by the k-means algorithm.Temperature differences can be corrected by subtracting the cluster centroid value from each observation, while uncertainties can be taken as thestandard deviations of each cluster distributions, then associated to each sensor+shield system depending on the specific environmental conditions(evaluated by ancillary measurements). This methodology reduces the overall uncertainty and improves the level of detail about sensors behaviourknowledge.ΔT (°C) Rad (W/m2) RH (%)Wind speed (m/s)Cluster 0 0.047 7 94.0 0.71Cluster 1 0.352 192 71.0 0.73Cluster 2 0.109 207 54.1 1.24Cluster 3 0.051 106 49.9 3.11The road siting effect evaluation experimentUnsupervised k-means clustering algorithm Cluster centroids of the 4 different parameters. 𝝈∆𝑻 (°C) VariationCluster 0 0.045 43%Cluster 1 0.120 115%Cluster 2 0.070 67%Cluster 3 0.055 53%Standard deviations of ΔT measurements and variation over previous evaluation. • A way to refine the evaluation of measurement uncertainties in air temperature, is presented.• A clustering algorithm to group together measurements taken in similar conditions is used• Centroids as bias corrections• Standard deviations of cluster as uncertainties• Uncertainties for the largest part of measurements are reduced• Extend to longer time periods (whole year)• Explore other clustering algorithms

Statistical analysis and clusterization of environmental parameters measurements from co-located sensors

https://iris.inrim.it/bitstream/11696/75099/1/Coppa_Cluster_v2.pdf

Statistical analysis and clusterization of environmental parameters measurements from co-located sensors

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