Experimental evaluation of the effect of presence of obstacles in the vicinity of sites hosting near surface meteorological measurement. The case of the road.

The accuracy of near surface measurements of meteorological variables is influenced by the environmental characteristics of the site where the instruments are placed. WMO guide #8 establishes a qualitative/quantitative classification, by itemizing different site conditions.In the framework of the MeteoMet2 project, to deliver a validated analysis aiming at possibly improving the WMO siting classification, a one-year lasting experiment has been devised for evaluating the effect of obstacles on near surface air temperature measurements. The experiment consists in a 100 m long array of identical thermometers equipped with aspirated solar shields, placed on a flat grass field at increasing distances from an obstacle, such that the farthest station fulfils current WMO requirements for a Class 1 site.Thermometers are Pt100 calibrated against reference standards and other quantities of influence are also measured: humidity, solar radiation, wind speed and direction. Three identical experimental setups have been designed, built and characterized to separately identify the effect of three different kind of obstacles: asphalt roads (Italy), trees (Czech Republic) and buildings (Spain).The work here presented focuses only on the road siting experiment.A statistical analysis based on Generalized Additive Model (GAM) was performed to understand the effect of each quantity of influence on the temperature measurements. The model was instrumental in understanding the best combination of environmental factor that would boost the effect. The largest temperature biases (extremes) have been then modelled through Extreme Values Analysis (EVA), which allowed for an evaluation of the asymptotic behaviour of these biases, and an estimation of theroad siting effect.Results show that the roads influence temperature readings more intensely during nights and when wind is absent. The magnitude of the effect has been evaluated at 1.7±0.4 °C for a return period of 100 year

Rock temperature variability in high-altitude rockfall-prone areas

In a context of cryosphere degradation caused by climate warming, rock temperature is one of the main driving factors of rockfalls that occur on high-elevation mountain slopes. In order to improve the knowledge of this critical relationship, it is necessary to increase measurement capability of rock temperature and its variability in different lithological and slope/aspect conditions, and also to increase local scale studies, increasing the quality and the comparability of the data. This paper shows an example of metrological characterization of sensors used for rock temperature measurement in mountain regions, by means of the measurement uncertainty. Under such approach, data and results from temperature measurements carried out in the Bessanese high-elevation experimental site (Western European Alps) are illustrated. The procedures for the calibration and field characterization of sensors allow to measure temperature in different locations, depths and lithotypes, within 0.10 °C of overall uncertainty. This work has highlighted that metrological traceability is fundamental to asses data quality and establish comparability among different measurements; that there are strong differences between air temperature and near-surface rock temperature; and that there are significant differences of rock temperature acquired in different aspect conditions. Finally, solar radiation, slope/aspect conditions and lithotype, seem to be the main driving factors of rock temperature

Intercomparison of Vaisala RS92 and RS41 Radiosonde Temperature Sensors under Controlled Laboratory Conditions

Radiosoundings are essential for weather and climate applications, as well as for calibration and validation of remote sensing observations. Vaisala RS92 radiosondes have been widely used on a global scale until 2016; although in the fall of 2013, Vaisala introduced the RS41 model to progressively replace the RS92. To ensure the highest quality and homogeneity of measurements following the transition from RS92 to RS41, intercomparisons of the two radiosonde models are needed. A methodology was introduced to simultaneously test and compare the two radiosonde models inside climatic chambers, in terms of noise, calibration accuracy, and bias in temperature measurements. A pair of RS41 and RS92 radiosondes has been tested at ambient pressure under very different temperature and humidity conditions, reproducing the atmospheric conditions that a radiosonde can meet at the ground before launch. The radiosondes have also been tested before and after fast (within ≈ 10 s) temperature changes of about ±20 °C, simulating a scenario similar to steep thermal changes that radiosondes can meet when passing from indoor to outdoor environment during the pre-launch phase. The results show that the temperature sensor of RS41 is less affected by noise and more accurate than that of RS92, with noise values less than 0.06 °C for RS41 and less than 0.1 °C for RS92. The deviation from the reference value, referred to as calibration error, is within ±0.1 °C for RS41 and the related uncertainty (hereafter with coverage factor k = 1) is less than 0.06 °C, while RS92 is affected by a cold bias in the calibration, which ranges from 0.1 °C up to a few tenths of a degree, with a calibration uncertainty less than 0.1 °C. The temperature bias between RS41 and RS92 is within ±0.1 °C, while its uncertainty is less than 0.1 °C. The fast and steep thermal changes that radiosondes can meet during the pre-launch phase might lead to a noise increase in temperature sensors during radiosoundings, up to 0.1 °C for RS41 and up to 0.3 °C for RS92, with a similar increase in their calibration uncertainty, as well as an increase in the uncertainty of their bias up to 0.3 °C