Solar radiation is one of the main factors which introduce significant deviations between thermometers reading and true air temperature value. Techniques to protect the sensors from direct radiative
influence have been adopted almost since the beginning of meteorological
observations. Reflected radiation from a snow-covered surface can also cause extra warming to thermometers hosted in solar shields, which are not always optimised to protect the sensors from this further radiative heat transfer. This phenomenon can cause errors in near-surface temperature measurements results, with a relevant impact on the quality of data records and series. This study experimentally evaluates the effect of reflected
radiation from a snow-covered surface on the accuracy of air temperature
measurements. The investigation is based on the evaluation of temperature
differences between pairs of identical instruments, positioned above ground
covered by natural vegetation, with one instrument in snow-free conditions and the other above a snow-covered surface, at the same time and at the same site. The work involved a representative number of sensors and shields, in terms of different typologies, technologies and engineering solutions, from different manufacturers. A mountain site with acceptable field conditions, offering long-lasting snow presence to maximise data availability, was selected to perform the experiment. Quantities of influence, such as relative humidity, wind speed and direction and solar radiation (global and reflected), were constantly measured. The main findings of this work show that none of the involved instruments were immune to the extra heating due to the snow-reflected radiation. Excluding nighttimes and days of high wind or low incident radiation, the differences among sensors positioned above natural soil and identical ones exposed to snow albedo ranged up to more than 3
The World Meteorological Organization (WMO), Commission for Climatology and
the Global Climate Observing System (GCOS) recommend the study and
definition of measurement methods for reference-grade networks and
installations to generate top quality data for meteorology and climate
studies (GCOS, 2019). A key requirement for a station taking part in a reference network is a documented traceability and the understanding of the total measurement uncertainty (Thorne et al., 2018). Consistent uncertainty calculations need complete knowledge of the measurement system, sensors' calibration uncertainty, characteristics of the site and effects of environmental parameters such as wind, solar radiation and precipitation. Among the numerous observed essential climate variables (ECVs), near-surface (1.25–2 m; WMO, 2012) atmospheric air temperature measurements have been collected for 150 years. Such data series form the basis of scientific knowledge on local and global climate trends (Camuffo and Jones, 2002). Land-based stations are equipped with different kinds of thermometers whose
performances have constantly improved. Today, top quality instruments
involve platinum resistance sensors and high-level reading and recording
electronics. Many efforts have also been made to minimise the effect of the
quantities of influence on measurement results, with the aim to reduce the
associated errors and measurement uncertainty. Solar radiation is one of the
main factors influencing the instruments, causing significant deviations
between sensors' readings and real air temperature. Techniques to protect
sensors have been adopted almost since the beginning of meteorological
observations. Shields to avoid direct solar radiation reaching the sensing
element have been developed, from Stevenson screens (Stevenson,
1864) and modern pagodas to naturally or mechanically ventilated solar
shields. Recent intercomparisons were organised by WMO (Lacombe et al., 2011) to evaluate the performances and differences among the numerous solutions adopted by manufacturers. While the practical/technical features offered by these shields are now optimised and prescribed (WMO, 2012), their capability to protect the thermometers from radiation reflected by the ground is rarely evaluated or taken into account in measurements or documented in data sheets. This is dependent on the so called albedo, indicated with
Only few studies in the literature evaluate the effect of the albedo of snow-covered land on temperature sensors; among them, the most significant work is from Huwald et al. (2009), which is based on a different approach and is limited to a single typology of sensor and screen.
The task of the present work is to observe, measure and quantify the effect of extra heating on different kinds of instruments positioned above snow-covered land in terms of deviations of the sensors' readings from actual temperature values. This work is the result of a seasonal in-field experiment, following a metrological protocol and experimental method defined and described in a previous study (Musacchio et al., 2019). The investigation is addressed at the evaluation of relative difference between the readings of pairs of identical sensors protected by solar shields as provided by manufacturers. One pair is positioned above a snow-covered surface, while the other is above grass-covered ground, at the same site and at the same time and under equal environmental and topoclimatic conditions.
The problem of albedo effect on air temperature instruments can be included as part of the general study on assessing data quality and uncertainty in near-surface air temperature measurements. This wider subject is now being analysed and discussed by the WMO expert teams of the Infrastructure Commission (INFCOM) and is a key aspect in the creation of the Climate Reference Networks for the Global Climate Observing System (GCOS). The complete knowledge and evaluation of uncertainty budget components on air temperature measurement is also included in the roadmaps of scientific activities of the working group for environment of the Comité Consultatif de Thermométrie (CCT; Consultative Committee for Thermometry) of the Bureau International des Poids et Mesures (BIPM; International Bureau of Weights and Measures; CCT, 2017).
The activities reported here have been carried out in the framework of the MeteoMet project (Merlone et al., 2015a, b, 2018), a funded joint research initiative of the European Metrology Research Project (EMRP), grouping a wide consortium of National Metrology Institutes (NMIs), research institutes, universities and national meteorological and hydrological services (NMHSs).
The experiment presented here follows the prescriptions and assumptions
proposed by Musacchio et al. (2019), where a measurement protocol is presented, following a theoretical study on the influence of various parameters such as wind speed and direction, snow cover thickness, incident solar radiation, snow conditions and humidity on air temperature measurements above snow-covered ground. In the cited work, the authors also give guidelines on the experiment design and the evaluation of uncertainty components, as well as laboratory characterisations of instruments and the treatment of all identified quantities of influence, both instrumental and environmental. Based on these considerations, a measurement protocol, prescribing the following, was prepared for the realisation of the field experiment:
design of the experimental set-up and definition of site requirements evaluation of the quantities of influence characterisation of the sensors in laboratory and in field evaluation of uncertainty components.
The albedo effect investigated here is defined as is the sensors'
overheating due to reflected radiation from snow, and it is measured as
differences in air temperature readings
These two measurement points are arranged in close vicinity and on a flat surface, free from obstacles, and are thus exposed to the same topoclimatic conditions; however, they are far enough apart to accommodate a significant area covered by snow at one point and a sufficient area (at least 5 m of radius) with natural ground left free from snow at the other point. Readings from each pair of sensors are recorded by means of a single data logger. The investigated effect is, therefore, the result of a relative analysis of temperature differences involving identical instruments and single reading unit; this allows for the minimisation of influencing factors and uncertainties. Halfway between the two measurement points, other instruments are deployed to measure the quantities of influence, which contribute as components to the uncertainty budget.
Following the experimental protocol described in Musacchio et al. (2019), the site hosting the experiment requires a number of specific features. It must be an open, flat surface of at least 50 m in diameter with a minimum presence of obstacles (e.g. trees, buildings or roads in the surrounding area) and have spatially uniform solar exposure during the daytime central hours. Snow must be present for a significant amount of time; underneath it, the ground must be covered with natural low vegetation. Other characteristics are related to logistic aspects such as electrical power being available throughout the winter, easy access for maintenance, no agricultural or sport activities, strictly reduced access to the public and no presence of vehicles. The experimental site scheme is described in Fig. 1.
Experiment layout scheme. Points
The main quantities of influence on temperature measurements for the evaluation of the albedo effect must be constantly monitored during the experiment. Musacchio et al. (2019) identified wind speed, air relative humidity and solar radiation as possible major contributors. As a matter of fact, as stated in the cited work, humidity should not have a measurable influence on the albedo effect; it was included in the present experiment simply because hygrometers are commonplace in weather stations, and its monitoring does not significantly increase the workload. A simple preliminary analysis of the humidity ruled out any contributions of it to the albedo effect.
Global (downward) and reflected (upward) solar radiations were measured in
the same position of each temperature sensor to associate the temperature
differences to the radiative budget. Without going into too much detail, which is available in the cited work, other quantities were identified as being important, like snow depth and conditions; they influence the albedo effect in terms of functional evaluation, but since this work aims at detecting the maximum value of the effect, they have been monitored (see Sect. 3.2) but are excluded from the analysis. Some other quantities, like snow density and solar zenith angle, have been considered but ultimately not monitored, e.g. following, for example, the findings of Bohren and Beschta (1979), who concluded that snowpack albedo was only weakly dependent upon snow density, and the theoretical study of Xiong et al. (2015), who showed that, at high values of albedo like those typical of snow, the dependence on the solar zenith angle is basically flat, while, at lower values, the dependence steepens, after
It is still possible that very thin snow covers of low density may be influenced by the dark ground surface. In this case, density could be a significant influencing quantity; however, this should reflect in lower values of reflected radiation (thus albedo) and is easy to spot during the analysis. As a matter of fact, no instances of this kind have happened in the days selected for data analysis.
Before starting the experimental activities in the field, temperature sensors have been characterised in order to understand their behaviour in different situations. The experimental protocol prescribes two different characterisation phases, i.e. in laboratory and in field conditions.
The laboratory characterisation is needed to evaluate possible systematic differences between pairs of sensors exposed to the same temperature under controlled conditions. Since the investigation is based only on relative temperature differences among pairs of identical instruments, the sensors' calibration is not strictly necessary as no traceable absolute temperature measurements are required for the evaluation of the albedo effect in field. This avoids the inclusion of the calibration uncertainty in the overall uncertainty budget and makes the adoption of this procedure easier, also for users willing to make similar analysis without the calibration costs and time required. Laboratory-controlled conditions also allow the evaluation of the sensors' stability and sensitivity and the resolution of the readout.
Different systematic biases can arise when the sensors are deployed in the field, due to environmental factors. For this reason, an in-field characterisation of the sensors is also needed to evaluate their behaviour in such conditions. Performing an estimation of the uncertainty components of on-site measurements is necessary to quantify the accuracy reached in the experiment. For more details, Musacchio et al. (2019) give an in-depth description of the whole method together with its assumptions and prescriptions.
The experimental activity reported in the present work was carried out in the framework of the MeteoMet2 project. Pairs of systems composed of different sensors and shields of different shapes and dimensions, either mechanically aspirated or naturally ventilated, were lent to us directly by the manufacturers, along with their data loggers, in order to have as many commonly used devices in as broad a range as possible. In the end, six different pairs of systems from four different producers were selected for the experiment and labelled from A to F; their main characteristics are described in Table 1.
Selected air temperature instruments and their main characteristics.
Ancillary measurement sensors and their positioning with reference to the scheme in Fig. 1.
Additional sensors for the measurement of the quantities of influence were installed, including a cup-and-vane anemometer, a thermo-hygrometer (both positioned in the central measurement point of the experimental area) and two albedometers, one for each measurement point (Table 2). The air temperature measured in the central point is neither included in the evaluation of the differences among the pairs of sensors under test, nor does it contribute to the uncertainty budget. This further air temperature value is recorded as another potential quantity of influence, both in terms of further possible dependence of the temperature differences on the temperature itself and, in addition, to the one investigated in laboratory.
Tests on the selected sensors were performed in the laboratory for the characterisation of the sensors and the complete system. This part of the work was performed in the new Climate Data Quality Laboratory of the Istituto di Ricerca per la Protezione Idrogeologica – Consiglio Nazionale delle Ricerche (IRPI-CNR). During this phase, a study of the different data loggers' working principles was also made, together with the evaluation of best mounting solutions.
The activities started with an evaluation of the differences between
readings by each pair of sensors, without shields, in stable temperature
conditions, to check for systematic biases. The sensors were then assembled
in the shields, and all the temperature measurements differences of each pair
of instruments,
Results of the evaluation of
Stability of the instruments was also tested in the laboratory, during a 1 month continuous acquisition, to check for longer-term drifts and potential maintenance required in the field. No failures or significant effects were observed.
The laboratory-controlled experimental conditions have been evaluated in the testing zone, using traceable reference sensors.
Room temperature drift was found to be Metrological convention allows for temperature to be expressed in degrees Celsius and temperature differences in Kelvin (BIPM, 2019).
The temperature homogeneity was measured and found to be
The total uncertainty contribution due to laboratory conditions was
evaluated as
The evaluation of possible systematic differences,
Example of laboratory characterisation. Shown is a 1-week acquisition, at 10 min sampling rate, of the differences between the readings of the two sensors of the pair. The E and F systems were not available at the time of the laboratory characterisation.
Finally, a check for possible sensor drifts was performed after the field
campaign and exposure to meteorological conditions. In particular, the drift
of
Since significant snow cover was needed for the experiment, a mountain site in the Alps was chosen to assure the presence of snow cover throughout the winter.
The measurement site, selected to meet the logistical and experimental
requirements, was found in the municipality of Balme at 1410 m elevation
(45
Only a 3 m wide local road with almost no traffic and a small unoccupied building were present in the area at a distance of more than 50 m from the measuring point. Coppa et al. (2021b) performed a metrological quantification of the influences on air temperature measurements introduced by the proximity of roads that revealed a significant effect only at closer distances (less than 50 m) and mainly at very low or even null values of incident radiation; the presence of this infrastructure was therefore considered negligible. According to a similar experiment for the evaluation of the effect of buildings (Garcia Izquierdo et al., 2021), a building of the size of the hut and at that distance causes no influence in air temperature records. Moreover, during the experimental set-up, great care has been taken in order to place both measurement points at similar distances from each possible source of heat and disturbance; thus, their potential influences affect both measurement points in the same way, cancelling out external influences during relative differential evaluations. It is possible that, due to asymmetrical winds, the building can sometimes influence one of the sites more than the other; however, this should affect only few measurements because strong winds were almost absent at that location.
Even though not perfect in terms of siting, the chosen area turned out to be a reasonable compromise between the necessity of an alpine location in terms of snow cover presence and duration and the logistics of an instrumented research site.
The equipment was installed following the protocol described in Musacchio et al. (2019). The experimental scheme in Fig. 1 was followed, with two external poles hosting the pairs of identical shielded thermometers and the albedometers and a central pole with the data loggers, the electric power connection to conduct the auxiliary measurements of humidity, wind speed, wind direction and central air temperature (Fig. 4a and b). The two instruments of each pair were positioned in the same orientation, in case of asymmetric shapes, following the manufacturers' specifications (i.e., ventilation aperture facing north).
After significant precipitation events, the snow was removed from a 5 m
radius area centred in point
As mentioned in Sect. 2.2, the experimental protocol mandates an evaluation
of snow depth and conditions for a full understanding of the quantities of
influence. Instruments have been positioned at 2 m from the ground, and
during the whole measurement campaign, the snow thickness never surpassed
40 cm (measured by a simple ruler), thus keeping sensors at a distance of at
least 1.5 m from the surface below (both above the natural soil and snow-covered area). In the measurement protocol, a recommendation to remove data in case of snow depth over 1 m was included to avoid other effects (extra cooling and turbulence) from introducing errors or uncertainties. Observing snow conditions was deemed unnecessary because observations were only used following snowfall and after site clearing; therefore, snow conditions at site
The theoretical method assumption is that, under the same conditions of snow
cover, the difference in air temperature measurements between the two
sensors at position
A specific measurement campaign was therefore performed on site, after each
snow event, before the snow removal from point data were recorded when snow was present below both the measurement points data were selected during the daytime, with incident solar radiation greater than zero data were selected when the reflected radiation difference was zero (identical readings of the two radiometers facing the soil).
The readings of the sensor pairs under these conditions have been recorded and systematic values
Results of the evaluation of
The overall uncertainty budget
The expression for the evaluation of overall uncertainty is defined as follows:
Contributions to the uncertainty budget evaluated in the laboratory and in-field characterisation.
As used in metrology, uncertainty is described in terms of the coverage factor (a number larger than one by which a combined standard measurement
uncertainty is multiplied to obtain an expanded measurement uncertainty; BIPM and Joint Committee For Guides In Metrology, 2008). Table 5 summarises the components of uncertainty, with the expanded uncertainty
The measurement campaign was performed between 8 September 2016 and 24 March 2017.
The sampling frequency of each pair of sensors was different, but in order
to retain comparability, recording frequency was set to 10 min for all of
them. During the campaign, an operator constantly accessed the experimental
site and marked the best days for the analysis, both in terms of sunny days
(maximum radiation conditions) and after a snowfall (highest albedo), when the snow below instruments at point
Snow was removed on 4 different days, namely 30 November and 22 December 2016 and 20 January and 23 February 2017. Each time, the snow was completely removed within the radius of 5 m, leaving the natural soil exposed. Salt was used to prevent the formation of ice, which would have changed the natural soil reflectivity, and to make snow removal easier and more complete. The data analysis was limited to measurements recorded in the days immediately after snow removal from point
A typical plot of a 1 d long acquisition (25 February 2017),
showing the effect in terms of temperature differences
Results showed that the albedo effect leads to larger
Plots of albedo
Figure 7 shows the evolution of albedo with time, for the whole duration of
the experiment, at both sites
Mean albedo of site
Plots of the albedo of site
There seem to be no direct relationship between albedo and temperature
differences, as they tend to be quite stable (at least during the few days
of the analysis); absolute values of radiation (global or reflected) seem to
be more important. Figure 8 shows that there is basically no relationship
between albedo and temperature differences as the two concentrations of data
shown by some instruments are due to the two values that the albedo assumes to be in site
Results of measured reflected radiation (the whole 10 min sampled
data set) recorded in position
Differences in incident radiation at the two measurement points have also
been evaluated and taken into account, in order to exclude the cases when
these differences were significant and due, for example, to asymmetric shadows from clouds or occurrences of the mountain peak shadow, as mentioned in Sect. 3.3. Having already excluded those values, measurements of incident
radiation were mostly consistent within instrumental uncertainty, which was
evaluated to be 35 W m
Frequency of temperature differences,
A threshold on the difference of reflected radiations,
Figure 9a and b show the reflected radiation recorded in position
Results of the evaluation of
On this subset, a further data selection is applied by excluding the values
of temperature differences among pairs of sensors that fall below the
As a preliminary analysis, records from the deployed instruments were
initially considered as a single set. The plot in Fig. 10 shows the
distribution of
The most frequent values of
Records were then segregated according to system types, as reported in the
following plots (Fig. 11). The analysis shows that no instrument is immune
to the effect, resulting in different values of
Given that we had only one type of actively ventilated shield, and many passively ventilated shields with different designs, it does not seem fair to draw general conclusions about actively vs. passively ventilated shields. As a matter of fact, there is no physical reason why actively ventilated shields should outperform passive ones. The albedo effect investigated here is purely radiative, so the amount of air flowing inside the shield should not influence the radiative heating that the sensor experiences. It is interesting to note, in fact, that actively ventilated shields are not necessarily the best performers; for instance, the type D system performance with a passive screen is similar to that of a type A system. It must be kept in mind, though, that A and D systems feature different screens but also different sensors (Pt100 vs. thermo-hygrometer), so a straightforward comparison is difficult. Helical shields may perform better with respect to other multi-plate shields, possibly because they maximise air intake and effectively cool down the sensor inside; this is something, however, to be investigated – perhaps with a theoretical study.
Table 6 summarises the maximum
Maximum difference,
Further data analysis was addressed to evidence the relations between temperature differences and the main quantities of influence, such as wind speed and radiation.
Temperature differences
Figure 12 shows
Temperature differences
In the same plot, measurements are coded in a cyan scale to underline the
difference in reflected radiation,
The analysis presented here shows that the reflected radiation from a snow-covered surface affects the reliability of meteorological thermometers by transferring extra heat. This effect results in a temperature increase, here evaluated between identical co-located sensors over snow-free ground.
The main considerations are summarised as follows:
Some typologies of instruments are more influenced than others, with significant differences (over 3 Out of the whole group of instruments, 95 % of temperature differences were found to be within 2.4 The lowest temperature mean differences have been recorded by forced ventilated shields, among naturally ventilated shields and by those with helical shapes. Most of the largest temperature differences were found in conjunction with the maximum reflected radiation differences between the two positions, as expected. The wind has the effect of reducing the highest temperature differences. The overall uncertainty on temperature differences in field conditions ranged between 0.1 and 0.4 The distribution of differences as a function of the reflected radiation was found, for most instruments, to be uniform; some instruments show a large scatter in this relation.
Although limited in number, the selected instruments covered most commercial
configurations of modern meteorological sensors, with a reasonable balance of
fan-aspirated, naturally ventilated and alternative designs. While the
duration of the experiment was limited by the duration of the funded project
that backed it, almost all meteorological conditions in the site were met,
including radiation and wind variability, during the November to March time span. Moreover, an appropriate site with easy access for maintenance, a
long-lasting presence of snow, electric power and staff presence is not an easy find, especially in Alpine valleys. Considerations on possible effects of the site features (trees, a small building and shadow) were made in any case to select data and correct for systematic effects.
For these reasons, these results are considered valid for understanding the order of magnitude of the effect. This work also gives an example of how to evaluate this phenomenon and take it into account in terms of correction and associated uncertainty. Following these guidelines, manufacturers and end-users are encouraged to characterise their own instruments to evaluate the albedo effect as a function of reflected radiation, wind speed, etc., to obtain a correction function. Since there is no certainty that a complete correction function can be calculated, also in the case of a single instrument, the level of approximation that can be achieved must be taken into account.
Very few are the examples in the scientific literature of similar evaluations, methods or prescriptions to quantify the studied effect on near-surface thermometers. The work by Huwald et al. (2009), mentioned in the introduction, where one meteorological station (featuring, among other ancillary sensors, albedometers, platinum thermometers and several three-dimensional sonic anemometers used as temperature references) was installed on a Swiss glacier, reaches the same conclusions in the sense that “Temperature errors decrease with decreasing solar radiation and increasing wind speed” and that this effect ranges in the order of degrees Celsius. With respect to the aforementioned study, the key improvement in the work presented here was the use of different sets of identical instruments; the effect is evaluated in a relative way, without the assumption that a sonic anemometer can be used as unbiased reference. It is agreed that non-contact thermometry is immune from some effects of the influencing quantities, but the accuracy achieved by using anemometers as thermometers is not sufficient for being considered a reference instrument (Burns et al., 2012; Richiardone et al., 2012). The method proposed here can be adopted just by using a second identical thermometer and shield, significantly reducing costs. The resulting uncertainties are reduced with respect to comparing different systems and even different physical principles in measuring air temperature. Finally, in this analysis, the investigation was extended to several different kinds of sensors and shields, thus making the results representative of a wider typology of solutions adopted in meteorology.
It must be noted that, since no reference air temperature independent from
radiation errors is available, the total uncertainty due to heating of the
sensor by solar radiation cannot be accurately and absolutely quantified. As
a matter of fact, albedo-induced uncertainty does not include radiative
errors due to heating of the sensor shield from incident solar radiation;
this should be added to determine a complete short-wave radiation-induced
uncertainty of air temperature measurements. In any case, this would go
beyond the scope of the work, given that it focused on relative differences
caused by reflected radiation only, and that there is much more literature
dealing with the effect of incident radiation. Erell
et al. (2005), for instance, showed that no shield provides complete
protection from incident radiation, with relative uncertainties up to
1.5
Beside delivering the numerical results, the key output of this work is a methodology for evaluating a factor affecting temperature data in climatology (and meteorology) and giving an example of how this can be implemented and adopted when selecting instruments and shields as in the case of surface stations of climatological networks.
The main purpose of the paper is to quantify the albedo effect involving
different configurations to obtain a result that is as general as possible. However, the analysis is still limited to some possible configurations, and the aim of the work is not to influence or direct the choice of a configuration. For this reason, no recommendation on which system to buy will be given in this paper because no general rule can be drawn; for instance, the fan-aspirated system performed generally well, but it was outperformed by
some of the passive screens, especially at winds around 2 m s
One of the main tasks of the MeteoMet project was to give metrological support to the meteo-climatology community, including data users, station staff and manufacturers (Merlone et al., 2018). A summary of the outcomes of this work has been presented at the WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation (CIMO TECO-2018; Musacchio et al., 2018) and sent to the WMO Commission for Instruments and Methods of Observation (CIMO) expert team on observation in situ technologies (now the expert teams on surface and subsurface measurements and on measurement uncertainties of the infrastructure commission, respectively).
Following the publication of the experimental method (Musacchio et al., 2019), indications on how to design and implement a field experiment for this purpose have been prepared and sent to WMO expert teams on Metrology, Surface Measurements and Measurement Uncertainties. Manufacturers should also evaluate and declare this effect on their product data sheets and, where possible, adopt solutions to minimise it.
The report to WMO is summarised as follows.
To evaluate the amplitude of the error due to reflected radiation from snow-covered soil on specific instruments, it is recommended that a specific analysis is performed, following the procedure reported here: Two identical systems (thermometers and shield, possibly using the same data logger) must be installed in proximity (between 20 and 50 m in distance), with one positioned above a snow-covered area and one above an area where snow is removed after any snow event. Further instrumentation is required to constantly record and monitor the environmental factors of influence, including global and reflected radiation in both areas, wind speed and direction and humidity. Readings should be recorded for at least one full snow season to meet most meteorological conditions of the sites and to evaluate the associated effects and factors of influence. A correction can then be generated in terms of the relationship between temperature reading differences with respect to the reflected radiation, wind speed and air temperature. The uncertainty budget associated to the correction is then evaluated through Gaussian propagation, where components of uncertainty are calculated by field analysis of systematic differences in temperature and by knowledge of each involved instrument performance, including radiometers and anemometers, and from the statistical analysis and interpolation.
The objective of the recommendation is to report and inform users and instrument manufacturers of what to consider, what to include in data products and possibly minimise and what the effect of reflected radiation from a snow-covered surface on has their systems. While the present study involved different typologies of solar shields as an overall analysis with a significant variety of systems available in the market, the recommendations are addressed to users and manufacturers for a direct evaluation of their specific system. More detailed analysis can then be adopted, and a correction curve, with associated uncertainty, can be obtained and applied to post-processed data. This correction can compensate only the relative differences, with and without snow, and not the overall radiation-induced biases.
The procedure and error evaluation processes are also relevant for the definition of data quality and instrument features by the GCOS and the WMO in promoting climatological reference stations, such as the GCOS Surface Reference Network (GSRN). For high-quality installations and climate reference stations, the analysis presented here can lead to data quality improvement by adding an evaluated relative correction and associated uncertainty.
The study presented here was performed to evaluate the accuracy of
near-surface air temperature data series recorded by thermometers in
radiation shields positioned above snow. The study strictly followed an
already published method and its associated experimental protocol. It
involved a representative number of modern sensors and solar shields,
including naturally ventilated, fan-aspirated and helical shields, provided
as commercially offered by manufacturers, equipped with dedicated data
loggers. The warming effect produced by reflected radiation was apparent for
all the systems, with maximum
The method was validated by the experimental results and can be considered a procedure for further similar investigations involving other typologies of sensors. This process can be adopted by manufacturers to test and characterise their product, as well as by station staff and data users to include this effect, correction and associated uncertainty to the records. A similar analysis should be performed when selecting instruments to use in a climate reference network, such as the planned GCOS Surface Reference Network GSRN, for those stations positioned on sites with snow presence.
Finally, further work can be done to evaluate correction curves in the form of the temperature difference relationship with reflected radiation and wind conditions. The calculation of a correction function requires longer time of field activities to meet the wider range of atmospheric conditions and having more data available for statistical analysis. The uncertainty budget associated to the curve will then be completed by including the statistical analysis and all components from the instruments involved (thermometers, anemometers and radiometers).
In a site where a high-quality installation is planned to be permanent, a study like this is recommended among the overall efforts to increase data quality and understand uncertainties in meteorological observations for climate.
Original raw data are available at
CM, GC and AM designed and ran the experiment, with contributions by all co-authors. CM, AM, GB, GN and FS worked on the set-up of the installation. GN and CHM worked on finding a suitable site and were responsible for logistic organizing of the experiment, with contributions from AM. LM performed the data analysis, with contributions from CM and GC. CM prepared the paper, with contributions from all co-authors. Revisions were handled by GC, with contributions from CM and AM.
The authors declare that they have no conflict of interest.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors wish to thank the Agenzia Regionale per la Protezione Ambientale del Piemonte, the Municipality of Balme and the Les Montagnards mountain hut, for the valuable support, as well as the manufacturers who took part in the experiment by providing the instrumentation.
This work is being developed within the framework of the EMRP (European Metrology Research Programme) joint research project of MeteoMet2. The EMRP is jointly funded by the EMRP-participating countries within EURAMET (European Association of National Metrology) and the European Union.
This research has been supported by the European Association of National Metrology Institutes (grant no. ENV58; MeteoMet2).
This paper was edited by Daqing Yang and reviewed by Hendrik Huwald and Craig Smith.