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This paper reports results from an inter-comparison effort involving different sensors and models used to measure the atmospheric boundary layer height (ABLH). The effort took place in the framework of the first Special Observing Period of the Hydrological Cycle in the Mediterranean Experiment (HyMeX-SOP1), with the Raman lidar system BASIL deployed in Candillargues (southern France) and operating in almost continuous mode over the time period September–November 2012. ABLH estimates were obtained based on the application of the Richardson number technique to Raman lidar and radiosonde measurements and to ECMWF-ERA5 reanalysis data. In the effort we considered radiosondes launched in the proximity of the lidar site, as well as radiosondes launched from the closest radiosonde station included in the Integrated Global Radiosonde Archive (IGRA). The inter-comparison effort also includes ABLH measurements from the wind profiler, which rely on the turbulence method, as well as measurements obtained from elastic backscatter lidar signals. The Richardson number approach applied to the on-site radiosonde data is taken as reference. Measurements were carried out throughout the month of October 2012. The inter-comparison is extended to both daytime and night-time data. Results reveal a very good agreement between the different approaches, with values of the correlation coefficient R2 for all compared data pairs in the range 0.94–0.98. Values of the slope of the fitting line in the regression analysis are in the range 0.91–1.08 for daytime comparisons and in the range 0.95–1.03 for night-time comparisons, which testifies to the presence of the very small biases affecting all five ABLH estimates with respect to the reference ABLH estimate, with slightly smaller bias values found at night. Results also confirm that the combined application of different methods to the sensors and model data allows us to get accurate and cross-validated estimates of the ABL height in a variety of weather conditions. Correlations between the ABLH measurements and other atmospheric dynamic and thermodynamic variables, such as CAPE (convective available potential energy), friction velocity and relative humidity, are also evaluated to infer possible mutual dependences.

In the last two decades, technological progress has not only seen improvements to the quality of atmospheric upper-air observations but also provided the opportunity to design and implement automated systems able to replace measurement procedures typically performed manually. Radiosoundings, which remain one of the primary data sources for weather and climate applications, are still largely performed around the world manually, although increasingly fully automated upper-air observations are used, from urban areas to the remotest locations, which minimize operating costs and challenges in performing radiosounding launches. This analysis presents a first step to demonstrating the reliability of the automatic radiosonde launchers (ARLs) provided by Vaisala, Meteomodem and Meisei. The metadata and datasets collected by a few existing ARLs operated by the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) certified or candidate sites (Sodankylä, Payerne, Trappes, Potenza) have been investigated and a comparative analysis of the technical performance (i.e. manual versus ARL) is reported. The performance of ARLs is evaluated as being similar or superior to those achieved with the traditional manual launches in terms of percentage of successful launches, balloon burst and ascent speed. For both temperature and relative humidity, the ground-check comparisons showed a negative bias of a few tenths of a degree and % RH, respectively. Two datasets of parallel soundings between manual and ARL-based measurements, using identical sonde models, provided by Sodankylä and Faa'a stations, showed mean differences between the ARL and manual launches smaller than ±0.2 K up to 10 hPa for the temperature profiles. For relative humidity, differences were smaller than 1 % RH for the Sodankylä dataset up to 300 hPa, while they were smaller than 0.7 % RH for Faa'a station. Finally, the observation-minus-background (O–B) mean and root mean square (rms) statistics for German RS92 and RS41 stations, which operate a mix of manual and ARL launch protocols, calculated using the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model, are very similar, although RS41 shows larger rms(O–B) differences for ARL stations, in particular for temperature and wind. A discussion of the potential next steps proposed by GRUAN community and other parties is provided, with the aim to lay the basis for the elaboration of a strategy to fully demonstrate the value of ARLs and guarantee that the provided products are traceable and suitable for the creation of GRUAN data products.

Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 m a. s. l. ; 40.60° N, 15.72° E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016. The intercomparison highlighted that the MiniMPL range-corrected signals (RCSs) show, on average, a fractional difference with respect to those of CNR-IMAA Atmospheric Observatory (CIAO) lidars ranging from 5 to 15 % below 2.0 km a.s.l. (above sea level), largely due to the use of an inaccurate overlap correction, and smaller than 5 % in the free troposphere. For the CL51, the attenuated backscatter values have an average fractional difference with respect to CIAO lidars Finally, additional tests performed during the campaign using the CHM15k ceilometer operated at CIAO showed the clear need to investigate the CHM15k historical dataset (2010–2016) to evaluate potential effects of ceilometer laser fluctuations on calibration stability. The number of laser pulses shows an average variability of 10 % with respect to the nominal power which conforms to the ceilometer specifications. Nevertheless, laser pulses variability follows seasonal behavior with an increase in the number of laser pulses in summer and a decrease in winter. This contributes to explain the dependency of the ceilometer calibration constant on the environmental temperature hypothesized during INTERACT.

In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m−2 at surface and 0.007 W m−2 at top of the atmosphere) and dust aerosol layers (0.7 W m−2 at surface and 0.85 W m−2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m−2 at surface and 2.7 W m−2 at top of the atmosphere) and opaque (7.7 W m−2 at surface and 11.8 W m−2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20–150 sr) than for clouds (20–35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

Abstract Background The query “are there animals at home?” is usually administered for collecting information on anamnesis. This modality to consider exposure to pet allergens constitutes a potential bias in epidemiological studies and in clinical practice. The aim of our study was to evaluate/quantify different modalities of exposure to cat/dog in inducing allergic sensitization. Methods Thirty Italian Allergy units participated in this study. Each centre was required to collect the data of at least 20 consecutive outpatients sensitized to cat/dog allergens. A standardized form reported all demographic data and a particular attention was paid in relieving possible modalities of exposure to cat/dog. Results A total 723 patients sensitized to cat/dog were recorded, 359 (49.65%) reported direct pet contact, 213 patients (29.46%) were pet owners, and 146 subjects (20.19%) were exposed to pets in other settings. Other patients were sensitized by previous pet ownership (150–20.75%) or indirect contact (103–14.25%), in 111 subjects (15.35%) any contact was reported. Conclusions Only 213 patients (29.46%) would be classified as “exposed to animals” and 510 (70.54%) as “not exposed” according to usual query. Our classification has shown that many “not-exposed” subjects (399–55.19%) were “really exposed”. The magnitude of exposure to pet allergens at home is not related exclusively to pet ownership. These considerations should be taken into account during the planning of epidemiological studies and in clinical practice for the management of pet allergic individuals.

There are numerous networks and initiatives concerned with the non-satellite-observing segment of Earth observation. These are owned and operated by various entities and organisations often with different practices, norms, data policies, etc. The Horizon 2020 project GAIA–CLIM is working to improve our collective ability to use an appropriate subset of these observations to rigorously characterise satellite observations. The first fundamental question is which observations from the mosaic of non-satellite observational capabilities are appropriate for such an application. This requires an assessment of the relevant, quantifiable aspects of the measurement series which are available. While fundamentally poor or incorrect measurements can be relatively easily identified, it is metrologically impossible to be sure that a measurement series is correct. Certain assessable aspects of the measurement series can, however, build confidence in their scientific maturity and appropriateness for given applications. These are aspects such as that it is well documented, well understood, representative, updated, publicly available and maintains rich metadata. Entities such as the Global Climate Observing System have suggested a hierarchy of networks whereby different subsets of the observational capabilities are assigned to different layers based on such assessable aspects. Herein, we make a first attempt to formalise both such a system-of-systems networks concept and a means by which to, as objectively as possible, assess where in this framework different networks may reside. In this study, we concentrate on networks measuring primarily a subset of the atmospheric Essential Climate Variables of interest to GAIA–CLIM activities. We show assessment results from our application of the guidance and how we plan to use this in downstream example applications of the GAIA–CLIM project. However, the approach laid out should be more widely applicable across a broad range of application areas. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs and in highlighting to network owners and operators areas for potential improvement.

Dust aerosols are very efficient ice nuclei, important for heterogeneous cloud glaciation even in regions distant from desert sources. A new generation of ice nucleation parameterizations, including dust as an ice nucleation agent, opens the way towards a more accurate treatment of cold cloud formation in atmospheric models. Using such parameterizations, we have developed a regional dust-atmospheric modelling system capable of predicting, in real time, dust-induced ice nucleation. We executed the model with the added ice nucleation component over the Mediterranean region, exposed to moderate Saharan dust transport, over two periods lasting 15 and 9 days, respectively. The model results were compared against satellite and ground-based cloud-ice-related measurements, provided by SEVIRI (Spinning Enhanced Visible and InfraRed Imager) and the CNR-IMAA Atmospheric Observatory (CIAO) in Potenza, southern Italy. The predicted ice nuclei concentration showed a reasonable level of agreement when compared against the observed spatial and temporal patterns of cloud ice water. The developed methodology permits the use of ice nuclei as input into the cloud microphysics schemes of atmospheric models, assuming that this approach could improve the predictions of cloud formation and associated precipitation.

In this paper we present the automated software tool ELDA (EARLINET Lidar Data Analyzer) for the retrieval of profiles of optical particle properties from lidar signals. This tool is one of the calculus modules of the EARLINET Single Calculus Chain (SCC) which allows for the analysis of the data of many different lidar systems of EARLINET in an automated, unsupervised way. ELDA delivers profiles of particle extinction coefficients from Raman signals as well as profiles of particle backscatter coefficients from combinations of Raman and elastic signals or from elastic signals only. Those analyses start from pre-processed signals which have already been corrected for background, range dependency and hardware specific effects. An expert group reviewed all algorithms and solutions for critical calculus subsystems which are used within EARLINET with respect to their applicability for automated retrievals. Those methods have been implemented in ELDA. Since the software was designed in a modular way, it is possible to add new or alternative methods in future. Most of the implemented algorithms are well known and well documented, but some methods have especially been developed for ELDA, e.g., automated vertical smoothing and temporal averaging or the handling of effective vertical resolution in the case of lidar ratio retrievals, or the merging of near-range and far-range products. The accuracy of the retrieved profiles was tested following the procedure of the EARLINET-ASOS algorithm inter-comparison exercise which is based on the analysis of synthetic signals. Mean deviations, mean relative deviations, and normalized root-mean-square deviations were calculated for all possible products and three height layers. In all cases, the deviations were clearly below the maximum allowed values according to the EARLINET quality requirements.

Since its establishment in 2000, EARLINET (European Aerosol Research Lidar NETwork) has provided, through its database, quantitative aerosol properties, such as aerosol backscatter and aerosol extinction coefficients, the latter only for stations able to retrieve it independently (from Raman or high-spectral-resolution lidars). These coefficients are stored in terms of vertical profiles, and the EARLINET database also includes the details of the range resolution of the vertical profiles. In fact, the algorithms used in the lidar data analysis often alter the spectral content of the data, mainly acting as low-pass filters to reduce the high-frequency noise. Data filtering is described by the digital signal processing (DSP) theory as a convolution sum: each filtered signal output at a given range is the result of a linear combination of several signal input data samples (relative to different ranges from the lidar receiver), and this could be seen as a loss of range resolution of the output signal. Low-pass filtering always introduces distortions in the lidar profile shape. Thus, both the removal of high frequency, i.e., the removal of details up to a certain spatial extension, and the spatial distortion produce a reduction of the range resolution. This paper discusses the determination of the effective resolution (ERes) of the vertical profiles of aerosol properties retrieved from lidar data. Large attention has been dedicated to providing an assessment of the impact of low-pass filtering on the effective range resolution in the retrieval procedure.

Despite their differences from more advanced and more powerful lidars, the low construction and operation cost of ceilometers (originally designed for cloud base height monitoring) has fostered their use for the quantitative study of aerosol properties. The large number of ceilometers available worldwide represents a strong motivation to investigate both the extent to which they can be used to fill in the geographical gaps between advanced lidar stations and also how their continuous data flow can be linked to existing networks of the more advanced lidars, like EARLINET (European Aerosol Research Lidar Network). In this paper, multi-wavelength Raman lidar measurements are used to investigate the capability of ceilometers to provide reliable information about atmospheric aerosol properties through the INTERACT (INTERcomparison of Aerosol and Cloud Tracking) campaign carried out at the CNR-IMAA Atmospheric Observatory (760 m a.s.l., 40.60° N, 15.72° E), in the framework of the ACTRIS (Aerosol Clouds Trace gases Research InfraStructure) FP7 project. This work is the first time that three different commercial ceilometers with an advanced Raman lidar are compared over a period of 6 months. The comparison of the attenuated backscatter coefficient profiles from a multi-wavelength Raman lidar and three ceilometers (CHM15k, CS135s, CT25K) reveals differences due to the expected discrepancy in the signal to noise ratio (SNR) but also due to changes in the ambient temperature on the short and mid-term stability of ceilometer calibration. Therefore, technological improvements are needed to move ceilometers towards operational use in the monitoring of atmospheric aerosols in the low and free troposphere.

The potential for measurement redundancy to reduce uncertainty in atmospheric variables has not been investigated comprehensively for climate observations. We evaluated the usefulness of entropy and mutual correlation concepts, as defined in information theory, for quantifying random uncertainty and redundancy in time series of the integrated water vapour (IWV) and water vapour mixing ratio profiles provided by five highly instrumented GRUAN (GCOS, Global Climate Observing System, Reference Upper-Air Network) stations in 2010–2012. Results show that the random uncertainties on the IWV measured with radiosondes, global positioning system, microwave and infrared radiometers, and Raman lidar measurements differed by less than 8%. Comparisons of time series of IWV content from ground-based remote sensing instruments with in situ soundings showed that microwave radiometers have the highest redundancy with the IWV time series measured by radiosondes and therefore the highest potential to reduce the random uncertainty of the radiosondes time series. Moreover, the random uncertainty of a time series from one instrument can be reduced by ~ 60% by constraining the measurements with those from another instrument. The best reduction of random uncertainty is achieved by conditioning Raman lidar measurements with microwave radiometer measurements. Specific instruments are recommended for atmospheric water vapour measurements at GRUAN sites. This approach can be applied to the study of redundant measurements for other climate variables.

With the establishment of ceilometer networks by national weather services, a discussion commenced to which extent these simple backscatter lidars can be used for aerosol research. Though primarily designed for the detection of clouds it was shown that at least observations of the vertical structure of the boundary layer might be possible. However, an assessment of the potential of ceilometers for the quantitative retrieval of aerosol properties is still missing. In this paper we discuss different retrieval methods to derive the aerosol backscatter coefficient βp, with special focus on the calibration of the ceilometers. Different options based on forward and backward integration methods are compared with respect to their accuracy and applicability. It is shown that advanced lidar systems such as those being operated in the framework of the European Aerosol Research Lidar Network (EARLINET) are excellent tools for the calibration, and thus βp retrievals based on forward integration can readily be implemented and used for real-time applications. Furthermore, we discuss uncertainties introduced by incomplete overlap, the unknown lidar ratio, and water vapor absorption. The latter is relevant for the very large number of ceilometers operating in the spectral range around λ = 905–910 nm. The accuracy of the retrieved βp mainly depends on the accuracy of the calibration and the long-term stability of the ceilometer. Under favorable conditions, a relative error of βp on the order of 10% seems feasible. In the case of water vapor absorption, corrections assuming a realistic water vapor distribution and laser spectrum are indispensable; otherwise errors on the order of 20% could occur. From case studies it is shown that ceilometers can be used for the reliable detection of elevated aerosol layers below 5 km, and can contribute to the validation of chemistry transport models, e.g., the height of the boundary layer. However, the exploitation of ceilometer measurements is still in its infancy, so more studies are urgently needed to consolidate the present state of knowledge, which is based on a limited number of case studies.

The quantification of measurement uncertainty of atmospheric parameters is a key factor in assessing the uncertainty of global change estimates given by numerical prediction models. One of the critical contributions to the uncertainty budget is related to the collocation mismatch in space and time among observations made at different locations. This is particularly important for vertical atmospheric profiles obtained by radiosondes or lidar. In this paper we propose a statistical modelling approach capable of explaining the relationship between collocation uncertainty and a set of environmental factors, height and distance between imperfectly collocated trajectories. The new statistical approach is based on the heteroskedastic functional regression (HFR) model which extends the standard functional regression approach and allows a natural definition of uncertainty profiles. Along this line, a five-fold decomposition of the total collocation uncertainty is proposed, giving both a profile budget and an integrated column budget. HFR is a data-driven approach valid for any atmospheric parameter, which can be assumed smooth. It is illustrated here by means of the collocation uncertainty analysis of relative humidity from two stations involved in the GCOS reference upper-air network (GRUAN). In this case, 85% of the total collocation uncertainty is ascribed to reducible environmental error, 11% to irreducible environmental error, 3.4% to adjustable bias, 0.1% to sampling error and 0.2% to measurement error.

The eruption of the Icelandic volcano Eyjafjallajökull in April–May 2010 represents a "natural experiment" to study the impact of volcanic emissions on a continental scale. For the first time, quantitative data about the presence, altitude, and layering of the volcanic cloud, in conjunction with optical information, are available for most parts of Europe derived from the observations by the European Aerosol Research Lidar NETwork (EARLINET). Based on multi-wavelength Raman lidar systems, EARLINET is the only instrument worldwide that is able to provide dense time series of high-quality optical data to be used for aerosol typing and for the retrieval of particle microphysical properties as a function of altitude. In this work we show the four-dimensional (4-D) distribution of the Eyjafjallajökull volcanic cloud in the troposphere over Europe as observed by EARLINET during the entire volcanic event (15 April–26 May 2010). All optical properties directly measured (backscatter, extinction, and particle linear depolarization ratio) are stored in the EARLINET database available at http://www.earlinet.org. A specific relational database providing the volcanic mask over Europe, realized ad hoc for this specific event, has been developed and is available on request at http://www.earlinet.org. During the first days after the eruption, volcanic particles were detected over Central Europe within a wide range of altitudes, from the upper troposphere down to the local planetary boundary layer (PBL). After 19 April 2010, volcanic particles were detected over southern and south-eastern Europe. During the first half of May (5–15 May), material emitted by the Eyjafjallajökull volcano was detected over Spain and Portugal and then over the Mediterranean and the Balkans. The last observations of the event were recorded until 25 May in Central Europe and in the Eastern Mediterranean area. The 4-D distribution of volcanic aerosol layering and optical properties on European scale reported here provides an unprecedented data set for evaluating satellite data and aerosol dispersion models for this kind of volcanic events.

During the eruption of Eyjafjallajökull in April–May 2010 multi-wavelength Raman lidar measurements were performed at the CNR-IMAA Atmospheric Observatory (CIAO), whenever weather conditions permitted observations. A methodology both for volcanic layer identification and accurate aerosol typing has been developed. This methodology relies on the multi-wavelength Raman lidar measurements and the support of long-term lidar measurements performed at CIAO since 2000. The aerosol mask for lidar measurements performed at CIAO during the 2010 Eyjafjallajökull eruption has been obtained. Volcanic aerosol layers were observed in different periods: 19–22 April, 27–29 April, 8–9 May, 13–14 May and 18–19 May. A maximum aerosol optical depth of about 0.12–0.13 was observed on 20 April, 22:00 UTC and 13 May, 20:30 UTC. Volcanic particles were detected at low altitudes, in the free troposphere and in the upper troposphere. Occurrences of volcanic particles within the PBL were detected on 21–22 April and 13 May. A Saharan dust event was observed on 13–14 May: dust and volcanic particles were simultaneously detected at CIAO at separated different altitudes as well as mixed within the same layer. Lidar ratios at 355 and 532 nm, the Ångström exponent at 355/532 nm, the backscatter-related Ångström exponent at 532/1064 nm and the particle linear depolarization ratio at 532 nm measured inside the detected volcanic layers are discussed. The dependence of these quantities on relative humidity has been investigated by using co-located microwave profiler measurements. The measured values of these intensive parameters indicate the presence of volcanic sulfates/continental mixed aerosol in the volcanic aerosol layers observed at CIAO. In correspondence of the maxima observed in the volcanic aerosol load on 19–20 April and 13 May, different values of intensive parameters were observed. Apart from the occurrence of sulfate aerosol, these values indicate also the presence of some ash which is affected by the aging during transport over Europe.

Long-term observations of aerosol and clouds are of crucial importance to understand the weather climate system. At the Istituto di Metodologie per l'Analisi Ambientale of the Italian National Research Council (CNR-IMAA) an advanced atmospheric observatory, named CIAO, is operative. CIAO (CNR-IMAA Atmospheric Observatory) main scientific objective is the long term measurement for the climatology of aerosol and cloud properties. Its equipment addresses the state-of-the-art for the ground-based remote sensing of aerosol, water vapour and clouds including active and passive sensors, like lidars, ceilometers, radiometers, and a radar. This paper describes the CIAO infrastructure, its scientific activities as well as the observation strategy. The observation strategy is mainly organized in order to provide quality assured measurements for satellite validation and model evaluation and to fully exploit the synergy and integration of the active and passive sensors for the improvement of atmospheric profiling. Data quality is ensured both by the application of protocols and dedicated quality assurance programs mainly related to the projects and networks in which the infrastructure is involved. The paper also introduces examples of observations performed at CIAO and of the synergies and integration algorithms (using Raman lidar and microwave profiler data) developed and implemented at the observatory for the optimization and improvement of water vapour profiling. CIAO database represents an optimal basis to study the synergy between different sensors and to investigate aerosol-clouds interactions, and can give a significant contribution to the validation programs of the incoming new generation satellite missions.

At CNR-IMAA, an aerosol lidar system has operated since May 2000 in the framework of EARLINET (European Aerosol Research Lidar Network), the first lidar network for tropospheric aerosol study on a continental scale. High quality multi-wavelength measurements make this system a reference point for the validation of data products provided by CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), the first satellite-borne lidar specifically designed for aerosol and cloud study. Since 14 June 2006, dedicated measurements have been performed at CNR-IMAA in coincidence with CALIPSO overpasses. For the first time, results on 1-year comparisons between ground-based multi-wavelength Raman lidar measurements and corresponding CALIPSO lidar Level 1 profiles are presented. A methodology for the comparison is presented and discussed in detail. Night-time cases are considered to take advantage from Raman capability of the ground based lidar. Cases with the detection of cirrus clouds in CALIPSO data are separately analysed for taking into account multiple scattering effects. For cirrus cloud cases, few cases are available to draw any conclusions. For clear sky conditions, the comparison shows good performances of the CALIPSO on-board lidar: the mean relative difference between the ground-based and CALIPSO Level 1 measurements is always within its standard deviation at all altitudes, with a mean difference in the 3–8 km altitude range of (−2±12)%. At altitude ranges corresponding to the typical PBL height observed at CNR-IMAA, a mean difference of (−24±20)% is observed in CALIPSO data, probably due to the difference in the aerosol content at the location of PEARL and CALIPSO ground-track location. Finally, the mean differences are on average lower at all altitude ranges for the closest overpasses (at about 40 km) respect to the 80-km overpasses.

Remotely sensed data, recorded by means of the MIVIS hyperspectral scanner in the framework of the research activity of the CNR Institutes IIA-LARA and IMAA, have been calibrated to reflectance values and then quantitatively compared with ground data. A new procedure for radiometric calibration has been defined by utilizing the MIVIS test-bench and applying a wider radiance range with respect to the one provided by the manufacturing company. New calibration curves have been determined and applied in the pre-processing chain. For validation purpose ground spectra were measured during the campaign by means of a portable spectroradiometer. The atmospheric correction has been carried out by implementing an IDL procedure to manage MODTRAN4 input and output cards. MIVIS test data acquired over Passo Corese (Roma) have shown how the new calibration coefficients significantly improve the radiometric accuracy. In particular, in the VIS spectral region the percentage error, with respect to a ground truth spectrum, is about half of that occurring if the standard calibration coefficients are used.