The water cycle strongly influence life on Earth and precipitation especially modifies the atmospheric column thermodynamics through the evaporation process and serving as a proxy for latent heat modulation. For this reason, a correct light precipitation parameterization at global scale, it is of fundamental importance, bedsides improving our understanding of the hydrological cycle, to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios. In this context we developed a full automatic algorithm based on morphological filters that, once operational, will make available a new rain product for the NASA Micropulse Lidar Network (MPLNET) and the European Aerosol Research Lidar Network (EARLINET) in the frame of WMO GALION Project

This document summarises the views expressed by the Italian Computing and Data Initiative (ICDI) in response to the open consultation for the EOSC Strategic Research and Innovation Agenda (SRIA), closed on the 31st of August. It provides insightful input and suggestions about the current draft of the SRIA document shared with the wider EOSC community, with the aim of helping to shape the future vision of the European Open Science Cloud.

A stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network's preparedness to offer insight into natural hazards that affect the aviation sector.

Two tailored configurations of the Robust Satellite Technique (RST) multi-temporal approach, for airborne volcanic ash and desert dust detection, have been tested in the framework of the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. The two algorithms, running on Spinning Enhanced Visible Infra-Red Imager (SEVIRI) data, were previously assessed over wide areas by comparison with independent satellite-based aerosol products. In this study, we present results of a first validation analysis of the above mentioned satellite-based ash/dust products using independent, ground-based observations coming from the European Aerosol Research Lidar Network (EARLINET). The aim is to assess the capabilities of RST-based ash/dust products in providing useful information even at local scale and to verify their applicability as a "trigger" to timely activate EARLINET measurements during airborne hazards. The intense Saharan dust event of May 18-23 2008-which affected both the Mediterranean Basin and Continental Europe-and the strong explosive eruptions of Eyjafjallajokull (Iceland) volcano of April-May 2010, were analyzed as test cases. Our results show that both RST-based algorithms were capable of providing reliable information about the investigated phenomena at specific sites of interest, successfully detecting airborne ash/dust in different geographic regions using both nighttime and daytime SEVIRI data. However, the validation analysis also demonstrates that ash/dust layers remain undetected by satellite in the presence of overlying meteorological clouds and when they are tenuous (i.e., with an integrated backscatter coefficient less than similar to 0.001 sr(-1) and with aerosol backscatter coefficient less than similar to 1 x 10(-6) m(-1)sr(-1)). This preliminary analysis confirms that the continuity of satellite-based observations can be used to timely "trigger" ground-based LIDAR measurements in case of airborne hazard events. Finally, this work confirms that advanced satellite-based detection schemes may provide a relevant contribution to the monitoring of ash/dust phenomena and that the synergistic use of (satellite-based) large scale, continuous and timely records with (ground-based) accurate and quantitative measurements may represent an added value, especially in operational scenarios.

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol-cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

Six months of stratospheric aerosol observations with the European Aerosol Research Lidar Network (EARLINET) from August 2017 to January 2018 are presented. The decay phase of an unprecedented, record-breaking stratospheric perturbation caused by wildfire smoke is reported and discussed in terms of geometrical, optical, and microphysical aerosol properties. Enormous amounts of smoke were injected into the upper troposphere and lower stratosphere over fire areas in western Canada on 12 August 2017 during strong thunderstorm-pyrocumulonimbus activity. The stratospheric fire plumes spread over the entire Northern Hemisphere in the following weeks and months. Twenty-eight European lidar stations from northern Norway to southern Portugal and the eastern Mediterranean monitored the strong stratospheric perturbation on a continental scale. The main smoke layer (over central, western, southern, and eastern Europe) was found at heights between 15 and 20 km since September 2017 (about 2 weeks after entering the stratosphere). Thin layers of smoke were detected at heights of up to 22-23 km. The stratospheric aerosol optical thickness at 532 nm decreased from values > 0.25 on 21-23 August 2017 to 0.005-0.03 until 5-10 September and was mainly 0.003-0.004 from October to December 2017 and thus was still significantly above the stratospheric background (0.001-0.002). Stratospheric particle extinction coefficients (532 nm) were as high as 50-200 Mm-1 until the beginning of September and on the order of 1 Mm-1 (0.5- 5 Mm-1) from October 2017 until the end of January 2018. The corresponding layer mean particle mass concentration was on the order of 0.05-0.5 ?g m-3 over these months. Soot particles (light-absorbing carbonaceous particles) are efficient ice-nucleating particles (INPs) at upper tropospheric (cirrus) temperatures and available to influence cirrus formation when entering the tropopause from above. We estimated INP concentrations of 50-500 L-1 until the first days in September and afterwards 5-50 L-1 until the end of the year 2017 in the lower stratosphere for typical cirrus formation temperatures of -55 ?C and an ice supersaturation level of 1.15. The measured profiles of the particle linear depolarization ratio indicated a predominance of nonspherical smoke particles. The 532 nm depolarization ratio decreased slowly with time in the main smoke layer from values of 0.15-0.25 (August-September) to values of 0.05-0.10 (October-November) and < 0.05 (December-January). The decrease of the depolarization ratio is consistent with aging of the smoke particles, growing of a coating around the solid black carbon core (aggregates), and thus change of the shape towards a spherical form. We found ascending aerosol layer features over the most southern European stations, especially over the eastern Mediterranean at 32-35? N, that ascended from heights of about 18-19 to 22-23 km from the beginning of October to the beginning of December 2017 (about 2 km per month). We discuss several transport and lifting mechanisms that may have had an impact on the found aerosol layering structures.

We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1%, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3%.

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).

The Cloud-Aerosol Transport System (CATS) onboard the International Space Station (ISS), is a lidar system providing vertically resolved aerosol and cloud profiles since February 2015. In this study, the CATS aerosol product is validated against the aerosol profiles provided by the European Aerosol Research Lidar Network (EARLINET). This validation activity is based on collocated CATS-EARLINET measurements and the comparison of the particle backscatter coefficient at 1064nm.

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

The EARLINET database is facing a complete reshaping to meet the wide request for more intuitive products and to face the even wider request related to the new initiatives such as Copernicus, the European Earth observation programme. The new design has been carried out in continuity with the past, to take advantage from long-term database. In particular, the new structure will provide information suitable for synergy with other instruments, near real time (NRT) applications, validation and process studies and climate applications.

The Aerosols, Clouds and Trace gases Research Infrastructure (ACTRIS) is a distributed infrastructure dedicated to high-quality observation of aerosols, clouds, trace gases and exploration of their interactions. It will deliver precision data, services and procedures regarding the 4D variability of clouds, short-lived atmospheric species and the physical, optical and chemical properties of aerosols to improve the current capacity to analyse, understand and predict past, current and future evolution of the atmospheric environment.

Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.

We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with lit-erature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59% (minimum) and 90% (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80 %. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.

The INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking) campaign, performed at the CNR-IMAA Atmospheric Observatory (760 m a.s.l., 40.60° N, 15.72° E), aims to evaluate the performances of commercial automatic lidars and ceilometers for atmospheric aerosol profiling, through the comparison with Potenza EARLINET (European Aerosol Research Lidar NETwork) lidars. The results of the campaign and the overall lesson learnt within INTERACT-I and INTERACT-II ACTRIS campaigns will be presented.

Particle depolarization ratio retrieved from lidar measurements are commonly used for aerosol-typing studies, microphysical inversion, or mass concentration retrievals. The particle depolarization ratio is one of the primary parameters that can differentiate several major aerosol components but only if the measurements are accurate enough. The accuracy related to the retrieval of particle depolarization ratios is the driving factor for assessing and improving the uncertainties of the depolarization products. This paper presents different depolarization calibration procedures used to improve the quality of the depolarization data. The results illustrate a significant improvement of the depolarization lidar products for all the selected lidar stations that have implemented depolarization calibration procedures. The calibrated volume and particle depolarization profiles at 532?EUR-nm show values that fall within a range that is generally accepted in the literature.

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 degrees N, 15.72 degrees 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 < 20-30% below 3 km and larger above. The variability of the CL51 calibration constant is within +/- 46 %. For the CS135, the performance is similar to the CL51 below 2.0 km a.s.l.:, while in the region above 3 km a.s.l. the differences are about +/- 40 %. The variability of the CS135 normalization constant is within +/- 47 %. 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.

The European Aerosol Research Lidar Network, EARLINET, established in 2000 as a research project, has the goal of creating a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution all over Europe (Pappalardo et al., 2014). At present, 31 stations distributed all over Europe are part of the network. A strong link with EARLINET data users has been established since the beginning of EARLINET in which EARLINET people acted as mentor and guide for users to assure a correct use and interpretation of the data. This was from one side a big effort for the EARLINET community as a whole,on the other side it was the opportunity to improve the knowledge about the user needs. In April 2010 a user web portal was made accessible to the public and since that date more than 3 and a half million products data files have been downloaded from the database. The number of registered external users has grown up year by year and nowadays this number is about 500. External users are spread all over the world, in particular, on 46 countries (16 out of Europe). EARLINET is a key component of the ACTRIS infrastructure (Aerosols, Clouds and Trace gases Research Infrastructure), which represents a big step towards a better coordination of the atmospheric observations in Europe towards the establishment of the European component of an Integrated Atmospheric Global System as part of GEOSS, the Global Earth Observation System of Systems (GEOSS, 2005). EARLINET is also a contributing network to the GAW Programme. The EARLINET database, also, takes advantage from the centralized data processing through the common standardized automatic analysis software developed within EARLINET, the Single Calculus Chain (SCC) (D'Amico et al., 2015). It is also possible the submission of data analyzed by using specific Quality Assured (QA) and documented software. The usage of the SCC assures the usage of QA retrieval algorithms and the full traceability of all EARLINET products. Currently the EARLINET database is the largest and most representative database of aerosol optical parameters obtained from LIDAR measurements on continental scale. It is freely available and it made possible several studies of atmospheric aerosols on European scale. In order to guarantee the highest possible quality of the final products, and at the same time to ensure the homogeneity of data from different types of lidar systems, EARLINET/ACTRIS network has implemented a rigorous quality assurance program that all affiliated stations are expected to meet (Freudenthaler et al., 2018; Matthais et al., 2004). The implementation of the quality assurance program has been, and still is, one of the main activities of EARLINET/ACTRIS network, and is a prerequisite to establish and monitor over time the performance of different Lidar systems that are part of the network. The quality control of EARLINET applies to both lidar instruments and analysis algorithms levels. Nowadays the EARLINET database is facing a complete reshaping to meet the wide request for more intuitive products and to face the even wider request related to the new initiatives such as Copernicus, the European Earth observation programme. The new design has been carried out in continuity with the past, to take advantage from long-term database. In this report we describe the EARLINET database Record Version Control system. Sometimes it can happen that a product already uploaded on the database is not fully optimized, moreover, products may need to be re-analysised if, for example, new retrieval algorithms are released. With the aid of a Record Version Control system it will be allowed (among others) to version a product, that is, allowing a product to exist in several versions at the same time.

The European Aerosol Research LIdar NETwork (EARLINET) is the first aerosol lidar network on a continental scale with the main goal to provide a comprehensive, quantitative, and statistically significant database for the aerosol distribution over Europe. The EARLINET database, also, takes advantage from the centralized data processing through the common standardized automatic analysis software developed within EARLINET, the Single Calculus Chain (SCC) (D'Amico et al., 2015). It is also possible the submission of data analyzed by using specific Quality Assured (QA) and documented software. The usage of the SCC assures the usage of QA retrieval algorithms and the full traceability of all EARLINET products. At present, 31 stations distributed all over Europe are part of the network. Each station of the network has a PI (Principal Investigator) and key personnel. EARLINET institutional members are obliged to follow the rules as defined in the EARLINET constitution. These include (among others) the performance of regular measurements and regular quality assessment. EARLINET members enabled to upload products on the database are called Data Originators (DO) while other users are usually referred as external users. Products can be uploaded through a Data Processor interface, which is a password protected web server interface. In this report we describe the EARLINET product uploading process which allows data originators to uploads their products.

EARLINET (European Aerosol Research Lidar Network) , established in 2000 as a research project, is the first aerosol lidar network which attempts to retrieve quantitative data on the vertical distribution of aerosol optical properties in a systematic and statistically significant approach and on a continental scale. Therefore, the main effort as well as the main achievements are in the development of methods. Strong emphasis is on making the results from all stations and all times comparable, because this is essential for the use of joint data sets in all studies involving several stations (Pappalardo et al., 2014). EARLINET is a key component of the ACTRIS infrastructure (Aerosols, Clouds and Trace gases Research Infrastructure), which represents a big step towards a better coordination of the atmospheric observations in Europe towards the establishment of the European component of an Integrated Atmospheric Global System as part of GEOSS, the Global Earth Observation System of Systems (GEOSS, 2005). EARLINET is also a contributing network to the GAW Programme. At present, 31 stations distributed all over Europe are part of the network (fig. 1). Each station of the network has a PI (Principal Investigator) and key personnel. EARLINET institutional members are obliged to follow the rules as defined in the EARLINET constitution. These include (among others) the performance of regular measurements and regular quality assessment. n order to guarantee the highest possible quality of the final products, and at the same time to ensure the homogeneity of data from different types of lidar systems, EARLINET/ACTRIS network has implemented a rigorous quality assurance program that all affiliated stations are expected to meet (Freudenthaler et al., 2018; Matthais et al., 2004). The implementation of the quality assurance program has been, and still is, one of the main activities of EARLINET/ACTRIS network, and is a prerequisite to establish and monitor over time the performance of different Lidar systems that are part of the network. The quality control of EARLINET applies to both lidar instruments and analysis algorithms levels. EARLINET users enabled to upload products on the database are called Data Originators (DO) while other users are usually referred as external users. Products can be uploaded through a Data Processor interface, which is a password protected web server interface. In this report we describe the EARLINET Quality Control procedures. When a data originator uploads a product, in order to be sure that it comes with the highest possible quality, two types of quality control are applied : ? Basic quality control (BQC) : it is a series of technical controls in order to verify the input product datafile integrity. ? Advanced quality control (AQC) : it is a set of physical controls applied to the content of the product. Products not passing all the BQC are not accepted by the datacenter. Products that overcome all the BQC and fail in any one of the AQC are accepted by the datacenter but are labeled as Level 1. Finally, products passing all the BQC and AQC are accepted and labeled as Level 2.