the automatic weather stations noann network of the

The automatic weather stations NOANN network of the NationalObservatory of Athens: operation and database

K. Lagouvardos*, V. Kotroni, A. Bezes, I. Koletsis, T. Kopania, S. Lykoudis, N. Mazarakis,K. Papagiannaki and S. VougioukasNational Observatory of Athens, Institute for Environmental Research and Sustainable Development, Athens, Greece

*Correspondence: K. Lagouvardos, National Observatory of Athens, Institute for Environmental Research and SustainableDevelopment, 15236 Penteli, Athens, Greece, E-mail: [email protected]

This research has been partly financed by the ‘Development Proposals of Research Entities – KRIPIS’ framework, which isfunded by N.P. ‘Competitiveness and Entrepreneurship’, Action: ‘THESPIA – Development of synergistic and integrated methodsand tools for monitoring, management and forecasting of environmental parameters and pressures’, and by Excellence ResearchProgramme “ARISTOTELIS Environment, Space and Geodynamics/activity 1 SOLAR”, in the framework of Programmatic Agree-ments between Research Centres and GSRT 2015–2017.

During the last 10 years, the Institute for Environmental Research and Sustainable Development of the National Observatory ofAthens has developed and operates a network of automated weather stations across Greece. The motivation behind the networkdevelopment is the monitoring of weather conditions in Greece with the aim to support not only the research needs (weathermonitoring and analysis, weather forecast skill evaluation) but also the needs of various communities of the production sector(agriculture, constructions, leisure and tourism, etc.). By the end of 2016, 335 weather stations are in operation, providing real-time data at 10-min intervals. This paper provides information about the logistics of this network, including real-time applicationsof the collected data as well as information on the quality control protocols, the construction of the station data and metadatarepository and the means through which the data are made available to users.

Geosci. Data J. (2017), doi: 10.1002/gdj3.44

Received: 26 January 2016, revised: 21 February 2017, accepted: 6 March 2017

Key words: weather station network, weather monitoring

Dataset

Identifier: http://stratus.meteo.noa.gr/frontCreator: Lagouvardos, K.Title: NOANET databasePublisher: National Observatory of Athens, GreecePublication year: 2015Resource type: DatasetVersion: 1.0

Introduction

Weather monitoring is among the highest priorities ofagencies and research organizations that are involvedin operational and/or research activities related toweather and climate. Moreover, since weather obser-vations are of paramount importance for many socioe-conomic activities, they have to be provided in atimely manner to potential end-users, through web-based platforms that permit access not only to the

current weather conditions but also to past weatherdata that are quality controlled and archived.

Observations provided by dense surface networks ofmeteorological stations, except their use in numericalweather prediction, are absolutely necessary for modelvalidation and verification (e.g. Kotroni and Lagouvar-dos, 2004; Akylas et al., 2007), for targeted studieswithin urban areas (Kotroni et al., 2011; Siu and Hart,2013), for climatological studies relating meteorologywith air quality (Perez-Martinez and Miranda, 2015), etc.

ª 2017 The Authors. Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution andreproduction in any medium, provided the original work is properly cited.

info:doi/10.1002/gdj3.44

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Over the last few decades, the number of auto-mated weather stations (AWSs) and networks havebeen considerably increased worldwide, including notonly those operated by the national weather services,but also the ones established and operated by univer-sities, research institutes, power grid operators or pri-vate companies. Non-national weather servicenetworks address the need for a denser distribution ofweather observations and contribute to the expansionand dissemination of high-quality weather-related dataneeded in the research on the weather hazards andassociated environmental and socioeconomic impacts(Meyer and Hubbard, 1992; Shafer et al., 2000; Papa-giannaki et al., 2013, 2015).

In the United States and Canada, the number ofnon-federal networks has particularly increased duringthe 1980s, mostly to meet needs related to agricul-ture, while the expansion continued since newresearch topics emerged to address climatic and envi-ronmental issues (Shafer et al., 2000). Among theexisting AWS networks that provide detailed documen-tation on their quality control protocols and technicalinformation is the Oklahoma Mesonetwork (Mesonet),which currently comprises 120 stations. The networkis operated by two Oklahoma universities since 1995,and has a significant positive impact on the researchconducted on the region (Brock et al., 1995; Shaferet al., 2000). The West Texas Mesonet and the Hel-sinki Testbed networks have also been acknowledgedfor the high quality and reliability of the produceddatasets by Muller et al. (2013), who provide a reviewof urban meteorological networks, with the aim toaddress the need for standardized metadata protocols.The authors stress the importance of documentingand making public the networks’ protocols in order toestablish common guidelines and ensure high-qualityservices for applications and urban research.

The Institute of Environmental Research and Sus-tainable Development of the National Observatory ofAthens (IERSD/NOA) took the initiative in 2006 tobuild a relatively low-cost surface weather observationnetwork with the aim to gradually cover the entireGreek territory (including the numerous Greek islands)with automated online stations that continuously

transmit their measurements through the web. Thisnetwork is hereinafter referred to as NOAAN (NOAAutomatic Network). The network expansion in termsof the number of deployed stations is shown in Fig-ure 1. In December 2016, the NOAAN comprised 335stations. Currently, NOAAN is the denser network ofautomated stations over Greece, complementing theHellenic National Meteorological Service network whichcounts ~90 stations. NOAAN has been built in a wayto address not only the needs of both the scientificcommunity but also of the various sectors of the econ-omy for real-time accessible weather observations.Indeed, the data from NOANN, both the real time andthe archived, are used for the study of the urban envi-ronment, the understanding of severe weather eventsand their impacts on the society, the model validationand verification, among others. Moreover, NOANNdata are used in the agriculture sector not only for thedefinition of the local microclimate, where the stationis installed, but also for the evaluation of the impactsof weather on the cultivations, in the constructionbusiness (definition of degree days, maximum rainfallsin a region, etc.), insurance companies (evaluation ofdamages), etc.

The next section is devoted to the general descriptionof the network, including site selection, geographic dis-tribution, etc. Then the construction of the correspond-ing database, the applied quality control proceduresand the description of the multiple ways a user canaccess and display the data, are discussed. Finally, thelast section presents the concluding remarks as well asthe prospects for the sustainability and future expansionof the network and the associated services.

1. The network

1.1 General description

Figure 2(a) shows the geographical distribution of the335 stations of NOAAN at the end of 2016. The net-work is designed to cover the totality of the Greek ter-ritory with a more or less homogenous geographicdistribution; however, in some places the network isapparently denser due to local research implications.

Figure 1. Number of installed meteorological stations per year.

2 K. Lagouvardos et al.

ª 2017 The Authors.Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 0: 1–13 (2017)

The network is very dense especially over the greaterAthens area where 40% of the Greek population lives(Figure 2(b)) with the aim to meet the needs of moni-toring this highly urbanized area.

The stations are installed at buildings or land par-cels owned by local authorities, schools, universities,monasteries, as well as on privately owned land and/or premises. From the stations shown in Figure 2(a):

• 27 stations are installed at an altitude higherthan 1000 m,

• 93 stations are installed in islands, includingCrete,

• 70 stations measure horizontal global irradiance(incoming solar radiation), and

• 25 stations measure the erythema inducing por-tion of the UV radiation.

The weather station type used is Davis Vantage Pro2 that measures ambient air temperature, relativehumidity, wind speed and direction, rainfall, atmo-spheric pressure, and solar and UV radiation (Davis

(a) (b)

Figure 2. Geographical distribution of the network of automatic stations over (a) Greece and (b) Greater Athens Area.

Table 1. Technical characteristics of the weather station used in NOAAN (Davis Instruments, 2010b).

Parameter Sensor type Range Resolution AccuracyUpdateinterval

Temperature Electronic – PNjunction silicondiode

�40 to +65 °C 0.1 °C, �23.3to +37.8 °C

0.2 °Cotherwise

�0.3 °C, +15.6–+37.8 °C�1.7 °C, �40 to +15.6 °C�1.1 °C, +37.8–+65 °C

10–12 s

Relativehumidity

Electronic – Filmcapacitor element

0–100% 1% �3%, 0–90%�4%, 90–100%

50–60 s

Wind direction Wind vane withpotentiometer

0–360° 1° �3° 2.5–3 s

Wind speed Wind cups withmagnetic switch

1–67 m/s, 3–241 km/h(large wind cups)

1.5–79 m/s, 5–282 km/h (small wind cups)

1 km/h,0.5 m/s

�max(5%, 3 km/h/1 m/s)(large wind cups)

�max(5%, 5 km/h/1.5m/s) (small wind cups)

2.5–3 s

Rainfall Tipping bucket (0–100 mm/h) 0.2 mm �max(3%, 0.2 mm), forrain rates up to 50 mm/h

�max(3%, 0.25 mm), otherwise

20–24 s

Atmosphericpressure

Electronic 540–1100 hPa 0.1 hPa �1.0 hPa 60 s

Solar radiation Silicon photodiodewith diffuser(400–1100 nm)

0–1800 W/m2 1 W/m2 �5% 50–60 s

The National Observatory of Athens weather station network 3

ª 2017 The Authors.Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 0: 1–13 (2017)

Instruments, 2010a). It also measures indoor air tem-perature and humidity at the location of the station’sconsole/datalogger (Davis Instruments, 2012) and cal-culates a suite of bioclimatic indices and derived mete-orological parameters (Davis Instruments, 2006).Table 1 provides the technical characteristics of thevarious sensors incorporated within each station(Davis Instruments, 2010b). It is beyond the scope ofthe current work to analyse the measurement differ-ences in Davis instrumentation against other manufac-turers of surface stations. For such a comparison, thereaders are referred to Bell et al. (2013, 2015).

In order to assure the sustainability of the network,three prerequisites are necessary for the installation ofa new station:

• To comply, as much as possible, with the guideli-nes for installing automatic weather stations,provided by WMO (2006, 2008) (as explained indetail in the ‘Site Selection’ section).

• To ensure the availability of uninterrupted Inter-net connection that permits the real-time trans-mission and display of the collected data. For thequasi-majority of the stations, the network con-nection is provided free of charge by the localauthorities so as to minimize the operationalcosts of the network.

• To ensure collaboration with local weather enthu-siasts and/or local authorities employees, whocan voluntarily support the station operation byintervening onsite in case of a sensor and/ortelecommunication deficiency.

1.2 Site selection

The selection of an appropriate site for the installationof a weather station is critical for obtaining represen-tative meteorological data. For this reason, there aretwo main criteria defined for the site selection ofNOAAN stations.

First, the site is chosen to be as exposed as possi-ble, and away from obstructions such as buildingsand trees. The anemometer of the rural meteorologi-cal stations is mounted over an open-level terrain ata height of 5 m above the ground, while for theroof-mounted weather stations in urban sites, theanemometer is mounted at a height of 3 m. Stan-dard anemometer height is at 10 m, but very oftenespecially for surface stations deployed by NationalWeather Services in airports, the anemometers areinstalled at 5 or 6 m height (Kotroni et al., 2014).The height of the anemometer being referred to inthe metadata helps the eventual users to makeadjustments if needed. The temperature and humid-ity sensors are located inside a fan-aspirated radia-tion shield apparatus, usually at 1.8–2.0 m abovebare soil or short grass, avoiding excessive vegeta-tion that could smother the sensors; in urban areas,the roof-mounted stations are located at the edge ofthe roof in order to avoid radiative heating, especially

during the summer months, from the underlyingcement or roof tiles. Furthermore, special attention isgiven in order to avoid locations near heat sourcesusually installed at the roofs of buildings (i.e. air con-dition outdoor units, external heating units, chimneys,etc.). The rain gauge is placed above the fan-aspi-rated shield of the temperature and humidity sensors,with the collection area of the gauge bucket at ahorizontal plane, open to the sky and approximately2.0–2.3 m above the ground. At this point it shouldbe noted that Davis Vantage Pro 2 rain gauge collec-tor opening has a diameter of 165 mm, which is15% smaller than that of typical gauges (~200 mm).Taking into account that rain gauge measurementsare less accurate with increasing wind speed, thisfeature might affect more a gauge with a smaller col-lector diameter. Another feature that might alsoaffect the rain measurement is when the gauge isinstalled on rooftops, but the siting information isincluded in the metadata. As it concerns solid precipi-tation, a rain collector heater is installed at those sta-tions where, climatologically, snowfall is expected atleast 4–5 days per year. When snowfall occurs over astation where no rain collector heater is installed, therain reports due to melting snow are deleted fromthe database, while in the metadata report a com-ment about the loss of solid precipitation is added.Moreover, regarding stations that are equipped withsolar and ultraviolet radiation sensors, special care istaken to avoid shadowing from adjacent obstaclesand to assure, as much as possible, an unobstructedhorizon. This last feature can be easily verified ateach location by inspection of the daily variation intotal solar radiation during a cloudless day.

The second criterion is to make the 10-min meteo-rological observations available in a real-time environ-ment to a wide variety of Internet users, andprimarily to ensure the data transmission using com-munication systems to relay data back to the centralserver of NOAAN. The largest part of the NOAANdata, collected by stations at public buildings andschools, is transmitted by the public Internet networkconnection ‘Syzefxis’, while for stations installed onprivately owned land, data transmission is ensured byprivate Internet connections. Syzefxis is a typical pro-ject for providing large-scale telecommunication anddata-transmitting services to major bodies of theGreek public sector (hospitals, libraries, town halls,port authorities, etc.). Schools are chosen for hostingmeteorological stations, as they are relatively secureand provide a stable Internet connection. Further-more, an additional benefit is that schools can usethe data of the station for educational activities,familiarizing students with scientific research. In someareas, such as ski centres or in the most distant andisolated villages where Internet connection is not fea-sible, the data could not be transmitted. In thesecases, the mobile telephony networks provide analternative solution for data transmission by using adata transmitter usb card with a relatively low



monthly cost which is funded by the local authoritiesor the owners of the ski centres.

Overall, approximately two thirds of the NOAAN sta-tions are installed on public buildings or public land,and the rest on privately owned premises. In bothcases, the stations are hosted free of charge. Permis-sion is also given to NOA, by each landowner, in orderto have access to the station under any weather con-ditions and at any time of the day. Among the 335stations of the NOAAN, 200 are installed on theground, while the rest 135 are installed on rooftops.At this point, it should be noted that it was a real chal-lenge to find the appropriate locations (with Internetaccess and ensured security) to strictly meet the WMOguidelines on installation of the stations over groundsurfaces, mainly in urban areas. Figure 3 shows a col-lection of representative station setups installed bothon the ground and on rooftops.

1.3 Network maintenance

The regular NOAAN stations maintenance includes sitevisit at each station every 24 months. During this visit,

the station is cleaned, the sensors are checked, theenvironment is also checked for any nearby installa-tions/obstacles that might have not be reported, therain gauge is calibrated, and the anemometer is alsochecked for its functionality and replaced if necessary.The temperature–humidity sensors are changed withnew ones after 6–7 years of operation (or before if aproblem is identified from the quality control, asdescribed later in the text). This time interval for thereplacement of the old sensors has been decided asthe calibration of the 20 oldest sensors in a certifiedlaboratory showed that all of them were still perform-ing within the range of error specified by the manufac-turer (Table 1).

1.4 Real-time data display

In the quasi-totality of the stations, data arerecorded at 10 min intervals and they are transferredto a server at NOA premises, dedicated for dataarchiving and quality control (as explained in detailin the next section). For each weather station, adedicated webpage is designed that provides the

(a) (b)

(c) (d)

Figure 3. Examples of various setups of installed stations: (a) Hydra island, Saronic Gulf (port authority roof), (b) Koufonissiisland, Southern Aegean (municipality office roof), (c) Arahova station, Central Greece (municipality water supply premises), and(d) Kryoneri, Peloponnisos (astronomical station).



most recent measurements, diagrams of measuredparameters during the last 24 h, as well as somebasic statistical information for the current and theprevious month. Apart from the direct measurementsof the main meteorological parameters, additionalinformation is provided, including heat index,daily minima and maxima of all parameters. Thesewebpages are bilingual (Greek/English) for easyaccess by both Greek and foreign users. Figure 4provides an example of a NOAAN station webpage

(http://www.meteo.gr/stations/lesvos-agiaparaskevi/).It should be noted that at about 55 out of the 335weather station locations, web cameras transmittingpictures of the nearby area and sky are operated,providing a more integrated overview of the weatherconditions in this particular area. The number ofpageviews of all station webpages during 2016reached 19 000 000, while in cases of adverseweather over large parts of the country the dailypageviews exceeded 190 000.

Figure 4. An example of a station web page showing on the left part the current measurements, and on the right part the 24-hdiagrams.



http://www.meteo.gr/stations/lesvos-agiaparaskevi/

2. Database

2.1 Quality control

Each station of the NOAAN generates a data messageevery 10 min that consists of measurements of thevarious meteorological variables (including the 10-minaverages, minimum and maximum values, etc.). TheNOAAN produces ~44 000 such data messages daily.In order to acquire and provide accurate data recordsfrom such a large number of observations, a concisequality control procedure is required. The quality con-trol of high-frequency meteorological data records isknown to be cumbersome and there is no establishedstandard procedure to apply (Von Arx et al., 2013).The quality control of the NOAAN high-frequencymeteorological data is performed in two steps.

In the first step, a primary quality control is appliedtwice daily by scanning the records of each meteoro-logical variable during the elapsed 12 h in order todetect and flag data of questionable quality. The auto-mated algorithms are able to detect erroneous dataand send error messages to the NOAAN administra-tors. These algorithms include checks for (1) confor-mance to the operational range of each sensor, (2)exceedance of predefined extreme values for eachvariable, (3) suspiciously persistent values (e.g. nullwind for many hours, successive and persistent precip-itation values of 0.2 mm). In this step, defectiveinstruments, communication problems, or other issuesaffecting data quality and availability are identified,and the appropriate maintenance procedures are initi-ated. Furthermore, a not automated procedureincludes the identification of suspicious data by com-parison of the closest weather stations observations.At this point it should be mentioned that automatiza-tion of the procedure that examines the spatial consis-tency of the dataset is underway. However, the verycomplex topography of the country adds difficulty inthe calibration of such an automated procedure.Although the first step is essential to identify erro-neous data, all flagged data are submitted to a secondstage of quality control.

The second step could also be characterized asthe ‘final decision’ step. All data candidate for inclu-sion in the database are re-examined in terms ofspatial and/or temporal inconsistencies both inter-nally and in comparison to neighbouring stations,and those found of unacceptable quality areexcluded. This step is carried out before incorporat-ing any new data in the database. Once the secondstep is completed, the remaining good-quality dataare archived and made available to the general pub-lic as explained in the following subsections. Itshould be noted that the erroneous data are notincluded in the final database, and the users can beinformed on the reason of these missing data fromthe station metadata section. Furthermore, as this isexplained later on, the users can obtain statistics onthe data availability for each required period.

2.2 Database structure

As described in the previous sections, the hugeamount of data from NOAAN collected on a daily basisraised the need for the creation of an advanced data-base system to archive them. Since the beginning of2014, new servers were set up and running, hosting anew database and a front-end interface to ease theaccess of the public to the meteorological data.Numerous scripts and queries were developed, andare regularly expanded to cover the needs that arise,to evaluate the data, populate and extract data fromthe database, and finally calculate climatological data.

After extensive research in the technologies avail-able to cover the needs of such a project and takinginto account the WMO guidelines and the existingtrends in research and development, it was concludedthat open-source products should be used. Linux ser-vers were set up and every technology used is eitheropen source or developed in house from scratch. Theback-end is written in PHP and hosts a MySQL data-base, while the front-end uses several open-sourcetechnologies and standards such as PHP, Javascript,HTML5, and SVG. The only commercial product usedso far is the Google Maps javascript API, mainlybecause of the need for using both maps and satelliteimagery as a base to project and display the archiveddata.

The general idea behind the creation of a databaseis the grouping and standardization of data in the leastredundant form, without the loss of information. In arelational database, such as the one developed in theframe of this work, the standardization is achievedwith the creation of separate tables depending on thetype of the variables stored and their relation withother data.

Each station is considered unique as well as each sta-tion’s 10-min entry. The variables are distributed intoseven groups as shown in Figure 5. Those groups holdall the unique data received from the stations withunique ‘keys’ generated by the combination of theentry’s time stamp, station id, and the entry’s interval(most of the stations use a 10-min interval thoughthere are a few exceptions that connectivity limitationsdictated 15, 30, 60, or 120-min recording intervals).Since no automated implementation is error-free anderrors may always be generated at random intervals,the entries retrieved from the stations may contain dataloss or fragmentation due to numerous reasons such assensor failure, Internet connection loss, connectivityproblems, or interference in the wireless communica-tion between the sensors and their interface (console/computer). Thus, the above coding structure allowsfaster accessibility to the data, reduces the size of thedatabase, and ensures the validity of the calculatedstatistics since the entries in the tables are generatedonly if there is data availability.

This method of data standardization allows theexpandability of the system and the network itself. Asalready mentioned, the system consists of 335



stations, and even though the sensors implementedso far are of meteorological nature, its structure allowsthe addition of other data groups such as agricultural(soil moisture, leaf wetness, etc.), hydrological (lakeor river levels, river flow, etc.), air quality (concentra-tion of particulate matter ozone, etc.), or other formsfacilitating the research on the correlation betweenthem.

The relational database created allows an additionalform of quality control of the available data. Eventhough there is a bundle of scripts running and pro-cessing the real-time data to highlight failures (asdescribed in the previous subsection), these are notable to detect long-term sensor irregularities or prob-lems. For example, a failing aspiration fan – that circu-lates the air in the temperature/relative humiditysensor enclosure – would result in a considerablechange in the recorded temperature during daytime.The database and the associated scripts permit theinvestigation of autocorrelation patterns within thedata of each station as well as cross-correlation pat-terns between the data from neighbouring stations,allowing faster responses when dealing with the prob-lems that arise, and keeping the data quality to highstandards.

2.3 Visualization and dissemination ofhistorical data

The collection of meteorological data described abovecreates the need for their processing and analysis toturn the raw data into information useful to the public.The first step in order to make the leap and convertthe data to information is to visualize them. For thispurpose, a web-GIS tool, based mostly on open-source software, was created that is able to providecomplete analytical geospatial data from NOAAN data-base. Through a user-friendly interface the user is able

to easily access, view, navigate, and download allforms of meteorological and climatological data gener-ated by the stations network and the processing routi-nes of the database. The safety of the database issecured by keeping weekly backups on a RAID 5 datastorage system hosted at NOA.

The idea behind this web-GIS tool is the creation ofa product that will overcome the cross-platform prob-lems when using geospatial data and provide the datain a simple and uniform way to make them accessibleeven by people that do not possess dedicated com-mercial products. For this purpose, a simple graphicaluser interface (GUI) was designed that visually resem-bles any other window-based application with toolbarsand a workspace. This GUI is accessed throughhttp://stratus.meteo.noa.gr/front.

The application separates the stations’ data in twolarge categories: ‘Live Data’ and ‘Database Data’, butvisualizes both in three view types ‘Map’, ‘List’, and per‘Station’. The user has the ability to easily searchthrough the stations, filter the results, and switchbetween live and archived data of specific date/timeor period.

The ‘Map’ view visualizes the distribution of NOAANstations over the Greek territory, by points on map(markers, Figure 6(a)). The markers are placed,based on the geographic location of the stations, onan interactive map layer that can be switchedbetween map and satellite view and can be zoomedin and out. These markers hold all the data collectedlive from the network and are refreshed in a regular10-min interval. The user has not only the ability toswitch between the various meteorological variablesbut also to be informed about each station’s activitystatus. If a station is offline (the latest entry is olderthan 60 min before present) or generates corruptedor false data, it is rendered on the map with differentmarkers respectively. In addition, the user has the

Figure 5. Schematic diagram of the NOANN database structure. Each rectangle represents a table in the database and thelines connecting them a combination of ‘foreign keys’ that mark a unique value in the table. As seen in the diagram, the sensorvariables are marked by the combination of ‘Date and Time’ (blue lines), ‘Interval’ of the measurement (red lines), and the ‘Sta-tions’ IDs (black lines) making them unique throughout each table.




ability to switch the marker type/colouring to threemore types if needed: by each station’s status, divi-sion, or prefecture. The ‘List’ view presents the samedata as the ‘Map’ view, but in a tabular form (Fig-ure 6(b)).

Through this view, the user is capable to searchquickly for the station of interest (by name or internalcode) or to filter the available stations by the combina-tions of filters that are provided from a menu simpleto use. The user can specify filtering methods basedon the stations’ geographical attributes (division, pre-fecture, point or within a radius, altitude) and stationsactivity period. With each search of filtering, the ‘Map’and the ‘List’ views are automatically updated to showonly the stations that fulfil the criteria selected (Fig-ure 7). Additionally, in ‘List’ view is the downloadableform of the filtered stations with their locations andthe values selected.

As mentioned earlier, the application separates the‘Live Data’ from the ‘Database Data’, and this is donebecause the data need to be processed before beingpublished. The user through a menu can switch from‘Live Data’ to a specific date/time or a period in thepast. By using a specific date/time the markers on themap are updated with the available station data ofthat time, whereas filtering by a time period the mark-ers are updated with the corresponding mean, min-ima, maxima, or total depending on the variable thatis being viewed. In this way, the user is able to visual-ize and download specific or climatological data. Atthis point it is worth mentioning that the yearly aver-age percentage of missing values in the database is ofthe order of 3.5%.

The third view type is the ‘Station’ view that can beaccessed by clicking any station’s marker or by select-ing a specific station from the ‘List’. Through this view,

(a) (b)

Figure 6. Web-GIS application and basic views: (a) map of stations with colour-coding according to the station status and char-acteristics and (b) tabular presentation of the data.

Figure 7. Area-filtering tool in the ‘Map’ view.



the user can access the station’s general info, activity,operational, and geographical info. Additionally, whenin ‘Live Data’ mode, all the stations sensor data aregathered there, whereas when in ‘Database Data’mode, all the data are projected in forms of chartsseparately for daily, monthly, and annual data, whichare also available for download (Figure 8). No dataare provided when the availability is lower than 25%.When downloading data, the users receive along withthe measurements information about the data avail-ability, following a four-level rating that ranges fromthe lowest (in the range 25–50%) to the highest(more than 95%) availability. It should be mentionedthat the users have no access to download the rawdata through automated scripts as this access isgranted after a relevant approval by NOANN.

Finally, an attempt was made to incorporate othermethods of visualization, such as gridding and con-touring, in addition to the point measurement presen-tation. For both of those visualization methodsnumerous algorithms exist, however, mainly due tothe excessive computational requirements of the moresophisticated ones and the need to be able to run theselected algorithm in almost any end-user system, itwas decided to implement a rather simple one. Amongall the methods for gridding, the nearest neighbour-

weighted interpolation was selected, as the one thatbalances computational needs and quality of theresulting grid, and its product is used as input to abicubic interpolation algorithm (Press et al., 1992) forthe contouring (Figure 9).

The user after selecting a meteorological variable,from live or past data, has the ability, through a simplemenu, to alter the parameterization of the algorithmsand change the result to meet his or her needs. Also,after the desired result is achieved, the map can bedownloaded in a vector open-source format (SVG) andbe used freely. It is in the authors’ plans to produce inthe near future gridded data where the elevationwould be also used as predictor in the regression anal-ysis. This feature will be incorporated in the nextrelease of the database.

At this point it is worth mentioning how useful theNOAAN network data have been so far for both theresearch community and the various sectors of theeconomy.

First, in the frame of the scientific works performedat the National Observatory of Athens, these datahave been crucial for the following studies: (1) societalanalyses of high-impact weather events, includingtheir database over Greece (Papagiannaki et al.,2013), and the analysis of flash flood occurrence in

(a) (b)

(c) (d)

Figure 8. Web-GIS application station views: (a) archived values, (b) daily charts, (c) monthly charts, and (d) annual charts.



relation to rainfall hazard over the Greater Athens Area(Papagiannaki et al., 2015); (2) numerical weatherprediction validation and verification of temperature(Kotroni et al., 2011), precipitation (Sindosi et al.,2012; Giannaros et al., 2015, 2016; Feidas et al.,2016), wind (Banks et al., 2016), and solar radiation(Kosmopoulos et al., 2015).

Furthermore, other national bodies or agencies sys-tematically use this database. Namely, the HellenicNational Meteorological Service uses these data forthe reports on high-impact weather delivered to WMO.The Hellenic Agricultural Insurance Agency uses theNOAAN database for the authentication of the weatherevents that impacted the cultivations. The Power GridDistribution Agency received up to the end of 2016operationally total solar radiation data from theNOAAN stations for the assessment of the energy pro-duced by the photovoltaic plants.

An additional measure of the usefulness of theNOAAN can be deduced by the number of queries fordata distribution that have been satisfied up to now.Since 2011, the number of queries for data that havebeen addressed exceeded 260 and about 1200 station-years have been delivered. Among these requests, 10%have been made from entities outside Greece.

2.4 Metadata

The existence of metadata information is consideredof paramount importance. All changes in the locationof the instruments, periodic maintenance, calibrationof sensors, etc., have to be meticulously noted inorder to be able to provide the end-users with all thenecessary information about the operation of the sta-tions. These metadata include:

• Detailed description of each site, with photos.• Detailed description of the dates of regular main-

tenance, problems encountered, and the calibra-tion of sensors.

• Detailed description of temporal coverage ofdata, with list of periods of lost data (partial ortotal loss).

• Reporting of date of station decommissioning, ifapplicable.

• Reporting of dates of sensor replacements, dueto failure, vandalism, and/or ageing.

The part of the metadata that is useful to the datausers (such as description of the site, temporal cover-age of data, loss of data, etc.) is available at theonline interface.

3. Concluding remarks

This paper is devoted to the presentation of the net-work of the automated meteorological stations devel-oped, maintained, and operated by the Institute ofEnvironmental Research and Sustainable Developmentof the National Observatory of Athens. The networkstarted in 2006 and by the end of 2016 reached 335stations over the Greek territory.

The network was built on the principles: (1) tocover the research needs of the scientists and thesocioeconomic needs of both the general public and ofend-users from various sectors (constructions, leisure,etc.), (2) to provide the measured data in real-time tothe end-users, (3) to build and populate a databasewith quality-controlled data that can be visualized anddownloaded in a user-friendly way. All relevant dataand information is available at http://stratus.meteo.noa.gr/front.

The network was built finally on the principle toassure the sustainability of its operation. For that rea-son the selected equipment is relatively low cost, thesite selection ensures the transmission of data throughthe Internet from existing infrastructure, and last butnot least the network is supported locally by a largenumber of weather enthusiasts that voluntarily inter-vene when necessary. It is worth mentioning that the

(a) (b)

Figure 9. Web-GIS application gridded and contouring data visualization: (a) nearest neighbour-weighted interpolation grid and(b) bicubic interpolation contours.





network has been financed with internal funds, dona-tions, and sponsorships while during the last 3 years italso received support by THESPIA and ARISTOTELIS/SOLAR national projects.

The network expansion will considerably slow downrelatively to what happened since its start in 2006(and shown in Figure 1) because the aim is to keepthe number of stations at a level that can be main-tained in a high operational standard taking intoaccount the available resources in both people andbudget. Only some gaps are expected to be filled, andthe plan is not to exceed the 400 stations.

Acknowledgements

This research has been partly financed by the ‘Devel-opment Proposals of Research Entities – KRIPIS’framework, which is funded by N.P. ‘Competitivenessand Entrepreneurship’, Action: ‘THESPIA – Develop-ment of synergistic and integrated methods and toolsfor monitoring, management and forecasting ofenvironmental parameters and pressures’, and byExcellence Research Programme “ARISTOTELIS Envir-onment, Space and Geodynamics/activity 1 SOLAR”, inthe framework of Programmatic Agreements betweenResearch Centres and GSRT 2015–2017.

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