SOFTWARE ARTICLE

Online weather data analysis and visualization toolsfor applications in ecoinformatics

Krisanadej Jaroensutasinee & Wittaya Pheera &

Mullica Jaroensutasinee

Received: 13 March 2013 /Accepted: 2 October 2013# Springer-Verlag Berlin Heidelberg 2013

Abstract As large quantities of physical data are alwayscollected for Ecoinformatics research, it is difficult for themto be cleaned, shared, visualized, and analyzed by researchcollaborators. To resolve this difficulty, this study presentsonline weather data analysis and visualization cyber-infrastructures consisting of (1) online weather data analysisand visualization tools and (2) near real-time onlineweather data portal. Firstly, these online tools at www.twibl.org/weather provide data sharing in three web pages:information on instruments and site; data access protected bysimple password security; data analysis and visualizationservices so-called “Ecoinfows”. Secondly, the near real-timeonline weather data portal for visualizing and forecasting weath-er data from cloud storage of many automatic weather stations isonline at www.twibl.org/aaportal. To overcome speed andaccessibility problems, we developed these tools with manytechnologies - i.e. cloud computing, online computing XML(webMathematica), and binary access data conversion.

Keywords Ecoinformatics .Weather data . Online services .

Online visualization tool . Data sharing

Introduction

At present various types of weather data, such as raw data,processed data, analyzed data, and visualized data are madeavailable through many organizations for a number of differentpurposes, such as weather forecasting and disasters monitoring

(e.g. Li et al. 2008; Kussul et al. 2009; Murgia et al. 2009;Cervato et al. 2011). With the increased need for access to data,including weather data via internet, web services and hardwarehave been rapidly developed to handle the enormous webworkloads and increased traffic (Grimshaw 1993; Glassman1994; Grimshaw et al. 1994; Katz et al. 1994; Bestavros et al.1995; Arbenz et al. 1997; Casanova and Dongarra 1997;Fatoohi 1997;Maltzahn et al. 1997; Sato et al. 1997). However,due to the high costs involved, the newly developed webservices and hardware are not available for all servers. As aresult, many web services face problems with high web work-load and data processing time.

Similarly, in order to analyze and visualize the massiveamount of data in Ecoinformatics such as weather data, highperformance computing resources are also needed. To trackthe effect of climate change at Khao Nan National Park,Thailand, using the weather data, the online weather dataanalysis and visualization tool was developed to increase thespeed, performance, and efficiency of data access, analysis,visualization, and synthesis. Issues with high web workloadand processing time were addressed by utilizing the dataprocessing time control strategy. Using this strategy, onlineweather data analysis and visualization tools can enhance ourability to observe, monitor, study, and understand weather andclimatic complexity.

This paper describes the services for online weather dataanalysis and visualization tool which was developed for thestudy of Ecoinformatics. This tool helps users to gain easieraccess to and a better understanding of Ecoinformatics data.To increase the performance of this tool, data processing timecontrol strategy was developed utilizing binary files in datamanipulation processes. The binary files format was utilizedinstead of the text files format to back up the weather data. Inaddition, data processing time of the original backup text filesand that of the binary files were compared. Data processingtimes for both file formats were measured using three ways of

Communicated by: H. A. Babaie

K. Jaroensutasinee :W. Pheera :M. Jaroensutasinee (*)Center of Excellence for Ecoinformatics, School of Science,WalailakUniversity, 222 Thaiburi, Thasala, Nakhon Si Thammarat 80161,Thailande-mail: jmullica@gmail.com

Earth Sci InformDOI 10.1007/s12145-013-0138-y

data access: (1) direct software interface on a PC; (2) a webbrowser to a local web server on a PC; (3) a web browser to aninternet web server. The use of a web browser to an internetweb server is our goal for data sharing. The analysis wasdesigned to investigate the possibility and efficiency of usingthe binary file format instead of original backup text format indata manipulation for the analytical online services. The newtechniques for online atmospheric analysis and visualizationtools are applicable to any computing and monitoring workthat involves atmospheric data, especially sensor collectedatmospheric data. In addition, a near-real time online weatherdata portal was developed to solve the problem of weatherdata visualization. With this weather data portal, users canmore easily and correctly interpret the weather data frommany sites. This near real-time online weather data portal isa very useful tool for users who are interested in the weather,environmental, and disaster monitoring system.

Computational details

Online weather data analysis and visualization web service

Weather data were obtained from eighteen automatic weatherstations (Davis Vantage Pro II Plus model, Davis InstrumentsCorporation) installed in eight provinces throughout Thailand,as well as from eight data loggers (HOBO Pro V2 model,Onset Computer Corporation) deployed at the headquartersand seven ranger stations in KhaoNanNational Park, Thailand.Weather data archived from these stations and loggers were

exported, cleaned-up, and converted to the determined form oftext file as monthly data. These text files were uploaded to theserver for backup and data access as a database (ftp://www.twibl.org) and for utilization in data analysis and visualizationalgorithms (www.twibl.org/ecoinfows). The workflow ofthis online weather data analysis and visualization tool isshown in Fig. 1.

Performance analysis of the online analysis and visualizationservice

For performance analysis, weather data obtained from theautomatic weather stations in Muang District, Nakhon SiThammarat, were selected because they were the most numer-ous and consistent data when compared with those from theother sites. Monthly data files, that are approximately 15–20MB each, were prepared in text format as described above.Using a script consisting of programs in advanced computa-tional software, the monthly data text files were converted intothe binary files as monthly (MD), 3 month (TMD), 6 month(SMD), and yearly data (YD) files.

Three ways of data access – i.e. using direct softwareinterface on a PC, using a web browser to a local web serveron a PC, and using a web browser to an internet web server -were compared. The scripts for all methods were programmedto start from a working directory, import data, and manipulatedata for a few steps to find the maximum, minimum, andaverage values of each data file. Performance analysis wasaccomplished by randomly processing data files using all threeways of data access. Script processing time was measured

Fig. 1 Workflow of the onlineweather data analysis andvisualization tool

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twelve times per analysis; the highest and lowest times were cutout of processing time data analysis; the final ten run timeswere averaged, analyzed, and visualized.

The hardware used in the performance analysis was a PCcompatible computer with an Intel® Core™ 2 Quad CoreQ8400@ 2.66 GHz 2.67 GHz CPU, 4 GB RAM, and a500 GB 5400 rpm hard disk drive. All performance analyseswere run in the Windows 7 Professional with a 64 Bit operat-ing system. For the data access using direct software interfaceon a PC, the analyses were performed using a script pro-grammed in the advanced analytical software. For the dataaccess using a web browser to local web server on a PC, theanalyses were performed using a script programmed for theonline computing XML. For the data access using a webbrowser to an internet web server, the client computer wasrun on an Apple MacBook Pro 13.3” with Intel® Duo™ CoreP7550 2.26 GHz CPU, 4 GB RAM, and a 500 GB 5400 rpmhard disk drive. The client computer was run in the Windows7 Professional with a 64 Bit operating system via Apple BootCamp software. The server was operated on the network with100Mbps network speed. The analyses were performed usingthe same computing XML script.

Near real-time online weather data portal

Weather data were collected from six automatic weather sta-tions which were Davis Vantage Pro II Plus models, DavisInstruments Corporation. They were installed in five prov-inces of Thailand: Phuket, Krabi, Trang, Surat Thani, andNakhon Si Thammarat. The data were automatically uploadedto the server via Cloud storage technology. These weatherstations were configured to take a reading every minute and

upload every 10min. Data forecasting for the next 10 min wasprocessed using the data collected during the previous 3 h - i.e.the last 180 records. Data visualization was generated andexported in .jpg file format. Finally, generated .jpg files werevisualized as webpages by a java script (.jsp) file. This nearreal-time online weather data portal was configured to auto-matically update every 10 min, just as the data was uploading.The workflow for the near real-time online weather data portalis shown in Fig. 2.

Contents in online weather data analysis and visualizationweb service

Online weather data analysis and visualization tool providedata sharing in three web pages: (1) information on instru-ments and site; (2) data access protected by simple passwordsecurity; (3) data analysis and visualization services so-called“Ecoinfows”. These services are available online at www.twibl.org/weather (Fig. 3).

In “Introduction” section, information on instruments andsite was summarized. Instrument specification sheets andmanuals are available for download. Google Map was usedas a geographic information system (GIS) tool to display thelocation of sites, boundaries, large water bodies, mountains,and roads. With GIS tools, users can understand the studyarea, find the relationships between data and sites, and com-pare data among sites more effectively.

In “Computational details” section, data obtained fromboth automatic weather stations and data loggers were backedup and converted to the text file format. Although this fileformat is not commonly used for weather data backup, it isuser friendly for teachers and students. Users can access the

Fig. 2 Workflow of the nearreal-time online weatherdata portal

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files at ftp://www.twibl.org with granted usernames andpasswords. A new user has to contact a web-administratorfor a first-time login.

In “Near real-time online weather data portal” section,online computing XML was used to provide online servicefor data analysis and visualization so-called “Ecoinfows”.This online tool provides eight data analyses and visualiza-tions, which are time-series, histogram, overlay histogram,3D-moving histogram, cluster analysis, correlation, regressionand data distribution (Fig. 4a–h). The most accessed graphicaldisplay is the time-series visualization. This may be becauseusers like to explore some long-term trends, e.g. increasing ordecreasing temperatures at that site, seasonal variations inweather data, and detecting spatial/temporal anomaly weatherdata). Ecoinfows makes it possible for general users withminimum mathematical and programming knowledge to ana-lyze and visualize weather data. Moreover, the tool provesuseful for researchers as the time required for the first step ofweather data accessing is reduced. This prototype analyticaland visualization algorithms can also be extended to supportweather data that are uploaded by users.

Performance analysis of data accessing

After the performance analysis was done, the results revealedthat processing the text files tookmuch longer than processingthe binary files. Details about the time required for each wayof data access were described below.

Data access using direct software interface on a PC

Accessing the data using direct software interface on a PC,text files and binary files that can be processed in less than 30 sare 6 (6 months: 23.17 s) and 48 (48 months: 29.38 s, Fig. 5a).For other binary files - i.e. TMD, SMD and YD - under thesame conditions, they can process 14 (42 months: 29.83 s), 6(36 months: 27.56 s), and 2 (24 months: 20.34 s) respectively(Fig. 5d). For 60 months data processing, text files requirenearly 6 times more processing time than binary files (i.e. at278.85 s for text files and 46.00 s for binary files). The order ofshortest to longest time taken for processing is MD, TMD,SMD, and YD file formats (Fig. 5d).

Data access using a web browser to a local web serveron a PC

Accessing the data using a web browser to a local webserver on a PC, the number of text files and binary files thatcan be processed in less than 30 s increases from 6 to 11(11 months: 28.27 s), and from 48 to 51 (51 months:29.71 s, Fig. 5b) files. For other binary files - i.e. TMD,SMD and YD - under the same conditions, the number ofbinary files that can be processed in less than 30 s increasesfrom 14 to 15 (45 months: 29.08 s) in TMD and from 2 to 3in YD (36 months: 28.37 s) (Fig. 5e). However, there is thesame number of binary files processed with SMD binaryfiles (36 months: 29.79 s) (Fig. 5e).

Fig. 3 Homepage of www.twibl.org/weather. A map shows eightprovinces where eighteenautomatic weather stations wereinstalled

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Fig. 4 The examples of weatherdata visualizations. a time-series,b histogram, c overlay histogram,d 3Dmoving histogram, e clusteranalysis, f correlation,g regression, and h datadistribution analysis

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Data access using a web browser to an internet web server

Accessing the data using a web browser to an internet webserver, the number of text files and binary files that can beprocessed in less than 30 s increases from 6 to 11 (11 months:29.76 s) and from 48 to 60 (60 months: 29.81 s) (Fig. 5c). Forother binary files - i.e. TMD, SMD and YD - under the sameconditions, the number of binary files that can be processed inless than 30 s increases from 14 to 20 (60 months: 29.76 s) inTMD, from 6 to 10 (60 months: 29.38 s) in SMD and from 2to 5 in YD (60 months: 28.76 s) (Fig. 5f).

Near real-time online weather data portal

Near real-time weather data from six study sites are visualizedonline as a weather data portal (www.twibl.org/aaportal)(Fig. 6a–c). Weather parameters - i.e. temperature, hu-midity, dew point, temperature-humidity-wind (THW) index,radiation, UV index, and pressure - at six sites are visualized

using arrow symbols as increase, decrease or no change inparameters between the time of visualization and the tenprevious minutes before visualization (Fig. 6a). Overall(O/A) temperature is composed of heat index (HI), hightemperature, low temperature, and wind chill factor (WCF))as shown in a temperature summary frame. Rainfall parame-ters are composed of rain rate, daily rainfall, monthly rainfall,and yearly rainfall as shown in a rainfall summary frame(Fig. 6b). Wind parameters are composed of wind speed anddirection shown in a wind rose with an average wind speed inkm/h unit (Fig. 6c).

Near real-time weather data from six study sites are shownin six panels - i.e. Koh Samui, Koh PP, Koh Racha, SMNFarm, Muang, Nakhon Si Thammarat and Walailak U. Eachpanel displays 10 frames for 10 weather parameters. Theupdated value of each parameter is displayed at the center ofeach frame. In each frame, data can be interpreted along the x-and y-axes. The y-axis shows the increasing and decreasingtrends while the x-axis shows the predictive trend of theparameter from the previous 10-min readings.

Fig. 5 Comparison of processingtime for text (black circle) andbinary (MD, white circle) fileformats in data access using (a)direct software interface on a PC,b a web browser to local webserver on a PC, and (c) a webbrowser to an internet web serverand comparison of processingtime for binary files as MD (whitecircle), TMD (white square),SMD (white triangle), YD (whitediamond) files in data accessusing (d) direct software interfaceon a PC, e a web browser to localweb server and (f) a web browserto an internet web server

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Along the y-axis, information about data from the tenprevious minutes and the current measurement is displayed.If data from the current measurement has decreased since theprevious measurement, e.g. previous time data: 26.0 °C, cur-rent time data: 25.0 °C, the previous time data will bedisplayed above the current time data. A blue arrow, pointingdownward, will appear between these two data (Fig. 6a). Incontrast, if the current time data has increased since theprevious time measurement, e.g. previous time data:24.0 °C, current time data: 25.0 °C, the previous time datawill be displayed below the current time data with a red arrow,pointing upward between these two data (Fig. 6a). If thecurrent time data and the previous time measurement areequal, e.g. both data are 25.0 °C, only the current time datawill be displayed. Two gray arrows will appear aboveand below, pointing upward and downward to the currenttime data.

Along the x-axis, information about data of the current timemeasurement and the forecasting value for the next 10 min isdisplayed. If the forecasted value is less than the current time

data, e.g. current time data: 25.0 °C, forecasted value: 23.5 °C,the forecasted value will be displayed on the left of the currenttime data. A blue arrow pointing downward will appear abovethe forecasted value (Fig. 6a). In contrast, if the forecastedvalue is greater than the current time data, e.g. current timedata: 25.0 °C, forecasted value: 26.5 °C, the forecasted datawill be displayed on the right of the current time data. A redarrow pointing upward will appear below the forecasted value(Fig. 6a). If the current time data and the forecasted value areequal, e.g. both data are 25.0 °C, the forecasted data will bedisplayed on the right of the current time data. Two grayarrows will appear above and below, pointing upward anddownward to the current time data.

Wind direction and velocity are visualized as wind rose(Fig. 6c). The color and scale of the arrow are designed todisplay the speed of the wind. The color range fromminimumto maximum wind velocity is dark blue, blue, green, yellow,orange, light red, and red. The scale of the arrow also corre-sponds with wind velocity with the larger arrow representshigher wind velocity. Thirteen scaling circles, based on the

a b c

Fig. 6 Visualizations of weatherdata in the www.twibl.org/aaportal: a weather parameters, boverall temperature and rainfall ina summary frame, and (c) windrose visualization

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Beaufort scale, are visualized in the wind rose. The smallestcircle corresponds to the Beaufort number “0” while thebiggest circle corresponds to the Beaufort number “12”. Thisscaling can help users interpret wind speed data more easily.

Although the automatic weather stations that are availablein this near real-time online weather data portal are configuredto collect data each minute, all data uploading and visualiza-tion is configured to process every 10 min. This configurationis designed to prevent the server from high web workload andhigh internet traffic problems. However, if this workflow isperformed on a higher performance computing server, allconfigurations could be modified for completion in a smallerdata interval for near real-time updates.

Discussion

The online weather data analysis and visualization tool wasdeveloped for sharing the information on Ecoinformatics withthe public. To increase the performance of this tool, dataprocessing time control strategy is utilized. Comparison ofprocessing time in data manipulation was done to observe theperformance of this time control strategy. The results demon-strate that using binary files can save time when comparedwith text files. Our results show that data access using a webbrowser to an internet web server can process the highestnumber of data within 30 s. However, processing times inour performance analysis were measured time that the advancedanalytical software kernel processed for the programmed script.Processing times did not include the time for data transfer acrossthe network.

With regard to monthly data manipulation,MD seems to bethe best solution for the limited process time problem as itsaves the most time and is most flexible for data manipulation.According to an earlier study, it is difficult to reduce theoverheads caused by TCP/IP processing and interrupt han-dling unless OS code modification or a hardware change isimplemented (Hu et al. 1999). This study shows that ourtechnique, which can greatly decrease the file processing time,may have significant positive implications for enhancing thecomputing performance of web server.

With this time control strategy, our online weather dataanalysis and visualization tools can achieve higher perfor-mance. Our tools can handle larger data sets and performmorecomplex data analysis and visualization which have beenlimited in the past. With a similar outcome to the works ofArbenz et al. (1997) and Casanova and Dongarra (1997), thepublic can more easily access and compute data via a user-friendly interface. With our tools, users can perform weatherdata analysis and visualization via online computational re-sources. In addition, this time control strategy has an advan-tage over other solutions (e.g. caching (Glassman 1994;Bestavros et al. 1995)), because it provides more flexible data

analysis and visualization. Although the data can be servedfaster for time control strategy using cache, the cache needs tobe computed or duplicated prior to data visualization whichlimits its use. On the other hand, as our time control strategy isa file format conversion the binary files are backed up in thesame format as the original text files. By utilizing this timecontrol strategy, our users can select more options and greaterdata ranges than by using the cache in the server strategy.

Our online weather data analysis and visualization tool is aprototype. Regarding the international standards such as OpenGeospatial Consortium (OGC: http://www.opengeospatial.org)and Open-source Project for a Network Data Access Protocol(OPeNDAP: http://opendap.org), our data portal is in the processof being integrated with the Environment InformatoRIUM(E-Rium), the project of Thailand National Electronics andComputer Technology Center, which uses sensor observationservice (SOS) protocols of OGC (Sahavechaphan et al. 2013).

Another similar distribution of weather data approach is theInternet Data Distribution (IDD) system. The IDD system isdeveloped to publicize near real-time scientific data andextending user’s community via internet technology (Cooper1985; Domenico 1989; Yoksas et al. 2006). The successfullyIDD system is the Unidata Internet Data Distribution Project(http://www.unidata.ucar.edu) with more than 400 internationalsites as user’s community operated on a 24×7 basis since 1995(Yoksas et al. 2006; Unidata Program Center 2012). Unidata’sLocal Data Manager (LDM) can handle data from NationalWeather Service “NOAAport channel 3” data streams,NEXRAD radar data, lightning data from the National Light-ning Detection Network, and GOES satellite imagery. Nowa-days, the Unidata IDD system deals with more than 10 GB/h(~100 products per second) with peak volumes of 15 GB/h(Unidata Program Center 2012). Our approach, however, uti-lized market-based tools and services that are publically andfreely available on the Internet such as cloud storage servicesand files based on popular operating systems. This is possiblebecause bandwidth and coverage of Internet increase dramati-cally in the past few years to accommodate larger data streamsuch as video and social network applications. Compared tothese, weather data require significantly less bandwidth and thuscan operate together on such networking seamlessly. Moreover,the coverage of mobile network is also expanding at a rapid rateto include remote areas where weather stations can be deployedand networked easily.

Our weather data portal supports the near-real time weatherdata from many sites on a local scale. This differentiates itfrom other available near-real time weather data visualizationtools which are mostly designed for weather data on a regionalor global scale. Our weather data portal is developed tovisualize weather data that users can more easily and correctlyinterpret. The visualization is designed to show the trend ofchanges, both of change from previous 10 min to current timeand from current time to the next 10 min. Users can interpret

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weather data from many sites, and can also compare the datafrom different sites at the same time. This near real-time onlineweather data portal is a very useful tool for users in weather,environmental, and disaster monitoring system.

Conclusions

The online weather data analysis and visualization tools andthe near real-time online weather data portal are useful forweather data accessing and monitoring. This study investi-gates and proposes practical and affordable solutions to thehigh web workload problems of web services that handle largedata sets. With this data processing time control strategy, largedata sets and more complex data analysis and/or visualizationcan be computed and accessed online. The proposed timecontrol strategy could be applied using any data analysisand/or visualization server. With the development of acyber-infrastructure to access weather data, these intelligentonline analytical and visualization tools can remove the ob-stacles in knowledge exploration and discovery. Moreover,these tools can be used for understanding and educatingstudents in the science of Ecology and Ecoinformatics.

Acknowledgments We thank Thana na Nagara, Sherri Lynn Conklin,John Endler and two anonymous referees for suggestions on earlierversions of this manuscript. This work was supported in part by TRF/Biotec special program for Biodiversity Research Training grant BRTT_351004, the Institute of Research and Development Fund WU50602,Walailak University Fund 07/2552, and Centre of Excellence forEcoinformatics, NECTEC and Walailak University.

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