Progressing urban climate research using a high-density monitoring network system

  • Ivan B. ŠećerovEmail author
  • Stevan M. Savić
  • Dragan D. Milošević
  • Daniela M. Arsenović
  • Dragan M. Dolinaj
  • Srdjan B. Popov


An increasing number of urban meteorological networks (UMNs) and automated data acquisition are irreplaceable tools in modern urban climate monitoring, evaluation, and analysis. The most serious issue in such systems is data loss, caused primarily by communication problems between stations and servers. The Novi Sad Urban Network (NSUNET) consists of 28 remote stations and 2 servers built solely on open-source technologies. It is used for monitoring climate peculiarities and acquiring long-term meteorological data from the urban area of Novi Sad, as well as for the early warning notification to the city emergency services of the current urban weather conditions. Since its deployment, the system has managed to overcome most of the problems related to today’s UMNs, to operate at a low Internet service fee, and ensure high reliability and performance on low-budget hardware. This study includes details on how to develop such a system and it presents a statistical analysis of the NSUNET system’s performances and the measurement data. Furthermore, this kind of monitoring system provides good results in the analysis of air/surface temperature and outdoor human thermal comfort in the local climate zones (LCZs) of urban and surrounding areas and can help identify hot spots/districts in the urban area.


Urban monitoring network NSUNET system Wireless sensor network Open-source technologies Cost-effective network Novi Sad (Serbia) 


Funding information

This study is a result of the following projects: (a) URBAN-PATH project funded by the Hungary-Serbia IPA Cross-border Co-operation EU Program (Project Grant: HUSRB/1203/122/166) and (b) state project funded by Serbian Ministry of Education, Science and Technological Development (Research Grant: 176020).


  1. Aguilar, E., Auer, I., Brunet, M., Peterson, T. C., Wieringa, J. (2003). Guidelines on climate data and homogenization. Report WMO-TD 1186, WCDMP-No. 53, Geneva, pp. 50.Google Scholar
  2. Ali, A. S., Zanzinger, Z., Debose, D., & Stephens, B. (2016). Open source building science sensors (OSBSS): a low-cost Arduino-based platform for long-term indoor environmental data collection. Building and Environment, 100, 114–126.CrossRefGoogle Scholar
  3. Bajšanski, I., Milošević, D., & Savić, S. (2015). Evaluation and improvement of outdoor thermal comfort in urban areas on extreme temperature days: applications of automatic algorithms. Building and Environment, 94(2), 632–643.CrossRefGoogle Scholar
  4. Basara, J. B., Basara, H. G., Illston, B. G., & Crawford, K. C. (2010). The impact of the urban heat island during an intense heat wave in Oklahoma City. Advances in Meteorology, volume, 2010, 10. Scholar
  5. Basara, J. B., Illston, B. G., Fiebrich, C. A., Browder, P. D., Morgan, C. R., McCombs, A., Bostic, J. P., McPherson, R. A., Schroeder, A. J., & Crawford, K. C. (2011). The Oklahoma City Micronet. Meteorological Applications, 18, 252–261.Google Scholar
  6. Bell, S., Cornford, D., & Bastin, L. (2013). The state of automated amateur weather observations. Weather, 68(2), 36–41.CrossRefGoogle Scholar
  7. Carlos-Mancilla, M., López-Mellado, E., & Siller, M. (2016). Wireless sensor networks formation: approaches and techniques. Journal of Sensors, volume, 2016, 18–18. Scholar
  8. Cetin, M. (2015). Determining the bioclimatic comfort in Kastamonu City. Environmental Monitoring and Assessment, 187(10), 640.CrossRefGoogle Scholar
  9. Cetin, M., Adiguzel, F., Kaya, O., & Sahap, A. (2018a). Mapping of bioclimatic comfort for potential planning using GIS in Aydin. Environment, Development and Sustainability, 20(1), 361–375.CrossRefGoogle Scholar
  10. Cetin, M., Yildirim, E., Canturk, U., & Sevik, H. (2018b). Investigation of bioclimatic comfort area of Elazig city centre. In Recent researches in science and landscape management (pp. 324–333). Newcastle upon Tyne: Cambridge Scholars Publishing Lady Stephenson Library.Google Scholar
  11. Chapman, L., Muller, C. L., Young, D. T., Cai, X.-M., Grimmond, C. S. B. (2012). An introduction to the Birmingham Urban Climate Laboratory, ICUC8 – 8th International Conference on Urban Climates, 6th–10th August, UCD, Dublin Ireland: pp. 3.Google Scholar
  12. Chapman, L., Muller, C. L., Young, D. T., Warren, E. L., Grimmond, C. S. B., Cai, X.-M., & Ferranti, E. J. S. (2015). The Birmingham urban climate laboratory: an open meteorological test bed and challenges of the smart city. Bulletin of the American Meteorological Society, 96, 1545–1560. Scholar
  13. Chapman, L., Bell, C., & Bell, S. (2017). Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations. International Journal of Climatology, 37(9), 3597–3605.CrossRefGoogle Scholar
  14. Chen, Y.-C., Yao, C.-K., Honjo, T., & Lin, T.-P. (2018). The application of a high-density street-level air temperature observation network (HiSAN): dynamic variation characteristics of urban heat island in Tainan, Taiwan. Science of the Total Environment, 626, 555–566.CrossRefGoogle Scholar
  15. Chiaradia, E. A., Facchi, A., Masseroni, D., Ferrari, D., Bischetti, G. B., Gharsallah, O., Cesari de Maria, S., Rienzner, M., Naldi, E., Romani, M., & Gandolfi, C. (2015). An integrated, multisensor system for the continuous monitoring of water dynamics in rice fields under different irrigation regimes. Environmental Monitoring and Assessment, 187(9), 586.CrossRefGoogle Scholar
  16. Dabberdt, W., Koistinen, J., Poutiainen, J., Saltikoff, E., & Turtiainen, H. (2005). The Helsinki mesoscale testbed: an invitation to use a new 3-D observation network. Bulletin of the American Meteorological Society, 86, 906–907.CrossRefGoogle Scholar
  17. Dong, B., Sutton, R., Shaffrey, L., Wilcox, L. (2016). The 2015 European heat wave. Bulletin of the American Meteorological Society.
  18. Fenner, D., Meier, F., Bechtel, B., Otto, M., & Scherer, D. (2017). Intra and inter ‘local climate zone’ variability of air temperature as observed by crowdsourced citizen weather stations in Berlin, Germany. Meteorologische Zeitschrift, 26(5), 525–547.CrossRefGoogle Scholar
  19. Gondi, V., Cooky, C. T., Hallstrom, J. O., Eidson, G., Post C. J. (2014). Ultra-scale environmental monitoring—the intelligent river. 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC), 10–13 Januray 2014,
  20. Grimmond, C. S. B. (2006). Progress in measuring and observing the urban atmosphere. Theoretical and Applied Climatology, 84(1–3), 3–22.CrossRefGoogle Scholar
  21. Han, H., Stevens, K. S. (2009). Clocked and asynchronous FIFO characterization and comparison. 17th IFIP/IEEE International Conference on Very Large-Scale Integration, Florianópolis, Brazil, 12–14 October, pp. 7.Google Scholar
  22. Hu, X. M., Xue, M., & Klein, P. M. (2016). Analysis of urban effects in Oklahoma City using a dense surface observing network. Journal of Applied Meteorology and Climatology, 55, 723–741.CrossRefGoogle Scholar
  23. Koskinen, J. T., Poutiainen, J., Schultz, D. M., Joffre, S., Koistinen, J., Saltikoff, E., Gregow, E., Turtiainen, H., Dabberdt, W. F., Damski, J., Eresmaa, N., Göke, S., Hyvärinen, O., Järvi, L., Karppinen, A., Kotro, J., Kuitunen, T., Kukkonen, J., Kulmala, M., Moisseev, D., Nurmi, P., Pohjola, H., Pylkkö, P., Vesala, T., & Viisanen, Y. (2011). The Helsinki testbed: a mesoscale measurement, research, and service platform. Bulletin of American Meteorological Society, 92, 325–342.CrossRefGoogle Scholar
  24. Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263.CrossRefGoogle Scholar
  25. Kulkarni, K. A., & Zambare, M. S. (2018). The impact study of houseplants in purification of environment using wireless sensor network. Wireless Sensor Network, 10(3), 59–69.CrossRefGoogle Scholar
  26. Lehnert, M., Geletič, J., Husák, J., & Vysoudil, M. (2015). Urban field classification by “local climate zones” in a mediumsized central European city: the case of Olomouc (Czech Republic). Theoretical and Applied Climatology, 122, 531–541.CrossRefGoogle Scholar
  27. Lelovics, E., Unger, J., Gál, T., & Gál, C. V. (2014). Design of an urban monitoring network based on local climate zone mapping and temperature pattern modeling. Climate Research, 60, 51–62.CrossRefGoogle Scholar
  28. Lin, F.-Y., Huang, K.-T., Lin, T.-P., & Hwang, R.-L. (2019). Generating hourly local weather data with high spatially resolution and the applications in bioclimatic performance. Science of the Total Environment, 653, 1262–1271.CrossRefGoogle Scholar
  29. Meier, F., Fenner, D., Grassmann, T., Otto, M., & Scherer, D. (2017). Crowdsourcing air temperature from citizen weather stations for urban climate research. Urban Climate, 19, 170–191.CrossRefGoogle Scholar
  30. Mikami, T., Ando, H., Morishima, W., Izumi, T., Shioda, T. (2003). A new urban heat island monitoring system in Tokyo. In Proceedings of the Fifth International Conference on Urban Climate, Lodz, September 2003.Google Scholar
  31. Milošević, D., Kresoja, M., Savić, S., Lužanin, Z. (2018). Intra-urban analysis of relative humidity and its cross-correlation with air temperature in Central-European city. In 10th International Conference on Urban Climate jointly with 14th Symposium on the Urban Environment, New York, NY, USA, 6–10 August 2018.Google Scholar
  32. Muller, C. L., Champan, L., Grimmond, C. S. B., Young, D. T., & Cai, X.-M. (2013a). Sensors and the city: a review of urban meteorological networks. International Journal of Climatology, 33, 1585–1600.CrossRefGoogle Scholar
  33. Muller, C. L., Champan, L., Grimmond, C. S. B., Young, D. T., & Cai, X.-M. (2013b). Toward a standardized metadata protocol for urban meteorological networks. Bulletin of the American Meteorological Society, 94, 1161–1185. Scholar
  34. Oke, T. R. (2006). Initial guidance to obtain representative meteorological observations at urban sites, IOM Report 81, WMO/TD no. 1250, Geneva, pp. 51.Google Scholar
  35. Poutiainen, J., Saltikoff, E., Dabberdt, W. F., Koistinen, J., Turtiainen, H. (2006). Helsinki testbed: a new open facility to test instrumentation technology for atmospheric measurements. WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation Geneva, Switzerland, 4–6 December 2006.Google Scholar
  36. Reina, D. G., Toral, S. L., Barrero, F., Bessis, N., & Asimakopoulou, E. (2013). The role of ad hoc networks in the internet of things: a case scenario for smart environments. In N. Bessis et al. (Eds.), Internet of things and inter-cooperative computational technologies for collective intelligence (pp. 89–113). New York: Springer. Scholar
  37. Savić, S., Lužanin, Z., Milošević, D., Kresoja, M. (2018a). Intra-urban analysis of air temperature in Central-European city. The 10th International Conference on Urban Climate (ICUC10) with the 14th Symposium on the Urban Environment (SUE), August 6-10, 2018, New York, USA, Extended Abstract, pp. 6.Google Scholar
  38. Savić, S., Marković, V., Šećerov, I., Pavić, D., Arsenović, D., Milošević, D., Dolinaj, D., Nagy, I., & Pantelić, M. (2018b). Heat wave risk assessment and mapping in urban areas: case study for a midsized Central European city, Novi Sad (Serbia). Natural Hazards, 91, 891–911.CrossRefGoogle Scholar
  39. Schroeder, A. J., Basara, J. B., & Illston, B. G. (2010). Challenges associated with classifying urban meteorological stations: the Oklahoma City Micronet example. The Open Atmospheric Science Journal, 4, 88–101.CrossRefGoogle Scholar
  40. Šećerov, I., Savić, S., Milošević, D., Marković, V., & Bajšanski, I. (2015). Development of an automated urban climate monitoring system in Novi Sad (Serbia). Geographica Pannonica, 19(4), 174–183.CrossRefGoogle Scholar
  41. Skarbit, N., Stewart, I. D., Unger, J., & Gál, T. (2017). Employing an urban meteorological network to monitor air temperature conditions in the ‘local climate zones’ of Szeged, Hungary. International Journal of Climatology, 37, 582–596.CrossRefGoogle Scholar
  42. Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93, 1879–1900. Scholar
  43. Suomi, J., & Käyhkö, J. (2012). The impact of environmental factors on urban temperature variability in the coastal city of Turku, SW Finland. International Journal of Climatology, 32, 451–463.CrossRefGoogle Scholar
  44. Tashtoush, Y., Al-Maolegi, M., & Arkok, B. (2014). The correlation among software complexity metrics with case study. International Journal of Advanced Computer Research, 4, 414–419.Google Scholar
  45. UN. (2014). World urbanization prospects—the 2014 revision (p. 32). New York: Department of Economic and Social Affairs.Google Scholar
  46. Unger, J., Savić, S., & Gál, T. (2011). Modelling of the annual mean urban heat island pattern for planning of representative urban climate station network. Advances in Meteorology, volume 2011, 9.
  47. Unger, J., Savić, S., Gál, T., & Milošević, D. (2014). Urban climate and monitoring network system in Central European cities (p. 103). Novi Sad: University of Novi Sad, University of Szeged.Google Scholar
  48. Unger, J., Savić, S., Gál, T., Milošević, D., Marković, V., Gulyás, Á., Arsenović, D. (2015). Urban climate monitoring networks based on LCZ concept. ICUC9 – 9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment, 20th-24th July, Toulouse, France, Extended Abstracts, pp. 6.Google Scholar
  49. Warren, E. L., Young, D. T., Chapman, L., Muller, C. L., Grimmond, C. S. B., & Cai, X.-M. (2016). The Birmingham urban climate laboratory—a high density, urban meteorological dataset, from 2012–2014. Scientific Data, 3(160038).
  50. Watras, C. J., Morrow, M., Morrison, K., Scannell, S., Yaziciaglu, S., Read, J. S., Hu, Y., Hanson, P. C., & Kratz, T. (2014). Evaluation of wireless sensor networks (WSNs) for remote wetland monitoring: design and initial results. Environmental Monitoring and Assessment, 186(2), 919–934.CrossRefGoogle Scholar
  51. Wilhelmi, O. V., & Hayden, M. H. (2010). Connecting people and place: a new framework for reducing urban vulnerability to extreme heat. Environmental Research Letters, 5, 1–7.CrossRefGoogle Scholar
  52. Young, D. T., Chapman, L., Bland, S. C., Muller, C. L. (2012). Assessing a ‘low-cost’ wireless temperature sensors for HiTemp. ICUC8 – 8th International Conference on Urban Climates, 6th-10th August, UCD, Dublin, Ireland, pp. 4.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Sciences, Climatology and Hydrology Research CentreUniversity of Novi SadNovi SadSerbia
  2. 2.Faculty of Technical Sciences, Department of Applied Computer ScienceUniversity of Novi SadNovi SadSerbia

Personalised recommendations