Spatio-temporal outlier detection and analysis in pressure and wind speed data to study climate change

Document Type : Original Article

Authors

1 Applied Climatology Division, Climatology Research Institute, ASMERC, Mashhad, Iran.

2 PhD in Agricultural Meteorology, Ferdowsi University of Mashhad, Mashhad, Iran

3 Applied Climatology Division, Climatology Research Institute, ASMERC, Mashhad, Iran

Abstract

Detecting outliers is one of the most important steps in data analysis. In climate data, an outlier can be an extreme event or an error due to measurement, observation and recording. If outliers which are error are not identified and deleted, they will be recorded as extreme data and cause the bias in result of the climate change studies. In this paper, the outliers of wind speed and pressure data for meteorological stations in the normal climatic period 1991-2020 were analyzed. For this purpose, first the spatial outliers were determined using the CLIMATOL algorithm and second, errors were identified by temporal and meteorological analyzes. In the first step, for the parameters of station pressure, sea level pressure, vapor pressure, wind speed and maximum wind speed were identified as 40, 42, 93, 52 and 41 outliers, respectively. In the second step, 20, 10, 56, 20 and 27 of those data were recognized error, respectively. These results have been reported by station and date, to be used by researchers in other studies, especially climate change studies.

Keywords


  1. دین­پژوه، یعقوب.، فاخری، احمد.، مقدم، محمد.، میرنیا، میرکمال.، و جهانبخش اصل، سعید. (1382). پهنه­بندی اقلیمی ایران با استفاده از تحلیل­های چند متغیره برای استفاده در مطالعات کشاورزی. م. دانش کشاورزی، 13(1)، 90-71.
  2. علیجانی، بهلول. (1390). اقلیم­شناسی سینوپتیک، چاپ چهارم، انتشارات سمت، تهران- ایران.
  3. مسعودیان، سید. ابوالفضل. (1382). نواحی اقلیمی ایران. م. جغرافیا و توسعه. 1(2)، 184-171.
  4. Barnett, V. and Lewis, T. (1994). Outliers in statistical data. John Wiley & Sons, 3rd edition.
  5. Badripour, H. (1992). Islamic Republic of Iran (FAO, Report on the round table on pastoralism. FAO Technical Cooperation Programme, Project TCP/IRA/2255, Rome. FAOSTAT. 2004 (http://www.fao.org/waicent/portal/statistics-en.asp).
  6. Barnett, V. and Lewis, T. (1994). Outliers in statistical data. John Wiley & Sons, 3rd edition.
  7. Coll, J., Domonkos, P., Guijarro, J., Curley, M., Elke Rustemeier, E., Aguilar, E., Walsh, S., and Sweeney, J. (2020). Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results. J. Climatol. 40 (14), 6169- 6188.
  8. Lu, C. and Liang, L.R. 2004. Wavelet Fuzzy Classification for Detecting and Tracking Region Outliers in Meteorological Data. GIS’04, pages 258-265.
  9. Daget, J. (1979). Les modèles mathematiques en écologie. Collection d’Écologie 8, 172, Masson, Paris.
  10. Eleonora Aruffo, E., Piero Di Carlo, P.D. (2019). Homogenization of instrumental time series of air temperature in Central Italy (1930−2015), Clim. Res. Vol. 77: 193–204, 193-203.
  11. Guijarro, J.A. (2018). Homogenization of climatic series with Climatol. Available at: http://www.Climatol.eu/homog_ Climatol-en.pdf [Accessed 28th March 2020].
  12. International Energy Agency (IEA) (2013). Wind Power Technology Roadmap 2013 Edition.
  13. Ma, , Gu, X. and Wang, B. 2017. Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network, Information 2017, 8(2), 60, https://doi.org/10.3390/info8020060.
  14. R. Adam, V.P. Janeja, and V. Atluri. Neighborhood based detection of anomalies in high dimensional spatio-temporal Sensor Datasets. SAC’04, pages 576-583, 2004.
  15. Paulhus, J.L.H., Kohler, M.A. (1952). Interpolation of missing precipitation records. Monthly Weather Review 80(8), 129-133.

 

  1. Prasanthi, M.L., Krishna Chaitanya, A., Sambasiva Rao, Dr. N. (2016). A Survey On Outlier Detection Methods In Spatio-Temporal Datasets, IJAERS, Vol-3, Issue-11, Nov-, 168-172.
  2. Rahimzadeh, F., Nassaji Zavareh, M., (2014). Effects of adjustment for non-climatic discontinuities n determination of temperature trends and variability over Iran. Int. J. Climatol. 34:2079–2096.
  3. Ranjan, K., Tripathy, D.S., Prusty, B.R., and Jena, D. (2020). An improved sliding window prediction-based outlier detection and correction for volatile time-series, Int J Numer 34:e2816.https://doi.org/10.1002/jnm.2816.
  4. Skrynyk, O., Aguilar, E., Guijarro, J., Yannick, L., Randriamarolaza, A., and Bubin, S .(2020). Uncertainty evaluation of Climatol's adjustment algorithm applied to daily air temperature time series. J. Climatol. 41 (S1), E2395-E2419.
  5. Schaeffer R. et al. (2012). Energy sector vulnerability to climate change: A review. Energy, 38, 1-12.
  6. Tavakoli, M., and Mohmoudian, H.A., (2011). GIS based considerations for development in different Iranian climatic regions. J. American. Science. 7(4), 182–187.
  7. Cheng, T. and Li, Z. (2004). A Hybrid Approach to Detect Spatial-temporal Outliers. In Proc. GeoInformaticas, p 173-178.
  8. Yuxiang, S., Kunqing, X., Xiujun, M., Xingxing, J., Wen, P., and Xiaoping, G., (2005). Detecting spatio-temporal outliers in climate dataset: a method study, 2005 IEEE International Geoscience and Remote Sensing Symposium. IGARSS'05.

24.              Wu, E., Liu, W., and Chawla, S. (2008). Spatio-temporal Outlier Detection in Precipitation Data.  Knowledge Discovery from Sensor Data. 115-133.