Journal of Meteorological Research

, Volume 31, Issue 4, pp 774–790 | Cite as

Reconstructing missing hourly real-time precipitation data using a novel intermittent sliding window period technique for automatic weather station data

Article
  • 72 Downloads

Abstract

Precipitation is the most discontinuous atmospheric parameter because of its temporal and spatial variability. Precipitation observations at automatic weather stations (AWSs) show different patterns over different time periods. This paper aims to reconstruct missing data by finding the time periods when precipitation patterns are similar, with a method called the intermittent sliding window period (ISWP) technique—a novel approach to reconstructing the majority of non-continuous missing real-time precipitation data. The ISWP technique is applied to a 1-yr precipitation dataset (January 2015 to January 2016), with a temporal resolution of 1 h, collected at 11 AWSs run by the Indian Meteorological Department in the capital region of Delhi. The acquired dataset has missing precipitation data amounting to 13.66%, of which 90.6% are reconstructed successfully. Furthermore, some traditional estimation algorithms are applied to the reconstructed dataset to estimate the remaining missing values on an hourly basis. The results show that the interpolation of the reconstructed dataset using the ISWP technique exhibits high quality compared with interpolation of the raw dataset. By adopting the ISWP technique, the root-mean-square errors (RMSEs) in the estimation of missing rainfall data—based on the arithmetic mean, multiple linear regression, linear regression, and moving average methods—are reduced by 4.2%, 55.47%, 19.44%, and 9.64%, respectively. However, adopting the ISWP technique with the inverse distance weighted method increases the RMSE by 0.07%, due to the fact that the reconstructed data add a more diverse relation to its neighboring AWSs.

Key words

automatic weather station intermittent sliding window period interpolation mean absolute error reconstruction of missing precipitation data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anjan, A., R. Pratap, U. K. Shende, et al., 2010: Comparison of automatic raingauge station with observatory and its performance in Indian subcontinent. TECO-2010-WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation, Helsinki, Finland, 1–10.Google Scholar
  2. Chai, T., and R. R. Draxler, 2014: Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247–1250, doi: 10.5194/gmd-7-1247-2014.CrossRefGoogle Scholar
  3. De Silva, R. P., N. D. K. Dayawansa, and M. D. Ratnasiri, 2007: A comparison of methods used in estimating missing rainfall data. The Journal of Agricultural Sciences, 3, 101–108.CrossRefGoogle Scholar
  4. Department of Agriculture & Cooperation Ministry of Agriculture, Government of India, Krishi Bhavan, New Delhi, 2012: A Technical Note on Automatic Weather Station (AWS) and Automatic Rain Gauge (ARG), Draft Report/Guidelines for setting up Automatic Weather Stations (AWSs) and Automatic Rain Gauge (ARGs) & Their Accreditation, Standardization, Validation and Quality Management of Weather Data for Implementation of Weather Based Crop Insurance Scheme (WBCIS), 3–31. Available at http://agricoop.nic.in/sites/default/files/GuidlinesforAWSandWeather%20Data- 15.04.pdf (accessed on April 9, 2017).Google Scholar
  5. Deshpande, N. R., A. Kulkarni, and K. K. Kumar, 2012: Characteristic features of hourly rainfall in India. Int. J. Climatol., 32, 1730–1744, doi: 10.1002/joc.v32.11.CrossRefGoogle Scholar
  6. Giri, R. K., P. Devendra, and A. K. Sen, 2015: Rainfall comparison of automatic weather stations and manual observations over Bihar region. Int. J. Phys. Math. Sci., 5, 1–22, doi: 10.9734/PSIJ.CrossRefGoogle Scholar
  7. Harada, L., 2003: An efficient sliding window algorithm for detection of sequential patterns. Proceedings of the Eighth International Conference on Database Systems for Advanced Applications, Kyoto, Japan, 26–28 March, IEEE Computer Society, 73–80.Google Scholar
  8. Hasan, M. M., and B. F. W. Croke, 2013: Filling gaps in daily rainfall data: A statistical approach. 20th International Congress on Modeling and Simulation, Adelaide, Australia, 1–6 December, 380–386.Google Scholar
  9. Kajornrit, J., K. W. Wong, and C. C. Fung, 2012: Estimation of missing precipitation records using modular artificial neural networks. Neural Information Processing: Lecture Notes in Computer Science. Huang, T. W., Z. G. Zeng, C. D. Li, et al., Eds., Springer, Berlin Heidelberg, 7666, 52–59, doi: 10.1007/978-3-642-34478-7_7.Google Scholar
  10. Lee, H., and K. Kang, 2015: Interpolation of missing precipitation data using Kernel estimations for hydrologic modeling. Adv. Meteor., 2015, 935868, doi: 10.1155/2015/935868.CrossRefGoogle Scholar
  11. Ly, S., C. Charles, and A. Degré, 2011: Geostatistical interpolation of daily rainfall at catchment scale: The use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrol. Earth. Syst. Sci,. 15, 2259–2274, doi: 10.5194/hess-15-2259-2011.CrossRefGoogle Scholar
  12. Ministry of Environment & Forests, Government of India, New Delhi, 2004: Executive Summary. India’s Initial First National Communication to The United Nations Framework Convention on Climate Change, 6–13. Available at http://unfccc.int/resource/docs/natc/indnc1.pdf (accessed on April 9, 2017).Google Scholar
  13. Noori, M. J., H. H. Hassan, and Y. T. Mustafa, 2014: Spatial estimation of rainfall distribution and its classification in Duhok Governorate using GIS. J. Water Resource Prot., 6, 75–82, doi: 10.4236/jwarp.2014.62012.CrossRefGoogle Scholar
  14. Simolo, C., M. Brunetti, M. Maugeri, et al., 2010: Improving estimation of missing values in daily precipitation series by a probability density function-preserving approach. Int. J. Climatol., 30, 1564–1576, doi: 10.1002/joc.1992.Google Scholar
  15. Tang, Q. H., A. W. Wood, and D. P. Lettenmaier, 2009: Real-time precipitation estimation based on index station percentiles. J. Hydrometeor., 10, 266–277, doi: 10.1175/2008JHM1017.1.CrossRefGoogle Scholar
  16. Technical Service Center, 2015: Weather- and Soil Moisture- Based Landscape Irrigation Scheduling Devices. Technical Review Report, 5th Edition, Denver, Colorado, 1–145.Google Scholar
  17. Teegavarapu, R. S. V., and V. Chandramouli, 2005: Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. J. Hydrol., 312, 191–206, doi: 10.1016/j.jhydrol.2005.02.015.CrossRefGoogle Scholar
  18. Verworn, A., and U. Haberlandt, 2011: Spatial interpolation of hourly rainfall-effect of additional information, variogram inference and storm properties. Hydrol. Earth Syst. Sci., 15, 569–584, doi: 10.5194/hess-15-569-2011.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering/Information TechnologyJaypee Institute of Information TechnologyNoidaIndia

Personalised recommendations