• Roby Hambali Department of Civil Engineering, Universitas Bangka Belitung, Kampus Terpadu UBB, Bangka, Indonesia
  • Djoko Legono Department of Civil and Environmental Engineering, Universitas Gadjah Mada, JL. Grafika No. 2, Yogyakarta, Indonesia
  • Rachmad Jayadi Department of Civil and Environmental Engineering, Universitas Gadjah Mada, JL. Grafika No. 2, Yogyakarta, Indonesia



Rain motion, displacement vector, optical flow, nowcastig, X-band MP radar


Short-duration rainfall characteristics in the form of certain intensity, time, and spatial distribution become valuable contribution for lahar flow disaster mitigation in a mountainous region. Due to mitigation purpose, such information can be provided through the rainfall nowcasting process. One of the promising rainfall nowcasting applications is the extrapolation-based method. Rain motion tracking is a crucial part of the rainfall nowcasting based on this method. This paper discusses the application of Pyramid Lucas-Kanade Optical Flow (PLKOF) method on the rain motion tracking analysis using 150x150m resolution radar image. The study of rain motion tracking is carried out using 112 successive rainfall images with 10-minutes time interval originating from Mt. Merapi X-band multiparameter radar. The rainfall movement patterns in short duration are presented in the displacement vector (u,v) images and scatter diagrams of rain motions at x- and y-directions. From the simulations, it was found that the average displacement of rain motions in the Mt. Merapi region is 9 pixels (8.3 km/h) with the dominant direction is northeast. The results show that PLKOF is relatively good at detecting small displacements, yet unable to identify the occurrence of rain growth and decay properly. The ability of PLKOF method in predicting the position of rain cell displacement is satisfied as indicated by the POD, CSI, and FAR indexes.


Otani, K., Legono, D., Darsono, S., and Suharyanto. 2018. Effects of Disaster Management Programs on Individuals’ Preparedness in Mount Merapi. Journal of the Civil Engineering Forum. 4(1): 79–90.

Lavigne, F., Thouret, J. C., Voight, B., Suwa, H., and Sumaryono, A. 2000. Lahars at Merapi Volcano, Central Java : An overview. Journal of Volcanology and Geothermal Research. 100: 423–456.

Hambali, R., Legono, D., Jayadi, R., and Oishi, S. 2018. Statistical Properties of Short-term Rainfall Time Series as Observed by XMP Radar (Case of Mt. Merapi Area). Proceeding of 21st IAHR-APD Congress 2018. 1317–1324.

Fathani, T. F., and Legono, D. 2013. The Application of Monitoring and Early Warning System of Rainfall-Triggered Debris Flow at Merapi Volcano, Central Java, Indonesia. In Progress of Geo-Disaster Mitigation Technology in Asia, Heidelberg: Springer Berlin Heidelberg. 263–275.

Fibriyanto, E. A. 2015. Development of Warning Criteria for Lahar Flow Disaster in Gendol River Area of Mount Merapi. Journal of the Civil Engineering Forum. 1(1): 17–22.

Van Horne, M. P. 2003. Short-Term Precipitation Nowcasting for Composite Radar Rainfall Fields. Massachusetts Institute of Technology.

Zahraei, A., Hsu, K., Sorooshian, S., Gourley, J. J., Hong, Y., and Behrangi, A. 2013. Short-Term Quantitative Precipitation Forecasting Using an Object-Based Approach. Journal of Hydrology. 483: 1–15.

Ganguly A. R., and Bras, R. L. 2003. Distributed Quantitative Precipitation Forecasting Using Information from Radar and Numerical Weather Prediction Models. Journal of Hydrometeorology. 4: 1168–1180.<1168:DQPFUI>2.0.CO;2

Liu, Y., Xi, D., Li, Z., and Hong, Y. 2015. A New Methodology for Pixel-Quantitative Precipitation Nowcasting Using a Pyramid Lucas Kanade Optical Flow Approach. Journal of Hydrology. 529: 354–364.

Golding, B. W. 1998. Nimrod : A System for Generating Automated Very Short Range Forecasts. Meteorological Applications. 16: 1–16.

Wang, P., Smeaton, A., Lao, S., Connor, E. O., Ling, Y., and Connor, N. O. 2009. Short-Term Rainfall Nowcasting : Using Rainfall Radar Imaging. Eurographics Ireland.

Zahraei, A., Hsu, K., Sorooshian, S.,Gourley, J. J., Lakshmanan, V., Hong, Y., and Bellerby, T. 2012. Quantitative Precipitation Nowcasting : A Lagrangian Pixel-Based Approach. Atmospheric Research. 118: 418–434.

Bechini R., and Chandrasekar, V. 2017. An Enhanced Optical Flow Technique for Radar Nowcasting of Precipitation and Winds. Journal of Atmospheric and Oceanic Technology. 34: 2637–2658.

Chu, H., Liu, M., Sun, M., and Chen, L. 2018. Rainfall Nowcasting by Blending of Radar Data and Numerical Weather Prediction [Online First]. In Numerical Analysis and Prediction for Meteorological and Atmospheric Systems, Shanghai, China: IntechOpen. 1–18.

Van Horne, M. P., Vivoni, E. R., Entekhabi, D., Hoffman, R. N., and Grassotti, C. 2006. Evaluating the Effects of Image Filtering in Short-term Radar Rainfall Forecasting for Hydrological Applications. Meteorological Applications. 13: 289–303.

Li, L., He, Z., Chen, S., Mai, X., Zhang, A., Hu, B., Li, Z., and Tong, X. 2018. Subpixel-Based Precipitation Nowcasting with the Pyramid Lucas – Kanade Optical Flow Technique. Atmosphere. 9(260): 1–22.

Grecu M., and Krajewski, W. F. 2000. A Large-Sample Investigation of Statistical Procedures for Radar-Based Short-Term Quantitative Precipitation Forecasting. Journal of Hydrology. 239: 69–84.

Germann U., and Zawadski, I. 2002. Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology. Monthly Weather Review. 130(4): 2859–2873.<2859:SDOTPO>2.0.CO;2

Montanari, L., Montanari, A., and Toth, E. 2006. A Comparison and Uncertainty Assessment of System Analysis Techniques for Short-term Quantitative Precipitation Nowcasting Based on Radar Images. Journal of Geophysical Research. 111(D14111): 1–12.

Berenguer, M., Sempere-torres, D., and Pegram, G. G. S. 2011. SBMcast – An Ensemble Nowcasting Technique to Assess the Uncertainty in Rainfall Forecasts by Lagrangian Extrapolation. Journal of Hydrology. 404(3–4): 226–240.

Woo, W., and Wong, W. 2017. Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting. Atmosphere. 8(48): 1–20.

Bowler, N. E. H., Pierce, C. E., and Seed, A. 2004. Development of a Precipitation Nowcasting Algorithm Based Upon Optical Flow Techniques. Journal of Hydrology. 288: 74–91.

Cheung P., and Yeung, H. Y. 2012. Application of Optical-Flow Technique to Significant Convection Nowcast for Terminal Areas in Hong Kong. In The 3rd WMO International Symposium on Nowcasting and Very Short-Range Forecasting. 1–10.

Park, S.-G., Maki, M., Iwanami, K., Bringi, V. N., and Chandrasekar, V. 2005. Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part II: Evaluation and Application. Journal of Atmospheric and Oceanic Technology. 22(11): 1633–1655.

Hambali, R., Legono, D., Jayadi, R., and Oishi, S. 2019. Improving Spatial Rainfall Estimates at Mt. Merapi Area Using Radar-Rain Gauge Conditional Merging. Journal of Disaster Research. 14(1): 69–79.

B. K. P. Horn and B. G. Schunck, “Determining Optical Flow,†Artif. Intell., vol. 17, pp. 185–203, 1981.

Gibson, J. J. 1950. The Perception of the Visual World. Boston: The Riverside Press, Cambridge.

Tauro, F., Tosi, F., Mattoccia, S., Toth, E., Piscopia, R., and Grimaldi, S. 2018. Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-based Filtering for Surface Streamflow Observations. Remote Sensing. 10(12): 1–24.

Lenzano, M. G., Lannutti, E., Toth, C., Rivera, A., and Lenzano, L. 2018. Detecting Glacier Surface Motion by Optical Flow. Photogrammetric Engineering & Remote Sensing. 84(1): 33–42.

Urbich, I., Bendix, J., and Müller, R. 2018. A Novel Approach for the Short-term Forecast of the Effective Cloud Albedo. Remote Sensing. 10(95): 1–16.

Hadhri, H., Vernier, F., Atto, A. M., and Trouvé, E. 2019. Time-lapse Optical Flow Regularization for Geophysical Complex Phenomena Monitoring. ISPRS Journal of Photogrammetry and Remote Sensing. 150: 135–156.

Ayzel, G., Heistermann, M., and Winterrath, T. 2019. Optical Flow Models as an Open Benchmark for Radar-based Precipitation Nowcasting (rainymotion v0.1). Geoscientific Model Development. 12(4): 1387–1402.

Fleet D. J., and Weiss, Y. 2005. Optical Flow Estimation. In Mathematical Models in Computer Vision: The Handbook. Springer. 239–258.

O’Donovan, P. 2005. Optical Flow: Techniques and Applications.

Xue, T., Mobahi, H., and Freeman, W. T. 2015. The Aperture Problem for Refractive Motion. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3386–3394.

Ravisankar, P., Sharmila, T. S., and Rajendran, V. 2018. Acoustic Image Enhancement Using Gaussian and Laplacian Pyramid – a Multiresolution Based Technique. Multimedia Tools and Applications. 77(5): 5547–5561.

Dash S., and Jena, U. R. 2017. Gaussian Pyramid Based Laws’ Mask Descriptor for Texture Classification. In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). 654–658.

Li, S., Hao, Q., Kang, X., and Benediktsson, J. A. 2018. Gaussian Pyramid Based Multiscale Feature Fusion for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing. 11(9): 3312–3324.

Barron, J. L., Fleet, D. J., and Beauchemin, S. S. 1994. Performance of Optical Flow Techniques. In International Joint Conference of Computer Vision. 43–77.

Konlambigue, S., Pothin, J.-B., Honeine, P., and Bensrhair. 2018. Fast and Accurate Gaussian Pyramid Construction by Extended Box Filtering. In 26th European Signal Processing Conference (EUSIPCO). 400–404.

Karpushin, M., Valenzise, G., and Dufaux, F. 2016. An Image Smoothing Operator for Fast and Accurate Scale Space Approximation. In IEEE International Conference on Acoustics, Speech, and Signal Processing.

Neustaedter, C. 2002. An Evaluation of Optical Flow Using Lucas and Kanade’s Algorithm. [Online]:

Ulbrich, U., Fink, A. H., Klawa, M., and Pinto, G. 2001. Three Extreme Storms over Europe in December 1999. Weather. 56: 70–80.




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