Abdullah Ali, S. Supriatna, Umi Sa'adah


Nowcasting, or the short-term forecasting of precipitation, is urgently needed to support the mitigation circle in hydrometeorological disasters. Pangkalan Bun weather radar is single-polarization radar with a 200 km maximum range and which runs 10 elevation angles in 10 minutes with a 250 meters spatial resolution. There is no terrain blocking around the covered area. The Short-Term Ensemble Prediction System (STEPS) is one of many algorithms that is used to generate precipitation nowcasting, and is already in operational use. STEPS has the advantage of producing ensemble nowcasts, by which nowcast uncertainties can be statistically quantified. This research aims to apply STEPS to generate stochastic nowcasting in Pangkalan Bun weather radar and to analyze its advantages and weaknesses. Accuracy is measured by counting the possibility of detection and false alarms under the 5 dBZ threshold and plotting them in a relative operating characteristic (ROC) curve. The observed frequency and forecast probability is represented by a reliability diagram to evaluate nowcast reliability and sharpness. Qualitative analysis of the results showed that the STEPS ensemble produces smoothed reflectivity fields that cannot capture extreme values in an observed quasi-linear convective system (QLCS), but that the algorithm achieves good accuracy under the threshold used, up to 40 minutes lead time. The ROC shows a curved upper left-hand corner, and the reliability diagram is an almost perfect nowcast diagonal line.


weather radar, nowcasting, Short Term Ensemble Prediction Systems (STEPS)

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Adi, S. (2013). Karakterisasi Bencana Banjir Bandang. Jurnal Sains dan Teknologi Indonesia, 15(1), 42-51.

Ali, A., Adrianto, R., & Saepudin, M. (2019). Preliminary Study of Horizontal And Vertical Wind Profile Of Quasi-Linear Convective Utilizing Weather Radar Over Western Java Region, Indonesia. International Journal of Remote Sensing and Earth Sciences (IJReSES), 15(2), 177-186.

Ali, A., Deranadyan, G., & Umam, I. H. (2020). An Enhancement to The Quantitative Precipitation Estimation Using Radar-Gauge Merging. International Journal of Remote Sensing and Earth Sciences (IJReSES), 17(1), 65-74.

Bowler, N. E., Pierce, C. E., & Seed, A. W. (2006). STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society: A Journal of the Atmospheric Sciences, Applied Meteorology and Physical Oceanography, 132(620), 2127-2155.

Bröcker, J., & Smith, L. A. (2007). Increasing the eliability of reliability diagrams. Weather and Forecasting, 22(3), 651-661.

Cao, C. Y., Chen, Y. Z., Liu, D. H., Li, C., Li, H., & He, J. (2015). The optical flow method and its application to nowcasting. Acta Meteor. Sinica, 73, 471-480.

Chen, M. X., Wang, Y. C., & Yu, X. D. (2007). Improvement and application test of TREC algorithm for convective storm nowcast. J. Appl. Meteor. Sci, 18, 690-701.

Crane, R. K. (1979). Automatic cell detection and tracking. IEEE Transactions on Geoscience Electronics, 17(4), 250-262.

Dixon, M., & Wiener, G. (1993). TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. Journal of Atmospheric and Oceanic Technology, 10(6), 785-797.

Han, L., Wang, H. Q., & Lin, Y. J. (2008). Application of optical flow method to nowcasting convective weather. Acta Scientiarum Naturalium Universitatis Pekinensis, 44(5), 751-755.

Johnson, J. T., MacKeen, P. L., Witt, A., Mitchell, E. D. W., Stumpf, G. J., Eilts, M. D., & Thomas, K. W. (1998). The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Weather and Forecasting, 13(2), 263-276.

Jolliffe, I. T., & Stephenson, D. B. (2003). Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley and Sons.

Kedem, B., & Chiu, L. S. (1987). On the lognormality of rain rate. Proceedings of the National Academy of Sciences, 84(4), 901-905.Miyakoda, K., & Talagrand, O. (1971). The assimilation of past data in dynamical analysis. I. Tellus, 23(4-5), 310-317.

Pulkkinen, S., Chandrasekar, V., & Harri, A. M. (2018). Nowcasting of precipitation in the high-resolution Dallas–Fort Worth (DFW) urban radar remote sensing network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2773-2787

Pulkkinen, S., Chandrasekar, V., von Lerber, A., & Harri, A. M. (2020). Nowcasting of convective rainfall using volumetric radar observations. IEEE Transactions on Geoscience and Remote Sensing, 58(11), 7845-7859.

Putri, Y. P. (2018). Arahan Kebijakan Mitigasi Bencana Banjir Bandang di Daerah Aliran Sungai (DAS) Kuranji, Kota Padang. Majalah Ilmiah Globe, 20(2), 87-98.

Rinehart, R. E., & Garvey, E. T. (1978). Three-dimensional storm motion detection by conventional weather radar. Nature, 273(5660), 287-289.

Veneziano, D., Bras, R. L., & Niemann, J. D. (1996). Nonlinearity and self‐similarity of rainfall in time and a stochastic model. Journal of Geophysical Research: Atmospheres, 101(D21), 26371-26392.

Wilson, J. W., Crook, N. A., Mueller, C. K., Sun, J., & Dixon, M. (1998). Nowcasting thunderstorms: A status report. Bulletin of the American Meteorological Society, 79(10), 2079-2100.

Woo, W. C., & Wong, W. K. (2017). Operational application of optical flow techniques to radar-based rainfall nowcasting. Atmosphere, 8(3), 48.

Yilmaz, A., Javed, O., & Shah, M. (2006). Object tracking: A survey. ACM Computing Surveys (CSUR), 38(4), 13-es.

Yu, X. D., Zhou, X. G., & Wang, X. M. (2012). The

advances in the nowcasting techniques on thunderstorms and severe convection. Acta Meteorologica Sinica, 70(3), 311-3


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