IMPLEMENTATION OF GABELLA METHOD AND RANDOM FOREST FOR GROUND CLUTTER DETECTION IN PADANG WEATHER RADAR DATA

Wildan Nurahman; Abdullah Ali; Riser Fahdiran

doi:10.21009/SPEKTRA.083.05

Authors

Wildan Nurahman Department of Physics, State University of Jakarta Jl. Rawamangun Muka, Jakarta 13220, Indonesia
Abdullah Ali Remote Sensing Data Management Division, Indonesia Agency for Meteorology and Geophysics Jl. Angkasa 1, Jakarta 10720, Indonesia
Riser Fahdiran Department of Physics, State University of Jakarta Jl. Rawamangun Muka, Jakarta 13220, Indonesia

DOI:

https://doi.org/10.21009/SPEKTRA.083.05

Keywords:

gabella method, ground clutter detection, random forest, weather radar

Abstract

Weather radar is an active remote sensing instrument for various hydrological and meteorological applications. One advantage of weather radar is its ability to detect rainfall in space and time with high spatial resolution. However, one of the issues that contaminate radar observations is ground clutter. Ground clutter is a signal or echo from non-meteorological objects on the earth’s surface that are stationary in the time domain. Detecting and mitigating clutter effects is crucial to achieve precise weather measurements. This research aims to implement the Gabella and random forest methods to detect ground clutter in Padang weather radar data and determine the optimal method between the two. The implementation of the Gabella method for detecting ground clutter in Padang weather radar data was suboptimal. This was due to the most duplicated data at the same point being only 15.97% of the total data. Meanwhile the random forest method obtained a kappa value of 92.03%. This indicates that the random forest model created using 2000 trees as the parameter performs well. Based on these results, the random forest method identified as the most optimal approach for detecting ground clutter in Padang weather radar data.

References

[1] N. Nanding and M. A. Rico-Ramirez, “Precipitation measurement with weather radars,” ICT for Smart Water Systems: Measurements and Data Science, pp. 235-258, 2021.
[2] A. A. Arbain, F. Sunarto and E. Mulyana, “Deteksi Es dan Hail di Atmosfer dengan Radar Polrimetrik X-Band Furuno WR-2100,” Jurnal Sains & Teknologi Modifikasi Cuaca, vol. 19, no. 1, pp. 21-31, 2018.
[3] R. Y. Mardyansyah et al., “Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan,” Elektron Jurnal Ilmiah, pp. 21-29, 2022.
[4] J. Yan and A. Bárdossy, “Short time precipitation estimation using weather radar and surface observations: With rainfall displacement information integrated in a stochastic manner,” Journal of Hydrology, vol. 574, pp. 672-682, 2019.
[5] D. Putri, E. Y. Nugroho and J. R. Pratama, “Kajian Impelementasi Quality Control Faktor Bright Band dan Atenuasi Radar Cuaca C-Band,” Jurnal Teori dan Aplikasi Fisika, vol. 9, no. 1, pp. 111-120, 2021.
[6] J. C. Hubbert, M. Dixon and S. M. Ellis, “Weather radar ground clutter. Part II: Real-time identification and filtering,” Journal of Atmospheric and Oceanic Technology, vol. 26, no. 7, pp. 1181-1197, 2009.
[7] M. H. Golbon-Haghighi and G. Zhang, “Detection of ground clutter for dual-polarization weather radar using a novel 3D discriminant function,” Journal of Atmospheric and Oceanic Technology, vol. 36, no. 7, pp. 1285-1296, 2019.
[8] Y. Wang et al., “A weather signal detection algorithm based on EVD in elevation for airborne weather radar,” Digital Signal Processing, vol. 116, 2021.
[9] K. Osródka and J. Szturc, “Improvement in algorithms for quality control of weather radar data (RADVOL-QC system),” Atmospheric Measurement Techniques (AMT), vol. 15, no. 2, pp. 261-277, 2022.
[10] Golbon-Haghighi et al., “Detection of ground clutter from weather radar using a dual-polarization and dual-scan method,” Atmosphere, vol. 7, no. 6, pp. 2-11, 2016.
[11] D. A. Warde and S. M. Torres, “Staggered-PRT sequences for Doppler weather radars. Part II: Ground clutter mitigation on the NEXRAD network using the CLEAN-AP filter,” Journal of Atmospheric and Oceanic Technology, vol. 34, no. 3, pp. 703-716, 2017.
[12] T. Y. Yu et al., “Variational analysis of oversampled dual-Doppler radial velocity data and application to the analysis of tornado circulations,” Journal of Atmospheric and Oceanic Technology (JAOT), vol. 24, no. 4, pp. 616-626, 2007.
[13] J. L. Speiser et al., “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Systems with Applications, vol. 134, no. 6, pp. 2-11, 2019.
[14] S. Jacobi and M. Heistermann, “Benchmarking attenuation correction procedures for six years of single-polarized C-band weather radar observations in South-West Germany,” Geomatics, Natural Hazards, and Risk, vol. 7, pp. 93-101, 2016.
[15] M. Gabella and R. Notarpietro, “Ground clutter characterization and elimination in mountainous terrain,” Proceedings of ERAD Use of radar observations in hydrological and NWP models, pp. 305-311, 2002.
[16] A. Ali et al., “Preliminary Study Of A Radio Frequency Interference Filter For Non-Polarimetric C-Band Weather Radar In Indonesia (Case Study: Tangerang Weather Radar),” International Journal of Remote Sensing and Earth Sciences, vol. 18, no. 2, pp. 189-202, 2022.
[17] R. Scovell, N. Gaussiat and M. Mittermaier, “Recent improvements to the quality control of radar data for the OPERA data centre,” In Proc. 36th Conference on Radar Meteorology, 2013.
[18] R. H. Anasiru, “Analisis Spasial dalam Klasifikasi Lahan Kritis di Kawasan Sub-DAS Langge Gorontalo,” 2017.
[19] Z. Rachmah, M. M. Rengkung and V. Lahamendu, “Kesesuaian lahan permukiman di kawasan kaki Gunung Dua Sudara,” Spasial, vol. 5, no. 1, pp. 118-129, 2018.