DOI: https://doi.org/10.18517/ijods.3.2.71-79.2022
Development of a Method for Classifying Convective and Stratiform Rains from Micro Rain Radar (MRR) Observation Data Using Artificial Neural Network
Abstract
This study examined the performance of Artificial Neural Network (ANN)-backpropagation to classify rain types from observations of Micro Rain Radar (MRR) in Serpong (6.359oSL; 106.673oEL). The inputs of ANN are radar reflectivity, Doppler velocity, and Liquid Water Content (LWC). Rain events on January 5, 2017; at 16.28 – 21.21 local time were used as training data. The ANN results were validated with rain classified by the Bright Band (BB) and Countour Frequency by Altitude Diagram (CFAD) methods. The most appropriate ANN-backpropagation architecture is the 3-6-1 architecture (input layer-hidden layer-output layer), with an activation-transfer function being competitive and a learning rate of 0.9. The Mean Square Error (MSE) of the training step was 0.0098735, and the average percentage of accuracy for the test step was 94%. A rain event with a single type of rain can be classified accurately by ANN and gives the same results as the CFAD method. Thus, the ANN can be a solution to the shortcomings of the BB method, which sometimes classification results of a single type of rain events is interspersed with another type, which is physically impossible.
Article Details
How to Cite
[1]
B. Aprilia, M. Marzuki, I. Taufiq, and F. Renggono, “Development of a Method for Classifying Convective and Stratiform Rains from Micro Rain Radar (MRR) Observation Data Using Artificial Neural Network”, Int. J. Data. Science., vol. 3, no. 2, pp. 71-79, Sep. 2022.
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References
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[3] R. Li, Q. Min, X. Wu, and Y. Fu, “Retrieving Latent Heating Vertical Structure from Cloud and Precipitation Profiles—Part II: Deep Convective and Stratiform Rain Processes,” Journal of Quantitative Spectroscopy and Radiative Transfer, vol. 122, pp. 47-63, Jun. 2013, doi: 10.1016/j.jqsrt.2012.11.029.
[4] W.K Tao, S. Lang, X. Zeng, S. Shige, and Y. Takayabu, “Relating Convective and Stratiform Rain to Latent Heating,” Journal of Climate, vol. 23, np. 7, pp. 1874–1893, Apr. 2010, doi: 10.1175/2009JCLI3278.1.
[5] A. Dai, “Precipitation Characteristics in Eighteen Coupled Climate Models,” Journal of Climate, vol. 19, no. 18, pp. 4605–4630, Sep. 2006, doi: 10.1175/JCLI3884.1.
[6] P. M. Austin and R. A. Houze Jr., “Analysis of the Structure of Precipitation Patterns in New England,” Journal of Applied Meteorology and Climatology, vol. 11, no. 6, pp. 926–935, Sep. 1972, doi: 10.1175/1520-0450(1972)011<0926:AOTSOP>2.0.CO;2.
[7] R. A. Houze Jr., “A Climatological Study of Vertical Transports by Cumulus-Scale Convection,” Journal of Atmospheric Sciences, vol. 30, no. 6, pp. 1112–1123, Sep. 1973, doi: 10.1175/1520-0469(1973)030<1112:ACSOVT>2.0.CO;2.
[8] D. D. Churchill and R. A. Houze Jr. “Development and Structure of Winter Monsoon Cloud Clusters on 10 December 1978,” Journal of Atmospheric Sciences, vol. 41, no. 6, pp. 933–960, Mar. 1984, doi: 10.1175/1520-0469(1984)041<0933:DASOWM>2.0.CO;2.
[9] M. Steiner, R. A. Houze Jr, and S. E. Yuter, “Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data,” Journal of Applied Meteorology and Climatology, vol. 34, no. 9, pp. 1978–2007, Sep. 1995, doi: 10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2.
[10] M. I. Biggerstaff and S. A. Listemaa, “An Improved Scheme for Convective/Stratiform Echo Classification Using Radar Reflectivity,” Journal of Applied Meteorology, vol. 39, no. 12, pp. 2129–2150, Dec. 2000, doi: 10.1175/1520-0450(2001)040<2129:AISFCS>2.0.CO;2.
[11] C. R. Williams, W. L. Ecklund, and K. S. Gage, “Classification of Precipitating Clouds in the Tropics Using 915-MHz Wind Profilers,” Journal of Atmospheric and Oceanic Technology, vol. 12, no. 5, pp. 996–1012, Oct. 1995, doi: 10.1175/1520-0426(1995)012<0996:COPCIT>2.0.CO;2.
[12] A. Tokay and D. A. Short, “Evidence from Tropical Raindrop Spectra of the Origin of Rain from Stratiform versus Convective Clouds.,” Journal of Applied Meteorology and Climatology, vol. 35, no. 3, pp. 355–371, Mar. 1996, doi: 10.1175/1520-0450(1996)035<0355:EFTRSO>2.0.CO;2.
[13] H. Wang, H. Lei, and J. Yang, “Microphysical Processes of a Stratiform Precipitation Event over Eastern China: Analysis Using Micro Rain Radar Data,” Advances in Atmospheric Sciences, vol. 34, no. 12, pp. 1472–1482, Nov. 2017, doi: 10.1007/s00376-017-7005-6.
[14] R. Ramadhan, Marzuki, M. Vonnisa, Harmadi, H. Hashiguchi, and T. Shimomai., “Diurnal Variation in the Vertical Profile of the Raindrop Size Distribution for Stratiform Rain as Inferred from Micro Rain Radar Observations in Sumatra,” Advances in Atmospheric Sciences, vol. 47, no. 8, pp. 832–846, Jul. 2020, doi: 10.1007/s00376-020-9176-9.
[15] A. Foth, J. Zimmer, F. Lauermann, and H. Kalesse-Los., “Evaluation of Micro Rain Radar-Based Precipitation Classification Algorithms to Discriminate between Stratiform and Convective Precipitation,” Atmospheric Measurement Techniques, vol. 14, no. 6, pp. 4565–37108, Jul. 2016, doi: 10.5194/amt-14-4565-2021.
[16] S. L. Zhang, and T. C. Chang, “A Study of Image Classification of Remote Sensing Based on Back-Propagation Neural Network with Extended Delta Bar Delta,” Mathematical Problems in Engineering 2015, Oct. 2015, doi: 10.1155/2015/178598.
[17] J. D. Paola and R. A. Schowengerdt, “A Review and Analysis of Backpropagation Neural Networks for Classification of Remotely-Sensed Multi-Spectral Imagery,” International Journal of Remote Sensing, vol. 16, no. 16, pp. 3033–3058, Apr. 2007, 2019, doi: 10.1080/01431169508954607.
[18] B. Aprilia, Marzuki, and I. Taufiq, “Performance of Backpropagation Artificial Neural Network to Predict El Nino Southern Oscillation Using Several Indexes as Onset Indicators,” Journal of Physics: Conference Series - IOP Publishing, vol. 1876, no. 1, Apr. 2021.
[19] S. E. Yuter and R. A. Houze Jr., “Three-Dimensional Kinematic and Microphysical Evolution of Florida Cumulonimbus. Part II: Frequency Distributions of Vertical Velocity, Reflectivity, and Differential Reflectivity,” Monthly Weather Review, vol. 123, no. 7, pp. 1941–1963, Jul. 1995, doi: 10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2.