A New Precipitation Nowcasting method Based on Extrapolation Technique and Random Forest
Authors: 
Ling Li, Xiongfa MaiAuthors Info & Claims
Ling Li, Xiongfa MaiAuthors Info & ClaimsPages 14 - 19
Abstract
The Precipitation Nowcasting is critical to the safe of region. Tradition extrapolate-based precipitation nowcasting used the simple extrapolation techniques, which accuracy decreased quickly after 30 minutes. In this study, a new precipitation forecasting scheme named as RF-SPLK has been developed that blends the extrapolation nowcasting method with machine learning technique. The proposed method can improve the accuracy of precipitation forecasting within the 2 h lead time. The experiments show that the statistical skill scores better than the compareable nowcasting methods and the forecast image is more continuity and close to the observed.
References
[1]
Ayzel, G., Heistermann, M., Sorokin, A., Nikitin, O. and Lukyanova, O. (2019) All convolutional neural networks for radar-based precipitation nowcasting. Procedia Computer Science 150, 186-192.
[2]
Behrangi, A., Khakbaz, B., Jaw, T.C., AghaKouchak, A., Hsu, K. and Sorooshian, S. (2011) Hydrologic evaluation of satellite precipitation products over a mid-size basin. Journal of Hydrology 397(3-4), 225-237.
[3]
Bellon, A., Lovejoy, S. and Austin, G. (1980) Combining Satellite and Radar Data for the Short-Range Forecasting of Precipitation.
[4]
Bento, A.P., Gaulton, A., Hersey, A., Bellis, L.J., Chambers, J., Davies, M., Kruger, F.A., Light, Y., Mak, L. and Mcglinchey, S. (2002) Classification and Regression by randomForest. R News 23(23).
[5]
Berenguer, M., M. Surcel, I. Zawadzki, M. Xue & F. Kong, 2012. The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting. Monthly Weather Review 140(8):2689-2705
[6]
Bonazza, P., J. Miteran, D. Ginhac & J. Dubois, 2019. Traditional Machine Learning or Deep Learning Methods for Embedded Computer Vision Study on Biometric Application. Biostatistics and Biometrics Open Access Journal 9.
[7]
D. Lucas, B. and Kanade, T. (1981) An iterative technique of image registration and its application to stereo.
[8]
Das, S., Chakraborty, R. and Maitra, A. (2017) A random forest algorithm for nowcasting of intense precipitation events. Advances in Space Research 60(6), 1271-1282.
[9]
Franch, G., D. Nerini, M. Pendesini, L. Coviello, G. Jurman & C. Furlanello, 2020. Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events. Atmosphere 11(3)
[10]
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.
[11]
Liu, Y., Xi, D.-G., Li, Z.-L. 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.
[12]
Mandapaka, P.V., Germann, U., Panziera, L., Hering, A., 2012. Can Lagrangian Extrapolation of Radar Fields Be Used for Precipitation Nowcasting over Complex Alpine Orography? Weather and Forecasting, 27(1): 28-49.
[13]
McGovern, A., 2011. Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Statistical Analysis and Data Mining, 4: 407-429.
[14]
McGovern, A., 2011. Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Statistical Analysis and Data Mining, 4: 407-429.
[15]
Shi, X., Z. Chen, H. Wang, D.-Y. Yeung, W. K. Wong & W.-c. Woo, 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.
[16]
SVETNIK, V. (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information & Computer Sciences 43.
[17]
Tuttle, J. and Gall, R. (1999) A Single-Radar Technique for Estimating the Winds in Tropical Cyclones.
[18]
Wang, G., W. K. Wong, Y. Hong, L. Liu, J. Dong and M. Xue (2014). "Improvement of forecast skill for severe weather by merging radar-based extrapolation and storm-scale NWP corrected forecast." Atmospheric Research 154.
[19]
Williams, J.K. (2014) Using random forests to diagnose aviation turbulence. Mach Learn 95(1), 51-70.
[20]
Wilson, J., E. E. Ebert, T. R. Saxen, R. Roberts, C. K. Mueller, M. Sleigh, C. Pierce and A. Seed (2004). "Sydney 2000 Forecast Demonstration Project: Convective Storm Nowcasting." Weather and Forecasting - WEATHER FORECAST 19: 131-150.
[21]
Xie, P., X. Li, X. Ji, X. Chen & Y. Ye, 2020. An Energy-Based Generative Adversarial Forecaster for Radar Echo Map Extrapolation. IEEE Geoscience and Remote Sensing Letters PP(99):1-5.
[22]
Zahraei, A., K.-l. Hsu, S. Sorooshian, J. J. Gourley, Y. Hong and A. Behrangi (2013). "Short-term quantitative precipitation forecasting using an object-based approach." Journal of Hydrology 483: 1-15.
[23]
Zhang, J.; Howard, K.; Langston, C.; Kaney, B.; Qi, Y.; Tang, L.; Grams, H.; Wang, Y.; Cocks, S.; Martinaitis, S., Multi-radar multi-sensor (mrms) quantitative precipitation estimation: Initial operating capabilities. Bulletin of the American Meteorological Society 2016, 97, 621-638.
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Published In
June 2021
71 pages
ISBN:9781450389648
DOI:10.1145/3471274
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Association for Computing Machinery
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Published: 26 August 2021
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HP3C'21
HP3C'21: 2021 5th International Conference on High Performance Compilation, Computing and Communications
June 18 - 20, 2021
Guangzhou, China
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