Ling Li
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.
- 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.Google Scholar
Digital Library
- 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.Google ScholarCross Ref
- Bellon, A., Lovejoy, S. and Austin, G. (1980) Combining Satellite and Radar Data for the Short-Range Forecasting of Precipitation.Google ScholarCross Ref
- 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).Google Scholar
- 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 doi:10.1175/mwr-d-11-00181.1.Google ScholarCross Ref
- 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.Google Scholar
- D. Lucas, B. and Kanade, T. (1981) An iterative technique of image registration and its application to stereo.Google Scholar
- 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.Google ScholarCross Ref
- 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) doi:10.3390/atmos11030267.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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. DOI:10.1175/waf-d-11-00050.1Google ScholarCross Ref
- McGovern, A. , 2011. Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Statistical Analysis and Data Mining, 4: 407-429. DOI:10.1002/sam.10128Google ScholarDigital Library
- McGovern, A. , 2011. Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Statistical Analysis and Data Mining, 4: 407-429. DOI:10.1002/sam.10128Google ScholarDigital Library
- 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.Google Scholar
- SVETNIK, V. (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information & Computer Sciences 43.Google Scholar
- Tuttle, J. and Gall, R. (1999) A Single-Radar Technique for Estimating the Winds in Tropical Cyclones.Google ScholarCross Ref
- 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.Google Scholar
- Williams, J.K. (2014) Using random forests to diagnose aviation turbulence. Mach Learn 95(1), 51-70.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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. DOI:10.1175/BAMS-D-14-00174.1.Google Scholar
Recommendations
Precipitation Nowcasting Using Deep Learning on Radar Data Augmented with Satellite Data
IAIT '21: Proceedings of the 12th International Conference on Advances in Information TechnologyPrecipitation nowcasting with ground-based weather radars and satellite-based precipitation data based on deep learning method will open a new avenue of weather prediction. However, it is limited to regions where ground-based weather radars can operate ...
Precipitation Nowcasting with Satellite Imagery
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningPrecipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service. Modern precipitation nowcasting algorithms rely on the extrapolation of observations by ground-based ...
Assessment of deterministic and probabilistic precipitation nowcasting techniques over New York metropolitan area
AbstractThis study evaluates spatiotemporal variability of deterministic and probabilistic precipitation nowcasting models' performance over the greater New York City area. Five deterministic (LINDA-D, ANVIL, S-PROG, RM-DR, RM-S) and two ...
Highlights- Spatiotemporal variability of precipitation nowcasting methods were evaluated.
- ...
Comments