Prediction of rainfall is important in terms of the impact of a rain event on various systems such as communication systems. Traditional approaches used to predict rain events are often sensitive to fluctuations in the datasets on which the predictions are made. The present paper therefore develops a robust machine learning based technique for accurate short term rain forecasting, based on experimentally collected data. Ground based microwave radiometer allows continuous monitoring of ambient temperature, water vapour and liquid water, and other hydrometeors through measurement of radiometric brightness temperature at different frequencies in clear and cloudy weather conditions. The radiometric brightness temperature outputs at 23.834 and 30 GHz are used to establish a relation where data trends which are precursors to rain events can be identified using this parameter. Spline equations are modeled by partitioning the dataset. The predictability of the occurrence of precipitation and the rainfall intensity has been studied based on the rise of brightness temperature from clear to cloudy weather conditions. The rise of brightness temperature at 23.834 and 30 GHz show that the precursory variations of this parameter preceding rain events are observable from 29 to 47 minutes prior to precipitation depending upon the nature of rainfall patterns. The data collected empirically displays trends that are used in this paper to provide a clear forecast of short term precipitation. Spline regression based machine learning models incorporating monthly trends, proposed in this paper improve the accuracy of prediction of short term rain events.
Radiometer,brightness temperature,microwave,propagation,rain,weather forecasting,
I. Ahuna, M. N., Afullo, T. J. and Alonge, A. A., “Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation,” in SAIEE Africa Research Journal, vol. 110, no. 1, pp. 11-18, March 2019.
II. Barbaliscia, F., Fionda, E., &Masullo, P. G., “Ground-based radiometric measurements of atmospheric brightness temperature and water contents in Italy,” Radio Sci.,Vol. 33, no. 3,pp. 697-706, 1998.
III. Bosisio, A. V., Fionda, E., Basili, P., Carlesimo, G., Martellucci, A., “Identification of rainy periods from ground based microwave radiometry,” European Journal of Remote Sensing, Vol. 45, pp: 41-50, 2012.
IV. Bosisio, A. V., Fionda, E., Ciotti, P., Fionda, E., Martellucci, A., “Rainy events detection by means of observed brightness temperature ratio,” “Rainy events detection by means of observed brightness temperature ratio,” 2012 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad), Rome, pp. 1-4, 2012.
V. Chan, P.W., & Tam, C.M., “Performance and application of a multi-wavelength, ground-based microwave radiometer in rain now-casting”, 9th IOAS-AOLS of AMS., 2005.
VI. Chan, P.W., “Performance and application of a multi-wavelength, ground based microwave radiometer in intense convective weather,” Meteorol. Z., Vol. 18, no.3,pp. 253–265, 2009.
VII. Chan, P.W., & Hon, K.K., “Application of ground-based, multichannel microwave radiometer in the now casting of intense convective weather through instability indices of the atmosphere,” Meteorol. Z., Vol. 20, no.4, pp. 431–440, 2011.
VIII. Doran, J.C., Zhong, S., Liljegren, J. C., &Jakob, C., “A comparison of cloud properties at a coastal and inland site at the North Slope of Alaska,” J. Geophys. Res.,Vol. 107 (D11), pp. 4120, doi:10.1029/2001JD000819., 2002.
IX. Dvorak, P., Mazanek, M., Zvanovec, S., “Short-term Prediction and Detection of Dynamic Atmospheric Phenomena by Microwave Radiometer,” Radioengineering, Vol. 21, no. 4, Dec. 2012.
X. Geerts, B., “Estimating downburst-related maximum surface wind speeds by means of proximity soundings in New South Wales,” Australia,Weather Forecast, Vol. 16, pp. 261–269, 2001.
XI. Güldner, J., &Spänkuch, D., “Results of year-round remotely sensed integrated water vapor by ground-based microwave radiometry”, J. Appl. Meteorol., Vol. 38, pp:981-988, 1999.
XII. Güldner, J., &Spänkuch, D., “Remote sensing of the thermodynamic state of the atmospheric boundary layer by ground-based microwave radiometry,” J. Atmos. Oceanic Technol., Vol. 18,pp: 925-933, 2001.
XIII. Hye, Y.W., Yeon-Hee, K., &Hee-Sang, L., “An application of brightness temperature received from a ground-based microwave radiometer to estimation of precipitation occurrences and rainfall intensity,” Asia-Pacific Journal of Atmospheric Sciences, Vol. 45, no. 1, pp: 55-69, 2009.
XIV. Karmakar, P. K., Maiti, M., Calheiros, A. J. P., Angelis, C. F., Machado, L. A. T., Da Costa, S. S., ”Ground based single frequency micro -wave radiometric measurement of water vapour ,” International Journal of remote sensing(UK) , vol. 32, No. 23, pp 1-11, 2011.
XV. Knupp, K., Ware, R., Cimni, D., Vandenberghe F., Vivekanandan, J., Westwater, E., Coleman, T., “Ground-based passive microwave profiling during dynamic weather conditions,” J. Atmos. Oceanic Technol., Vol. 26, pp. 1057–1072, 2009.
XVI. Liu, G. R., “Rainfall intensity estimation by ground-based dual-frequency microwave radiometers,” Journal of Applied Meteorology, Vol. 40, pp: 1035-1041, 2001.
XVII. Lee, O.S.M., “Forecast of strong gusts associated with thunderstorms based on data from radiosonde ascents and automatic weather stations in China”, 21st Guangdong–Hong Kong–Macao Technical Seminar on Meteorological Science and Technology, Hong Kong, 24–26 Jan. 2007.
XVIII. Madhulatha, A., Rajeevan, M., Ratnam, M. Venkat, Bhate, Jyoti, & Naidu, C. V., “Now casting severe convective activity over southeast India using ground-based microwave radiometer observations,” Journal of Geophysical Research, Vol. 118, pp.1-13, 2013.
XIX. Manandhar, S., Lee, Y. H., Meng, Y. S., Yuan, F. and Ong, J. T., “GPS-Derived PWV for Rainfall Nowcasting in Tropical Region,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4835-4844, Aug. 2018.
XX. Manzato, A., “A climatology of instability indices derived from Friuli Venezia Giulia soundings, using three different methods,” Atmos. Res., 67–68, 417–454, 2003.
XXI. McCann, D. W., “WINDEX—A new index for forecasting microburst potential,” Weather Forecast, Vol. 9, pp. 532–541, 1994.
XXII. Ojo, J. S., Ajewole, M. O., &Sarkar, S. K., “Rain Rate Attenuation Prediction for Satellite communication in Ku and Ka Bands over Nigeria,” Progress In Electromagnetics Research B, Vol. 5, 207–223, 2008.
XXIII. Qiu, Minghui, Zhao, Peilin, Zhang, Ke, Huang, Jun, Shi, Xing, Wang, Xiaoguang& Chu, Wei, “A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks,” 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, 2017, pp. 395-404.
XXIV. Rivero, C. Rodriguez, Pucheta, J., Herrera, M., Sauchelli, V. and Laboret, S., “Time Series Forecasting Using Bayesian Method: Application to Cumulative Rainfall,” in IEEE Latin America Transactions, vol. 11, no. 1, pp. 359-364, Feb. 2013.
XXV. Rose, T. &Czekala H.,” Filter bank radiometers for atmospheric profiling,” Sixth International Symposium on Tropospheric Profiling: Needs and Technologies. Meckenhiem, Germany, 2003.
XXVI. Ulaby, F. T., Moore, R. K., Fung, A. K., “Microwave Remote Sensing Active and Passive,” Vol. 1, Microwave Remote Sensing Fundamentals and Radiometry, Addison-Wesley, 1981.
XXVII. Ware, R., Solheim, F., Carpenter, R., Gueldner, J., Liljegren, J., Nehrkorn, T., &Vandenberghe, F., “A multi-channel radiometric profiler of temperature, humidity and cloud liquid,” Radio Sci., vol. 38, no. 4, 8079, pp.1–13, 2003.
XXVIII. Yang, H.Y., Chang, K.H., & Oh, S. N., “Measurements of precipitable water vapor and liquid water path by dual-channel microwave radiometer during 2001-2003,” Proceedings of the Autumn Meeting of KMS, 104-105, 2006.
XXIX. Zhang, Pengcheng, Jia, Yangyang, Gao, Jerry, Song, Wei, and Leung, Hareton K. N., “Short-term Rainfall Forecasting Using Multi-layer Perceptron,” in IEEE Transactions on Big Data, 2018.