Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 4019–4034, 2021
Geosci. Model Dev., 14, 4019–4034, 2021

Model description paper 29 Jun 2021

Model description paper | 29 Jun 2021

MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data

Dawei Li et al.

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Cited articles

Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine Learning for Precipitation Nowcasting from Radar Images [cs, stat], arXiv [preprint], arXiv:1912.12132, December 2019. 
Ayzel, G., Heistermann, M., and Winterrath, T.: Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1), Geosci. Model Dev., 12, 1387–1402,, 2019. 
Adrianto, I., Trafalis, T. B., and Lakshmanan, V.: Support vector machines for spatiotemporal tornado prediction, Int. J. Gen. Syst., 38, 759–776,, 2009. 
Chandra, R. and Kapoor, A.: Bayesian neural multi-source transfer learning, Neurocomputing, 378, 54–64,, 2020. 
Chandra, R., Cripps, S., Butterworth, N., and Muller, R. D.: Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning, Environ. Model. Softw., 139, 105002,, 2021. 
Short summary
In the daily weather forecast business, numerical weather prediction is mainly used to forecast precipitation, but its performance for nowcasting tasks within 0–2 h is very poor. Hence, we hope to use machine learning to improve the accuracy and resolution of quantitative precipitation nowcasting (QPN) tasks. Previous works focused on the extrapolation of radar echo without using abundant meteorological data. Therefore, we designed a model using three kinds of data for QPN in eastern china.