Submitted as: model description paper 21 Dec 2020

Submitted as: model description paper | 21 Dec 2020

Review status: this preprint is currently under review for the journal GMD.

MSDM: a machine learning model for precipitation nowcasting over east China using multi-source data

Dawei Li, Yudi Liu, and Chaohui Chen Dawei Li et al.
  • Institute of Meteorology and Oceanography, National University of Defense Technology, Changsha 410003, China

Abstract. East China is one of the most economically developed and most densely populated areas in the world. Due to its special geographical location and climate, East China is affected by different weather systems like monsoon, shear line, typhoon and extratropical cyclone, in the imminent future the rainfall rate affected by which is difficult to precisely predict. Traditional physics-based methods like Numerical Weather Prediction (NWP) tend to perform poorly for the nowcasting problem due to its spinup issue. Meanwhile, various meteorological stations are distributed here, generating a large amount of observation data every day, which has a great potential to be applied to data-driven methods. Thus, it is important to train a data-driven model from scratch that suitable to the specific weather situation of East China. We collect three kinds of data (radar, satellite, precipitation) in flood season from 2017 to 2018 of this area and preprocess them into ndarray (256 × 256) that cover East China with a domain of 12.8 × 12.8°.The Multi-Source Data Model (MSDM) which we developed combines the Optical flow, Random forest and Convolutional Neural Network (CNN). It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with a interval of 30 minutes as inputs and predicts radar echo intensity at a lead time of 30 minutes. To reduce the smoothing caused by convolution, we use Optical flow to predict satellite data in the following 120 minutes. The predicted radar echo from MSDM together with satellite data from Optical flow are recursively implemented in MSDM to achieve 120 minutes lead time. The predictions from MSDM are comparable to those of other baseline models with a high temporal resolution of 6 minutes. To solve the blurry image problems, we applied a modified SSIM as a loss function. Furthermore, we use Random forest with predicted radar and satellite data to estimate the rainfall rate, the results outperform those of the traditional Z-R relationship. The experiments confirm that machine learning with multi-source data provides more reasonable predictions and reveals a better non-linear relationship between radar echo and precipitation rate. Besides the algorithms will be developed, exploiting the potential of multi-source data will bring more improvements.

Dawei Li et al.

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for authors/topical editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Dawei Li et al.

Dawei Li et al.


Total article views: 373 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
283 86 4 373 4 4
  • HTML: 283
  • PDF: 86
  • XML: 4
  • Total: 373
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 21 Dec 2020)
Cumulative views and downloads (calculated since 21 Dec 2020)

Viewed (geographical distribution)

Total article views: 309 (including HTML, PDF, and XML) Thereof 309 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 15 Apr 2021
Short summary
In the daily weather forecast business, NWP is mainly used to forecast precipitation, but its performance on nowcasting tasks within 0–2 hours is very poor. Hence, we hope to use the machine learning to improve the accuracy and resolution of quantitative precipitation nowcasting(QPN) task. Previously works focus on the extrapolation of radar echo without using abundant meteorological data generated everyday. Hence, we designed a model using three kinds of data for QPN in east china.