Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 4019–4034, 2021
https://doi.org/10.5194/gmd-14-4019-2021
Geosci. Model Dev., 14, 4019–4034, 2021
https://doi.org/10.5194/gmd-14-4019-2021

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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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Dawei Li on behalf of the Authors (11 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (29 May 2021) by Rohitash Chandra
AR by Dawei Li on behalf of the Authors (01 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (01 Jun 2021) by Rohitash Chandra
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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.