Preprints
https://doi.org/10.5194/gmd-2021-230
https://doi.org/10.5194/gmd-2021-230

Submitted as: development and technical paper 25 Aug 2021

Submitted as: development and technical paper | 25 Aug 2021

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

Representation of the Autoconversion from Cloud to Rain Using a Weighted Ensemble Approach

Jinfang Yin1, Xudong Liang1, Hong Wang2, and Haile Xue1 Jinfang Yin et al.
  • 1State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences (CAMS), Beijing 100081, China
  • 2Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration (CMA), Guangzhou 510080, China

Abstract. Cloud and precipitation processes remain among the largest sources of uncertainties in weather and climate modeling, and considerable attention has been paid to improve the representation of the cloud and precipitation processes in numerical models in the last several decades. In this study, we develop a weighted ensemble (named as EN) scheme by employing several widely used autoconversion (ATC) schemes to represent the ATC from cloud water to rainwater. One unique feature of the EN approach is that ATC rate is a weighted mean value based on the calculations from several ATC schemes within a microphysics scheme with a negligible increase of computation cost. The EN scheme is compared with the several commonly used ATC schemes by performing a real case simulations. In terms of accumulated rainfall and extreme hourly rainfall rate, the EN scheme provides better simulations than that by using the single Berry-Reinhardt scheme which was originally used in the Thompson scheme. It is worth emphasizing, in the present study, we only pay our attention to the ATC process from cloud water into rainwater with the purpose to improve the modeling of the extreme rainfall events over southern China. Actually, any (source/sink) term in a cloud microphysics scheme can be dealt with the same approach. The ensemble method proposed herein appears to have important implications for developing cloud microphysics schemes in numerical models, especially for the models with variable grid resolution, which would be expected to improve of the representation of cloud microphysical processes in the weather and climate models.

Jinfang Yin et al.

Status: open (until 02 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-230', Anonymous Referee #1, 10 Sep 2021 reply

Jinfang Yin et al.

Data sets

Representation of the Autoconversion from Cloud to Rain Jinfang Yin, Xudong Liang, Hong Wang, Haile Xue https://doi.org/10.5281/zenodo.5052639

Model code and software

Representation of the Autoconversion from Cloud to Rain Jinfang Yin, Xudong Liang, Hong Wang, Haile Xue https://doi.org/10.5281/zenodo.5052639

Jinfang Yin et al.

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Short summary
An ensemble (EN) approach was designed to improving autoconversion (ATC) from cloud water to rainwater in cloud microphysics schemes. One unique feature of the EN approach is that the ATC rate is a mean value based on the calculations from several widely used ATC schemes. The ensemble approach proposed herein appears to be helpful for improving the representation of cloud and precipitation processes in weather and climate models.