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Impact of different cumulus convective parameterization schemes on the simulation of precipitation over China Cover

Impact of different cumulus convective parameterization schemes on the simulation of precipitation over China

By:  and    
Open Access
|Jan 2017

Figures & Tables

Fig. 1.

Model domain and topography (units: m) over china. Boxes represent the selected regions: Northeast China (NE), North China (NC), Yangtze River (YZ), Southeast China (SE), Northwest China (NW), Tibetan Plateau (TB) and Southwest China (SW).

Table 1.

Indicator, acronym and definition of three precipitation extreme indices used in the study.

IndicatorAcronymDefinitionUnitsSimple daily intensitySDIIAnnual precipitation amount on wet days (daily precipitation larger than 1 mm)mm/dayHeavy precipitation daysR10Number of days with daily precipitation above 10 mmdaysExtreme precipitation amountR95pAnnual precipitation amounts of daily precipitation above the 95th percentile of wet daysmm
Fig. 2.

Spatial distributions of seasonal mean (DJF (a, d, g), JJA (b, e, h), ANN (c, f, i)) precipitation (units: mm) during 1982–2004 from observation (a–c), WRF model with Kain–Fritsch (d–f) and Grell (g–i) cumulus schemes. Note that the colourbar scales are different.

Fig. 3.

Annual cycle of precipitation (units: mm) over sub-regions during 1982–2004 from observation (solid black line), and WRF model with Kain–Fritsch (dotted red line) and Grell cumulus (dashed green line) schemes. The annual cycle correlation coefficients of the result of Kain–Fritsch and Grell cumulus schemes with observation are labelled at the top of each panel.

Fig. 4.

Interannual anomalies of area averaged summer precipitation (units: mm) over sub-regions during 1982–2004 from observation (solid black line), and WRF model with Kain–Fritsch (dotted red line) and Grell cumulus (dashed green line) schemes. The interannual correlation coefficients of the result of Kain–Fritsch and Grell cumulus schemes with observation are labelled at the top of each panel.

Fig. 5.

Taylor diagrams for the summer mean precipitation over dry (left panel) and wet (right panel) regions from WRF model with Kain–Fritsch (red circles) and Grell (blue circles) cumulus schemes.

Fig. 6.

Spatial distributions of summer extreme precipitation indices (SDII (a, d, g), units: mm/day; R10 (b, e, h), units: days; R95p (c, f, i), units: mm) from observation (a–c), WRF model with Kain–Fritsch (d–f) and Grell (g–i) schemes. Note that the colourbar scales are different.

Fig. 7.

Spatial distributions of ensemble summer mean precipitation (a), (Pr, units: mm) and extreme precipitation indices (b–d) (SDII (b), units: mm/day; R10 (c), units: days; R95p (d), units: mm). Note that the colourbar scales are different.

Table 2.

RMSE of summer precipitation and extreme precipitation indices between Kain–Fritsch, Grell, Ensemble and observation.

KFGREnsemblePr3.423.483.40SDII3.392.892.73R106.356.296.01R95p14.5411.3910.48

[i] Note: Figures in bold indicate improvement (lower RMSE) compared to KF and GR.

Fig. 8.

Spatial distributions of 1982–2004 temporal correlation coefficient of summer mean precipitation for WRF model with Kain–Fritsch (a), Grell cumulus schemes (b) and their ensemble result (c).

Fig. 9.

Spatial distributions of 1982–2004 biases of summer mean precipitation (a, d, g) (Pr, units: mm) and extreme precipitation indices (b, c, e, f, h, i) (SDII (b, e, h), units: mm/day; R10 (c, f, i), units: days) from WRF model with Kain–Fritsch (a–c), Grell (d–f) cumulus schemes and their ensemble result (g–i). Note that red colours represent a dry bias and blue colours represent a wet bias and the colourbar scales are different.

Language: English
Page range: 1406264 - 1406264
Submitted on: May 15, 2017
Accepted on: Nov 11, 2017
Published on: Jan 1, 2017
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2017 Danlian Huang, Shibo Gao, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.