Main Technical Improvements of GRAPES_Meso V4.0 and Verification

Huang Liping; Chen Dehui; Deng Liantang; Xu Zhifang; Yu Fei; Jiang Yuan; Zhou Feifei

doi:10.11898/1001-7313.20170103

Vol.28, NO.1, 2017

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Huang Liping, Chen Dehui, Deng Liantang, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. DOI: 10.11898/1001-7313.20170103.

Citation:

Huang Liping, Chen Dehui, Deng Liantang, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. DOI: 10.11898/1001-7313.20170103.

Huang Liping, Chen Dehui, Deng Liantang, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. DOI: 10.11898/1001-7313.20170103.

Citation:

Huang Liping, Chen Dehui, Deng Liantang, et al. Main technical improvements of GRAPES_Meso V4.0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. DOI: 10.11898/1001-7313.20170103.

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Main Technical Improvements of GRAPES_Meso V4.0 and Verification

DOI: 10.11898/1001-7313.20170103

Numerical Weather Prediction Center of CMA, Beijing 100081

Received Date: 2016-03-22
Rev Recd Date: 2016-10-12
Publish Date: 2017-01-31

Abstract

Abstract

After operational implementation of GRAPES_Meso V3.0 in March 2013, some problems are found, which include over-prediction of precipitation, integration instability, large 2 m temperature forecast errors, insufficient observations assimilated, and coarser resolution. To deal with these problems, a lot of changes are made, mainly including introducing variational quality control scheme, applying the bias correction for sounding humidity observation, assimilating GPS/PW data, FY-2E cloud drift wind and radio occultation observation, increasing resolution of the model, using the fourth horizontal diffusion scheme, adjusting the coupling scheme between dynamic core and WSM6 microphysics parameterization, optimizing land surface model, and improving diagnostic algorithm of composite radar reflectivity. GRAPES_Meso is also upgraded from Version 3.0 to Version 4.0 by integrating all of the progresses mentioned above. One month hindcast experiments are implemented and results show that, compared with GRAPES_Meso V3.0, ETS scores of precipitation forecasts for GRAPES_Meso V4.0 are obviously higher for all five thresholds of 24 h accumulated precipitation, and the bias is largely decreased for light, moderate and heavy rainfall thresholds. The monthly mean precipitation pattern and intensity are both closer to observation, and the detail precipitation distribution can be reproduced better. Daily time evolutions of root mean square errors for 2 m temperature forecasts are very similar, while the amount of V4.0 is much less than that of V3.0. Monthly mean errors are reduced about 1-2℃ over most region of China and even 3-5℃ over some region for 24 h forecast. It is apparent that GRAPES_Meso V4.0 performs better for height, temperature and wind fields, as anomaly correlation coefficients of these fields at 500 hPa are larger and root mean square errors of these fields at 850 hPa are less than those by GRAPES_Meso V3.0. The forecast skill of GRAPES_Meso is largely improved from Version 3.0 to Version 4.0. Also, the unified process control has been implemented for GRAPES_Meso and GRAPES_RAFS (Rapid Analysis and Forecast System), which can reduce the system maintenance and management costs significantly. GRAPES_Meso V4.0 is transitioned into operational run at China National Meteorological Center with horizontal resolution of 0.1°×0.1° and vertical resolution of 50 levels from July 2014 and the whole system running is stable.
- GRAPES_Meso V4.0;
- FY-2E data assimilated;
- physics parameterization;
- variational quality control;
- model resolution

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References(26)

Figures and Tables

Fig. 1 Equitable threat score for 24 h accumulated precipitation forecast with and without GPS/PW data assimilated from 20 Jun to 20 Jul in 2013 (a)0-24 h, (b)24-48 h

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Fig. 2 Equitable threat score for 24 h accumulated precipitation forecast with and without FY-2E atmospheric wind vector data assimilated from 20 Jun to 20 Jul in 2013 (a)0-24 h, (b)24-48 h

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Fig. 3 Comparison of different GRAPES_Meso level schemes (a) all levels, (b) below 3000 m

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Fig. 4 Correlation coefficients of 24 h and 48 h forecast with and without the 4th horizontal diffusion in Jul 2013 (a)500 hPa height, (b)500 hPa temperature

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Fig. 5 Time evolution of total water vapor (a) and cloud water (b) by GRAPES_Meso V3.0 on 15 Jul 2008

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Fig. 6 Observed and simulated 24-hour accumulated precipitation from 0000 UTC 16 Jul to 0000 UTC 17 Jul in 2008 (a) observation, (b)18-42 h forecast by GRAPES_Meso V3.0, (c)18-42 h forecast after adjusting moisture flux scheme and couple between WSM6 and dynamical core

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Fig. 7 The root mean square error for 2 m temperature of 12 h and 36 h forecast before and after the improvement of GRAPES_Meso surface energy balance equation

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Fig. 8 Scores for 24 h accumulated precipitation by GRAPES_Meso V3.0 and V4.0 from 20 Jun to 20 Jul in 2013 (a) ETS of 0-24 h forecast, (b) ETS of 24-48 h forecast, (c) Bias of 0-24 h forecast, (d) Bias of 24-48 h forecast

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Fig. 9 Monthly mean 24 h accumulated precipitation distribution from 20 Jun to 20 Jul in 2013 (a) observation, (b)24 h forecast of V4.0, (c)24 h forecast of V3.0, (d)48 h forecast of V4.0, (e)48 h forecast of V3.0

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Fig. 10 Monthly mean error of 2 m temperature of GRAPES_Meso 24 h forecast from 20 Jun to 20 Jul in 2013 (a) V3.0, (b) V4.0

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Fig. 11 The anomaly correlation coefficient and root mean square error (RMSE) of GRAPES_Meso 24 h and 48 h forecast from 20 Jun to 20 Jul 2013 (a) correlation coefficient of 500 hPa height, (b) RMSE of 850 hPa height, (c) correlation coefficient of 500 hPa temperature, (d) RMSE of 850 hPa temperature, (e) correlation coefficient of 500 hPa zonal wind, (f) RMSE of 850 hPa zonal wind

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Table 1 Differences between GRAPES_Meso V3.0 and V4.0

项目	GRAPES_Meso V3.0	GRAPES_Meso V4.0
观测资料	AOB	AOB，GPS/PW，FY-2E
水平分辨率	0.15°	0.1°
垂直层次数	L33	L50
分析系统	无变分质量控制	增加变分质量控制
分析系统	无探空湿度偏差订正	增加探空湿度偏差订正
微物理参数化	WSM6	改进耦合方案的WSM6
陆面参数化	NOAH	改进地表辐射平衡的NOAH
辐射参数化	RRTM	RRTM (新)
积云参数化	BMJ	KF

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