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Main Technical Improvements of GRAPES_Meso V4.0 and Verification
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摘要: 针对GRAPES_Meso V3.0存在的降水量偏大、模式运行不稳定、近地面温度预报偏差较大、可同化资料偏少以及分辨率偏低等问题，开展了多方面的改进工作：引入变分质量控制以及探空湿度的偏差订正，实现了GPS/PW资料、FY-2E云导风资料以及无线电掩星资料的同化应用，提高了模式分辨率，引入四阶水平扩散方案，调整了微物理参数化方案与动力框架的耦合方案，完善了地面辐射能量平衡方程以及优化了后处理雷达组合反射率因子的诊断方案，并集成所有改进成果形成新的业务化GRAPES_Meso V4.0。批量试验结果表明：GRAPES_Meso V4.0降水ETS评分普遍提高，同时预报偏差明显降低，月平均降水更接近实况，且能够较好地刻画雨带细节；2 m温度预报偏差有较为显著的改善，大部分地区24 h预报有1~2℃左右的降低，有些地区有3~5℃的降低；GRAPES_Meso V4.0对高度场、温度场和风场的改进效果比较显著，500 hPa的温度、风速、位势高度场的相关系数均有显著提高，850 hPa的均方根误差也明显降低，整体性能明显高于GRAPES_Meso V3.0。
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关键词:
- GRAPES_MesoV4.0;
- FY-2E资料同化;
- 物理过程参数化;
- 变分质量控制;
- 模式分辨率
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. -
图 1 2013年6月20日-7月20日有无GPS/PW资料同化应用连续试验降水预报的ETS评分(a)0~24 h, (b)24~48 h
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
图 2 2013年6月20日-7月20日有无FY-2E云导风资料同化应用连续试验降水预报的ETS评分(a)0~24 h, (b)24~48 h
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
图 3 GRAPES_Meso不同垂直分层方案对比(a)全部层次, (b)3000 m以下
Fig. 3 Comparison of different GRAPES_Meso level schemes (a) all levels, (b) below 3000 m
图 4 2013年7月有无四阶水平扩散方案24 h和48 h预报逐日距平相关系数(a)500 hPa高度场, (b)500 hPa温度场
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
图 5 2008年7月15日GRAPES_Meso V3.0全场总水汽(a)和云水(b)时间演变
Fig. 5 Time evolution of total water vapor (a) and cloud water (b) by GRAPES_Meso V3.0 on 15 Jul 2008
图 6 观测及GRAPES_Meso V3.0模拟的24 h降水分布(a)实况，(b) GRAPES_Meso V3.0 18~42 h预报，(c)优化地表水汽通量计算以及微物理动力耦合后18~42 h预报
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
图 7 2012年7月GRAPES_Meso地表能量平衡方案改进前后12 h和36 h 2 m温度预报逐日均方根误差
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
图 8 2013年6月20日-7月20日GRAPES_Meso V3.0和V4.0连续试验降水预报检验评分(a)0~24 h预报ETS评分，(b)24~48 h预报ETS评分，(c)0~24 h预报Bias评分，(d)24~48 h预报Bias评分
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
图 9 2013年6月20日-7月20日观测及模拟月平均24 h累积降水分布(a)实况，(b) V4.0 24 h预报，(c) V3.0 24 h预报，(d) V4.0 48 h预报，(e) V3.0 48 h预报
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
图 10 2013年6月20日-7月20日GRAPES_Meso 2 m温度24 h预报月平均偏差分布(a) V3.0，(b) V4.0
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
图 11 2013年6月20日-7月20日GRAPES_Meso 24 h及48 h预报逐日距平相关系数和均方根误差(a)500 hPa高度场距平相关系数，(b)850 hPa高度场均方根误差，(c)500 hPa温度场距平相关系数(d)850 hPa温度场均方根误差，(e)500hPa纬向风场距平相关系数，(f)850 hPa纬向风场均方根误差
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
表 1 GRAPES_Meso V3.0和V4.0系统主要差别
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 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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