GRAPES_MesoV4.0主要技术改进和预报效果检验

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。

Main Technical Improvements of GRAPES_Meso V4.0 and Verification

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.