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Verification and Diagnostics for Data Assimilation System of Global GRAPES
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摘要: 中国气象局数值预报中心新近升级的GRAPES全球三维变分同化系统的大气基本状态变量在物理属性与定义的网格和坐标上与预报模式保持一致，是一个完全针对GRAPES预报模式的同化系统。该系统不仅有利于减小分析误差，也是构建GRAPES四维变分同化系统的基本环节之一。该文通过与观测资料的对比、与国际其他业务中心分析场的对比，以及中期数值预报的检验，对新的GRAPES全球三维变分同化系统性能进行较全面讨论，并通过对这一系统的检验，探索资料同化系统性能的检验方法，尤其是观测资料同化效果的定量评价方法。诊断结果表明：在宏观特征上，GRAPES变分同化系统的分析场与欧洲中期数值预报中心和美国国家环境预测中心的分析场十分相似, 但细节上仍有差别。这些差别主要源自GRAPES同化系统中探空、地面报、掩星以及飞机报观测的贡献偏大，而卫星垂直探测仪观测资料的作用尚未充分发挥。从探测单要素来讲，风及湿度观测的作用发挥不够。此外，青藏高原周围地区、模式高层及赤道地区分析场偏差较大，它们与模式地形及高层的处理等有关系，这些问题有待进一步改进。Abstract: Numerical Weather Prediction Center of China Meteorological Administration has upgraded the global GRAPES (Global/Regional Assimilation and PrEdiction System) variation data assimilation system. The new data assimilation system employs the same coordinate, grids and atmospheric state variables as those of the GRAPES model. It can reduce analysis errors due to the interpolation and variable transformations, and also provide basics for developing GRAPES four-dimension variation assimilation system. Some key characteristics of the new global GRAPES data assimilation system are discussed, and then the performance is evaluated in detail, by comparing with observations, analysis or reanalysis data from advanced operational numerical weather prediction centers, and the medium-range forecast from background and analysis fields and different forecast models. Some guidelines for further optimizing the system is also given based on diagnosis and quantitatively estimating the impact of observations. Results show that the GRAPES data assimilation system assimilates conventional observations, satellite radiances and radio occultation observations effectively, making analyses closer to the real atmosphere and improving the forecast skill. The analysis of GRAPES are similar to those of European Centre for Medium-Range Weather Forecasts and National Center for Environmental Prediction at the large-scale circulation fields. However, some differences still remains, which actually expose issues of GRAPES. These differences are related to overlarge contributions from radiosonde, surface, ships, aircraft and radio occultation observations, and the relatively weaker influence of satellite radiance observations.There is broad consensus among the global numerical weather prediction centers that these types of observations tend to be the highest-ranked contributors to forecast skill: Microwave temperature sounder, hyper-spectral infrared sounder, radiosondes, aircraft observations, radio occultation and atmospheric motion vectors, although not necessarily uniformly in this order. However, contributions of the microwave temperature sounder and hyper-spectral infrared sounder in GRAPES are not dominant, because GRAPES still uses less radiance data, and on the other hand, the bias correction effect is not so good.Contributions of wind and humidity observation are less in GRAPES. Additionally, biases in regions of the Tibet Plateau, upper levels of the model and the tropics are relatively larger compared to observations and the reanalysis, which are related to the treatment method of topography and upper boundary of model. To gain better analysis and forecast skill, there is a requirement to place more emphasis on the above issues.
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图 1 探空、飞机、地面、船舶、云导风、掩星观测以及NOAA18卫星垂直探测器温度计的da与db的概率分布
Fig. 1 Probability of da and db for TEMP, AIREP, SYNOPS, SHIPS, SATOB, RO and AMSUM-A of NOAA18 satellite
图 2 探空气压观测的BMO和AMO的平均值及均方根误差在500 hPa高度的水平分布
Fig. 2 Distribution of mean and root mean square of BMO and AMO for radionsonde pressure observations at 500 hPa
图 3 青藏高原地区探空观测的BMO和AMO的平均值和均方根误差廓线
(a) 气压，(b) 相对湿度, (c)u分量, (d)v分量
Fig. 3 Profile of mean and root mean square of BMO and AMO for the radiosonde observation in the Tibet Plateau for pressure (a), relative humidity (b), u wind (c), v wind (d)
图 4 青藏高原地区卫星云导风经向风和纬向风的da与db的概率分布
Fig. 4 Probability da and db of atmospheric motion vectors observations over the Tibet Plateau
图 5 G-3DVar与ERA-Interim在850，500 hPa和250 hPa位势高度的相关系数和均方根误差的时间演变
(a) 北半球的相关系数, (b) 南半球的相关系数, (c) 北半球的均方根误差, (d) 南半球的均方根误差
Fig. 5 Time evolution of correlation coefficients and root mean square errors between G-3DVar and ERA-Interim at 850, 500 hPa and 250 hPa for the Northern Hemisphere and the Southern Hemisphere
(a) correlation coefficient of the Northern Hemisphere, (b) correlation coefficient of the Southern Hemiphere, (c) root mean square error of the Northern Hemisphere, (d) root mean square error of the Southern Hemiphere
图 6 北半球、南半球以及热带地区G-3DVar的背景场、分析场与ERA-Interim分析场的差的平均值和均方根误差的垂直分布廓线
Fig. 6 Mean and root mean square error of GRAPES geopertential height, temperature and zonal wind analysis compared to ERA_Interim (GRAPES minus ERA-Interim) in the Northern Hemisphere, the Southern Hemisphere and the tropics
图 7 G-3DVar与ERA-Interim分析场月平均偏差的纬度-高度剖面图
(a) 位势高度 (单位：gpm)，(b) 温度 (单位：K)，(c) 比湿 (单位：kg·kg-1)，(d)u分量 (单位：m·s-1)，(e)v分量 (单位：m·s-1)
Fig. 7 Analysis comparisons between GRAPES and ERA_Interim for geopertential height (unit:gpm)(a), temperature (unit:K)(b), specific humidity (unit:kg·kg-1)(c), u wind (unit:m·s-1)(d) and v wind (unit:m·s-1)(e)
图 8 北半球 (a)、南半球 (b) 以及东亚地区 (c)500 hPa的距平相关系数随着预报时效的变化曲线
Fig. 8 Anomaly correlation coefficient (ACC) of the 500 hPa height for the 8-day forecast in the Northern Hemisphere (a), the Southern Hemisphere (b) and the East Asia (c)
表 1 估计的新息与分析余差的平均值和均方差等统计信息
Table 1 Statistics of mean and variance for innovation and residual
观测 要素 ξb ξa σb σa rab 探空 气压/hPa -0.4014 -0.0217 0.8037 0.4522 0.32 u分量/(m·s-1) -0.006 0.0054 3.7618 2.812 0.56 v分量/(m·s-1) -0.0202 0.014 3.6782 2.7799 0.57 相对湿度/% 3.0262 -1.0595 22.2519 15.7933 0.50 地面 地表气压/hPa -0.2361 -0.021 0.941 0.6754 0.52 船舶 地表气压/hPa -0.1467 0.0107 1.0522 0.7872 0.56 云导风 风的u分量/(m·s-1) 0.3533 0.1679 3.823 2.8708 0.56 飞机 温度/K 0.4076 0.1766 1.4039 1.1394 0.66 u分量/(m·s-1) 0.0121 -0.0123 3.6421 2.7596 0.57 v分量/(m·s-1) -0.1173 -0.0149 3.6005 2.7251 0.57 掩星 折射率/% -0.00073 -0.00048 0.00013 0.00005 0.15 微波温度计
(NOAA18)通道5亮温/K 0.1113 0.0755 0.2291 0.2119 0.86 通道6亮温/K -0.0567 0.0776 0.1976 0.1612 0.67 通道7亮温/K 0.0377 0.0179 0.2105 0.1793 0.73 通道8亮温/K 0.0945 0.0709 0.2319 0.2057 0.79 通道9亮温/K 0.2925 0.1876 0.2803 0.2126 0.58 通道10亮温/K 0.4785 0.4873 0.2485 0.2087 0.70 AIRS 通道平均亮温/K 0.1492 0.1117 0.52 0.48 0.85 
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表 2 背景场检查的相关统计信息
Table 2 Statistics information of background check
观测 要素 资料使用
率/%系统指定
的αqc本文式 (4) 统
计的αqc质控范围 探空 气压/hPa 98 5.0 2.9 [-2.6, 1.7] (400~850 hPa) u, v分量/(m·s-1) 100 9.0 2.5 [-7.5, 7.5] 相对湿度/% 97 3.0 1.6 [-41, 45] 船舶 地表气压/hPa 73 5.0 3.7 [-2.1, 2.3] 地面 地表气压/hPa 73 3.0 2.4 [-2.1, 1.7] 云导风 u, v分量/(m·s-1) 99 5.0 1.5 [-7.3, 8.0] 飞机 u, v分量/(m·s-1) 99 4.0 1.8 [-7.3, 7.3] 温度/K 99 4.0 2.2 [-2.4, 3.2] 掩星 折射率/% 90 4.0 2.0 [-1.2, 1.2] 微波温度计 AMSU-A亮温/K 74 2.0 2.0 高光谱 AIRS亮温/K 8 2.0
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