青藏高原东部及周边地区地势呈阶梯状分布, 是天气预报模式预报的难点。为了了解新版GRAPES_Meso模式在该区域的预报性能, 利用统计方法对比了GRAPES_Meso模式新版(V3.1模式)与旧版(V2.5模式)在2011年夏季(6-8月)预报结果, 还对比了V3.1模式在高、 中、 低三种地形高度的预报差异, 得到以下主要结论: (1)同化系统升级后, 各要素的误差明显减小, 初始场质量有了较大提高, 尤其在对流层中高层, 各要素的改善幅度依次为相对湿度>纬向风>经向风>位势高度>温度; (2)对于降水预报, 无论是6 h还是24 h累加降水, V3.1模式的TS评分都高于V2.5模式, 尤其是大雨和暴雨预报, V3.1模式能明显减小漏报率、 提高日平均降水准确率, 但对空报率和云南以西的虚假降水改善不明显; (3)V3.1模式预报的位势高度、 温度、 风场和相对湿度误差垂直分布廓线与V2.5模式的不同, V3.1模式可以改进整层的经向风、 纬向风和相对湿度、 对流层中高层的位势高度、 500 hPa以下的温度, 但位势高度误差增长速度大于V2.5模式, 对流层中低层的南风预报偏强, 500 hPa以上相对湿度误差仍很大; (4)V3.1模式在不同地形高度的预报结果对比发现, 除700 hPa的位势高度和温度外, 三种地形高度的各要素误差都是在初始时刻差异最小, 随着预报时长增长, 误差差异不断增大, 这表明V3.1模式各要素的误差增长受地形影响明显。
The eastern Qinghai-Xizang Plateau and its surrounding areas, which have a character of a ladder-like topography distribution, are not only the strong signal areas for many significant weather systems upstream, but also the difficulty of many weather forecast models. In order to understand the prediction performance of GRAPES_Meso V3.1 in these areas, two forecast outputs of GRAPES_Meso V3.1 and V2.5 in summer (from June to August) of 2011 are compared using statistical methods. The main conclusions are shown as follows: (1) Since assimilation system is upgraded, the initial fields have been greatly improved, and the mean error (ME) and root mean square error (RMSE) of various elements are reduced obviously, especially in the middle-upper levels of troposphere. According to the error-decreasing amplitude, various elements are sorted from large to small as follows: relative humidity, zonal wind, meridional wind, geopotential height, temperature. (2) TS scores of the V3.1 model, either every 6 h or 24 h accumulated precipitation forecast, are higher than that of V2.5 model, especially 25 mm and 50 mm precipitation forecast. The missed event rate of V3.1 model has improved significantly, and the average daily rainfall is better than that of V2.5 model, but it can not improve the false precipitation in the west of Yunnan. (3) The vertical-error profiles of V3.1 model for geopotential height, temperature, wind and relative humidity forecast are different with V2.5 model. V3.1 model mainly improves the whole-layer meridional wind, zonal wind and relative humidity, as well as geopotential height in middle-high-layer troposphere and temperature below 500 hPa. It is noteworthy that the geopotential height errors of V3.1 model are growing faster than that of V2.5 model and new model forecast stronger south wind than true wind in low-layer troposphere. (4) Compared forecast results in three different terrain height of V3.1 model, it is found that, in addition to the geopotential height and temperature on 700 hPa, the differences of the same element error are small in the initial moment, but gradually expand with forecast length increasing. Those show that the errors of V3.1 model are affected significantly by topography.
[1]徐祥德, 陶诗言, 王继志, 等. 青藏高原—季风水汽输送“大三角扇型”影响域特征与中国区域旱涝异常的关系[J]. 气象学报, 2002, 60(3): 257-266.
[2]王雨. 若干数值模式对2003年夏季青藏高原中南部降水预报检验[J]. 高原气象, 2004, 23(增刊): 53-58.
[3]吴秋霞, 史历, 翁永辉, 等. AREMS/973模式系统对2004年中国汛期降水实时预报检验[J]. 大气科学, 2007, 31(2): 298-310.
[4]屈鹏, 杨梅学, 郭东林, 等. RegCM3模式对青藏高原夏季气温和降水的模拟[J]. 高原气象, 2009, 28(4): 738-744.
[5]公颖, 王叶红, 赖安伟. 三个模式对2008 年夏半年西南区降水预报效果的检验[J]. 高原气象, 2010, 29(6): 1441-1451.
[6]肖玉华, 赵静, 蒋丽娟. 数值模式预报性能的地域性特点初步分析[J]. 暴雨灾害, 2010, 29(4): 322-327.
[7]沈沛丰, 张耀存. 四川盆地夏季降水日变化的数值模拟[J]. 高原气象, 2011, 30(4): 860-868.
[8]公颖, 李俊, 鞠晓慧. 2009年我国汛期降水形势总结与三个常用模式预报效果检验[J]. 热带气象学报, 2011, 27(6): 823-833.
[9]陈德辉, 沈学顺. 新一代数值预报系统GRAPES研究进展[J]. 应用气象学报, 2006, 17(6): 773-777.
[10]张人禾, 沈学顺. 中国国家级新一代业务数值预报系统GRAPES的发展[J]. 科学通报, 2008, 53(20): 2393-2395.
[11]陈德辉, 薛纪善, 杨学胜, 等. GRAPES新一代全球/区域多尺度统一数值预报模式总体设计研究[J]. 科学通报, 2008, 53(20): 2396-2407.
[12]叶成志, 欧阳里程, 李象玉, 等. GRAPES中尺度模式对2005年长江流域重大灾害性降水天气过程预报性能的检验分析[J]. 热带气象学报, 2006, 22(4): 393-399.
[13]王叶红, 赖安伟, 林春泽, 等.基于GRAPESMESO模式的非静力三维变分同化方案性能分析[J]. 高原气象, 2013, 32(3): 689-706, doi: 10.7522/j.issn.1000-]0534.2012.00065.
[14]徐双柱, 张兵, 谌伟. GRAPES模式对长江流域天气预报的检验分析[J]. 气象, 2007, 33(11): 65-71.
[15]张利红, 沈桐立, 王洪利. AMSU资料变分同化及在暴雨数值模拟中的应用研究[J]. 高原气象, 2007, 26(5): 1004-1012.
[16]李勇, 王雨. 2007年夏季GRAPES MESO 15及30km模式对比检验[J]. 气象, 2008, 34 (10): 81-89.
[17]宋煜, 叶成志, 黄振. GRAPES模式对2005年登陆强台风预报检验分析[J]. 热带气象学报, 2008, 24(6): 694-699.
[18]张兵,钟敏. GRAPES模式对湖北省汛期强降水预报的分类检验分析[J]. 暴雨灾害, 2009, 28(2): 137-142.
[19]王雨, 李莉. GRAPES_Meso V3.0模式预报效果检验[J]. 应用气象学报, 2010, 21(5): 524-233.
[20]彭新东, 常燕, 王式功. GRAPES模式对2008年两次强降水过程的数值预报检验[J]. 高原气象, 2010, 29(2): 321-330.
[21]郝民, 张华, 陶士伟, 等. 变分质量控制在区域GRAPES3DVAR中的应用研究[J]. 高原气象, 2013, 32(1): 122-132, doi: 10.7522/j.issn.10000534.2013.00013.
[22]王遂缠, 胡向军, 张新荣, 等. 雷达资料同化在甘肃局地暴雨天气个例中的应用[J]. 高原气象, 2011, 30(3): 711-718.
[23]Cressman G. An observational objective analysis system[J]. Mon Wea Rev, 1959, 87: 367-374.