The impact of high-resolution orography data on the CMA-MESO model prediction of ground meteorological elements
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摘要: 真实地形包含各自不同尺度的地形特征，对各种时空大气运动有深刻影响。不同尺度的地形效应很难在数值模式的离散格点中准确刻画，是发展数值模式的难点问题之一。随着模式向亚千米级高分辨率发展，高分辨模式要求刻画出更高准确度的地形数据。本研究在CMA-MESO中引入ASTER-1s高精度地形数据和改进地形滤波函数，在滤去波长接近模式网格的小尺度地形的同时保留更多地形细节，以提高模式对地形的刻画准确度。通过冬、夏各1个月批量模拟试验结果与2万多个地面观测站点观测数据的对比，发现单独采用ASTER-1s地形而不改变CMA-MESO的地形滤波函数，模式对2 m气温和10 m风速的整体预报准确度提升较小，采用ASTER-1s地形并改进地形滤波函数明显提高了模式对2 m气温和10 m风速的预报准确度，对气温和风速的月平均均方根误差提升分别为6.4%和4.9%。夏季1个月批量试验显示，改进的新地形方案对降水预报提升较弱，未造成非真实的细碎降水分布或异常值。此外，动能谱分析新引入的地形和滤波函数未造成高频能量积累。研究结果表明新地形方案能够明显改进低层气温和风速的预报准确度，并且在数值上稳定可靠。Abstract: Orography influences atmospheric circulation on a variety of spatial and temporal scales. The representation of its impact in numerical weather prediction models remains a challenging issue since the orographic spectrum can only be partially resolved in models. As numerical atmospheric models develop towards running in sub-kilometer resolutions, the need for accurate depiction of orography details becomes increasingly important. In this study, a new method to process orography is implemented in the CMA-MESO model by incorporating a new high-resolution orographic database ASTER-1s and an improved orography filter. The new method can remove harmful noises and retain more detailed small-scall orography features in the model, which greatly improves the representation of orographic effects. This new orography processing method is evaluated in the CMA-MESO based on simulations in June and December 2020. Comparison with observations collected at more than 20000 sites indicates that using ASTER-1s data without changing the filter does not significantly improve the prediction of 2 m temperature and 10 m wind speed. Using ASTER-1s data together with a new filter can greatly improve the prediction, resulting in a reduction of mean root mean square errors by 6.4% and 4.9% for the monthly mean 2 m temperature and 10 m wind speed, respectively. The prediction of monthly mean precipitation is also improved but not as significantly as that for the temperature and wind speed. The energy spectrum analysis shows that the new orography processing method does not show unrealistic energy accumulation at high frequencies, indicating the reliability of this method. Results of the study indicate that the new orography processing method can significantly improve the accuracy of near-surface temperature and wind speed forecast and is numerically stable and reliable.
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Key words:
- CMA-MESO /
- High-Resolution orography /
- Filter /
- ASTER-1s
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图 1 （a） 模式格点上ASTER-1s和 GTOPO30地形高度差 （ASTER-1s地形经网格平均到模式格点，GTOPO30经插值到模式格点，均未做地形滤波）；（b） 模式网格上两种地形高度的散点图
Figure 1. （a） Orography height difference between ASTER-1s and GTOPO30 on the model grids （no terrain filter is applied）； （b） scatterplot of different orography data on the model grids
图 2 （a） Zadra地形滤波函数不同参数
$ {r}_{c} $ 和 p下的振幅响应函数；（b） 不同滤波函数下的地形谱分布 （30s_RAW和1s_RAW分别为GTOPO30和ASTER-1s地形未做滤波处理，30s_CMA_SMTH和1s_CMA_SMTH分别为GTOPO30和ASTER-1s地形采用CMA-MESO滤波函数，1s_new_SMTH1和1s_new_SMTH2为ASTER-1s地形分别采用不同参数的Zadra 滤波函数，该谱分布为模式网格上地形在东西方向做傅里叶变换并在南北方向平均得到）Figure 2. （a） Amplitude response functions of the Zadra-filter with different filter parameters
$ {r}_{c} $ and p；（b） Power spectra of the orography field on CMA-MESO model grids with different filters （30s_RAW and 1s_RAW represent the spectra without filter for the GTOPO30 and ASTER-1s orography data respectively，30s_CMA_SMTH and 1s_CMA_SMTH represent the spectra with CMA-MESO filter for the GTOPO30 and ASTER-1s orography data respectively，1s_new_SMTH1 and 1s_new_SMTH2 represent the spectra with Zadra-filter for the ASTER-1s orography data）
图 3 模拟区域的地形及站点分布 （红点）
Figure 3. Simulation domain and locations of observation sites （marked by red dots）
图 4 不同模拟试验 2 m气温差异的月平均分布 （左列为2020年6月，右列为2020年12月；a、b. 30s_CMA_SMTH与站点观测之差；c、d. 1s_CMA_SMTH 与 30s_CMA_SMTH 之差；e、f. 1s_new_SMTH1与30s_CMA_SMTH之差；g、h. 1s_new_SMTH1与1s_new_SMTH2之差）
Figure 4. Differences in monthly mean temperature at 2 m above ground level between observations and simulations （left panel for June 2020 and right panel for December 2020；a，b. temperature differences between 30s_CMA_SMTH and observations；c，d. temperature differences between 1s_CMA_SMTH and 30s_CMA_SMTH；e，f. temperature differences between 1s_new_SMTH1 and 30s_CMA_SMTH；g，h. temperature differences between 1s_new_SMTH1 and 1s_new_SMTH2）
图 5 四组试验月平均2 m气温的均方根误差 （a、b） 及偏差 （c、d） （左列为2020年6月平均，右列为2020年12月平均）
Figure 5. （a，b） Root-mean-square errors of monthly mean temperature at 2 m above ground level from the four cases；（c，d） biases for monthly mean temperature at 2 m above ground level from the four cases （Left panel for June 2020 and right panel for December 2020）
图 8 四组试验2020年6月平均降水分布 （单位：mm/d；a. 30s_CMA_SMTH，b. 1s_CMA_SMTH，c. 1s_new_SMTH1，d. 1s_new_SMTH2）
Figure 8. Monthly mean precipitation for four cases （unit：mm/d; a. 30s_CMA_SMTH，b. 1s_CMA_SMTH，c. 1s_new_SMTH1，d. 1s_new_SMTH2）
图 9 四组试验的2020年6月降水ETS评分
Figure 9. Equitable threat scores （ETS） of precipitation for the four cases in June 2020
图 10 四组试验850 hPa等压面动能的能谱
Figure 10. Energy spectra of kinetic energy at 850 hPa for the four cases
表 1 模式试验设计
Table 1. List of simulation cases
试验名 地形数据 滤波函数 30s_CMA_SMTH GTOPO30 原CMA-MSEO滤波函数 1s_CMA_SMTH ASTER-1s 原CMA-MSEO滤波函数 1s_new_SMTH1 ASTER-1s Zadra滤波函数（$ {r}_{c} $=3，p=5） 1s_new_SMTH2 ASTER-1s Zadra滤波函数（$ {r}_{c} $=5，p=10） 
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表 2 四组数值试验对2020年6和12月2 m气温和10 m风速预报的偏差和均方根误差
Table 2. Biases and RMSEs of simulated 2 m temperature and 10 m wind speed for the four cases inJune 2020 and December 2020
试验名 2 m气温偏差（K） 2 m气温均方根误差（K） 10 m风速偏差（m/s） 10 m风速均方根误差（m/s） 6月 12月 6月 12月 6月 12月 6月 12月 30s_CMA_SMTH −0.75 −0.73 1.75 1.85 3.35 3.19 3.95 4.02 1s_CMA_SMTH −0.75 −0.73 1.75 1.86 3.36 3.21 3.96 4.03 1s_new_SMTH1 −0.54 −0.49 1.62 1.75 3.14 2.96 3.77 3.81 1s_new_SMTH2 −0.68 −0.65 1.70 1.80 3.29 3.13 3.90 3.96
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