南海海面风的动力降尺度模拟
发布时间：2018-11-05 11:51
【摘要】：现有的全球大气观测资料在时空分布上存在着非常严重的非均一性问题,而全球大气模式的时空分辨率一般较低,难以满足研究人员对区域气候精细化研究的需求。用全球模式的结果驱动适合区域模拟的有限区域模式,从而得到时空分辨率较高,符合区域气候特征的气象要素分布是一个不错的选择。目前动力降尺度在气温和降水上的应用比较广泛,但是对海面风的研究还不够深入,尤其是南海海面风。本文首先通过对2003年南海海面风场的模拟探究了中尺度天气预报模式WRF区域气候模拟的能力,并比较分析了两种驱动方式与多种全球(再)分析资料用于南海海面风的降尺度研究,最后选择质量较高的驱动场和驱动方式对2001-2008年南海进行降尺度,分析降尺度后的海面风场相比于驱动场的增加价值,并探究谱逼近方法在南海海面风降尺度中的作用,主要结论如下：1)WRF连续长期积分模拟会导致系统误差累积,使模拟结果偏离观测值,定期更新初始场的方法能够有效改善长期积分积累的误差,每个月风向风速的均方根误差(RMSE)和空间相关系数(SCC)都得到优化。2) ERA-Interim作为模式强迫场降尺度后的结果优于FNL和CFSR,主要体现在ERA-Interim作为强迫场得到的年平均风速SCC最大,达到0.909,年平均风向空间RMSE最小,仅17.99°,比表现最差的CFSR改善了8.3%°同时,区域平均的风速风向RMSE和CC都表现最优,相比风速表现最差的FNL,风速ROSE改善了8.5%,CC提高了9.2%,相比风向表现最差的CFSR RMSE改善了13.8%,CC提高了5.8%。3)降尺度能较好缓解再分析资料存在整体风速强度过低的问题,同时一定程度上修正再分析资料对风速的空间分布模态刻画不够准确,出现虚假高值中心或观测上存在的高值中心没有刻画出来的问题,但是值得注意的是,降尺度偶尔会使得原本已刻画较好的风速提高到高于观测的水平。进一步对不同风速等级的降尺度效果进行检验发现,降尺度对于高风速能产生增加价值,但是对于低风速的刻画能力不足,会导致低风速模拟过高。4)谱逼近在本次试验中影响较小,表现偏中立,其效果不显著的原因与采用5天更新一次初始场的方式驱动有关,每次积分时间过短,初始场的作用还较强,谱逼近的作用未能得到体现。5)降尺度结果显示,2001-2008年南海大风发生次数分布存在两个极大值中心,分别位于吕宋海峡和越南东南沿海,极大值为125天。南海北部的大风日数高于75天的高值区在这8年的时间内呈现先减小后增长的趋势,拐点出现在2004年,从2004年往后,该区域范围一直呈增大趋势,到2008年甚至与南海南部的高值区连通。南海大风频次表现出明显的季节变化特征,冬季大风发生频次最高,春季大风发生频次最低,且在2001到2008年这8年时间,南海冬季大风频次呈增加趋势,而夏季大风频次呈减小趋势。
[Abstract]:There is a very serious problem of heterogeneity in the temporal and spatial distribution of the existing global atmospheric observation data, but the spatial and temporal resolution of the global atmospheric model is generally low, so it is difficult to meet the needs of researchers for the fine study of regional climate. It is a good choice to use the results of the global model to drive the finite regional model which is suitable for regional simulation so that the spatial and temporal resolution is higher and the distribution of meteorological elements in accordance with the regional climate characteristics is a good choice. At present, dynamic downscaling is widely used in temperature and precipitation, but the study of sea surface wind is not deep enough, especially in the South China Sea. In this paper, the capability of the mesoscale weather forecast model WRF regional climate simulation is studied by simulating the sea surface wind field in the South China Sea in 2003. Two driving modes and several global (reanalysis) data are compared to study the downscaling of the South China Sea (SCS) wind. Finally, the high quality driving field and the driving mode are selected to downscale the South China Sea from 2001 to 2008. The value of downscaling sea surface wind field compared with driving field is analyzed, and the function of spectral approximation method in South China Sea sea surface wind downscaling is explored. The main conclusions are as follows: 1) WRF continuous long-term integral simulation will lead to the accumulation of system errors. The method that the simulation results deviate from the observed values and the initial field is updated regularly can effectively improve the error of long-term integral accumulation. The root mean square error (RMSE) and spatial correlation coefficient (SCC) of monthly wind direction and wind speed are optimized. 2) ERA-Interim is superior to FNL and CFSR, as the result of model forced field downscaling. The main results are as follows: the annual mean wind speed (SCC) obtained by ERA-Interim as a forced field is the largest (0.909), and the annual mean wind direction spatial RMSE (RMSE) is the smallest, only 17.99 掳, which is 8.3% better than the worst performing CFSR. The regional average wind speed and wind direction RMSE and CC both performed best. Compared with FNL, wind speed ROSE, which had the worst wind speed performance, the regional average wind speed direction increased by 9.2% compared with FNL, wind speed ROSE, and improved by 13.8% compared with CFSR RMSE, which had the worst wind direction performance. CC improved 5.8.3) downscaling can better alleviate the problem that the overall wind speed intensity is too low in the reanalysis data, and at the same time, to some extent, the reanalysis data is not accurate enough to describe the spatial distribution mode of wind speed. The problem of false high value center or observed high value center does not exist, but it is worth noting that the downscaling occasionally makes the wind speed which has already been well described raise to higher than the observed level. Further tests on the downscaling effect of different wind speed grades show that downscaling can add value to high wind speed, but the ability to depict low wind speed is insufficient. The results show that the low wind speed simulation is too high. 4) the effect of spectral approximation is relatively small and neutral in this experiment. The reason why the effect is not significant is driven by the method of updating the initial field once in 5 days, and the time of integration is too short. The effect of the initial field is still strong, and the effect of spectral approximation is not reflected. 5) the downscaling results show that there are two maximum centers in the frequency distribution of strong winds in the South China Sea from 2001 to 2008, located in Luzon Strait and off the southeast coast of Vietnam, respectively. The maximum value is 125 days. The high value area with gale days above 75 days in the northern part of the South China Sea showed a trend of first decreasing and then increasing in the period of 8 years. The inflection point appeared in 2004, and since 2004, the area has been increasing. By 2008 even with the South China Sea south of the high value of the region connected. The frequency of strong wind in the South China Sea shows obvious seasonal variation characteristics, the frequency of strong wind in winter is the highest, and the frequency of strong wind in spring is the lowest, and the frequency of strong wind in winter of the South China Sea increased in the 8 years from 2001 to 2008. However, the frequency of strong wind decreased in summer.
【学位授予单位】：中国海洋大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：P732
本文编号：2312000
[Abstract]:There is a very serious problem of heterogeneity in the temporal and spatial distribution of the existing global atmospheric observation data, but the spatial and temporal resolution of the global atmospheric model is generally low, so it is difficult to meet the needs of researchers for the fine study of regional climate. It is a good choice to use the results of the global model to drive the finite regional model which is suitable for regional simulation so that the spatial and temporal resolution is higher and the distribution of meteorological elements in accordance with the regional climate characteristics is a good choice. At present, dynamic downscaling is widely used in temperature and precipitation, but the study of sea surface wind is not deep enough, especially in the South China Sea. In this paper, the capability of the mesoscale weather forecast model WRF regional climate simulation is studied by simulating the sea surface wind field in the South China Sea in 2003. Two driving modes and several global (reanalysis) data are compared to study the downscaling of the South China Sea (SCS) wind. Finally, the high quality driving field and the driving mode are selected to downscale the South China Sea from 2001 to 2008. The value of downscaling sea surface wind field compared with driving field is analyzed, and the function of spectral approximation method in South China Sea sea surface wind downscaling is explored. The main conclusions are as follows: 1) WRF continuous long-term integral simulation will lead to the accumulation of system errors. The method that the simulation results deviate from the observed values and the initial field is updated regularly can effectively improve the error of long-term integral accumulation. The root mean square error (RMSE) and spatial correlation coefficient (SCC) of monthly wind direction and wind speed are optimized. 2) ERA-Interim is superior to FNL and CFSR, as the result of model forced field downscaling. The main results are as follows: the annual mean wind speed (SCC) obtained by ERA-Interim as a forced field is the largest (0.909), and the annual mean wind direction spatial RMSE (RMSE) is the smallest, only 17.99 掳, which is 8.3% better than the worst performing CFSR. The regional average wind speed and wind direction RMSE and CC both performed best. Compared with FNL, wind speed ROSE, which had the worst wind speed performance, the regional average wind speed direction increased by 9.2% compared with FNL, wind speed ROSE, and improved by 13.8% compared with CFSR RMSE, which had the worst wind direction performance. CC improved 5.8.3) downscaling can better alleviate the problem that the overall wind speed intensity is too low in the reanalysis data, and at the same time, to some extent, the reanalysis data is not accurate enough to describe the spatial distribution mode of wind speed. The problem of false high value center or observed high value center does not exist, but it is worth noting that the downscaling occasionally makes the wind speed which has already been well described raise to higher than the observed level. Further tests on the downscaling effect of different wind speed grades show that downscaling can add value to high wind speed, but the ability to depict low wind speed is insufficient. The results show that the low wind speed simulation is too high. 4) the effect of spectral approximation is relatively small and neutral in this experiment. The reason why the effect is not significant is driven by the method of updating the initial field once in 5 days, and the time of integration is too short. The effect of the initial field is still strong, and the effect of spectral approximation is not reflected. 5) the downscaling results show that there are two maximum centers in the frequency distribution of strong winds in the South China Sea from 2001 to 2008, located in Luzon Strait and off the southeast coast of Vietnam, respectively. The maximum value is 125 days. The high value area with gale days above 75 days in the northern part of the South China Sea showed a trend of first decreasing and then increasing in the period of 8 years. The inflection point appeared in 2004, and since 2004, the area has been increasing. By 2008 even with the South China Sea south of the high value of the region connected. The frequency of strong wind in the South China Sea shows obvious seasonal variation characteristics, the frequency of strong wind in winter is the highest, and the frequency of strong wind in spring is the lowest, and the frequency of strong wind in winter of the South China Sea increased in the 8 years from 2001 to 2008. However, the frequency of strong wind decreased in summer.
【学位授予单位】：中国海洋大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：P732
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1 张萌;南海海面风的动力降尺度模拟[D];中国海洋大学;2014年
，本文编号：2312000
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