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首页> 外文期刊>Atmospheric chemistry and physics >Wind extraction potential from 4D-Var assimilation of stratospheric O_3, N_2O, and H_2O using a global shallow water model
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Wind extraction potential from 4D-Var assimilation of stratospheric O_3, N_2O, and H_2O using a global shallow water model

机译:利用整体浅水模型从平流层O_3,N_2O和H_2O的4D-Var同化中提取风的潜力

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摘要

The wind extraction due to assimilation of stratospheric trace gas (tracer) data is examined using a 4DVar (four-dimensional variational) data assimilation system based on the shallow water equations coupled to the tracer continuity equation. The procedure is outlined as follows. First, a nature run is created, simulating middle stratospheric winter conditions. Second, ozone (O_3), nitrous oxide (N_2O), and water vapor (H_2O) (treated in this study as passive tracers) are initialized using Aura Microwave Limb Sounder (MLS) mixing ratios at 850K potential temperature and are advected by the nature run winds. Third, the initial dynamical conditions are perturbed by using a 6 h offset. Fourth, simulated hourly tracer observations on the full model grid are assimilated with a 4D-Var system in which tracer and winds are coupled via the adjoint of the tracer continuity equation. Multiple assimilation experiments are performed by varying the amount of random observation error added to the simulated measurements. Finally, the wind extraction potential (WEP) is calculated as the reduction of the vector wind root mean square error (RMSE) relative to the maximum possible reduction. For a single 6 h assimilation cycle with the smallest observation error, WEP values are ~60% for all three tracers, while 10-day multi-cycle simulations result in WEP of ~90 %, wind errors of ~0.3ms~(?1), and height errors of ~13 m. There is therefore sufficient information in the tracer fields to almost completely constrain the dynamics, even without direct assimilation of dynamical information. When realistic observation error is added (based on MLS precisions at 10 hPa), the WEP after 10 days is 90% for O_3, 87% for N_2O, and 72% for H_2O. O_3 and N_2O provide more wind information than H_2O due to stronger background gradients relative to the MLS precisions. The RMSE for wind reach a minimum level of ~0.3-0.9ms~(?1) for the MLS precisions, suggesting a limit to which realistic tracers could constrain the winds, given complete global cover age. With higher observation noise levels, the WEP values decrease, but the impact on the winds is still positive up to noise levels of 100%(relative to the global mean value) when compared to the case of no data assimilation.
机译:使用基于平流层示踪剂连续性方程的浅水方程组的4DVar(多维变分)数据同化系统,研究了平流层痕量气体(示踪剂)数据同化引起的风提取。该过程概述如下。首先,创建自然跑步,模拟平流层中部冬季条件。其次,使用Aura微波Limb Sounder(MLS)混合比在850K潜在温度下初始化臭氧(O_3),一氧化二氮(N_2O)和水蒸气(H_2O)(在本研究中被视为被动示踪剂),并且受自然界的约束奔风。第三,通过使用6 h偏移量来扰动初始动力条件。第四,将全模型网格上的模拟每小时示踪剂观测值与4D-Var系统进行同化,在该系统中,示踪剂和风通过示踪剂连续性方程的伴随关系耦合。通过改变添加到模拟测量结果中的随机观察误差的数量来执行多次同化实验。最后,将抽风潜力(WEP)计算为矢量风均方根误差(RMSE)相对于最大可能减少量的减少量。对于一个具有最小观测误差的6 h同化周期,所有三个示踪剂的WEP值均为〜60%,而10天的多周期模拟得出的WEP为〜90%,风力误差为〜0.3ms〜(?1 ),高度误差约为13 m。因此,即使没有直接吸收动态信息,在跟踪器字段中也有足够的信息来几乎完全约束动态。当添加实际观察误差时(基于10 hPa的MLS精度),O_3在10天后的WEP为90%,N_2O为87%,H_2O为72%。 O_3和N_2O比H_2O提供了更多的风信息,这是因为相对于MLS精度而言,背景梯度更强。对于MLS精度,风的均方根误差(RMSE)达到〜0.3-0.9ms〜(?1)的最低水平,这表明在给出完整的全球覆盖年龄的情况下,逼真的示踪剂可以约束风的极限。观察噪声级较高时,WEP值会降低,但与无数据同化的情况相比,在达到100%(相对于全局平均值)的噪声级时,对风的影响仍然是正的。

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