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谢飞, 田文寿, 郑飞, 等. 2022. 基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究[J]. 大气科学, 46(6): 1300−1318. doi: 10.3878/j.issn.1006-9895.2104.21014
引用本文: 谢飞, 田文寿, 郑飞, 等. 2022. 基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究[J]. 大气科学, 46(6): 1300−1318. doi: 10.3878/j.issn.1006-9895.2104.21014
XIE Fei, TIAN Wenshou, ZHENG Fei, et al. 2022. Stratospheric Assimilation, Weather Forecast, and Climate Prediction Model Based on Data Assimilation Research Testbed and Whole Atmosphere Community Climate Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1300−1318. doi: 10.3878/j.issn.1006-9895.2104.21014
Citation: XIE Fei, TIAN Wenshou, ZHENG Fei, et al. 2022. Stratospheric Assimilation, Weather Forecast, and Climate Prediction Model Based on Data Assimilation Research Testbed and Whole Atmosphere Community Climate Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1300−1318. doi: 10.3878/j.issn.1006-9895.2104.21014

基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究

Stratospheric Assimilation, Weather Forecast, and Climate Prediction Model Based on Data Assimilation Research Testbed and Whole Atmosphere Community Climate Model

  • 摘要: 本论文基于WACCM(Whole Atmosphere Community Climate Model)模式最新版本WACCM6和DART(Data Assimilation Research TestBed)同化工具最新版本Manhattan,开发了中高层大气温度、臭氧和水汽卫星资料的同化接口,搭建了一个包含完整平流层过程的数值同化、天气预报和短期气候预测模型(此后简称模型);本模型对2020年3~4月平流层大气变化进行了同化观测资料的模拟,并以同化试验输出的分析场作为初值,对5~6月的平流层大气进行了0~30天天气尺度预报以及31~60天短期气候尺度预测的回报试验。结果表明:本模型能较好地重现2020年3、4月北极平流层出现的大规模臭氧损耗事件随时间的演变特征,模拟结果和Microwave Limb Sounder(MLS)卫星观测结果很接近;而未进行同化的模拟试验,虽然可以模拟出北极臭氧损耗现象,但是模拟的臭氧损耗规模相比MLS卫星观测结果要低很多;利用同化试验4月末输出的分析场作为初值,预报的5月北极平流层臭氧体积混合比变化与MLS卫星观测值的差值小于0.5,预测的6月北极平流层臭氧变化只在10~30 hPa之间的区域,与观测之间的差异达到了1 ppm(ppm=10−6)。本模型不但改善了北极平流层化学成分变化的模拟,也显著地提升了北极平流层温度和环流的模拟。本模型同化模拟的3~4月、预报预测的5~6月北极平流层温度和纬向风变化与Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2)再分析资料结果具有很好的一致性,仅在北极平流层顶部,预报预测的温度和纬向风分别与再分析资料之间的均方根误差(RMSE)约为3 K和4 m s−1。未进行同化的试验模拟的3~4月、预报预测的5~6月北极平流层的温度和纬向风与MERRA2再分析资料之间的RMSE在大部分区域都达到6 K及5 m s−1以上。从全球范围来看,本模型对平流层中低层模拟性能改善最为显著,其预报预测结果与观测值之间的差异,比未进行同化试验的结果,减少了50%以上。

     

    Abstract: Herein, an assimilation interface for temperature, ozone, and water vapor data in the middle and upper atmosphere is developed, and an assimilation model with complete stratospheric processes, including assimilation, weather forecasting, and short-term climate prediction system, is built based on the latest version of the whole atmosphere community climate model (WACCM6) and the data assimilation research testbed. Using the assimilation analysis field as the initial value, the system performed assimilation simulations of the stratospheric atmosphere in March and April 2020 and provided 0–3 days, 4–15 days, and 16–30 days forecast, as well as 1–60 days short-term climate prediction for stratospheric atmosphere changes in May and June. The results show that the system can accurately reflect the time evolution of the very unusual ozone depletion event in the Arctic stratosphere in March and April 2020, which is very similar to the microwave limb sounder (MLS) satellite observations. While simulations without assimilation can simulate the Arctic ozone depletion event, the ozone depletion magnitude is less than that of the MLS satellite. The assimilation model system improves the simulation of not only the chemical composition of the Arctic stratosphere but also the Arctic stratospheric temperature and circulation changes. The simulated March and April and predicted May and June Arctic stratospheric temperature and zonal wind variability are in good agreement with the assimilation and reanalysis results using Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2). The root mean square error (RMSE) between the predicted temperature and zonal wind and the reanalysis data is about 3 K and 4 m s−1, respectively, for the top of the stratosphere. The RMSE between the temperature and zonal wind of the Arctic stratosphere simulated by the experiment without assimilation in March-April and forecasted in May-June and the MERRA2 reanalysis data reach about 6 K and 5 m s−1 or more in most regions. On a global scale, this system has the most significant improvement in the simulation of the middle and lower stratosphere, with the RMSE in the prediction results reduced by more than 50% when compared to unassimilated simulation experiment prediction results.

     

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