ATOVS卫星资料同化对三江源暴雨预报影响的试验研究
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第二次青藏高原综合科学考察研究项目(2019QZKK0105);国家自然科学基金资助项目(41971308)


Experimental study of ATOVS satellite data assimilation influencing rainstorm prediction in the Three Rivers Source area
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    摘要:

    选取3个三江源的典型降水个例(2018年6月30日、7月5日、8月24日),利用NCEP FNL再分析数据,并加入ATOVS湿度探测器MHS资料同化,基于WRF模式及其三维变分同化系统对三江源区域3次降水过程进行循环同化试验,分析3次事件的模拟状况,并定量分析降水结果。结果表明,3次降水事件在加入MHS资料同化后,1)模拟的水汽增大,在中层体现最明显且更符合实际情况,高空水汽和风模拟较好,地面温度预报欠佳;2)MHS资料对降水预报的影响主要体现在降水区面积和降水量的增大,提高了降水预报水平,但也带来较多空报区域;3)从TS、ETS、POD评分结果来看,有两个降水试验的提升较为明显,其中“0630”试验TS评分结果在0.5~10 mm之间提升了0.05~0.1,ETS在5 mm提升超过0.08,在10~20 mm之间也有少量提升,POD检验在0.5~20 mm之间均有提升,在0.5~10 mm之间提升最为明显,提升了0.1~0.25,“0824”试验TS与ETS在10~20 mm之间提升超过0.1,POD检验在6~20 mm之间提升了0.1~0.4,同化后降水预报有所改善,且在大阈值降水尤为明显;4)MHS资料同化对“0705”试验降水预报改善不明显,说明同化并不是每次都能给结果带来正效应,因此在使用MHS资料时不能过于信赖它,但总体上,同化MHS资料能够提升预报质量。

    Abstract:

    The Three Rivers Source area is located in the southern part of Qinghai Province,China.It is the country's largest nature reserve,and the world's nature reserve with the highest concentration of biodiversity in a high-altitude area.Summer is when the most concentrated precipitation occurs there.Once rainstorm occurs,disasters such as landslides and flash floods very easily occur,which not only pose a serious threat to the safety of local people's lives and property,but also damage the local economic development.Improving the accuracy of numerical weather forecast in the Three Rivers Source area can improve the forecast level of rainstorm and effectively reduce losses due to disaster.Numerical weather forecasting involves initial boundary value problems,and the more accurate the initial field is,the more accurate the forecast results will be.The essence of data assimilation is to study how to process various unconventional observation data with different accuracies and integrate them reasonably with conventional observation data into an organic whole,so as to provide a more accurate initial field for numerical models and achieve the goal of improving the accuracy of model forecasting.Compared to other assimilation data,advantages of satellite data include consistent observation data,wide coverage,high spatiotemporal resolution,and being unaffected by geographical conditions.The Three Rivers Source area has high and complex terrain,vast area,and few meteorological observation stations,thus there are significant problems with the initial value quality in numerical models.However,satellite radiance data has the characteristics of wide coverage and high spatiotemporal resolution,thus it is expected to improve the current situation of insufficient conventional observation data in the Three Rivers Source area.For this reason,it is imperative to carry out research of MHS (microwave humidity sounder) data assimilation in the Three Rivers Source area,which lacks conventional data.The experimental research process was as follows:First,we selected three typical precipitation cases in the Three Rivers Source area:(June 30,2018,July 5,2018,and August 24,2018).Next,we used reanalysis data from NCEP (National Centers for Environmental Prediction) FNL (final operational global analysis),and added ATOVS (Advanced TIROS Operations Vertical Sounder) humidity detector MHS data assimilation.Then we conducted a cyclic assimilation test of three precipitation processes in the Three Rivers Source area based on the WRF (Weather Research and Forecasting) model and its three-dimensional variational assimilation system,to analyze the simulation status of the three events and perform quantitative analysis of the precipitation results.After the assimilation of the three precipitation events with the MHS data,the results show the following:(1) The amount of water vapor predicted increases,which is most obvious and highly consistent with the actual situation in the middle layer,and has good simulation of water vapor and wind,yet poor prediction of temperature at the ground level.(2) The impact of MHS data on precipitation forecasts is mainly reflected in the expansion of precipitation areas and the increase of precipitation forecasts,but it increases the number of empty reporting areas.(3) From the results of the TS (threat score),ETS (equitable threat score),and POD (probability of detection) score,two precipitation tests showed significant improvement.Among them,the TS score of “0630” test improved by 0.05—0.1 for 0.5—10 mm,ETS improved by more than 0.08 for 5 mm,and also improved by a small amount of approximately 10—20 mm,and the POD test improved between 0.5—20 mm.The most obvious improvement was 0.1—0.25 in the range of 0.5—10 mm,while the TS and ETS of the “0824” test improved by more than 0.1 in the range of 10—20 mm,and the POD test improved by 0.1—0.4 in the range of 6—20 mm.The assimilation improved the precipitation forecast,which was particularly evident in the large-threshold precipitation.(4) The assimilation of MHS data did not significantly improve the precipitation forecast of the “0705” experiment,which indicates that assimilation does not always yield positive effects in the results.Therefore,one should not overly trust it when using MHS data.In general,assimilating MHS data can improve the forecast quality.In addition,there remain the following shortcomings in this experiment:The selected assimilation data is not highly varied,and the three selected precipitation cases are all strong precipitation in shortwave troughs,which may not be applicable to precipitation in other weather systems.Therefore,in the future,higher precision satellite data and microwave humidity data from other satellites can be considered to conduct assimilation experiments on various types of precipitation,so as to further verify the assimilation effect of satellite data in the assimilation prediction system of the Three River Source area.

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钟浩斌,王磊,李谢辉,陈旭,梁周彤,2024. ATOVS卫星资料同化对三江源暴雨预报影响的试验研究[J].大气科学学报,47(3):460-475. ZHONG Haobin, WANG Lei, LI Xiehui, CHEN Xu, LIANG Zhoutong,2024. Experimental study of ATOVS satellite data assimilation influencing rainstorm prediction in the Three Rivers Source area[J]. Trans Atmos Sci,47(3):460-475. DOI:10.13878/j. cnki. dqkxxb.20221101001

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  • 收稿日期:2022-11-01
  • 最后修改日期:2023-06-19
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  • 在线发布日期: 2024-06-15
  • 出版日期: 2024-05-28

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