Advances in Atmospheric Sciences

, Volume 35, Issue 10, pp 1217–1230 | Cite as

Value-added Impact of Geostationary Hyperspectral Infrared Sounders on Local Severe Storm Forecasts—via a Quick Regional OSSE

  • Zhenglong Li
  • Jun LiEmail author
  • Pei Wang
  • Agnes Lim
  • Jinlong Li
  • Timothy J. Schmit
  • Robert Atlas
  • Sid-Ahmed Boukabara
  • Ross N. Hoffman
Original Paper


Accurate atmospheric temperature and moisture information with high temporal/spatial resolutions are two of the key parameters needed in regional numerical weather prediction (NWP) models to reliably predict high-impact weather events such as local severe storms (LSSs). High spectral resolution or hyperspectral infrared (HIR) sounders from geostationary orbit (GEO) provide an unprecedented source of near time-continuous, three-dimensional information on the dynamic and thermodynamic atmospheric fields—an important benefit for nowcasting and NWP-based forecasting. In order to demonstrate the value of GEO HIR sounder radiances on LSS forecasts, a quick regional OSSE (Observing System Simulation Experiment) framework has been developed, including high-resolution nature run generation, synthetic observation simulation and validation, and impact study on LSS forecasts. Results show that, on top of the existing LEO (low earth orbit) sounders, a GEO HIR sounder may provide value-added impact [a reduction of 3.56% in normalized root-mean-square difference (RMSD)] on LSS forecasts due to large spatial coverage and high temporal resolution, even though the data are assimilated every 6 h with a thinning of 60 km. Additionally, more frequent assimilations and smaller thinning distances allow more observations to be assimilated, and may further increase the positive impact from a GEO HIR sounder. On the other hand, with denser and more frequent observations assimilated, it becomes more difficult to handle the spatial error correlation in observations and gravity waves due to the limitations of current assimilation and forecast systems (such as a static background error covariance). The peak reduction of 4.6% in normalized RMSD is found when observations are assimilated every 3 h with a thinning distance of 30 km.

Key words

OSSE hyperspectral sounder high-impact weather 

摘 要

具有高时空间分辨率的精确的大气温度和湿度信息, 是利用区域数值天气预报模式, 准确预报局地强风暴天气的两个关键参数. 来自地球静止轨道的高光谱分辨率红外探测仪, 能提供大气的动力和热力的近乎连续的三维信息. 这些前所未有的关于大气垂直结构的信息, 对临近预报和基于数值天气模式的预报具有重要意义. 为了展示地球静止轨道高光谱红外探测仪对局地强风暴预报的价值, 我们开发了一个快速的区域观测系统模拟试验框架, 包括高分辨率的自然大气场景(nature run)的生成, 观测的模拟和验证, 以及对局地强风暴预报的影响研究. 结果表明, 与现有的低地球轨道大气探测仪的相比, 地球静止轨道高光谱红外探测仪对区域模式具有更大的空间覆盖率和更高的时间分辨率. 在目前的每6小时一次, 稀疏距离为60公里的业务同化设置下, 能够改进局地强风暴预报, 减少整体分析和预报误差3.56%. 此外, 更频繁的同化和更小的数据稀疏距离能使更多的观测数据被同化, 从而进一步增加地球静止轨道高光谱红外探测仪的正影响效果.


观测系统模拟试验 高光谱 探测仪 高影响天气 


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This work is partly supported by the NESDIS OPPA OSSE program (Grant No. NA15NES4320001). The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or U. S. government position, policy, or decision. Thanks to the Joint Center for Satellite Data Assimilation for providing the “S4” supercomputer [Supercomputer for Satellite Simulations and Data Assimilation Boukabara et al. (2016b)], physically located at SSEC, University of Wisconsin-Madison, as the main computational resource for this research study.


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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Zhenglong Li
    • 1
  • Jun Li
    • 1
    Email author
  • Pei Wang
    • 1
  • Agnes Lim
    • 1
  • Jinlong Li
    • 1
  • Timothy J. Schmit
    • 2
  • Robert Atlas
    • 3
  • Sid-Ahmed Boukabara
    • 4
  • Ross N. Hoffman
    • 3
  1. 1.Cooperative Institute for Meteorological Satellite StudiesUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Center for Satellite Applications and ResearchNOAAMadisonUSA
  3. 3.Atlantic Oceanographic and Meteorological LaboratoryOAR/NOAAMiamiUSA
  4. 4.Center for Satellite Applications and ResearchNOAACollege ParkUSA

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