GSTDTAP  > 地球科学
DOI10.1016/j.atmosres.2016.11.004
Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts
Jones, Thomas A.1,2; Koch, Steven2; Li, Zhenglong3
2017-04-01
发表期刊ATMOSPHERIC RESEARCH
ISSN0169-8095
EISSN1873-2895
出版年2017
卷号186页码:43733
文章类型Article
语种英语
国家USA
英文摘要

Assimilation of hyperspectral sounder data into numerical weather prediction (NWP) models has provenvital to generating accurate model analyses of tropospheric temperature and humidity where few conventional observations exist. Applications to storm-scale models are limited since the low temporal resolution provided by polar orbiting sensors cannot adequately sample rapidly changing environments associated with high impact weather events. To address this limitation, hyperspectral sounders have been proposed for geostationary orbiting satellites, but these have yet to be built and launched in part due to much higher engineering costs and a lack of a definite requirement for the data.


This study uses an Observation System Simulation Experiment (OSSE) approach to simulate temperature and humidity profiles from a hypothetical geostationary-based sounder from a nature run of a high impact weather event on 20 May 2013. The simulated observations are then assimilated using an ensemble adjustment Kalman filter approach, testing both hourly and 15 minute cycling to determine their relative effectiveness at improving the near storm environment Results indicate that assimilating both temperature and humidity profiles reduced mid-tropospheric both mean and standard deviation of analysis and forecast errors compared to assimilating conventional observations alone. The 15 minute cycling generally produced the lowest errors while also generating the best 2-4 hour updraft helicity forecasts of ongoing convection. This study indicates the potential for significant improvement in short-term forecasting of severe storms from the assimilation of hyperspectral geostationary satellite data. However, more studies are required using improved OSSE designs encompassing multiple storm environments and additional observation types such as radar reflectivity to fully define the effectiveness of assimilating geostationary hyperspectral observations for high impact weather forecasting applications. (C) 2016 Elsevier B.V. All rights reserved.


英文关键词Hyperspectral sounders Ensemble data assimilation Storm-scale data assimilation OSSE
领域地球科学
收录类别SCI-E
WOS记录号WOS:000392557700002
WOS关键词ENSEMBLE KALMAN FILTER ; SYSTEM SIMULATION EXPERIMENTS ; EXPLICIT FORECASTS ; PART I ; IMPACT ; RADIANCE ; RADAR ; IMPLEMENTATION ; MODEL ; AIRS/AMSU/HSB
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/38382
专题地球科学
作者单位1.Univ Oklahoma, Cooperat Inst Mesoscale Meteorol Studies, Norman, OK 73019 USA;
2.NOAA, Natl Severe Storms Lab, Norman, OK 73069 USA;
3.Univ Wisconsin, Cooperat Inst Meteorol Satellite Studies, Madison, WI USA
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Jones, Thomas A.,Koch, Steven,Li, Zhenglong. Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts[J]. ATMOSPHERIC RESEARCH,2017,186:43733.
APA Jones, Thomas A.,Koch, Steven,&Li, Zhenglong.(2017).Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts.ATMOSPHERIC RESEARCH,186,43733.
MLA Jones, Thomas A.,et al."Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts".ATMOSPHERIC RESEARCH 186(2017):43733.
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