Collaborative Research: Atmospheric Data Assimilation Techniques for Antarctic Applications

摘要：“本奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。”南极大陆常规气象观测的缺乏对天气预报和气候分析构成了重大挑战。这使得卫星数据的有效使用得到了重视，包括非传统的方法，例如台湾/美国COSMIC/FormoSat-3联合任务中目前可用的GPS无线电掩星（RO）探测。这个由6个低地球轨道平台组成的极地轨道星座利用全球定位卫星网络信号的大气折射进行反卫星探测，以高时空分辨率测量大气的温度、压力和水分含量。考虑到南大洋和南极大陆几乎完全缺乏无线电探空仪数据，研究人员和观测团体都对最有效地利用这一强大观测系统的方法感兴趣。NCAR和俄亥俄州立大学的研究人员建议发展有效和高效的数据同化方法，将COSMIC RO探测结果纳入南极天气预报和其他大气（例如气候和大气系统变量）数据记录。现有的3或4维变分（3/ 4d - var）和基于集合（即概率）的数据同化方案将与天气研究与预报（WRF）模式结合使用，以确定哪种数据同化方案最适合南极和南大洋大气环境的天气分析和再分析。

Abstract"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."The paucity of conventional meteorological observations over the Antarctic continent represents a major challenge to both weather forecasts and climate analysis. This puts a premium on the productive use of satellite data, including non-traditional approaches such as is exemplified by the GPS radio occultation (RO) soundings, currently available from the joint Taiwan/US COSMIC/FormoSat-3 mission. This polar orbiting constellation of 6 low earth orbiting platforms (LEOs) uses RO limb sounding of the atmospheric refraction of the global positioning satellite network signals to yield, amongst other things, atmospheric soundings of temperature, pressure and moisture content of the atmosphere at high spatial and temporal resolution. Given the virtual total lack of radiosonde data over the Southern Ocean and the Antarctic continent, methods to most productively utilize this powerful observing system are of interest to researchers and observational groups alike. Investigators from NCAR and the Ohio State University propose to develop effective and efficient data assimilation approaches to incorporating COSMIC RO soundings into Antarctic weather prediction and other atmospheric (e.g. climate and atmosphere system variables) data records. Existing 3 or 4 dimensional variational (3/4D-Var) and ensemble based (i.e. probabalistic) data assimilation schemes will be used in conjunction with the Weather Research and Forecast (WRF) model in order to specify which data assimilation scheme is best suited to weather analysis, and reanalysis, in the Antarctic and Southern Ocean atmospheric environments.