Collaborative Research: Atmospheric Data Assimilation Techniques for Antarctic Applications

“该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。“南极大陆缺乏常规气象观测，这对天气预报和气候分析都是一个重大挑战。这就更加重视卫星数据的有效利用，包括非传统方法，例如目前台湾/美国联合COSMIC/FormoSat-3使命中提供的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轨道星座利用全球定位卫星网络信号的大气折射反渗透临边探测，除其他外，以高空间和时间分辨率对大气的温度、压力和湿度进行大气探测。鉴于南大洋和南极大陆上空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如何最有效地利用这一强大的观测系统感兴趣。NCAR和俄亥俄州州立大学的研究人员建议开发有效且高效的数据同化方法，将COSMIC RO探测纳入南极天气预测和其他大气（例如气候和大气系统变量）数据记录中。现有的三维或四维变分（3/4D-Var）和基于集合（即概率）的数据同化方案将与天气研究和预报（WRF）模式一起使用，以指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。

"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.