Advances in Atmospheric Sciences

, Volume 34, Issue 11, pp 1263–1264 | Cite as

A step forward toward effectively using hyperspectral IR sounding information in NWP

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摘 要

搭载在气象卫星上的作为全球观测资料重要来源之一的高光谱分辨率红外(IR)探测仪器, 例如搭载于 Aqua 卫星上的 AIRS, Metop-A/B 卫星上的 IASI 以及 SNPP 卫星上的 CrIS, 其观测数据通过同化应用到业务数值天气预报(NWP)模式, 能够改进数值天气预报. 在全部的卫星观测中, 红外和微波探测器的观测对数值天气预报技术具有最重要的影响(Joo 等, 2013; Cucurull 和Anthes, 2014). 虽然先进的红外探测器在数值天气预报系统中占有重要地位, 但由于其观测数据量巨大, 在全部可用的通道中也只有数百个通道的观测可被同化利用. 例如, AIRS, IAS I和CrIS 分别拥有 2372, 8461 和 1305 个观测通道, 也只有部分观测通道被主要的业务中心同化应用到了数值天气预报模式中. 为了有效地将高光谱红外遥感信息同化到数值天气预报模式中, 提出了多种筛选通道的方法. 例如, Li 和 Huang(1994)提出一种基于逐步回归的方法, 用于 AIRS 的通道筛选; Collard(2007)提出了基于信息内容分析以及相关限制条件的筛选方案; Rabier 等(2002)研究了基于雅可比矩阵及迭代方法的筛选方案, 用于顺序筛选通道以获得最大信息量; 在标准方法的基础上, Ventress 和 Dudhia(2014)发展的改进方案在通道筛选过程中可以更好的模拟和量化光谱相关误差; Migliorini(ECMWF技术备忘录, 编号727, 2014)研究了基于最优流依赖在有云情况下的通道筛选方案. 这些方法都可以有效地筛选一套通道, 为同化辐射量提供最优化信息, 尤其是地球同步卫星上的先进的高光谱探测器; 例如, 搭载在风云四号卫星上的地球同步干涉红外探测器(Yang 等, 2017). 综上所述, 通道筛选方案都是基于信息内容分析, 同时考虑非线性及其他因素(例如, 相关误差)的影响. 信息内容分析方法的一大局限在于采用线性方法筛选具有高度非线性的吸收通道(例如, 与温度相比, 水汽吸收通道的辐射量与大气中水汽的含量之间具有较大的非线性关系). 这类通道筛选方法的另一个局限在于不同吸收区间的通道被独立筛选, 由于不同通道的权重不同, 将导致主观的通道筛选结果. 最近, 一个新的通道筛选方法被提出 (Noh 等, 2017). 采用这种方法, 通过计算每个独立添加的通道对一维变分分析结果的改进来筛选通道. 在通道筛选过程中, 定义通道评分指数(CSI)作为成功筛选的标准. 在 314 个 EUMETSAT 的 IASI通道中, 通过计算每个独立通道的 CSI 贡献, 200个通道被系统自动成功筛选. 在 UKMO的统一模式中, 与目前业务使用的183个通道相比, 采用重新筛选的通道对上层大气中的水汽及总降水量的预报都具有改进作用(水汽预报误差减小). 与其他方法相比, 该方法有效地考量了非线性的作用, 尤其在水汽通道的筛选中, 使得更多的水汽通道最终被成功筛选. 该工作对于将高光谱红外探测器的遥感信息, 尤其是水汽遥感信息有效地应用到数值天气预报模式中, 具有至关重要的发展作用.

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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 2017

Authors and Affiliations

  1. 1.Cooperative Institute for Meteorological Satellite StudiesUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Numerical Weather Prediction CenterChina Meteorological AdministrationBeijingChina

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