Background error statistics for aerosols
Background error statistics for aerosols
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DOI：
10.1002/qj.37
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发表时间：
2007
影响因子：
8.9
通讯作者：
A. Benedetti;M. Fisher
中科院分区：
文献类型：
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作者：
A. Benedetti;M. Fisher
作为欧洲全球环境与安全监测倡议（GMES）的一部分，利用卫星和现场数据进行全球和区域地球系统监测（GEMS）项目已获得资助，以提供对气溶胶、温室气体和反应性气体等大气成分的预测和分析。在这方面，欧洲中期天气预报中心（ECMWF）于2005年3月开始开发基于四维变分（4D-Var）同化的气溶胶分析系统。作为该系统的主要组成部分，气溶胶混合比的误差背景协方差矩阵是基于气溶胶预报构造的背景统计量建立的。这些统计数据是使用NMC方法从海盐，沙漠尘埃和大陆颗粒的气溶胶混合比的48小时和24小时预测之间的差异中产生的。这种方法已被许多数值天气预报中心广泛用于建立状态变量的背景误差统计。这是第一次，据我们所知，它已被应用到一个新的变量，如气溶胶混合比，在全球模式的背景下。气溶胶同化系统已经成功地运行了观测导出或模型规定的误差协方差矩阵与预先指定的相关长度。在这项研究中，气溶胶的垂直和水平结构的这些误差相关性，推导出一个国家的最先进的模式的帮助下进行了深入的调查和讨论。这些误差的相关性建模通过一个频谱/小波的方法，并用于构建一个背景误差协方差矩阵。在单次观测4D-Var实验的背景下应用该背景矩阵，成功地对总气溶胶混合比进行了初步分析。版权所有© 2007皇家气象学会
As part of the European initiative for Global Monitoring for Environment and Security (GMES), the Global and regional Earth‐system Monitoring using Satellite and in situ data (GEMS) project has been funded to provide forecasts and analysis of atmospheric constituents such as aerosols, and greenhouse and reactive gases. In this context, the development of an aerosol analysis system based on a four‐dimensional variational (4D‐Var) assimilation was started in March 2005 at the European Centre for Medium‐Range Weather Forecasts (ECMWF). As part of the main building blocks of the system, an error background covariance matrix for aerosol mixing ratio was built based on background statistics constructed from aerosol forecasts. These statistics were generated from the differences between the 48‐hour and 24‐hour forecasts of aerosol mixing ratios for sea salt, desert dust and continental particulate, using the NMC method. This method has been widely used by many numerical weather prediction centres to build background error statistics for state variables. This is the first time, to our knowledge, that it has been applied to a novel variable such as an aerosol mixing ratio, in the context of a global model. Aerosol assimilation systems have been successfully run with either observationally‐derived or model‐prescribed error covariance matrices with pre‐assigned correlation lengths. In this study, the aerosol vertical and horizontal structures of these error correlations as derived with the aid of a state‐of‐the‐art model are investigated and discussed in depth. Successively these error correlations are modelled through a spectral/wavelet approach and used to construct a background error covariance matrix. Application of this background matrix in the context of a single observation 4D‐Var experiment produced successful preliminary analyses of the total aerosol mixing ratio. Copyright © 2007 Royal Meteorological Society