A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility
A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility
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DOI：
10.1002/2013jd020763
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发表时间：
2013-12
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通讯作者：
Ying Li;G. Kirchengast;B. Scherllin-Pirscher;Suqin Wu;M. Schwaerz;J. Fritzer;S. Zhang;B. Carter-B.-Car
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作者：
Ying Li;G. Kirchengast;B. Scherllin-Pirscher;Suqin Wu;M. Schwaerz;J. Fritzer;S. Zhang;B. Carter-B.-Car
基于全球导航卫星系统（GNSS）的无线电掩星（RO）是一种卫星遥感技术，可为天气和气候应用提供地球大气层的精确轮廓。然而，在约30公里的高度以上，统计优化是一个关键的过程，用于初始化RO弯曲角度，以优化检索的大气剖面的气候监测效用。在这里，我们介绍了一种先进的动态统计优化算法，该算法使用来自欧洲中期天气预报中心（ECMWF）短期预报和分析场的多天弯曲角，以及平均观测弯曲角，以获得背景剖面和相关的误差协方差矩阵，并每天更新地理变化的背景不确定性估计。新的算法进行评估对现有的韦格纳中心掩星处理系统版本5.4（OPSv5.4）算法，使用几天的模拟MetOp和观测CHAMP和COSMIC数据，1月和7月的条件。与OPSv5.4相比，我们发现新方法的性能如下：1。它显著地减小了随机误差（标准偏差），减小到其大小的大约一半，并且在优化的弯曲角度中留下更少或大约相等的残余系统误差（偏差）; 2.）仅背景误差相关矩阵的动态（每日）估计就已经改进了优化的弯曲角度; 3.）随后检索的放射性活度廓线和大气（温度）廓线因改进的误差特性而受益，特别是在约30公里以上。基于这些令人鼓舞的结果，我们的工作，采用类似的动态误差协方差估计也为观测到的弯曲角度，并将该方法应用到整个月，随后整个气候数据记录。
Global Navigation Satellite System (GNSS)‐based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium‐range Weather Forecasts (ECMWF) short‐range forecast and analysis fields, together with averaged‐observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.