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Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations

机译:通过与重新分析背景序列和邻近站的组合比较,对全球探空仪温度数据集进行均质化

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This article describes progress in the homogenization of global radiosonde temperatures with updated versions of the Radiosonde Observation Correction Using Reanalyses (RAOBCORE) and Radiosonde Innovation Composite Homogenization (RICH) software packages. These are automated methods to homogenize the global radiosonde temperature dataset back to 1958. The break dates are determined from analysis of time series of differences between radiosonde temperatures (obs) and background forecasts (bg) of climate data assimilation systems used for the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and the ongoing interim ECMWF Re-Analysis (ERA-Interim). RAOBCORE uses the obs-bg time series also for estimating the break sizes. RICH determines the break sizes either by comparing the observations of a tested time series with observations of neighboring radiosonde time series (RICH-obs) or by comparing their background departures (RICH- tau ). Consequently RAOBCORE results may be influenced by inhomogeneities in the bg, whereas break size estimation with RICH-obs is independent of the bg. The adjustment quality of RICH-obs, on the other hand, may suffer from large interpolation errors at remote stations. RICH- tau is a compromise that substantially reduces interpolation errors at the cost of slight dependence on the bg. Adjustment uncertainty is estimated by comparing the three methods and also by varying parameters in RICH. The adjusted radiosonde time series are compared with recent temperature datasets based on (Advanced) Microwave Sounding Unit [(A)MSU] radiances. The overall spatiotemporal consistency of the homogenized dataset has improved compared to earlier versions, particularly in the presatellite era. Vertical profiles of temperature trends are more consistent with satellite data as well.Digital Object Identifier http://dx.doi.org/10.1175/JCLI-D-11-00668.1
机译:本文介绍了通过使用重新分析的探空仪观测校正(RAOBCORE)和探空仪创新复合均质化(RICH)软件包的更新版本来实现全球探空仪温度均质化的进展。这些是使全球无线电探空仪温度数据集均匀化的自动化方法,可以追溯到1958年。中断日期是通过分析40年来使用的气候数据同化系统的探空仪温度(obs)和背景预报(bg)之间的时间序列差异确定的欧洲中距离天气预报中心(ECMWF)重新分析(ERA-40)和正在进行的临时ECMWF重新分析(ERA-Interim)。 RAOBCORE还使用obs-bg时间序列来估计中断大小。 RICH通过比较测试时间序列的观测值与相邻无线电探空仪时间序列的观测值(RICH-obs)或比较其背景偏差(RICH-tau)来确定间隔大小。因此,RAOBCORE结果可能受bg中的不均匀性影响,而使用RICH-obs估算的断裂大小与bg无关。另一方面,RICH-obs的调整质量可能会在远程站遭受较大的插值误差。 RICHTAU是一种折衷方案,它以稍微依赖于bg为代价,大大减少了内插误差。通过比较这三种方法以及通过改变RICH中的参数来估计调整不确定性。将调整后的探空仪时间序列与基于(高级)微波探测单元[(A)MSU]辐射率的最新温度数据集进行比较。与早期版本相比,同质化数据集的总体时空一致性得到了改善,尤其是在卫星前时代。温度趋势的垂直剖面也与卫星数据更加一致。数字对象标识符http://dx.doi.org/10.1175/JCLI-D-11-00668.1

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