The Estimation of Analysis Error Characteristics Using an Observation System Simulation Experiment

在应用大气观测作为数值天气预报的初始条件之前，必须对其进行分析。现代分析是使用数据同化系统进行的。这就提出了一个问题，即分析引入了哪些错误。这个问题无法在操作环境中得到回答，因为用于检查分析的唯一可用数据是编写分析时所用的数据。在这个项目中，国家环境预测中心的业务同化系统的误差统计将通过将其应用于数值模型的输出来评估，与观测到的大气不同，数值模型的真实状态是完全已知的。该模式的“自然运行”将提供待吸收的“数据”，它是欧洲中期天气预报中心（ECMWF）高分辨率预报模式已经存在13个月的模拟。观测系统模拟实验（OSSE）将吸收自然运行的模拟观测。OSSE的结果将被验证，内插到与自然运行相同的网格中，并计算分析误差的统计。这些包括基本的统计数据，如偏差和误差方差，以及更复杂的统计数据，这些统计数据将揭示分析误差的空间结构及其对大气流动的依赖。该项目的更广泛影响是向社会提供广泛用于业务天气预报和研究的分析数据集的不确定性估计。

Before atmospheric observations can be applied as initial conditions for numerical weather forecast, they must be analyzed. Modern analyses are carried out using data assimilation systems. This raises the question of what errors are introduced by the analysis. This question cannot be answered in an operational setting, for which the only data available for checking the analysis are those from which it was prepared. In this project the error statistics of the National Center for Environmental Prediction's operational assimilation system will be assessed by applying it to the output of a numerical model, for which, unlike the observed atmosphere, the true state is perfectly known.The "nature run" of the model, which will provide the "data" to be assimilated is an already extant 13-month simulation of the European Centre for Medium Range Weather Forecasting (ECMWF) high resolution forecast model. The observing system simulation experiment (OSSE) will assimilate simulated observations of the nature run. The results of the OSSE will be validated, interpolated to the same grid as the nature run, and the statistics of analysis errors will be computed. These include basic statistics, such as biases and error variances, and more complex statistics that will reveal the spatial structures of analysis errors and their dependence on the atmospheric flow.The broader impacts of this project are in providing to the community estimates of uncertainty in analyzed data sets that are widely used in operational weather forecasting and for research.