Evaluation of adjoint‐based observation impacts as a function of forecast length using an Observing System Simulation Experiment
Evaluation of adjoint‐based observation impacts as a function of forecast length using an Observing System Simulation Experiment
复制标题
使用观测系统模拟实验评估伴随观测影响作为预测长度的函数
DOI：
10.1002/qj.3909
复制
发表时间：
2020
影响因子：
8.9
通讯作者：
Amal El Akkraoui
中科院分区：
文献类型：
--
作者：
N. Privé;R. Errico;R. Todling;Amal El Akkraoui
数值天气预报模式的伴随线可用于观测值预测敏感性（FSO），以监测观测数据对短期预报技能的贡献。然而，由于缺乏真正独立的数据集进行验证，短期预测误差的计算是困难的。在观测系统模拟实验（OSSE）框架中，自然运行能够提供真实和完整的验证数据集，并允许对短期预测误差进行准确评估。在这项工作中，使用美国国家航空航天局全球模拟和同化办公室开发的OSSE来探索6-48小时范围内观测数据对预报的影响。采用全球地球观测系统模型的一个伴随模型来比较使用自分析验证和真实自然运行验证估计的观测影响。发现自我分析验证在早期预报期间夸大了估计的预报误差增长，导致对观测影响的高估，特别是在6-12小时预报范围内。到48小时，预测误差和观测影响的自我分析验证估计值更接近真实值。当使用自我分析验证时，在较短的预测时间内，有益观测的比例也会过度膨胀。单个观测值或数据类型的影响的进展取决于每个观测值所影响的初始条件误差的增长特征。
Adjoints of numerical weather prediction models may be employed for forecast sensitivity to observations (FSO) in order to monitor the contribution of ingested observation data on short‐term forecast skill. However, the calculation of short‐term forecast error is difficult, due to the lack of a truly independent dataset for verification. In an Observing System Simulation Experiment (OSSE) framework, the Nature Run is able to provide a true and complete verification dataset and allows accurate evaluation of short‐term forecast errors. In this work, an OSSE developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office is used to explore the impact of observational data on forecasts in the 6–48 hour range. An adjoint of the Global Earth Observing System model is employed to compare the observation impacts estimated using both self‐analysis verification and the true Nature Run verification. Self‐analysis verification is found to inflate the estimated forecast‐error growth during the early forecast period, resulting in overestimations of observation impacts, particularly in the 6–12 hour forecast range. By 48 hours, the self‐analysis verification estimates of forecast error and observation impacts match the true values more closely. The fraction of beneficial observations is also overinflated at short forecast times when self‐analysis verification is used. The progression of impacts of an individual observation or data type depends on the character of growth of the initial‐condition error that each observation affects.