The present investigation evaluates the assimilation of synthetic data which has properties similar to actual Global‐scale Observations of the Limb and Disk (GOLD) level‐2 (L2) temperatures and other conventional lower atmospheric observations. The lower atmospheric and GOLD L2 temperature (T_disk) are assimilated in the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension using Data Assimilation Research Testbed. It is found that inclusion of the GOLD T_disk improves the forecast root mean square error (RMSE) and bias by 5% and 71%. When compared to lower atmosphere only assimilation, the improvements in RMSE and bias are 20% and 94%. An investigation of the global diurnal westward‐propagating wavenumber 1 (DW1) and local diurnal tidal characteristics shows that inclusion of the GOLD temperatures improves the DW1 by about 8% and diurnal tide by more than 17%. Larger percentage improvements in the tides are seen in the lower thermosphere. Considerable improvements in the model state are also seen at times and locations where there are no GOLD observations available. These results and the background data assimilation procedure are presented here, which demonstrate that GOLD thermospheric temperature is an excellent data set that can be used for thermospheric assimilation studies and operational purposes.

中文翻译：

---

提取的金热层温度对整个大气数据同化模型的影响

本研究评估了合成数据的同化，该合成数据的性质与实际的全球范围的肢体和椎间盘（GOLD）-2级（L2）温度以及其他常规的低层大气观测相似。使用数据同化研究试验台，在大气圈和电离层扩展的整个大气群落气候模型中，吸收了较低的大气温度和金L2温度（T_盘）。发现包含了GOLD T_盘将预测的均方根误差（RMSE）和偏差提高了5％和71％。与仅吸收低层大气的情况相比，RMSE和偏差的改善分别为20％和94％。对全球昼夜向西传播波数1（DW1）和局部昼间潮汐特征的研究表明，包含金温度可将DW1改善约8％，将日潮提高17％以上。在较低的热圈中可以看到潮汐涨幅较大。在没有可用的GOLD观测值的时间和位置，也可以看到模型状态的显着改善。这些结果和背景数据同化过程在此处介绍，