The direct assimilation of space‐borne cloud radar and lidar observations into a global numerical weather prediction model has not previously been attempted for several reasons. Firstly, the modification of a data assimilation system to handle space‐borne profiling observations is a technical challenge. Secondly, the relationship between model‐scale control variables and the relatively narrow footprint of the radar and lidar instruments were thought to be unrepresentative and too nonlinear for a variational assimilation system that is based on assumptions of linearity. However, motivation was provided when previous experiments assimilating cloud radar and lidar profiles showed, using an intermediary step to generate pseudo‐observations of temperature and humidity, that there was potential for the observations to improve both the analysis and the subsequent forecast. This article presents the developments made to facilitate the direct assimilation of cloud radar and lidar observations into the four‐dimensional variational assimilation (4D‐Var) system of the European Centre for Medium‐Range Weather Forecasts (ECMWF). A review of the observation operators shows how they have been optimised for data assimilation with an emphasis on efficiency, consistency and differentiability. A key part of this work is the specification of a new fully flow‐dependent characterisation of the observation error. By taking an error inventory approach, we account for different sources of error in a physical way. Also, to avoid degrading the analysis, careful screening, quality control and bias correction is necessary and is described herein. Finally, an initial assessment of the impact of the observations is achieved through single‐analysis tests that show that the analysis fit to the new observations is improved. In‐depth results demonstrating the impact of these observations are shown in the second part of this two‐part series.

中文翻译：

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星载云雷达反射率和激光雷达后向散射的直接4D-Var同化。第一部分：观测算子及其实现

出于多种原因，之前尚未尝试将星云云雷达和激光雷达观测直接同化为全球数值天气预报模型。首先，修改数据同化系统以处理星载剖面观察是一项技术挑战。其次，对于基于线性假设的变分同化系统，模型规模的控制变量与雷达和激光雷达仪器相对狭窄的占地面积之间的关系被认为是不具代表性的，而且过于非线性。但是，当先前的实验将云雷达和激光雷达的廓线进行同化时，便提供了动力，它使用中间步骤生成温度和湿度的伪观测，观察有可能改善分析和后续预测。本文介绍了促进将云雷达和激光雷达观测结果直接同化到欧洲中距离天气预报中心（ECMWF）的四度变分同化（4D-Var）系统中的进展。对观测算子的回顾表明，它们是如何针对数据同化进行优化的，重点是效率，一致性和可区分性。这项工作的关键部分是对观测误差进行全新的，与流量完全相关的表征。通过采用错误清单方法，我们以物理方式解决了不同的错误来源。另外，为避免降低分析质量，请仔细筛选，质量控制和偏差校正是必要的，在此进行描述。最后，通过单项分析测试对观察结果的影响进行了初步评估，结果表明，该分析适合于新观察结果。这个由两部分组成的系列文章的第二部分显示了证明这些观察结果影响的深入结果。