Hourly 4D‐Var in the Met Office UKV operational forecast model
Hourly 4D‐Var in the Met Office UKV operational forecast model
复制标题
英国气象局 UKV 业务预报模型中的每小时 4D-Var
DOI：
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发表时间：
2020
影响因子：
8.9
通讯作者：
M. Wlasak
中科院分区：
文献类型：
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作者：
M. Milan;B. Macpherson;R. Tubbs;G. Dow;G. Inverarity;M. Mittermaier;Gemma Halloran;G. Kelly;Dingmin Li;A. Maycock;T. Payne;C. Piccolo;L. Stewart;M. Wlasak
2017年7月，气象局的对流尺度UKV预报模式中实施了每小时循环的四维变分资料同化（4D-Var），取代了之前的每小时循环的三维3D-Var方案。新系统是基于以前的临近预报示范项目（NDP），开发和运行在真实的时间超过英国南部域的伦敦2012年奥运会和残奥会，并侧重于降水预报。新的业务系统将这种能力扩展到整个英国（及周边）地区，提供适合融入临近预报产品的输出，并对全方位的气象变量进行一般预报。我们描述了气象局4D-Var系统的一般配方和一些特殊成分。新系统及其NDP和UK 3D-Var前因之间的差异进行了讨论的同化算法和观测输入。为了说明对预测性能的影响，我们使用两种配置的每小时周期比较了3D-Var和4D-Var的技能。对于降水技巧，我们还将三小时3D-Var和每小时4D-Var与参考临近预报系统进行了比较，以突出新方法在非常短的预报范围内的改进。未来的途径开发系统的概述。
Hourly cycling four‐dimensional variational data assimilation (4D‐Var) was implemented operationally in the Met Office's convective‐scale UKV forecast model in July 2017, replacing the previous three‐hourly cycling three‐dimensional 3D‐Var scheme. The new system was based on a previous Nowcasting Demonstration Project (NDP), developed and run in real time over a southern UK domain for the London 2012 Olympic and Paralympic Games and focusing on precipitation forecasts. The new operational system extends this capability to the full UK (and surrounding) area, delivering outputs suitable for blending into nowcasting products and for general forecasting of a full range of meteorological variables. We describe the general formulation of the Met Office 4D‐Var system and some particular ingredients. Differences between the new system and its NDP and UK 3D‐Var antecedents are discussed for both the assimilation algorithm and the observational inputs. As an illustration of the impact on forecast performance, we compare the skill of 3D‐Var and 4D‐Var using an hourly cycle for both configurations. For precipitation skill, we also compare three‐hourly 3D‐Var and hourly 4D‐Var with a reference nowcasting system, in order to highlight the improvement from the new method at very short forecast ranges. Future avenues for developing the system are outlined.
用于同化湿度和云敏感观测的湿度增量算子：公式和初步结果
影响因子：
8.9
作者：
Migliorini, Stefano;Lorenc, Andrew C.;Bell, William
通讯作者：
Bell, William