A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign
A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign
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DOI：
10.1016/j.atmosres.2021.105858
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发表时间：
2021-09
影响因子：
5.5
通讯作者：
M. E. Dillon;P. Maldonado;P. Corrales;Yanina García Skabar;J. Ruiz;M. Sacco;Federico Cutraro;L. Mingari;C. Matsudo;Luciano Vidal;Martín Rugna;M. P. Hobouchian;P. Salio;S. Nesbitt;C. Saulo;E. Kalnay;T. Miyoshi
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文献类型：
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作者：
M. E. Dillon;P. Maldonado;P. Corrales;Yanina García Skabar;J. Ruiz;M. Sacco;Federico Cutraro;L. Mingari;C. Matsudo;Luciano Vidal;Martín Rugna;M. P. Hobouchian;P. Salio;S. Nesbitt;C. Saulo;E. Kalnay;T. Miyoshi
本文介绍了自适应地面观测遥感电化、闪电和中/微尺度过程野外活动(2018年11月至12月，阿根廷中部)密集观察期内实施区域集合数据同化和预报系统所取得的经验教训。该系统基于天气研究和预报(WRF)模型和局部集合变换卡尔曼滤波(LETKF)的耦合。它结合了全球和本地可用的多种数据源，如高分辨率地面网络、来自当地飞机飞行的AMDAR数据、测深、AIRS反演、高分辨率GOES-16风速估计和当地雷达数据。网格间距为10公里的每小时分析与暖启动36小时集合预报一起产生，这些预报是从每三小时一次的快速刷新分析中初始化的。初步评估表明，由于同化观测，预报误差得到了减小。然而，从全球预报系统分析中初始化的冷启动预报略优于本文所讨论的区域同化系统中的冷启动预报。该系统采用多种物理方法，侧重于使用不同的积云和行星边界层方案，使我们能够对阿根廷中部上空的不同模式配置进行评估。我们发现，预报地表变量的最佳组合与预报降水的最佳组合不同，方案之间的差异往往主导着降水等变量的预报总体分布。从这一试验系统中吸取的经验教训是南美洲开发先进业务数据同化系统的Relampago实地运动的一部分。
This paper describes the lessons learned from the implementation of a regional ensemble data assimilation and forecast system during the intensive observing period of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (central Argentina, November–December 2018). This system is based on the coupling of the Weather Research and Forecasting (WRF) model and the Local Ensemble Transform Kalman Filter (LETKF). It combines multiple data sources both global and locally available like high-resolution surface networks, AMDAR data from local aircraft flights, soundings, AIRS retrievals, high-resolution GOES-16 wind estimates, and local radar data. Hourly analyses with grid spacing of 10 km are generated along with warm-start 36-h ensemble-forecasts, which are initialized from the rapid refresh analyses every three hours. A preliminary evaluation shows that a forecast error reduction is achieved due to the assimilated observations. However, cold-start forecasts initialized from the Global Forecasting System Analysis slightly outperform the ones initialized from the regional assimilation system discussed in this paper. The system uses a multi-physics approach, focused on the use of different cumulus and planetary boundary layer schemes allowing us to conduct an evaluation of different model configurations over central Argentina. We found that the best combinations for forecasting surface variables differ from the best ones for forecasting precipitation, and that differences among the schemes tend to dominate the forecast ensemble spread for variables like precipitation. Lessons learned from this experimental system are part of the legacy of the RELAMPAGO field campaign for the development of advanced operational data assimilation systems in South America.