A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign.

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. • A LETKF-WRF system was run successfully in real-time to support RELAMPAGO operations. • A reduction in forecast error was shown due to data assimilation cycles. • 60-member RRR analyses and forecasts are available for the research community. [ABSTRACT FROM AUTHOR]