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• This is the first time EFSOI is applied in southern South America. • On average all the observation sources had a beneficial impact on the 6-h forecasts. • Applying EFSOI method can efficiently suggest data selection criteria. • Surface pressure was the only variable with periods of negative effect on forecasts. • NSWS data denial experiment supported EFSOI beneficial impact results. Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently achieved by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study we explore the application of the ensemble formulation of FSOI (EFSOI) in a regional data assimilation system over south-eastern South America and evaluate the observations that result beneficial or detrimental to the forecast. In this paper we focus on the impact of two types of surface weather stations: human-operated conventional surface weather station networks from the National Meteorological Service and non-conventional automatic surface weather stations from different public and private networks (CSWS and NSWS respectively). To achieve this, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter is used with 20 members. The experiment was carried out during 30 days of the intensive observing period of the RELAMPAGO-CACTI (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations-Clouds, Aerosols, and Complex Terrain Interaction) field campaign that was conducted during the 2018–2019 austral warm season in the center of Argentina. 20 km resolution analyses were obtained every 6-h, assimilating data from soundings, aircrafts, GOES derived motion winds, AIRS retrievals, CSWS and NSWS. It is shown that, considering the entire period, all the observation sources had a positive impact on the 6-h forecasts. However, when each variable is analyzed individually, surface pressure observations from both CSWS and NSWS show on average a negative impact. Two data denial experiments were carried out in order to support EFSOI results. This paper is the first approximation to quantify the impact of different observation sources and observed variables at a regional scale over south-eastern South America, a data sparse region where unconventional networks can help close large geographical data gaps. The results of this work help identify observation data sources detrimental for the data assimilation system, suggesting data selection criteria to improve the regional forecast accuracy. [ABSTRACT FROM AUTHOR]

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]

• The impact of different observation platforms in a regional scale data assimilation is investigated over South America. • Assimilating frequent surface temperature and moisture observations improves precipitation forecasts associated with a huge mesoscale convective system. • Clear sky radiances improve the first-guess hourly accumulated precipitation but lead to neutral impacts in short-range precipitation ensemble forecasts. This paper evaluates the impact of assimilating high-resolution surface networks and satellite observations using the WRF-GSI-LETKF over central and north eastern Argentina where the surface and upper air observing networks are relatively coarse. A case study corresponding to a huge mesoscale convective system (MCS) that developed during November 22, 2018 was used. The accumulated precipitation associated with this MCS was quite high, exceeding 200 mm over northern Argentina and Paraguay. The MCS developed during the Intense Observing Period (IOP) of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. The GSI-4DLETKF data assimilation package is used to produce analyses by assimilating observations every hour with 10-km horizontal grid spacing and a 60-member multiphysics ensemble. Four assimilation experiments are conducted using different sets of observations: CONV, consisting of conventional observations from NCEP's prepBUFR files; AWS, combining CONV and dense automatic surface weather station networks (AWS), SATWND, combining AWS with satellite-derived winds, and RAD, including SATWND; and satellite radiances from different microwave and infrared sensors. The assimilation of observations with high temporal and spatial frequency generates an important impact on the PBL, primarily on the precipitable water content, that leads to the development of deep convection and heavy precipitation closer to the observed in this case study. The assimilation of radiance observations produces a better development of the convection mainly during the mature state of the MCS leading to an increase in the accumulated precipitation. Ensemble forecasts initialized from each experiment were also simulated to evaluate their skill to predict precipitation. The hourly assimilation of the observations in AWS, SATWND, and RAD helped to improve the precipitation forecast. [ABSTRACT FROM AUTHOR]