Hourly Assimilation of Different Sources of Observations Including Satellite Radiances in a Mesoscale Convective System Case During RELAMPAGO campaign

Abstract In this paper, we evaluate 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. We conducted a case study corresponding to a huge mesoscale convective system (MCS) that developed during November 22, 2018. 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 RELAMPAGO field campaign. We used the GSI-4DLETKF data assimilation package to produce analyses assimilating observations every hour with 10-km horizontal grid spacing and a 60-member multiphysics ensemble.

We conducted four assimilation experiments 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, SATWND, combining AWS with satellite-derived winds and RAD, including SATWND; and satellite radiances from different microwave and infrared sensors. We found that the assimilation of observations with high temporal and spatial frequency generate 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. We also ran ensemble forecasts initialized from each experiment and evaluated their skill to predict precipitation. We found that the hourly assimilation of the observations in AWS, SATWND, and RAD helped to improve the precipitation forecast.