Assimilation of Airborne Radio Occultation (ARO) Observation Using MPAS-JEDI for an 11-flight Sequence of California ARs

During the 2022-2023 AR Recon campaign, an unprecedented sequence of Airborne Global Navigation Satellite System (GNSS) radio occultation (ARO) observations was possible between 6 to 17 January 2023. ARO data collection increased the sampling of AR’s beyond that of traditional dropsondes during the 11 flights of the NOAA GIV aircraft. The assimilation of retrieved ARO observations presents an opportunity to investigate the impact for these events. Assimilation of ARO refractivity profiles have been investigated in past studies, but the assimilation of ARO bending angle profiles are yet to be explored. The assimilation of bending angle presents two advantages in that a more accurate intermediate product is assimilated, and that the assimilation can take advantage of advanced observation operators that account for horizontal variations in atmospheric structure along the horizontal sampling raypath. Recently, the 2D bending angle observations operator of the EUMETSAT ROMSAF Radio Occultation Processing Package (ROPP) has been modified to simulate ARO bending angle and is available using the Unified Forward Operator (UFO) of the Joint Effort for Data assimilation Integration (JEDI). In this study, the potential impacts of assimilating ARO bending observations on the analysis and prediction of ARs is investigated using MPAS-JEDI with a global quasi-uniform 60km grid and the LETKF method. A benchmark experiment is conducted using all available global conventional data for this time period along with AMV winds retrieved from geostationary satellite sensors. A comparison experiment in which ARO observations are added to the benchmark is also conducted and results show a positive impact on the analyses since the first cycle. Assimilating ARO observations positively impacts the analysis and MPAS 6-h forecasts and can reduce the overestimation of the precipitation found in the benchmark experiment. This study provides crucial information on the status of ARO data assimilation and plans for next steps to maximize the impact of these data on AR forecasts.