An assessment of dropsonde sampling strategies for Atmospheric River Reconnaissance

Using these observations, data denial experiments were conducted with a regional modeling and data assimilation system to explore the impacts of research flight frequency and spatial resolution of dropsondes on model analyses and forecasts.

Results indicate that dropsondes significantly improve the representation of ARs in the model analyses and positively impact the forecast skill of ARs and quantitative precipitation forecasts (QPF), particularly for lead times . 1 day. Both reduced mission frequency and reduced dropsonde horizontal resolution degrade forecast skill. On the other hand, experiments that assimilated only G -IV data and experiments that assimilated both G -IV and C-130 data show better forecast skill than experiments that only assimilated C-130 data, suggesting that the additional information provided by G -IV data is necessary for improving forecast skill.