Impacts of dropsonde and satellite observations on the forecasts of two atmospheric-river-related heavy rainfall events

Abstract

This study focuses on two intensive observing periods (IOPs) that cause heavy rainfall over California during the atmospheric river (AR) Reconnaissance Program in 2019. The impacts of dropsonde and satellite observations on the forecasts of the two AR-related heavy rainfall are investigated through both forecast sensitivity to observations (FSO) and observing system experiments (OSEs). In the first case (IOP3), satellite and dropsonde data coverage were relatively independent, whereas in the second case (IOP5), the satellite coverage was more extensive and substantially overlapped with dropsonde coverage. The FSO experiments indicate that the dropsondes improve the atmospheric forecast by a greater contribution per observation than that of an individual satellite instrument. In the OSEs, the heavy rainfall forecast presents a higher improvement when assimilating both dropsondes and satellite radiances. In IOP3, the dropsonde data slightly amplify the improvement achieved from the satellite data in terms of structure and location of the AR and attendant precipitation. In IOP5, the improvement from dropsonde data is more evident in the forecast of heavy precipitation. The influence of the dropsonde data in each case is broadly consistent with the relative coverage of dropsonde and satellite data. The best forecast performance acquired from the assimilation of both dropsonde and satellite data indicates the complementarity between the two data sources. Based on circulation analyses and further experiments assimilating satellite radiances from temperature channels and humidity channels separately, the improved rainfall forecasts are found to be related to the improvements in the three-dimensional circulation structure impacted by temperature characteristics.