Impact of Atmospheric River Reconnaissance Dropsonde Data on the
Assimilation of Satellite Data in GFS
Abstract
Satellites provide the primary dataset for monitoring the earth system
and constraining analyses in numerical models. A challenge for utilizing
satellite radiances is the estimation of their biases. High-accuracy
non-radiance data are typically employed to anchor radiance bias
corrections. This study provides the first assessment of impacts of
dropsonde data collected during the Atmospheric River (AR)
Reconnaissance program that samples ARs over the Northeast Pacific on
the radiance assimilation using the Global Forecast System (GFS) and the
Global Data Assimilation System. Including this dropsonde dataset has
provided better anchoring for bias corrections and improved model
background, leading to an increase of ~5-10% in the
amount of assimilated microwave radiance in the lower/middle troposphere
over the Northeast Pacific and North America. The impact on tropospheric
infrared radiance is small but also beneficial. This result points to
the usefulness of dropsondes, along with other conventional data, in the
assimilation of satellite radiance.