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Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Data in GFS
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  • Minghua Zheng,
  • Luca Delle Monache,
  • Xingren Wu,
  • Brian Kawzenuk,
  • F. Martin Ralph,
  • Yanqiu Zhu,
  • Ryan Torn,
  • Vijay Tallapragada,
  • Zhenhai Zhang,
  • Keqin Wu
Minghua Zheng
Scripps Institution of Oceanography, University of California San Diego

Corresponding Author:[email protected]

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Luca Delle Monache
University of California San Diego
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Xingren Wu
National Oceanic and Atmospheric Administration
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Brian Kawzenuk
Scripps Institution of Oceanography
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F. Martin Ralph
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Yanqiu Zhu
Global Modeling and Assimilation Office
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Ryan Torn
University at Albany
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Vijay Tallapragada
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Zhenhai Zhang
UC San Diego
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Keqin Wu
I. M. Systems Group Inc. at National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP)/ Environmental Modeling Center (EMC)
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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.