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Indirect Impact of Atmospheric River Reconnaissance Dropsonde Data on Data Assimilaiton
  • +7
  • 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
SIO
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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
NCEP/EMC
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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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Abstract

Dropsonde data are high-accuracy, high-vertical-resolution data collected from dropwindsondes  deployed from reconnaissance research aircraft. Aircraft from Atmospheric River (AR) Reconnaissance campaign deploy dropsondes data in the upper troposphere or near the tropopause. These data include meteorological variables that are essential to observing and modeling atmospheric structures. Research showed that these dropsondes data can influence data assimilation via direct impact on the intial conditions and indirect impact on the model background and satellite bias correction.
13 Sep 2024Submitted to ESS Open Archive
15 Sep 2024Published in ESS Open Archive