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The Data Assimilation Research Testbed; a Suite of Tools for Understanding the Earth System with Confidence.
  • +7
  • Kevin Raeder,
  • Jeffrey Anderson,
  • Moha El Gharamti,
  • Benjamin Johnson,
  • Benjamin Gaubert,
  • Soyoung Ha,
  • Craig Schwartz,
  • Nedjeljka Zagar,
  • Shixuan Zhang,
  • Helen Kershaw
Kevin Raeder
National Center for Atmospheric Research

Corresponding Author:[email protected]

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Jeffrey Anderson
NCAR
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Moha El Gharamti
King Abdullah University of Science and Technology
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Benjamin Johnson
University of Maryland
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Benjamin Gaubert
National Center for Atmospheric Research
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Soyoung Ha
NCAR
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Craig Schwartz
NCAR/MMM
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Nedjeljka Zagar
University of Ljubljana
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Shixuan Zhang
University of Utah
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Helen Kershaw
NCAR
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Abstract

Society’s ability to make wise decisions depends onan accurate understanding of the current state of Earthand on an ability to predict future states.The Data Assimilation Research Testbed (DART) is an example of a suite of toolsdesigned to improve our understanding through the combination of observationswith our theoretical understanding embodied in forecast models.DART’s ensemble based data assimilation provides uncertainty quantification as a function of time, location, and variable.Current research using DART includes: Improving streamflow prediction during intense rainfall events, which lead to flooding, using DART and the Weather Research and Forecasting model and the Noah-MP land model (WRF-Hydro). Building an integrated atmosphere and ocean forecasting system using DART and WRF for the Red Sea Initiative. Understanding air pollution using a global meteorology-aerosol-chemistry prediction system that assimilates aerosol optical depth, carbon monoxide, and weather observations into the Community Atmosphere Model with Chemistry (CAM-Chem). Assimilating observations of the Earth system from satellites into the Model for Prediction Across Scales (MPAS; regional and global) using observation operators from the Joint Effort for Data assimilation Integration (JEDI), bias correction for satellite retrievals from the Gridpoint Statistical Interpolation (GSI), and the assimilation environment of DART. Deciphering the flow dependency of forecast errors in the tropics and the relative importance of wind and mass information for tropical analyses. Connecting the U.S. Department of Energy’s E3SM atmospheric model with a broad spectrum of observations to perform short ensemble hindcast simulations for model development and evaluation. Generating atmospheric reanalysis data sets from CAM, which enables efficient data assimilation in other components of the Earth system; ocean, land, cryosphere, … Improving DART by giving users more control over how observations are assimilated, and supporting the assimilation of additional observations, such as radiances through the use of the RTTOV software.