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Article Type:
Research Article

An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part I: System Description and 3D-Hybrid Results

Daryl T. Kleist Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

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Kayo Ide Department of Atmospheric and Oceanic Science, and Earth System Science Interdisciplinary Center, and Institute for Physical Science and Technology, and Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park, College Park, Maryland

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Print Publication:
01 Feb 2015
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Sixth WMO Data Assimilation Symposium
DOI:
https://doi.org/10.1175/MWR-D-13-00351.1
Page(s):
433–451
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Abstract

An observing system simulation experiment (OSSE) has been carried out to evaluate the impact of a hybrid ensemble–variational data assimilation algorithm for use with the National Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE provides a controlled framework for evaluating analysis and forecast errors since a truth is known. In this case, the nature run was generated and provided by the European Centre for Medium-Range Weather Forecasts as part of the international Joint OSSE project. The assimilation and forecast impact studies are carried out using a model that is different than the nature run model, thereby accounting for model error and avoiding issues with the so-called identical-twin experiments.

It is found that the quality of analysis is improved substantially when going from three-dimensional variational data assimilation (3DVar) to a hybrid 3D ensemble–variational (EnVar)-based algorithm. This is especially true in terms of the analysis error reduction for wind and moisture, most notably in the tropics. Forecast impact experiments show that the hybrid-initialized forecasts improve upon the 3DVar-based forecasts for most metrics, lead times, variables, and levels. An additional experiment that utilizes 3DEnVar (100% ensemble) demonstrates that the use of a 25% static error covariance contribution does not alter the quality of hybrid analysis when utilizing the tangent-linear normal mode constraint on the total hybrid increment.

Corresponding author address: Dr. Daryl T. Kleist, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Computer and Space Sciences Building, College Park, MD 20742. E-mail: daryl.kleist@noaa.gov

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

Abstract

An observing system simulation experiment (OSSE) has been carried out to evaluate the impact of a hybrid ensemble–variational data assimilation algorithm for use with the National Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE provides a controlled framework for evaluating analysis and forecast errors since a truth is known. In this case, the nature run was generated and provided by the European Centre for Medium-Range Weather Forecasts as part of the international Joint OSSE project. The assimilation and forecast impact studies are carried out using a model that is different than the nature run model, thereby accounting for model error and avoiding issues with the so-called identical-twin experiments.

It is found that the quality of analysis is improved substantially when going from three-dimensional variational data assimilation (3DVar) to a hybrid 3D ensemble–variational (EnVar)-based algorithm. This is especially true in terms of the analysis error reduction for wind and moisture, most notably in the tropics. Forecast impact experiments show that the hybrid-initialized forecasts improve upon the 3DVar-based forecasts for most metrics, lead times, variables, and levels. An additional experiment that utilizes 3DEnVar (100% ensemble) demonstrates that the use of a 25% static error covariance contribution does not alter the quality of hybrid analysis when utilizing the tangent-linear normal mode constraint on the total hybrid increment.

Corresponding author address: Dr. Daryl T. Kleist, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Computer and Space Sciences Building, College Park, MD 20742. E-mail: daryl.kleist@noaa.gov

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

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