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Cloud-dependent Piecewise Assimilation Based On A Hydrometeor-included Background Error Covariance and Its Impact On Regional Numerical Weather Prediction


The background error covariance ( B ) behaves differently and needs to be carefully defined in cloudy areas due to larger uncertainties caused by the models’ inability to correctly represent complex physical processes. This study proposes a new cloud-dependent B strategy by adaptively adjusting the hydrometeor-included B in the cloudy areas according to the cloud index (CI) derived from the satellite-based cloud products. The adjustment coefficient is determined by comparing the error statistics of B for the clear and cloudy areas based on the two-dimensional geographical masks. The comparison highlights the larger forecast errors and manifests the necessity of using appropriate B in cloudy areas. The cloud-dependent B is then evaluated by a series of single observation tests and 3-week cycling assimilation and forecasting experiments. The single observation experiments confirm that the cloud-dependent B allows cloud dependency for the multivariate analysis increments and alleviates the discontinuities at the cloud mask borders by treating the CI as an exponent. The impact study on regional numerical weather prediction (NWP) demonstrates that the application of the cloud-dependent B reduces analysis and forecast bias and increases precipitation forecast skills. Diagnostics of a heavy rainfall case indicate that the application of the cloud-dependent B enhances the moisture, wind, and hydrometeors analyses and forecasts, resulting in more accurate forecasts of accumulated precipitation. The cloud-dependent piecewise analysis scheme proposed herein is extensible, and a more precise definition of CI can improve the analysis, which deserves future investigation.

Article / Publication Data
Available Metadata
Early Online Release
September 01, 2021
Fiscal Year
Peer Reviewed
Publication Name
Monthly Weather Review
Published On
September 10, 2021
Publisher Name
American Meteorological Society
Print Volume
Submitted On
December 23, 2020


Not available


Authors who have authored or contributed to this publication.

  • Deming Meng - lead None
  • Hongli Wang - fourth Gsl
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory