Impacts of Initial Condition Perturbation Blending in 10- and 40-Member Convection-Allowing Ensemble Forecasts

Details

• Journal Title:
  Monthly Weather Review
• Personal Author:
  Johnson, Aaron ; Wang, Xuguang
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office)
• Description:
  A series of convection-allowing 36-h ensemble forecasts during the 2018 spring season are used to better understand the impacts of ensemble configuration and blending different sources of initial condition (IC) perturbation. Ten- and forty-member ensemble configurations are initialized with the multiscale IC perturbations generated as a product of convective-scale data assimilation (MULTI) and initialized with the MULTI IC perturbations blended with IC perturbations downscaled from coarser-resolution ensembles (BLEND). The forecast performance of both precipitation and nonprecipitation variables is consistently improved by the larger ensemble size. The benefit of the larger ensemble is largely, but not entirely, due to compensating for underdispersion in the fixed-physics ensemble configuration. A consistent improvement in precipitation forecast skill results from blending in the 10-member ensemble configuration, corresponding to a reduction in the ensemble calibration error (i.e., reliability component of Brier score). In the 40-member ensemble configuration, the advantage of blending is limited to the ∼18–22-h lead times at all precipitation thresholds and the ∼35–36-h lead times at the lowest threshold, both corresponding to an improved resolution component of the Brier score. The advantage of blending in the 40-member ensemble during the diurnal convection maximum of ∼18–22-h lead times is primarily due to cases with relatively weak synoptic-scale forcing, while advantages at later lead times beyond ∼30-h lead time are most prominent on cases with relatively strong synoptic-scale forcing. The impacts of blending and ensemble configuration on forecasts of nonprecipitation variables are generally consistent with the impacts on the precipitation forecasts.
• Keywords:
  Numerical Weather Forecasting Probability Forecasts (Meteorology) Numerical Weather Forecasting Probability Forecasts (Meteorology) Ensembles Numerical Weather Prediction Probability Distribution Probability Models
• Source:
  Mon. Wea. Rev., 152, 1421–1441
• DOI:
  https://doi.org/10.1175/MWR-D-23-0188.1
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA22OAR4590532
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:f438eb3e4eaee085780b4182810e2ead5b7a308efc79ff24b4aa5d74c5dabced0cd0cad9eb1cacfe8b2e98fe600c4fbea4b11d1f24e931083e1924813073e232
• Download URL:
  https://repository.library.noaa.gov/view/noaa/71970/noaa_71970_DS1.pdf
• File Type:
  [PDF - 34.08 MB ]