Evaluating Forecast Performance and Sensitivity to the GSI EnKF Data Assimilation Configuration for the 28 – 29 May 2017 Mesoscale Convective System Case

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  • 1 School of Meteorology and
  • 2 Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072
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Abstract

In an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) realtime Spring Forecast Experiments are performed. These experiments are followed by 6-hour forecasts for an MCS on 28 – 29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations.

The results show experiments that assimilate radar observations more frequently (i.e., 5 – 10 minutes) are initially better at suppressing spurious convection. However, assimilating observations every 5 minutes causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance towards optimizing the configuration of the GSI EnKF system. Among DA the configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for realtime use.

Corresponding author address: Jonathan Labriola, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David Boren Blvd, Room 2500, Norman OK 73072, jonathan.labriola@noaa.gov

Abstract

In an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) realtime Spring Forecast Experiments are performed. These experiments are followed by 6-hour forecasts for an MCS on 28 – 29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations.

The results show experiments that assimilate radar observations more frequently (i.e., 5 – 10 minutes) are initially better at suppressing spurious convection. However, assimilating observations every 5 minutes causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance towards optimizing the configuration of the GSI EnKF system. Among DA the configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for realtime use.

Corresponding author address: Jonathan Labriola, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David Boren Blvd, Room 2500, Norman OK 73072, jonathan.labriola@noaa.gov
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