Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

Kevin Bachmann, Martin Weissmann, Christian Keil, George C. Craig, Christian A. Welzbacher

Publications: Contribution to journalArticlePeer Reviewed

Abstract

We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.
Original languageEnglish
Pages (from-to)63-81
Number of pages19
JournalMonthly Weather Review
Volume148
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Austrian Fields of Science 2012

  • 105206 Meteorology

Keywords

  • ATMOSPHERIC PREDICTABILITY
  • CLIMATE
  • Data assimilation
  • Deep convection
  • ERROR GROWTH
  • IMPACT
  • MESOSCALE PREDICTABILITY
  • Numerical weather prediction
  • Operational forecasting
  • Orographic effects
  • PRECIPITATION
  • PREDICTION
  • Radar observations
  • Radars
  • SCALE DEPENDENCE
  • VERIFICATION METHODS
  • WARM-SEASON
  • forecasting

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