School of Meteorology (Defense)
Ensemble Kalman Filter Methods For Convective-Scale Radar Data Assimilation And Multi-Scale Data Assimilation Of The 13 June 2010
Tornadic Supercell Environment
OU School of Meteorology
01 August 2014, 11:00 AM
National Weather Center, Room 5820
120 David L. Boren Blvd.
University of Oklahoma
Ensemble data assimilation at convective-scales will need to solve a number of scientific and technical issues prior to being usable for operational numerical weather prediction. This research contributes to this goal by first comparing the Local Ensemble Transform Kalman Filter (LETKF) to the Ensemble Square Root Filter (EnSRF) to examine whether either method consistently produces more accurate analyses and forecasts. Second, multi-scale data assimilation strategies are explored to improve the analysis of complex environmental conditions and subsequent convective forecasts.
While theoretically the LETKF and EnSRF filters should behave the same for ideal systems, a comparison between the serial and simultaneous filters has not previously been explored at the convective-scale where significant non-linear effects are present. The results from an Observing System Simulation Experiment (OSSEs) and a real-data case suggest the EnSRF and LETKF approaches are equally capable methods for radar data assimilation at convective-scales.
A multi-scale data assimilation framework is developed for an ensemble assimilation and prediction system using the Weather Research and Forecasting (WRF) model and the Data Assimilation Research Testbed (DART). The multi-scale framework is used to create analyses and forecasts of the complex convective environment and a tornadic storm on 13 June 2010. Several aspects of multi-scale DA cycling are investigated through comparisons of ensemble forecast performance relative to a control 6-hourly cycled analysis system. Results indicate that increased cycling frequency improves forecasts of the mesoscale storm environment and convection. The addition of radar observations in hourly DA cycling leads to further improvement in forecast skill, which is tied to better forecasts of the outflow boundary from overnight convection and subsequent convective evolution. Lastly, the initial conditions for the multi-scale data assimilation cycling system are found to have an impact on the characteristics of the near-storm environment.
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