Convective Meteorology (Mesoscale Dynamics)

Forecasts of convection initiation and early evolution on 29 May 2012 using EnKF-based assimilation of surface and radar data

Ryan Sobash
OU School of Meteorology

25 October 2013, 3:00 PM

National Weather Center, Room 5600
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

The 29 May 2012 convective episode produced very large hail (> 4” diameter), 80 mph wind gusts, and a brief tornado, near and within the OKC metropolitan area, with estimated losses totaling 500 million dollars. Analyses and forecasts of this event were produced using the ensemble Kalman filter (EnKF) by assimilating surface and radar datasets. Surface data, including data from surface mesoscale networks (mesonets), were assimilated at 5-minute intervals between 18 UTC and 21 UTC. Both surface and WSR-88D data were assimilated at 5-minute intervals between 21 UTC and 23 UTC, following convection initiation (CI). 50-member, 6-hour, ensemble forecasts were initialized each hour between 18 UTC and 23 UTC.

The first part of the presentation will focus on the ensemble predictions of CI. The frequent assimilation of surface data, especially the use of mesonet data, improved the forecast of CI timing and placement within the domain, especially for convection developing along a surface dry line. Surface data assimilation reduced a surface moisture bias that was present due to model error. Experiments where mesonet data were withheld, or where surface data were assimilated less frequently, produced less accurate forecasts of CI and possessed larger surface moisture errors. The improved surface state at 21 UTC also led to changes in the forecast convective mode after 00 UTC. The ability of sub-hourly assimilation of mesonet data to improve forecasts of CI has not been previously documented.

The second part of the presentation will focus on the forecasts following CI, when WSR-88D and surface data were assimilated. The 23 UTC forecast captured much of the observed convective evolution, including the tracks of several long-lived supercells. Surface data assimilation played a significant role during this period as well. Forecasts from an experiment that assimilated only radar data contained several large errors due to a poor representation of the mesoscale environment. Some parts of the forecast were especially sensitive to the assimilation of reflectivity observations and the vertical localization of those observations. Finally, using innovation statistics, several reflectivity biases were identified in the analyses due to errors in the microphysics parameterization, the reflectivity forward operator, and biases in the environmental wind profile. The applicability of these results for future warn-on-forecast systems will be discussed.

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