Assimilating Surface Mesonet Observations with the EnKF to Improve Ensemble Forecasts of Convection Initiation on 29 May 2012

Abstract

urface data assimilation (DA) has the potential to improve forecasts of convection initiation (CI) and short-term forecasts of convective evolution. Since the processes driving CI occur on scales inadequately observed by conventional observation networks, mesoscale surface networks could be especially beneficial given their higher temporal and spatial resolution. This work aims to assess the impact of high-frequency assimilation of mesonet surface DA on ensemble forecasts of CI initialized with ensemble Kalman filter (EnKF) analyses of the 29 May 2012 convective event over the southern Great Plains.Mesonet and conventional surface observations were assimilated every 5 min for 3 h from 1800 to 2100 UTC and 3-h ensemble forecasts were produced. Forecasts of CI timing and location were improved by assimilating the surface datasets in comparison to experiments where mesonet data were withheld. This primarily occurred due to a more accurate representation of the boundary layer moisture profile across the domain, especially in the vicinity of a dryline and stationary boundary. Ensemble forecasts produced by assimilating surface observations at hourly intervals, instead of every 5 min, showed only minor improvements in CI.The 5-min assimilation of mesonet data improved forecasts of the placement and timing of CI for this particular event due to the ability of mesonet data to capture rapidly evolving mesoscale features and to constrain model biases, particularly surface moisture errors, during the cycling period.