Assimilation of cloud top temperature and radar observations of an idealized splitting supercell

The Geostationary Operational Environmental Satellite-R Series (GOES-R) will provide frequent cloud-top observations on the convective scale that could be used to help initialize convective storms in numerical weather prediction models. To evaluate the potential value of cloud-top temperature observations for data assimilation, an Observing System Simulation Experiment (OSSE) approach is used. Synthetic cloud-top temperature observations from an idealized splitting supercell created using the Weather Research and Forecasting (WRF) model are assimilated along with synthetic radar reflectivity and radial velocity using an Ensemble Kalman Filter (EnKF) from the Data Assimilation Research Testbed (DART). The “Truth” run has a 320 km x 240 km domain with 41 vertical levels and 2 km horizontal grid spacing. A 50-member ensemble, initialized only with random perturbations to low-level moisture and the u/v fields, has the same domain specifications. Observations are assimilated every 5 min for 2.5 h, and additive noise is used to maintain ensemble spread.

Four assimilations are completed to explore the relative value of cloud-top temperature and radar observations. One experiment only assimilates radar data, while another experiment only assimilates cloud-top temperature data. Two experiments assimilate combined radar/satellite observations with the observation types assimilated in different order. A rather weak correlation is found between cloud-top temperature and dynamical variables, while larger correlations are found between cloud top temperature and microphysical variables. Results show that the assimilation of cloud-top temperature data produces a convective storm in the ensemble, although the storm fails to develop a mesocyclone and is larger than in the truth run. The addition of radar observations greatly improves the storm structure and reduces the over-prediction of storm extent. A 1-hour post-assimilation forecast is made for each experiment to gain some insight into the overall efficiency of assimilating radar data along with cloud-top temperature.