Application of radar observations to the evaluation and improvement of cloud permitting regional model simulations of MJO Samson M. Hagos, Zhe Feng, Kiranmayi Landu, Chuck Long, Kyo-Sun Lim and Chidong Zhang

Objectives To evaluate and improve simulation of cloud statistics in cloud permitting models using radar data from dynamo field campaign. Understand the mechanism of transition from shallow to deep convection and the initiation of MJO using the cloud permitting regional model simulations.

Part 1: Evaluation of model microphysics schemes using radar observations. Simulation domain (2km grid spacing) One month long simulation. Initialized only once. ERA-Interim surface, initial and boundary conditions. Boundary conditions updated every 6 hours.

Rain rate in November Rain is generally overestimated by the model.

Cloud statistics evaluation strategy The built in radar simulator in WRF is used to calculate reflectivity. Steiner et al. (1995) algorithm is used to identify convective and stratiform areas for both radar and WRF Cell Area 10DBZ Echo top height Frequency Rain rate

Convective regions log10(count) The convective region counts from the model are fairly consistent with the radar. Note the variance related to scanning strategy.

Stratiform regions log10(count) Distributions are fairly consisted with the radar, except there are more of them in the model simulations.

Rain rate (mm/hr) from convective regions More variance among the models than the radar scanning strategies.

Rain rate (mm/hr) from Stratiform regions Most of the schemes overestimate rain rate from large, deep convective cells.

Sensitivities to rain and cloud liquid water particle size parameters 1/12/2019 Sensitivities to rain and cloud liquid water particle size parameters Limiting rain drop (and cloud liquid water) size through shattering etc improves the count as well as rain rate distribution. In the WDM6 V02, we modified the slope parameter of cloud water as well as that of rain.

Part 2: Shallow to deep transition and the initiation of MJO. Run for two months at 3km grid spacing, with revised Morrison microphysics scheme (V02)

Comparison with TRMM 3B42. The red dots indicate the locations shallow to deep transitions identified by cloud tracking using brightness temperature and radar reflectivity The triangle marks an example Case 11-07-07.

Methodology Track contiguous cloudy area (Hagos et al. 2013; Feng et al. 2012) using simulated brightness temperature (Tb < 250K to include congestus). With in the area, define shallow convection, deep convection and transition using simulated radar reflectivity & cloud classification (Feng et al. 2011). Study the meso-scale environmental conditions leading to and following the transitions and put them in the context of the large-scale initiation of MJO.

14 transitioning systems are identified. Corresponding 12 hour long subset files are extracted from the raw WRF domain. Simulated radar echo top height evolution

Transition to deep convection An example (Case 11-07-07) Convective activity and large cold pool to the southeast. Shading: Temperature (K) at 2m. Contours 10DBZ echo at 10km. Arrows 10m winds.

Latitudinal cross section Shading: Relative humidity. Contours 10DBZ echo. The mid-tropospheric moistening responsible for transition to deep convection is supplied by evaporation of hydrometeors from ITCZ!

Observations TOP: Time-series of relative humidity from soundings at Gan site. BOTTOM: Same but masking out the hours when there are clouds as detected by any of the radars, KAZR, SMART-R and S-Pol radars. In the absence of local clouds, the mid-troposphere is moistened by detrained, falling, evaporated hydrometeors advected from elsewhere.

Summary The meridional advection of moisture in the form of detrained hydrometeors might hold the key to understanding the initiation of MJO. Regional scale cloud permitting modeling could be useful for cloud process studies and as bridges from field campaigns like DYNAMO to future global cloud permitting models (GCPMs) as well as conventional GCMs. Thank you!