Altu ğ Aksoy National Center for Atmospheric Research Collaborators:

1. ASSIMILATION of RADAR DATA at CONVECTIVE SCALES with the EnKF:PERFECT-MODEL EXPERIMENTSUSING WRF / DART Altuğ Aksoy National Center for Atmospheric Research Collaborators: Chris Snyder (NCAR) and David Dowell (CIMMS)

2. MAIN OBJECTIVES / CHALLENGES OF THE PROJECT • Exploration of the EnKF using the WRF / DART system • Real radar data for multiple convective cases with differing behavior (supercell, linear, multi-cellular). • Some of the challenges / questions: • Can we implement a certain filter configuration to assimilate in situations with varying convective characteristics? • How do we best assimilate reflectivity observations? • How do we best initialize our ensemble to account for the differing characteristics across cases? • How do we deal with the varying number of observations on the radar grid (too many near the radar vs. too little away from the radar)? • How do we deal with mature cells propagating into the domain? (For this project, we will limit ourselves to convective initiation within the domain.)

3. CURRENT WRF / DART SYSTEM CAPABILITIES • System has been improved to simulate and assimilate radar observations (reflectivity and radial velocity) • WRF (Weather Research Forecast) model V. 2.1: • Can be run in “idealized” mode • Open boundary conditions • Initial state obtained from a given sounding • Perturbations are obtained through placement of thermal bubbles • DART (Data Assimilation Research Testbed) V. iceland: • Offers different ensemble-based schemes • Capable of assimilating radar observations • 2D or 3D localization • Covariance inflation in model space • Adaptive covariance inflation in observation space

4. EXPERIMENT DETAILS - WRF • 150 km x 150 km x 18 km at 2-km horizontal resolution • Domain centered at KOUN radar (Norman, Oklahoma) • Flat terrain • WRF standard sounding for the “quarter-circle shear supercell test case” • Perturbations: • Control: 5-K thermal bubble near the radar (60 x 60 km) • Two initialization methods explored: • First method: 5 thermal bubbles randomly placed in a sub-domain (location selection from uniform distribution) • Second method: 2 thermal bubbles randomly placed around observed cell (Gaussian distribution with standard deviation of 8 km) • In both methods, thermal amplitude is selected from Gaussian (4K, 2K) • No perturbations applied to the background environment

5. CONTROL RUN (60 min FORECAST) 3-km Reflectivity (dBZ) and Horizontal Winds 7-km Vertical Wind (m/s, colored) and Surface Negative Temperature Perturbation (2K Contours) 20 Minutes 40 Minutes 60 Minutes

6. EXPERIMENT DETAILS - EnKF • Ensemble size: 50 members • 2-D localization with influence half width of ~6 km • No covariance inflation • Ensemble forecast initialized at 10-min control time (when first >10 dBZ reflectivity is observed and the cell is identified) • First assimilation performed at 20-min ens. forecast (30-min control) • 5 assimilation cycles performed with 5-min ens. forecasts in-between

7. EXPERIMENT DETAILS - OBSERVATIONS • Both reflectivity and radial velocity assimilated • Only observations with reflectivity > 10 dBZ assimilated • Observations simulated on radar grid. There are critical differences from generating obs at model grid: • Because obs locations do not generally overlap with model grids, the forward operator involves spatial approximation to the model grid • Number of observations assimilated depends on the relative locations of the storm and the radar: • When the observed storm is near the radar, one faces the danger of assimilating too many observations, hence filter divergence. • When the observed storm is away from the radar, there may not be enough observations to approach the truth. • In this case, number of observations (R > 10 dBZ) grows from ~6,000 at the first cycle to ~13,000 at the fifth cycle (mainly due to storm intensification). • Observation error (std. dev.): 5 dBZ for reflectivity and 2 m/s for radial velocity

8. COMPARISON OF GAUSSIAN and UNIFORM INITIAL DISTRIBUTION of BUBBLES (RAINWATER MIXING RATIO, kg/kg) At Observation Locations Over Entire Domain RMS Error Ratio of Variance to Square Mean Error Ne / (Ne+1) Time (minutes) Time (minutes)

9. ERROR and SPREAD GAUSSIAN INITIAL BUBBLE DISTRIBUTION (VERTICAL WIND, m/s) At Observation Locations At Model Reflectivity > 10 dBZ RMS Error Ne / (Ne+1) Ratio of Variance to Square Mean Error Time (minutes) Time (minutes)

10. ERROR and SPREAD GAUSSIAN INITIAL BUBBLE DISTRIBUTION (ZONAL WIND, m/s) At Observation Locations At Model Reflectivity > 10 dBZ RMS Error Ne / (Ne+1) Ratio of Variance to Square Mean Error Time (minutes) Time (minutes)

11. ERROR and SPREAD GAUSSIAN INITIAL BUBBLE DISTRIBUTION (TEMPERATURE, K) At Observation Locations At Model Reflectivity > 10 dBZ RMS Error Ne / (Ne+1) Ratio of Variance to Square Mean Error Time (minutes) Time (minutes)

12. COMPARISON OF ENSEMBLE MEAN TO THE CONTROL FORECAST HOR. DISTRIBUTION OF 7-km VERTICAL WIND (m/s, colored) and SFC COLD POOL (2-K contours) 30 min Assim. Cycle 1 50 min Assim. Cycle 5 Forecast Analysis Control (Truth)

13. COMPARISON OF ENSEMBLE MEAN TO THE CONTROL FORECAST VERTICAL CROSS SECTION OF TEMPERATURE PERT. (K, colored) and VERTICAL WIND (4 m/s contours) 30 min Assim. Cycle 1 50 min Assim. Cycle 5 Forecast Analysis Control (Truth)

14. FINAL THOUGHTS • Initiation with bubbles with Gaussian displacement is observed to perform better, but may need tuning. • Thresholding of reflectivity has been the only successful way to deal with the large number of observations. However, • There is valuable information in 0-dBZ observations we would like to utilize, especially when members have spurious cells • Methods exist to adaptively apply covariance inflation when observation density is high. Could be expensive… • Adaptive assimilation could also be performed depending on the magnitude of innovations