1. Potential Benefits of Multiple-Doppler Radar Data to Quantitative Precipitation Forecasting: Assimilation of Simulated Data Using WRF-3DVAR System Soichiro Sugimoto 1,2, N. Andrew Crook 2, Juanzhen Sun 2, Dale M. Barker 2, and Qingnong Xiao 2 1.Central Research Institute of Electric Power Industry, Japan 2.National Center for Atmospheric Research, USA ・ Main goal of our project: Improvement of QPF by assimilation of Doppler radar data ・ 3DVAR function in WRF-VAR: The second stage of evaluation from the initial stage of development ・ Main objective : Evaluation of potential of WRF-3DVAR by Observing System Simulation Experiments (OSSEs)

2. Introduction and Motivation 2100Z, June 12, 2002 6-hrs forecasts 0000Z, June 13, 2002 9-hrs forecasts 2100Z, June 12, 2002 9-hrs forecasts 0000Z, June 13, 2002 12-hrs forecasts WRF simulations for a dryline case (1-h accumulated precipitation) Initial: 1500Z, June 12Initial: 1200Z, June 12 -The use of a NWP model for QPF of convective weather is plagued by … 1.Poor specification of the initial and boundary conditions 2.The improper evolution of convection -Better initial and boundary conditions are required. Difference between forecasts motivates to assimilate radar data. -Evaluation of the maximum potential of the methodology with simulated data

3. Setup of OSSE 25 NEXRAD sites in our domain Tips 1200Z For making the truth and observations 1500Z 2100Z June 13, 2002 0000Z 0300Z For making the background (no-assimilation) # Two WRF simulations use the same model configuration. radar data background Assim. and Fcst. An example of time schedule - Horizontal grid spacing: 4 km (300x300) (right panel) - WRF model: Version 2.0.3.1 - Unbiased normal distribution (observation error) - Background error estimated by the NMC method (tuning horizontal lengthscale and eigenvectors/eigenvalues of the vertical component)

4. Radar Data Assimilation using 3DVAR Cost function x: a vector of analysis X b : a vector of background (first-guess) y: a vector of model-derived observation (y=H(x); observation operator) y o : a vector of observation B: the background error covariance matrix R: the observation and representative error covariance matrix Importance to note #1 Water substance: Only rainwater Vertical fallspeed of hydrometeor Reflectivity factor #2 Control variable of moisture: Total water mixing ratio “qt” (water vapor, rainwater, cloud water) A warm rain regime is considered for partitioning of qt increment to increments of model variables in control variable transformation. Modification of the background by a cloud analysis based on water budget analysis. #3 Background close to the correct state is important.

5. Application to a dryline case occurred in the central U.S. Result 1: Wind retrieval using radial velocity data with the cold-start mode. An idealized situation that observations are available on all grid points. Result 2: Wind retrieval using radial velocity data with the cycling mode. Data within a storm is used. 15-min interval of cycling. Result 3: Retrieval of temperature and microphysical variables by additional use of reflectivity factor data. Data within a storm is used. Result 4: Precipitation forecasting Comparisons among cases of “no-assimilation”, “radial velocity assimilation”, and “assimilation of both of radial velocity and reflectivity factor”

6. Wind Retrieval Using Idealized Data of Radial Velocity with Cold-Start Mode U-wind component Observation (O) Minus Background (B) (innovation) Analysis (A) Minus Background (B) (Increment) The retrieval is successful at a scale relatively larger than the convective scale. W-wind component

7. Wind Retrieval Using Idealized Data of Radial Velocity (Cont’d) U-wind component W-wind component RMS error for each level Benefit is likely smaller for the vertical wind than the one for horizontal winds. RMS error in the wind field is reduced by 3DVAR analysis.

8. Benefit of the Use of Cycling Mode (Storm Data) RMS error in V-component Each of 3DVAR analysis reduces error. The use of cycling 3DVAR prevents from rapid increase of error. Initiation Developing to Mature 2100Z – 0000Z (15 min. interval) This is due to degraded accuracy of W-component. Retrieval of vertical wind component is challenging especially when the convection is in the developing stage.

9. Retrieval of Temperature and Microphysical Variables with Reflectivity Data Temperature O-B Innovation Low-level Middle-level A-B Increment Overall reasonable result Water vapor retrieval is difficult In lower levels. Water vapor

10. Retrieval of Temperature and Microphysical Variables with Reflectivity Data Rainwater O-B Innovation Cloud Water O-B Innovation A-B Increment RMS error in each variable is reduced by 3DVAR & cloud analysis.

11. Impact on Precipitation Forecasting Truth No-Assimilation 1-h accumulated precipitation June 13, 2002 0000Z June 13, 2002 0100Z 1-hr ahead Radial velocityBoth 1-hr ahead A little better Better (lasting over 3 hrs ahead) Assimilation at the mature stage of convection Thread score for 3-hrs forecast

12. Impact on Precipitation Forecasting Truth No-Assimilation 1-h accumulated precipitation June12, 2002 2100Z Radial velocityBoth Better (larger impact than the previous case) Better than only radial velocity assimilation. Assimilation at the initiation stage of convection June13, 2002 0000Z 3-hrs ahead Thread score for 3-hrs forecast

13. Summary 1.Radial velocity assimilation works reasonably well in recovering key features of the wind field at scales relatively larger than the convective scale. 2. A more sophisticated dynamic framework with a flow-dependent background error statistics will be need for the retrieval at the convective scale. 3. The use of cycling mode 3DVAR serves to prevent the rapid increase of model error with time. 4. Positive impact on precipitation forecasting is found only with radial velocity assimilation, especially when the convection is in the initiation stage. 5. Additional use of reflectivity factor is encouraging for improving QPF if the convection is in the developing or mature stage. 6. The combined use of 3DVAR and a cloud analysis works to some extent for the retrieval of unobserved variables even when the background has no convection whereas the observation indicates the existence of convection.