1. IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Allen Zhao 1, John Cook 1, Qin Xu 2, and Paul Harasti 3 1 Naval Research Laboratory, Monterey, California, USA 2 National Severe Storms Laboratory, Norman. Oklahoma, USA. 3 University Corporation for Atmospheric Research, Boulder, Colorado, USA. Phone: (831) 656-4700Fax: (831) 656-4769zhao@nrlmry.navy.mil

2. Nowcasting and Data Assimilation  Mesoscale NWP models provide a practical means for nowcasting A physical-based approach Provide all atmospheric parameters for nowcasting convective storms and other hazardous atmospheric conditions (e.g., low ceiling & visibility) Smooth transition from nowcasting (0-6h) to forecasting (6-72h)  0-6 hour represents a hard period for mesoscale NWP models Inaccurate initial conditions due to the lack of (or poor) observational data and inadequate data assimilation procedures Imperfectness in model dynamics & physical parameterization  Recent developments in high-resolution data assimilation pave the way to use NWP models for nowcasting More and more high-resolution data are available from radars, satellites and other sensors New techniques, such as variational methods and ensemble-based approaches, have been developed for mesoscale data assimilation Objective: To study the opportunity and capability of improving 0-6 hour NWP forecasts by assimilation of high-resolution observational data

3. COAMPS  is a registered trademark of the Naval Research Laboratory NAVDAS Conventional Observations COAMPS  Forecast T, P, Z, U, V, q v COAMPS ® Forecast 3D Cloud Analysis Radar reflectivity q v, q c, q i, q r, q s, q g Satellite data Blending 3D Wind Analysis Radar radial velocity U, V, W, T, P or Data Assimilation Procedures

4. 23:08 UTC May 09, 2003 Radar Radius = 150 km Morehead City, NC (KMHX) Norfolk, VA (KAKQ) Raleigh, NC (KRAX) Model domain (100x100, 6km) 3-D radar reflectivity on COAMPS ® grid (Isosurface = 20 dBZ) 18 16 14 12 10 8 6 4 2 Height (km) 0 20 10.015.020.025.030.035.040.045.050.055.060.0 South – North (600 km) A Convective Storm Case  A strong convective storm system on 9 May 2003 was moving southward along the east coast of the United States  The storm system entered the study area at about 1800 UTC and reached its mature stage at about 2300 UTC  Data from three WSR-88D radars in that area were collected every 5-minutes  GOES-12 IR and vis data were also collected every 30 minutes COAMPS  is a registered trademark of the Naval Research Laboratory

5. Forecast CNTL No Data Assimilation Forecast from 12 UTC 9 May 1-hour forecast 1-hour forecast 1-hour forecast Forecast ALL Satellite IR and vis, Radar reflectivity and radial velocity Forecast from 12 UTC 9 May 22 UTC 21 UTC20 UTC19 UTC 1-hour forecast 1-hour forecast 1-hour forecast Forecast CLD Satellite IR and vis data Forecast from 12 UTC 9 May 22 UTC 21 UTC20 UTC19 UTC 1-hour forecast 1-hour forecast 1-hour forecast Forecast CLD+PR Satellite IR and vis data, Radar Reflectivity Forecast from 12 UTC 9 May 22 UTC 21 UTC20 UTC19 UTC 1-hour forecast 1-hour forecast 1-hour forecast Forecast WIND Radar radial velocity Forecast from 12 UTC 9 May 22 UTC 21 UTC20 UTC19 UTC  Five experiments have been conducted: CNTL: no radar data assimilation CLD: Cloud fields from satellite observations are assimilated hourly CLD+PR: Cloud fields from satellite observations and precipitations from radar reflectivity data are assimilated hourly WIND: Radar radial velocity data are assimilated hourly ALL: All these fields are assimilated hourly  12-hour forecasts were made starting at 22 UTC 9 May 2003 in all five experiments Experiment Design

6. Correlation coefficients and RMS errors of 1-hour forecast radial velocity (V r f ) verified against radar observations of all scans (Raleigh radar station, 23:00 UTC 9 May 2003)

7. Wind Forecast Improvements with Forecast Time 0.55 0.65 0.75 0.85 0.95 12345 CNTLCLD CLD+PRWIND ALL 5 7 9 12345 CNTLCLD CLD+PRWIND ALL Forecast Hour 5 7 9 11 12345 CNTLCLD CLD+PRWIND ALL Forecast Hour 0.5 0.65 0.8 0.95 12345 CNTLCLD CLD+PRWIND ALL Ele. Angle  = 2.37 o RMS Error (m 1 s -1 )Correlation Coefficient Ele. Angle  = 1.49 o

8.  The data assimilations affected all dynamical and hydrological fields.  The effects of the implicit latent heat from the assimilated satellite and radar reflectivity data were seen in the temperature changes and affected the wind fields significantly.  The data assimilation impacts remained in the forecasts of winds, temperature and water vapor for several hours, but decreased rapidly in the precipitation fields as the storm system weakened.  Radar radial velocity assimilation led to the biggest improvement in wind forecast, while reflectivity assimilation was the major cause of the improvement in storm location and strength prediction.  The combined data assimilation did not have the best results in each individual field forecast, but was the best in overall improvement. Conclusions

9. Data Assimilation Systems Used For This Study  The cloud analysis system developed by Albers et al. (1996) and modified by Zhang et al. (1998) is used for estimating three-dimensional hydrological structures of storms Estimates cloud top heights from satellite data Determines cloud ceiling from surface observations Retrieves storm internal structures from radar reflectivity  The radar wind analysis system developed by Xu et al. (2001) is employed to retrieve three-dimensional winds from radar observations of radial velocity A variational approach that uses three time levels of data Thermodynamical retrievals that dynamically balance the retrieved winds in model initial conditions Capability of using data from multiple radars  The background fields for the retrievals are provided by the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS ®, Hodur 1997). The same model is also used for testing the data assimilation system Nonhydrostatic Multiple nested grids Advanced physical parameterizations COAMPS  is a registered trademark of the Naval Research Laboratory.

10. 3-D wind verification  Compute the forecast radar radial velocity (V r f ) at the radar observational grid points from model prediction of (u, v, w)  Verify V r f against the observations (V r o ) and calculate the statistics  Advantages over single point verification: 3-D wind pattern and fine features in 3D winds Large amount of observational data available Storm location and intensity validation  Calculation of radar reflectivity from the model forecasts (Atlas 1954; Brown and Braham 1963; Douglas 1964): Rain water:Z=2.4x10 4 (  q r ) 1.82 Snow aggregates:Z=3.8x10 4 (  q s ) 2.2 Graupel/Hail (dry):Z =9.4x10 5 (  q g ) 1.12 Graupel/Hail (wet):Z =5.4x10 6 (  q g ) 1.21 Where Z is radar reflectivity factor (mm 6 m -3 ), and q is water mixing ratio (g/kg) obtained from the model forecasts of different hydrometeors.  Compare the predicted storms with radar observations Data Assimilation Assessment

11. Improvement in Storm Location and Intensity Predictions 1 Hour (23:00) OBSCNTLCLD+PRALL 2 Hour (00:00) FCST TIME