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) Fax: (831)

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

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

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) Height (km) 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

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

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)

Wind Forecast Improvements with Forecast Time CNTLCLD CLD+PRWIND ALL CNTLCLD CLD+PRWIND ALL Forecast Hour CNTLCLD CLD+PRWIND ALL Forecast Hour CNTLCLD CLD+PRWIND ALL Ele. Angle  = 2.37 o RMS Error (m 1 s -1 )Correlation Coefficient Ele. Angle  = 1.49 o

 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

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.

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

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