The use of WSR-88D radar data at NCEP Shun Liu 1 David Parrish 2, John Derber 2, Geoff DiMego 2, Wan-shu Wu 2 Matthew Pyle 2, Brad Ferrier 1 1 IMSG/ National Centers of Environmental Prediction, Camp Springs, Maryland 2 NOAA/National Centers of Environmental Prediction, Camp Springs, Maryland

OUTLINE Radar data processing at NCEP High resolution forecast initialization with radar data

WSR88D-Radar Data Processing at NCEP Problems of radar data processing in operations: 1. the relatively large volume of radar data restricting the data to be transmitted to the operational center in real time 2.the radar data decoding software and storage taking excessive computational resources 3.the quality control (QC) problems of radar data further limiting the applications of radar data for operational use

WSR88D-Radar Data Processing at NCEP Radar data received at NCEP: 1.Digital Precipitation Arrays (DPA) 2.VAD wind (velocity azimuth display) 3.WSR88D Level-III (NIDS) data 4.WSR88D Level 2.5 data 5.WSR88D Level-II data

Flow chart of level-II radar data processing at NCEP

Radar data QC at NCEP

QC Parameters Mean reflectivity (MRF) Velocity data coverage (VDC) Along-beam perturbation velocity sign changes (VSC) Along-beam velocity sign changes(SN) Standard deviation of radial wind (STD)

Recorded QC parameters

KFWS KBUF % 6% Along beam velocity sign change (SN) Threshold to reject data

Performance of radar data QC Observation (m/s) analysis (m/s) Observation (m/s) before QC after QC

With QC Goes image

Zoom-in area

HiRes Initialization with radar data The radial wind is directly analyzed by GSI. The cloud analysis package developed by GSD is modified and used to analyze reflectivity with NCEP’s forecast model background. Hourly cycle is used

HiRes Initialization with radar data After complex radial wind quality control, level II radial winds are used in GSI analysis. 3D reflectivity mosaic from level II data are used in cloud analysis. Metar and Satellite observations are used in cloud analysis to detect cloud. Latent heat estimated from reflectivity is used to adjust background temperature. After radial wind and reflectivity assimilation, wind, temperature, rain water mixing ratio, cloud water and cloud ice mixing ratio and specific humidity are upgraded.

Test case on Forecast start at 00z CTL: forecast without Radar data assimilation EXP: forecast with radar data assimilation

Low level divergence High level CREF obs CREF ctl EXP-CTL at the end of data assimilation cycle wind divergence conv div conv

psf (mb) uv (m/s)T (k)Rh (%) bias rms biasrmsbiasrmsbiasrms 4h fcst ctl exp h fcst ctl exp h fcst ctl exp Conventional data verification

Forecast hour Domain average of absolute pressure change per 3h

CREF ETS score 10 day parallel run from ctl exp

Vector wind Analysis Increment GSI analyzed wind increment Most of analyzed wind increments are along the radial direction. Challenges of radar data assimilation do we get cross- beam wind information with current 3DVAR system?

Challenges of radar data assimilation How to get balance between wind and other model variables? 1.Examine if DFI can distribute wind increment from Vr assimilation to other model variables. T increment through DFI is small. Q increment is relative large through DFI 2. Add new constrains in GSI to get balance between wind and other variables.

Future plan  Continue to test current hourly data assimilation system for HiRes initialization. Will try to extend current 2 hour assimilation window to 6 hour or change assimilation interval to half hour.  Consider including diabatic digital filter treatment.  May consider to use radar data in hybrid ensemble data assimilation system.  Improvement of radar data quality control package at NCEP is constantly needed. We will need to process TDWR, Dual-pol and Canadian radar data in near future.