220 likes | 372 Views
Assimilation of Dual- Polarimetric Radar and GPM Observations with GSI in regional WRF. Xuanli Li 1 , John Mecikalski 1 , Bradley Zavodsky 2 , and Jayanthi Srikishen 3 1 Department of Atmospheric Sciences, University of Alabama in Huntsville 2 NASA Marshall Space Flight Center
E N D
Assimilation of Dual-Polarimetric Radar and GPM Observations with GSI in regional WRF Xuanli Li1, John Mecikalski1, Bradley Zavodsky2, and JayanthiSrikishen3 1Department of Atmospheric Sciences, University of Alabama in Huntsville 2NASA Marshall Space Flight Center 3Universities Space Research Association 21-23 May 2014 12th JCSDA Science Workshop on Satellite Data Assimilation, College Park, MD 1
Outline Background and objectives Previous work: Assimilation of ground-based dual-pol radar data with GSI Comparison of dual-pol radar data assimilation using GSI vs. WRFVAR Work plan and current progress: assimilation of GPM ground validation data with GSI
Background and Goals • ROSES-13 A.33 project to assimilate GPM DPR reflectivity and GMI products, collaborated with NASA SPoRTCenter. • NWS WSR-88D network has been updated to include dual-polarimetric capability. The dual-pol radar can provide more information on cloud and precipitation particles. Assimilation of the dual-pol radar data is a relatively new area. • GPM has been launched and DPR and GMI data will be available soon. Broader coverage than the ground-based radar system, better measurement for snow storm events. • Project goal is to develop methodology to implement GPM DPR and GMI data with GSI into regional WRF model, and investigate the potential of using GPM observation in convective scale NWP for operational environment. 3
Dual-Polarimetric Radar Horizontal and vertical signals: more info about the type, shape, and size of the hydrometeors – more accurate estimates of precipitation and cloud particles. Variables: ZH: Horizontal reflectivity VR: Radial velocity ZDR: Differential reflectivity ZDR = 10 log10(ZH/ZV) ρHV: Correlation coefficient, the coefficient between the horizontal and vertical power returns. ΦDP: Differential phase, the measured phase shift between horizontal and vertical pulses SW: Spectrum width, measures the consistence of the phase shifts 4
Dual-Pol Radar Data Assimilation with GSI • WRF model ARW v3.5 • GSI v3.2 • Assimilation procedure: • Reflectivity is used by the Global Systems Division (GSD) cloud analysis to improve precipitation analysis • ZDR information is added in calculation of rain amount. GSD Cloud Analysis for rain: • Kessler (1969): With ZDR, using Ulbrich and Atlas (1984): 5
WSR-88D Dual-Pol Radar Observation ZDRZH 2 km height 0631 UTC 2 September 2013 VR 6
Reflectivity at 0600 UTC 2 September 2013 Model starts at 0000 UTC, no convection in model simulation at 0600 UTC 8
Reflectivity 0900 UTC 2 September 2013 ZH VR NEXRAD ZHZDR 10 Zdr data shows impact on the initial reflectivity and hydrometeor fields
Temperature 0900 UTC 2 September 2013 ZH VR Impact found in low level temperature field ZHZDR 11
Moisture 0900 UTC 2 September 2013 ZH VR Stronger dry region found in low level moisture field ZHZDR 12
Forecast Validation 1200 UTC 2 September 2013 VR ZH NEXRAD ZHZDR 13 Zdr data assimilation shows impact on the convective scale model forecast
Dual-Pol Radar Data Assimilation: GSI vs. WRFVAR GSI: GSD cloud analysis system Indirect incorporation of dual-pol radar data Convert to cloud type, precipitation amount WRFVAR: direct assimilation of dual-pol radar data moist control variables: water vapor, rain water, and cloud water mixing ratio Assimilation: using Ulbrich and Atlas (1984): cycled assimilation at 0600 and 0900 UTC 14
Dual-Pol Radar Data Assimilation: GSI vs. WRFVAR CTRL GSI WRFVAR Analysis field at 0600 UTC for reflectivity: more significant increment in WRFVAR than GSI 15
Dual-Pol Radar Data Assimilation: GSI vs. WRFVAR WRFVAR GSI Analysis field at 0600 UTC for low level temperature: more significant temperature change in WRFVAR than GSI 16
Dual-Pol Radar Data Assimilation: GSI vs. WRFVAR WRFVAR GSI Analysis field at 0600 UTC for low level moisture: higher value of moisture in GSI field than WRFVAR 17
Dual-Pol Radar Data Assimilation: GSI vs. WRFVAR GSI WRFVAR 1200 UTC 2 September 2013 6 h forecast: similar location and storm pattern NEXRAD 18
Dual-Pol Radar Data Assimilation: GSI vs. WRFVAR GSI WRFVAR NEXRAD 1500 UTC 2 September 2013 6h forecast: different pattern Storm dissipate quicker than WRFVAR 19
Work Plan • Generate a dual-frequency radar and microwave radiometer observation dataset, analyze GPM data -- Ground validation data now available • Develop a methodology for assimilation of GPM DPR and GMI observations with GSI • -- Test first with ground validation data • Assimilation of real GPM data • Generate a dual-frequency radar and microwave radiometer observation dataset, analyze GPM data • Develop a methodology for assimilation of GPM DPR and GMI observations with GSI • Transition the GPM daa assimilation into operational forecast
Current Work • Case study: 2012-02-24 snowstorm observed by GPM ground validation GCPExfield campaign. WRF model control run NEXRAD 1700 UTC 24 February 2012 • Generate a dual-frequency radar and microwave radiometer observation dataset, analyze GPM data • Develop a methodology for assimilation of GPM DPR and GMI observations with GSI • Transition the GPM daa assimilation into operational forecast