Presentation on theme: "VDRAS and 0-6 Hour NWP - Recent activities"— Presentation transcript:
1 VDRAS and 0-6 Hour NWP - Recent activities Juanzhen SunRAL/NESL, NCARVDRAS and its recent applicationsRetrospective studies of 0-6h NWP with radar DA4DVAR development
2 Overview of VDRASVDRAS is an advanced data assimilation system for high-resolution (1-3 km) and rapid updated (6-18 min) analysisProduce Low-level wind, temperature, and humidity analysisVDRAS assimilates mesoscale model data, surface data, and radar radial velocity and reflectivity data from single or multiple radarsThe core is a 4-dimensional data assimilation scheme based on a warm-rain cloud-scale modelIt has been installed at nearly 20 sites for nowcasting applications since 1998 and currently running over 10 domains in and outside of U.S.
4 Summary of recent VDRAS activities Continuing collaboration with BMB- Analysis of convective cases of 2008 and 2009- Understanding of convective initiation in Beijing- Development of forecast indexImplementation for CWB of Taiwan- Study of terrain-induced convection- Support of ANC for nowcasting in TaiwanWind energy applications- Evaluation of VDRAS performance for 80m wind analysis- Development of techniques for 0-2 hour wind forecastOthers: 10 instances of VDRAS are running in andoutside of U.S
6 VDRAS for wind energy in Northern Colorado VDRAS wind and temperature 08 June 2010X location of wind farm0314 UTCx0208 UTCx0135 UTCx0240 UTCx
7 VDRAS for wind energy in Northern Colorado VDRAS wind vector and speed June 2010X location of wind farmx0244 UTCx0353 UTCx0207 UTCx0320 UTCx0053 UTCx2237 UTCx2311 UTCx2344 UTCx0016 UTCx0130 UTC
8 Verification of VDRAS wind against Turbine wind 10-11 July, 2010, Northern Colorado04-05 AUG, 2010, TexasTurbine windVDRAS windTurbine windVDRAS windQuestions raised for the phase shift on Aug, 2010 caseDiscrepancy between radar and turbine observationsIssue of inadequate vertical resolution in radar obs.?Reliability of turbine wind?
9 Wind nowcasting based on VDRAS Feature extrapolation- Convergence line- Temperature gradient- Simple and efficientDirect integration of VDRAS model- Use a 2-D advection wind- May be more accurate than feature extrapolation- More computation
10 Summary of 0-6 h NWP research IHOP retrospective study through NCAR’s STEP program- Emphasize radar data assimilation and connection betweenmodel and nowcasting- Techniques include nudging, 3DVAR, 4DVAR, EnKF- Sensitivity of initial conditions vs. physicsWRF 3DVAR operational pre-testing (collaboration with BMB)Further development of advanced techniques, 4DVAR & EnKFEvaluation of 0-6 h NWP with radar DA over Front Range- Strategies for improved 0-6 h NWP for nowcasting purposes- Evaluate pros and cons of different techniques- Running systems of Nudging, 3DVAR, DDFI, EnKF over the samedomain and the same period
11 IHOP retrospective study Lesson 1: 0-12 hour forecasts highly sensitive to initial conditionsForecast skill over one-week10-16 June 2002OBSCTRLPhysics experimentsInitial condition experimentsRadarWSM6
12 IHOP retrospective study Lesson 2: Short-term forecast sensitivity depends on storm type“Easy to forecast” stormOBSWRF fcst NAMWRF fcst GFS“Hard to forecast” stormOBSWRF 3-h fcstWRF 3-h fcstNo radarWith radar
13 Lesson 3: radar data impact depends on storm type IHOP retrospective studyLesson 3: radar data impact depends on storm typeforecast skill over one-week10-16 June 2002Preliminary findings:Radar data has less impact onequilibrium and elevated convectionRadar data assimilation providestriggers for surface-based convectionChallenge: optimal fit toconvective-scale whilemaintaining large-scale balanceWith radarWSM6 microphysicsPositive impact of radarNegative impact of radar15 June13 JuneWith radarWith radar
14 FRONT - future STEP testbed S-Pol: N of Hwy 52 betweenI-25 and Hwy 85; near Firestone.Operational ~Summer,2012 after DYNAMO deploymentTestbed for- software development- data assimilation- instrument/modelintercomparison/validation- QPE/QPF and nowcastingPawneeCHILL42 km67 km73 kmS-Pol48 km
15 June 2009 Front-Range Convection Retrospective Studies Evaluation of radar data assimilation systemsEnKF Mesoscale and storm-scale data assimilation and predictionRTFDDA latent heat nudging of radar reflectivityWRF 3DVAR radar data assimilationNOAA/ESRL HRRR radar reflectivity initializationMesoscale data assimilation on CONUS domainStorm-scale DA on Front Range15 km3 km1515
16 WRF/DART EnKF storm-scale data assimilation June over Front Range regionFrequency of updraft helicity over a 6hr ensemble forecastNo radar DAWith radar DA
17 Development of WRFDA-4DVAR for Radar 1. Radar reflectivity assimilation- Assimilating retrieved rainwater from RF;- The error of retrieved rainwater is specifiedby error of RF.2. New control variables and background error covariance- Cloud water (qc), rain water (qr);- Recursive filter is used to model horizontalcorrelation ;- Vertical correlation is considered by EOFs;3. Microphysics scheme- Linear/adjoint of a Kessler warm-rain scheme;- Incorporated into WRF tangent/adjoint model.
18 WRF-4DVAR: Impact of reflectivity WSM6ThompsonThreshold: 5 mm/hrThreshold: 5 mm/hr4DVAR-RV+RF4DVAR-RV+RF4DVAR-RV4DVAR-RV3DVAR3DVARFCST Time (hour)FCST Time (hour)Reflectivity improves the forecast skill.
19 Impact of RV outside rain region WSM6Threshold: 5 mm/hrThompsonThreshold: 5 mm/hrRF-RVallRF-RVRF-RVRF-RVall3DVAR3DVARFCST Time (hour)FCST Time (hour)RV outside rain region improves forecast skill with Thompson microphysics.
20 Hourly rainfall at 01Z 13 June ObsBG4D-R2-T15-RVallWSM6Thompson3D-R24D-R2-T154D-R2-T15WSM6WSM6Thompson
21 Hourly rainfall at 06Z 13 June ObsBG4D-R2-T15-RVallWSM6Thompson3D-R24D-R2-T154D-R2-T15WSM6WSM6Thompson
22 Summary VDRAS analysis is an valuable addition to the existing precipitation nowcasting systemsRecent applications to wind energy prediction showed promisesActive research is being pursued to improve 0-6 hour NWP for nowcsting applicationsA joint workshop with MWG is being planned on “NWP for nowcasting”
23 The analysis Surface wind (vector), surface temperature (contour) Precipitable water (shaded)3D-RF-RV4D-RF-RVall
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