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RUC Impact Experiments: Enhancements, Statistical Results, and Forecast Accuracy

This talk provides a general description of the RUC 1-hour cycle, recent enhancements to its assimilation system, previous results from data impact experiments, and statistical results on forecast accuracy. It also discusses the use of surface observations and the impact of RUC enhancements on severe weather forecasting.

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RUC Impact Experiments: Enhancements, Statistical Results, and Forecast Accuracy

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  1. Stan Benjamin,Tracy Lorraine Smith, Bill Moninger, Brian JamisonNOAA Forecast Systems LaboratoryBoulder, CO GLFE Real-time TAMDAR Impact Experiments with the 20km RUC

  2. Outline of talk Part 1: General description of RUC 1h cycle - Data assimilated - Spatial effects of assimilated data Recent enhancements to RUC assimilation Previous results from RUC data impact experiments Design of RUC parallel experiments – Dev / Dev2 Examples of Dev/Dev2 difference fields (Brian Jamison) Part 2: Actual statistical results

  3. Purpose for Rapid Update Cycle (RUC) model run operationally at NCEP • Provide high-frequency mesoscale analyses, short-range model forecasts • Use all available observations • Users: • aviation/transportation • severe weather forecasting • general public forecasting • Focus on 1-12 hour forecast range • Accuracy of surface fields very important

  4. Rapid Update Cycle Model Features Vert Coord Hybrid sigma-isentropic Stable clouds, NCAR mixed-phase (cloud water, precipitationrain water, snow, graupel, ice, ice particle number. concen. Sub-grid Grell-Devenyi ensemble scheme precipitation (144 members, mean to model) Land-surfaceRUC LSM - 6-level soil/veg model, 2-layer snow model (Smirnova) Current operational RUC: 20-km Planned upgrade 2005: 13-km

  5. RUC Assimilation System Updates 1-h cycle Analysis 3DVAR on q/s surfaces (y/c/Zunb, q, lnQ) Details Balanced height (NMC method) Length-scales modified from OI Sfc Obs/ Use surface obs through PBL PBL Structure Lapse-rate checks Noise Adiabatic digital filter initialization Clouds/Cloud analysis (GOES cloudtop pres, moisture radar reflectivity, METAR clouds) Cycling of cloud, hydrometeor, land-surface fields

  6. 3DVAR 3DVAR 3DVAR 3DVAR 3DVAR Obs Obs Obs Obs Obs RUC Hourly Assimilation Cycle 1-hr fcst 1-hr fcst 1-hr fcst 1-hr fcst 1-hr fcst 1-hr fcst Background Fields Analysis Fields Time (UTC) 11 12 13 14 15 16

  7. 12-h fcst 12-h fcst 3DVAR 3DVAR 3DVAR 3DVAR 3DVAR Obs Obs Obs Obs Obs RUC Hourly Assimilation Cycle 3-hr fcst 3-hr fcst 3-hr fcst 3-hr fcst Background Fields Analysis Fields EC Time (UTC) 11 12 13 14 15 16

  8. Cloud anx variables Observations used in RUC Data Type ~Number Freq. -------------------------------------------------- Rawinsonde 80 /12h NOAA profilers 30 / 1h VAD winds 110-130 / 1h Aircraft (V,temp) 1400-4500 / 1h Surface/METAR1500-1700 / 1h Buoy/ship 100-150 / 1h GOES precip water 1500-3000 / 1h GOES cloud winds 1000-2500 / 1h GOES cloud-top pres~10km res / 1h SSM/I precip water 1000-4000 / 6h -------------------------------------------------- GPS precip water ~300 / 1h Mesonet ~5000 / 1h PBL – prof/RASS ~20 / 1h Radar refl / lightning4 km res -------------------------------------------------- NCEP operational FSL experimental

  9. Application of Digital Filter Initialization in RUC • 45 min forward, 45 min backward – no physics • Average over DFI period

  10. RUC Analysis • 3-d effect of observations dependent on statistically determined forecast error covariance • vertical – dependent on  • horizontal – smaller near surface, larger aloft,

  11. RUC20 • Wind forecast • Accuracy • Sept-Dec • 2002 6 9 1 3 12 Analysis ~ ‘truth’ 6 8 10 12 (kts) Verification against rawinsonde data over RUC domain RMS vector difference (forecast vs. obs) RUC is able to use recent obs to improve forecast skill down to 1-h projection for winds

  12. Results from fall 2002 – better moisture results in RUC13 Potential for more improvement from TAMDAR – V, T, RH

  13. Use of surface obs information throughout boundary layer in the RUC analysis • Problem • Information from surface • observation not used through • depth of PBL by RUC analysis • Surface observation not • retained in model forecast Original analysis Dewpoint Temperature * Surface Observation

  14. Use of surface obs information throughout boundary layer in the RUC analysis • Problem • Information from surface • observation not used through • depth of PBL by RUC analysis • Surface observation not • retained in model forecast • Solution • Use METAR observation throughout PBL depth • (from background field) • Better model retention • of surface observations Original analysis Analysis with use of PBL depth Dewpoint Temperature * Surface Observation

  15. CAPE impact from two RUC enhancements 0000 UTC 21 Apr 2004 3h fcst WITH enhancements 3h fcst OPERATIONAL • RUC enhancements: • Use of METAR obs through • boundary-layer depth(Sept 04) • Assimilation of GPS precipitable • water observations(May 2005) Severe reports NWS SPC Norman, OK

  16. RUC Cloud Analysis • Use GOES CTP, radar reflectivity, lightning, • METAR (clouds,wx,vis) to modify moisture fields • Construct 3-d logical arrays (YES/NO/UNKOWN) • for clouds and precipitation from all info • Clear/build (change qc, qi, qv) with logical arrays • Safeguards for pressure-level assignment problems • (marine stratus, convective clouds) • Use nationwide mosaic radar data to modify water • vapor, hydrometeor fields • Lightning data used as a proxy for radar reflectivity • Feedback to cumulus parameterization scheme

  17. 100 200 300 400 500 600 700 800 900 999 PRES dBZ Qi Qs Qr Rainwater, snow, cloud ice and reflectivity before ( ) and after (----) GOES radar/lightning adjustment GOES cloud top pressure Radar/lightning data 100 200 300 400 500 600 700 800 900 999 PRES RH Qv Qc Cloudwater, water vapor and relative humidity before ( ) and after (----) GOES Cloud- top pressure adjustment

  18. NESDIS GOES Verification cloud-top prs 3h 20km fcst WITH GOES cloud assim 1200 UTC 9 Dec 2001 Cloud-top pressure (mb) 3h 40km fcst NO GOES cloud assim Sample 20-km RUC forecast impact from GOES cloud-top pres. assimilation

  19. Assimilation of METAR cloud/wx/vis Better analysis, prediction of ceiling and visibility - Nearest station up to 100 km distance - Maps info to 3-d cloud, precip. Y/N/U arrays - Change qc, qi, qv as follows: Build for BKN / OVC / Vertical Visibility - 40 mb thick layer (2+ model levels) - 150 mb thick for precip + GOES clouds - Can build multiple broken layers Clear - Up to cloud base (if needed) - to 12 kft for CLR report

  20. analysis – with METAR cloud/ visibility obs Cloud water mixing ratio Sample modification of cloud water (qc) from METAR cloud/weather/ Visibility obs 1700 UTC 27 Jan 2004 Cloud water mixing ratio RH,  Background – 1h fcst

  21. LIFR IFR MVFR VFR CLR Sample ceiling analysis impact Analysis WITH cld/wx/ vis obs Ceiling from RUC hydrometeors Observations 1800 UTC 17 Nov 2003 Aviation Flight Rules Analysis NO cld/wx/ vis obs cloud ceiling height (meters)

  22. LIFR IFR MVFR VFR CLR Sample ceiling forecast impact 3h fcst WITH cld/wx/ vis obs Ceiling from RUC hydrometeors Observations 2100 UTC 17 Nov 2003 Aviation Flight Rules 3h fcst NO cld/wx/ vis obs cloud ceiling height (meters)

  23. Planned upgrades to RUC model • 2005  13-km operational at NCEP • Assimilation of new observations • - METAR cloud/vis/current weather • - Mesonet • - GPS precipitable water • - Boundary layer profilers, RASS temperatures • - Radar data (when available at NCEP) • Model improvements – new versions of: • - Mixed-phase cloud microphysics (NCAR-FSL) • - Grell-Devenyi convective parameterization •  Planned operational implementation • of WRF-based rapid-refresh

  24. 3-d RUC weather data updated hourly 20km x 50 vertical levels x 14 variables Turbulence Ceiling/visibility Convection - 2-12h forecast Terminal / surface Icing Better weather products require improved high-frequency high-resolution models with high-refresh data to feed them Winds

  25. Wind forecast ‘errors’ - defined as rawinsonde vs. forecast difference Anx Fit - ‘truth’ Cntl = using all obs Exp = deny profiler obs Difference in errors between Cntl and Exp experiments w/ RUC - 4-17 Feb 2001 Positive difference means CNTL experiment with profiler data had lower error than the EXP-P no-profiler experiment

  26. Wind forecast impact – US National domain 3h 6h 6h 3 h • Impact generally greatest for shorter forecast durations. • Decreases with projection except raobs • Raob impact largest at 12h – raob frequency is 12h. • Aircraft - largest overall impact at 3h, profiler next (much smaller) • Modest VAD and METAR impact aloft; (METARs improve low-level height field, which helps zp mapping needed for VADs and profilers) 12h 12 h

  27. Real-time TAMDAR impact experiment design • Parallel 20km RUC 1-h cycles • Dev cycle – all obs data but no TAMDAR • Dev2 cycle – dev + TAMDAR data • Lateral boundary conditions – same for Dev and Dev2 • Control design • Initialize Dev and Dev2 runs at exact same time • Reset dev and dev-2 background field at 1000z every 48 h (even Julian dates) • Ensure against any computer logistics differences • Evolution of dev vs. dev2 is different • Example – buddy check QC in each cycle may occasionally differ for non-TAMDAR data • Slight difference in gravity waves • Can propagate difference throughout domain • Shows up in sfc temps, convection, esp. 925, 850 mb

  28. 0900z --------- 1200z • Sunday 10 April 2005 • Reset of dev-dev2 difference at 1000z • by copying Dev RUC 1-h forecast from 0900z as background for Dev2 analysis at 1000z • Reset is effective (although

  29. Dev-Dev2 difference – 12h fcst Init 1200z 10 April 2005 – 500 mb

  30. Dev-Dev2 difference – 12h fcst Init 1800z 10 April 2005 – 700 mb

  31. Real-time TAMDAR impact experiment design • Parallel 20km RUC 1-h cycles • Dev cycle – all obs data but no TAMDAR • Dev2 cycle – dev + TAMDAR data • Lateral boundary conditions – same for Dev and Dev2 • Control design • Initialize Dev and Dev2 runs at exact same time • Reset dev and dev-2 background field at 1000z every 48 h (even Julian dates) • Ensure against any computer logistics differences • Evolution of dev vs. dev2 is different • Example – buddy check QC in each cycle may occasionally differ for non-TAMDAR data • Slight difference in gravity waves • Can propagate difference throughout domain • Shows up in sfc temps, convection, esp. 925, 850 mb

  32. Part 2 – Statistical results

  33. Verification regions for FSL-RUC TAMDAR impact Large region (eastern half of US) -- 38 RAOB sites Small region (Great Lakes) includes 14 RAOBs

  34. Wind forecast ‘errors’ - defined as rawinsonde vs. forecast difference Anx Fit - ‘truth’ Cntl = using all obs Exp = deny profiler obs Difference in errors between Cntl and Exp experiments w/ RUC - 4-17 Feb 2001 Positive difference means CNTL experiment with profiler data had lower error than the EXP-P no-profiler experiment

  35. Temperature shows notable improvement for 850 mb, 3-h forecast in large (E.US) region

  36. Even clearer improvement in the small (Gt Lakes) region

  37. Temperature bias: small improvement for 850 mb, 3-h forecast

  38. Much improved temperature bias in small region

  39. Temperature: some improvement for 700 mb, 3-h forecast

  40. Winds: not much difference

  41. Relative Humidity: not much difference

  42. Results – wind – Great Lakes region only 1 March – 12 April 2005 Only 00z times -------------- V m/s average diff by level pres 0h-an 12h 3h 6h 9h 1h 850 0.04 0.02 -0.07 -0.08 0.02 0.04 0.07 -0.01 0.09 700 -0.01 0.01 0.08 0.03 -0.05 -0.04 -0.02 0.03 0.06 500 -0.07 0.00 0.04 -0.04 -0.08 -0.04 -0.06 0.04 -0.08 400 -0.03 0.07 0.05 -0.06 -0.03 -0.02 0.03 -0.06 -0.04 300 0.00 0.06 0.04 0.09 -0.01 0.00 0.05 -0.02 0.00 250 0.00 -0.07 0.04 0.02 -0.05 0.03 0.02 0.00 0.05 200 0.01 0.07 -0.01 -0.04 -0.07 0.03 0.02 -0.01 0.04 150 0.02 0.01 0.07 -0.01 0.01 0.05 -0.01 -0.02 0.04

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