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Numerical Model Applications and Impact for Aircraft Observations

Stan Benjamin NOAA Earth System Research Laboratory (ESRL) Global Systems Division (GSD) Boulder, CO USA. Numerical Model Applications and Impact for Aircraft Observations. Stan.Benjamin@noaa.gov http://ruc.noaa.gov. Outline Numerical weather prediction

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Numerical Model Applications and Impact for Aircraft Observations

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  1. Stan Benjamin NOAA Earth System Research Laboratory (ESRL) Global Systems Division (GSD) Boulder, CO USA Numerical Model Applications and Impact for Aircraft Observations Stan.Benjamin@noaa.gov http://ruc.noaa.gov

  2. Outline Numerical weather prediction - How to get model initial conditions History of aircraft observations for NWP - AIREPs, automated reports NWP impact of aircraft observations - Relative impact of aircraft vs. raob, profiler, VAD, surface - Regional – Rapid Update Cycle model - Feb 2001 2-week period, 9-11 Sept 2001 - Global – ECMWF model Conclusions

  3. 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

  4. Themes • Uniqueness of aircraft observations • - from a private sector and government partnership • Part of composite observing system on regional and global scales • Automated reporting of accurate weather reports and continued growth of commercial aviation • allowed (and necessitated) high-frequency numerical weather prediction (NWP) (3h-1h updating) • contributes strongly to improvements in global NWP

  5. Some background history Late 1970s – International “fuel crisis” 1983 – Complaints from airlines to FAA on poor quality in upper-level wind forecasts, even over CONUS, with increasing costs to their operations 1984-86 – FAA-sponsored Aviation Weather Forecasting Task Force (AWFTF) – Recommendations - Implement automated aircraft reporting system as component of national meteo weather obs system - Upgrade aviation weather NWP system to higher frequency than 2x/day 1985-1989 – Initial data assimilation tests with automated aircraft observations over US

  6. History of aircraft reports for NWP • AIREPs (Aircraft Reports) • Voice-relayed reports • Made for transoceanic flights at “waypoints” every 10 deg longitude • Data quality known to be poor – observation errors usually set as high for assimilation • Only aircraft data type until late 1980s as automated aircraft reports • Automated aircraft met obs • Started in 1980s. Initially, Delta/United, then Northwest. (ARINC/ACARS-enabled) • Was company-confidential data initially, meant only for internal airline met offices for competitive advantage. Breakthrough for govt/private sharing – AWFTF, Russ Crawford-Delta, Carl Knable – United.

  7. Initial tests with assimilation of automated aircraft observations – February 1986 (200-hPa winds) Raobs only Addition of Delta and United automated reports

  8. RUC History – NCEP (NMC) implementations • 1994- First NCEP/NMC operational implementation of RUC • 60km resolution, 3-h cycle • Assimilation of aircraft, profiler, raob data initially • 1998 – 40km resolution, 1-h cycle, • - cloud physics, land-sfc model • 2002 – 20km resolution • - addition of GOES cloud data in assimilation • 2005 – 13km resolution, new obs (METAR clouds, GPS moisture), new model physics • 2006 – Improved convection, improved use of GOES cloud data, improved moisture/cloud forecasts (July) • 2009 – WRF-based Rapid Refresh planned to replace RUC

  9. Models running at the NOAA National Centers for Environmental Prediction (NCEP) used for aviation forecasting • Rapid Update Cycle (RUC) model • 1-h update frequency, 13-km resolution • North American Mesoscale (NAM) model • 6-h update frequency, 12-km resolution, continental scale • Global Forecast System (GFS) model • 6-h update, ~60-km resolution

  10. RUC is used over the CONUS domain in the Current Icing Potential (CIP), Forecast Icing Potential (FIP), Graphical Turbulence Guidance (GTG), and the National Convective Weather Forecast (NCWF) aviation weather products Planned WRF-Rapid Refresh domain - 2008 13km resolution RCPF 1500 Z + 6-h forecast RCPF Current RUC-13 CONUS domain Turbulence - GTG AWC 2100 Z verification Icing FIP

  11. RUC • Wind forecast • Accuracy • Sept-Dec • 2002 6 9 1 3 12 Analysis ~ ‘truth’ 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. Wind Height % Reduction of 12-h RUC forecast error Sept-Dec 2002 Temperature RH

  13. 3 6 1 Analysis ~ ‘truth’ 9 Wind errors Dec06-Jan07

  14. Analysis ~ ‘truth’ Temp errors Dec06-Jan07

  15. Analysis ~ ‘truth’ RH errors Dec06-Jan07

  16. RUC vector (m/s) forecast error – 1200 UTC 8 Feb 2001 – 250 hPa 12h init 12z 9h init 03z 6h init 06z 3h init 09z

  17. Obs Sens Experiments (OSEs) w/ RUC Data Type ~Number Freq. Rawinsonde 80 /12h NOAA 405 MHz profilers 31 / 1h VAD winds (WSR-88D radars) 110-130 / 1h Aircraft (automated) (V,temp) 1800-5500 / 1h Surface/METAR (T,Td,V,p) 1500-1700 / 1h Buoy/ship 100-150 / 1h GOES precipitable water 1500-3000 / 1h GOES cloud drift winds 1000-2500 / 1h GOES cloud-top pressure/temp ~10km res / 1h SSM/I precipitable water 1000-4000 / 6h GPS precipitable water ~300 / 1h Radar reflectivity / lightning 4 km Mesonet 6000 / 1h METAR cloud/vis/wx obs / 1h OSEs

  18. Spatial coverage for OSE obs types Data Type ~Number Freq. Rawinsonde 80 /12h horizontal - covers land area vertical – sfc to above 50 hPa NOAA 405 MHz profilers 31 / 1h horizontal - central US only vertical - surface to 18 km profiles VAD winds (WSR-88D radars) 110-130 / 1h horizontal – over continental US vertical - winter: sfc to 150-300 hPa above surface Aircraft (ACARS) (V,temp) 1800-6000 / 1h horizontal – over land area, irregular in time vertical – much at 300-200 hPa, ascent/descent Surface/METAR – T/Td/V/p 1500-1700 / 1h horizontal – covers land area completely vertical – surface only

  19. 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

  20. Experiment design • 7 experiments • Control • No raob • No aircraft • No profiler • No VAD • No surface • All obs denied (BC only) • Operational version of RUC run retrospectively for 4-17 Feb 2001 • 20km, 1h cycle, 3DVAR, DFI • Eta boundary conditions contain all data sources • Verification performed on twice daily rawinsonde data over 2 domains for standard variables (Z, T, RH, winds) for 3, 6, 9, 12 h fcsts • Two verification domains • Test verification performed using NOAA profiler data (winds only) National domain – 91 raobs Midwest domain – 26 raobs

  21. 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; 12h 12 h

  22. Wind forecast impact – US Midwest domain 3h 6h • Aircraft still dominant at 200-300 hPa, impact grows from 3h to 6h • Profiler impact large at 3h but drops off quickly – effect of regional network (not national) • METAR – important for 850 hPa for 3-6h • Hourly data – has more ‘room’ to help < 12h 12h

  23. 3h 12h Temperature forecast impact 3 h projection valid 00, 12UTC 6h

  24. 3h 12h Height forecast impact 3 h projection valid 00, 12UTC 6h

  25. RUC OSE (3-12h CONUS) Conclusions • Limited domain obs system – impact fades more quickly with time in mean statistics. • Raob most important ob type for all variables at 12h except jet-level winds. Importance “grows” from 3h until 12h, matching raob frequency • Wind profiler impact – complements aircraft data over midwest US at 3-6h, less so at 12h • Aircraft continue to dominate over upper troposphere • METAR/VAD have a small effect thru troposphere. • Short-range forecast improvement very significant to aviation, severe storm forecasting, NASA, energy industry, sfc transportation… • Safety net of multiple obs sources – no catastrophic result when any one is excluded.

  26. The impact of missing ACARS data on RUC wind forecast skill • 11-13 September 2001 • weekends (fewer package delivery reports)

  27. Hourly ACARS volume2-15 Sept 01(starting 00z 2 Sept) 2-8 Sept 01 Su Mo Tu We Th Fr Sa 9-16 Sept 01 Su Mo Tu We Th Fr Sa

  28. Improvement in 3h over 12h wind fcst- September 2001 • RUC 250 mb • Wind forecasts • Verification • against raob data Forecasts from operational RUC run at NCEP 11-13 Sep

  29. Forecast errors – RUCdev (no TAMDAR), RUCdev2 (w/ TAMDAR) Temp RH wTAM noTAM wTAM noTAM See AMS-IIPS TAMDAR session – Thurs AM • TAMDAR impact study with RUC parallel cycles • 2005-2007 (ongoing) • Further improvement in • RH, temperature, wind • Regional coverage (more airports w/ ascent/descent) • RH sensor Wind wTAM noTAM

  30. Trade-offs for obs system network size vs. duration of forecast impact • regional obs networks • may help very short-range forecasts • may contribute modestly or not at all to 2-5 day skill • duration of impact can be extended if network area can be expanded • accuracy and representativeness offsets network size

  31. EUCOS terrestrially based Observing System StudiesJean-Noël Thépaut and Graeme Kelly ECMWF email: jean-noel.thepaut@ecmwf.int

  32. Overview • Experimental Set Up • List of experiments • Model and assimilation configuration • Winter Results • Summer results • Conclusions and Perspectives

  33. Experimental Set-Up: List of experiments • (i): BASELINE: all current satellite observations used in NWP (radiances, cloud-drift winds, scatt winds)+ GUAN R/S (RADIOSONDE) network+hourly GSN surface land data+hourly buoys (no ship data) • (ii): (i) BASELINE + aircraft data • (iii): (i) BASELINE + non-guan R/S (incl. ASAP) wind profiles • (iv): (i) BASELINE + non-guan R/S (incl. ASAP) temperature and wind profiles • (v): (i) BASELINE + wind-profiler data • (vi): (iv) + aircraft data • (vii): (iv) + R/S (incl. ASAP) humidity profiles • (viii): CONTROL: full combined observing system

  34. GUAN = GCOS Upper-Air Network (minimal global raob network – e.g., 6 stations over CONUS) GCOS Global Climate Obs System

  35. Exp Set Up (ctd): Model/assim configuration • Winter Experiments: • 2004120400 until 2005012512 (including 10 day warm up) • Resolution : • Model: T511 (40km) L 60 • Assimilation: T511/T159 L60. 12h 4DVAR • Model cycle 29R1 • Summer experiments: • 2005071500 until 2005091512 (including 10 day warm up) • Resolution: idem winter experiments • Model cycle 29R2 • NOAA18 included (AMSU-A and MHS)

  36. Winter results: Control-Baseline (Z500)Normalised forecast error difference FC+72h

  37. Summer: CONTROL – BASELINEnormalised fcerr + 72H

  38. Winter results: Z scores (impact of aircrafts) 1000 hPa 500 hPa NH EUR

  39. Winter results: Wind scores (impact of aircrafts) 850 hPa 300 hPa NH EUR

  40. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+12h

  41. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+24h

  42. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+36h

  43. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+48h

  44. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+60h

  45. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+72h

  46. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+84h

  47. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+96h

  48. Winter results: [Baseline + aircrafts] - Baseline (Z500)Normalised forecast error difference FC+108h

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