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Presentation by Earth System Research Lab / Global Systems Division - Bill Moninger 23 March 2009

Presentation by Earth System Research Lab / Global Systems Division - Bill Moninger 23 March 2009 Impact of the AMDAR observations to aviation weather forecast, public weather service, and numerical weather prediction request of Mr. Hasegawa from JMA

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Presentation by Earth System Research Lab / Global Systems Division - Bill Moninger 23 March 2009

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  1. Presentation by Earth System Research Lab / Global Systems Division- Bill Moninger23 March 2009 • Impact of the AMDAR observations to aviation weather forecast, public weather service, and numerical weather prediction • request of Mr. Hasegawa from JMA • Demonstration of ESRL/GSDs real-time display of AMDAR data—used by weather services worldwide

  2. Bill Moninger, what I look like and where I work

  3. What is ESRL/GSD? • ESRL/GSD is located in Boulder, Colorado • ESRL has about 500 employees • GSD has about 200 employees • We are in the Research branch of NOAA • (NWS is an Operational branch of NOAA) • We develop NWP models from global to local scales • we focus on data assimilation • we focus on transferring our work to operations (NWS) • We provide data to researchers and operational weather forecasters world-wide

  4. What we have • ESRL/GSD operates several large supercomputers • We gather large amounts of weather data • including experimental data such as • WVSS-II • TAMDAR • We are a research & development organization • with the flexibility to test new models • and new data sources

  5. Models we run • Global models (will not be discussed further today) • Mesoscale models: • The Rapid Refresh (RR) • The High Resolution Rapid Refresh (HRRR) • The Rapid Update Cycle (RUC)

  6. Rapid Refresh domain Current RUC-13 CONUS domain HRRR domain • RR: • 13-km grid • covers North America • runs hourly • HRRR • 3-km grid • covers NE US • soon to cover 2/3 of US • runs every 15-60 minutes • RUC • 13-km grid • covers US • runs hourly • operational for 15+ years (in various forms)

  7. RUC/RR - backbone for high-frequency aviation products National Convective Weather Forecast (NCWF), Icing Potential (FIP), Graphical Turbulence Guidance (GTG), and the aviation weather products Rapid Refresh domain – 2009 13km resolution RCPF 1500 Z + 6-h forecast RCPF Current RUC-13 CONUS domain Turbulence - GTG AWC 2100 Z verification Icing FIP

  8. Purpose for the RUC/ Rapid Refresh • Provide high-frequency mesoscale analyses, short-range model forecasts • Assimilate all available observations • Focus on aviation and surface weather: • Thunderstorms, severe weather • Icing, ceiling and visibility, turbulence • Detailed surface temperature, dewpoint, winds • Upper-level winds • Users: • aviation/transportation • severe weather forecasting • general public forecasting • Support from Federal Aviation Administration “Situational Awareness Model”

  9. Operational Rapid Update Cycle Hourly updated short-range model run at NCEP (aviation, severe weather, general forecast applications) • Hybrid isentropic coordinate • Hourly 3DVAR update cycle • Extensive use of observations • 13-km horizontal resolution • Explicit 5-class microphysics 1-hr fcst 1-hr fcst 1-hr fcst Back- ground Fields Analysis Fields 3DVAR 3DVAR Obs Obs Time (UTC) 11 12 13

  10. 1-hr fcst 1-hr fcst 1-hr fcst Background Fields Analysis Fields RUC 3dvar 3dvar Obs Obs Time (UTC) 11 12 13 Observations assimilated Cycle hydrometeor, soil temp/moisture/snow plus atmosphere state variables Hourly obs in 2008 RUC Data Type ~Number Rawinsonde (12h) 80 NOAA profilers 30 VAD winds 110-130 PBL – profiler/RASS ~25 Aircraft (V,temp) 1400-7000 TAMDAR (V,T,RH) 0 - 800 Surface/METAR 1800-2000 Buoy/ship 100- 200 GOES cloud winds 1000-2500 GOES cloud-top pres 10 km res GPS precip water ~300 Mesonet (temp, Td) ~7000 Mesonet (wind) ~4500 METAR-cloud-vis-wx ~1600 Radar reflectivity 1km RUC Hourly Assimilation Cycle

  11. Commercial aircraft observations - winds and temperature - recently – water vapor, turbulence

  12. Impact of AMDAR data on RUC Forecasts • Study 1: weekend/weekday skill differences • Study 2: AMDAR cutoff after 11 Sept 2001 terrorist attacks • Study 3: Recent relative impact studies of AMDAR and other data sources

  13. Study 1: Weekend-Weekday RUC skill differences • 20,000 fewer reports every 12 hours on weekends because package carriers (FedEx and UPS) do not fly: • 0000-1200 UTC AMDAR volume average (2001) Weekday (Tu-Sa) 35,000 reports Weekend (Su-Mo) 15,000 reports • Result: a 7% increase in 3h wind forecast error at 200 hPa on weekends Study period: January-October2001; Stan Benjamin, ESRL/GSD

  14. 3 hr RUC Wind Forecast Errors (with respect to RAOBs) Weekend (Reduced AMDAR) minus weekday Jan-Oct 2001 0.35 m/s / ~5.0 m/s = 7% better forecasts during weekdays due to more AMDAR reports

  15. Study 2: Effect of 11-13 Sept 2001 on RUC Skill • No AMDAR data due to terrorist attack • 20% loss of 3h RUC wind forecast skill at 250mb

  16. Hourly AMDAR 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

  17. Improvement in 3h over 12h wind forecast- September 2001 • RUC 250 mb • Wind forecasts • Verification • against RAOB data without AMDAR data, 3-h forecast are no better than 12-h 11-13 Sep

  18. Relative Impact Studies These require substantial computer time GSD has a research supercomputer on which we run… …multiple retrospective runs, each with a controlled change against a standard to make detailed tests Including TAMDAR evaluation, funded by the FAA

  19. Retrospective 10-day experiments We used the 2007 version of operational RUC model/assimilation software run at 20km resolution, with all observations assimilated in operational RUC except radar reflectivity Two periods: August 2007 and Nov-Dec 2006 Each 10 days long (takes ~6 days to run) 30 experiments performed on the ’06 period

  20. Retrospective 10-day experiments (2) 13 experiments were completed for the ’07 period The following data types were excluded AMDAR TAMDAR TAMDAR winds TAMDAR “rejected” aircraft Profilers NEXRAD VAD wind profiles GPS Integrated Precipitable Water (IPW) Surface observations (METAR and Mesonet)

  21. Temperature relative impact (1) This shows the impact of each data source shown for the US Great Lakes Region, during winter 2006, for Temperature forecasts below 6000 ft (800 mb). AMDAR (red) has the greatest impact of all data sources investigated for 3h and 6h forecasts in this region. Surface observations have the second greatest impact at 3h and 6h. AMDAR has relatively little impact for 12h forecasts. Observation types: Red: AMDAR, including TAMDAR Blue: Profiler Pink: NEXRAD VAD Brown: RAOB Blue: surface (inc. Mesonets) Green: GPS-IPW Graphs show the error increase when each observation type is removed.

  22. Temperature relative impact (2) This shows relative AMDAR and TAMDAR impact for 3h Temperature forecasts valid at 0 UTC during winter 2006. TAMDAR is responsible for about 40% of the total AMDAR impact below 6000 ft. in this region and during this period. As a specific example, TAMDAR alone reduces 3-h temperature errors by 0.5 K at 900 mb (3000 ft.), whereas all AMDAR data (including TAMDAR) reduces temperature errors by 1.1 K at 900 mb. More precisely: removing TAMDAR alone increases temperature errors by 0.5 K, and removing all AMDAR data increases errors by 1.1 K.

  23. Temperature relative impact (3) This shows the impact of each data source shown for the Great Lakes Region, during Summer 2007, for Temperature forecasts. AMDAR (red) has the greatest impact of all data sources investigated for 3h, 6h and 12h forecasts in this region. Surface observations have the second greatest impact. Observation types: Red: AMDAR, including TAMDAR Blue: Profiler Pink: NEXRAD VAD Brown: RAOB Blue: surface (inc. Mesonets) Green: GPS-IPW

  24. RH relative impact Observation types: Red: AMDAR, including TAMDAR Blue: Profiler Pink: NEXRAD VAD Brown: RAOB Blue: surface (inc. Mesonets) Green: GPS-IPW Relative Humidity forecast impact for winter (left) and summer (right), below 6000 ft (800 mb). AMDAR has the greatest impact of all data sources studied for 3h and 6h in the winter (left), and for 3h, 6h, and 12h in the summer (right). TAMDAR is the only AMDAR data source that provides RH information to the RUC currently. (We do not yet ingest WVSS-II data.)

  25. RH relative impact This shows relative AMDAR and TAMDAR impact for 3h Relative Humidity forecasts valid at 0 UTC during winter 2006. In this altitude range (the lowest 6000 ft.), TAMDAR is responsible for about 60% of the total AMDAR impact for RH in this region and during this period.

  26. Wind impact: 3-h wind forecasts (22 - 28 April 2005) Wind errors are reduced by 1.4 m/s at 200 mb due to the inclusion of AMDAR data

  27. Direct forecaster use of AMDAR data (1) • Forecasters have direct access to AMDAR data through • ESRL/GSDs web display (to be shown to you soon) • And through NWS workstations • (This was covered by Carl Weiss earlier) • As a radiosonde substitute when there is none nearby (Vancouver, CAN and Houston, US) • To accurately forecast the onset of severe storms (near airports with timely flights) • To forecast and monitor low-level wind shear • To monitor jet stream location • To forecast downslope windstorms • To verify/correct model guidance (Montana, US) • Fire weather support • To forecast urban air quality Many other uses detailed at http://amdar.noaa.gov

  28. Direct forecaster use of AMDAR data (2) • Mountain weather forecasts in support of rescue operations (Seattle, US) • Improved control of aircraft spacing on descent (Ft. Worth, US) • Improved forecast of jet-stream-induced turbulence • Used in aircraft accident investigations (U.S. National Transportation Safety Board) • To initialize a city-scale model used in on-shore breeze forecasting (Chicago, US)

  29. Ongoing AMDAR observation monitoring We generate daily and weekly aircraft-model differences These are used by us (and others) to monitor aircraft data quality We automatically generate daily aircraft reject lists that are used in our backup and development RUC models

  30. This view sorted by std RH Clicking on an ID number gives a time series for that aircraft. Typical output from one of our evaluation web pages

  31. Typical output from another of our evaluation web pages This shows aircraft - model vector wind differences. The aircraft by the cursor has a 43 kt wind difference with the model. Uniform differences between many aircraft and the model in a particular difference suggest model problems; otherwise, differences suggest aircraft problems.

  32. Distribution of AMDAR data from GSD • Data are quality-controlled at GSD • Binary and text data are distributed via GSD’s MADIS program • http://madis.noaa.gov/ • Used by many weather service offices • Used by many research institutions • Soon to be transferred to operations • Graphical data available over the web • http://amdar.noaa.gov/

  33. Demonstration of GSD’s real-time AMDAR display • http://amdar.noaa.gov • Real-time displays are restricted • JMA has had an account since 2001 • requested by Dr. Masanori OBAYASHI • but not used recently

  34. http://amdar.noaa.gov/java/

  35. Zooming in on Japan

  36. Can display wind barbs

  37. Zooming in on Narita

  38. Clicking on an ascent or descent gives a sounding

  39. Clicking on “Get Text” gives the sounding as text

  40. A close look at Monday Morning’s accident

  41. Ascent sounding from aircraft JP9Z4Y55took off at 2142 UTCNote strong wind direction shear in lowest levels

  42. Higher resolution sounding from aircraft HL7718 (Korean) took off at 2023 UTCNote better vertical resolution lowest levels

  43. Zooming in on the soundingNote 49 kt wind at 1400 ft (AGL)

  44. This site is used by weather services and researchers world-wide • US NWS • US FAA • Contributing US airlines • US military • State air quality forecasters • AMDAR and E-AMDAR management • Australia, Brazil, Canada, Denmark, Dubai, France, Russia, Serbia-Montenegro, So. Africa, Spain, Switzerland, others. • Korean Meteorological Organization has adapted our software to make their own displays…

  45. Summary • AMDAR data improves NWP forecasts • AMDAR data improves forecasts made by humans • AMDAR quality monitoring is performed in several locations, including GSD • GSD impact studies show AMDAR is the most important data source for many short-term, mesoscale forecasts • AMDAR data are available from GSD’s MADIS program to approved users • AMDAR data are available on the web to approved users at http://amdar.noaa.gov/ • in plan view • as soundings

  46. Thank you! William R. (Bill) Moninger NOAA/ESRL/GSD R/GSD1 325 Broadway Boulder, CO 80304 303-497-6435 Bill.Moninger@noaa.gov

  47. ‘Off-time’ assimilation • Traditionally, a model is initialized with RAOBs at one ‘on-time’ (say, 0 UTC) • and validated with RAOBs at the next ‘on-time’ 12 h later. • The RUC and other modern models can assimilate data at ‘off-times’… • And generate forecasts to be validated with raobs at the next ‘on-time’ • (Off-time data consist of much more than AMDAR, but we’ll focus on AMDAR)

  48. Each cycle gains the benefit of all ‘off-time’ observations. There is now enough AMDAR data to cycle every hour On Off Off On Off Validate withRaob AMDAR AMDAR AMDAR Raob + AMDAR 3-h 6-h 9-h 12-h 9 6 12 0 3 Time (UTC)

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