1 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 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 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 minutes RUC 13-km grid covers US runs hourly operational for 15+ years (in various forms) Rapid Refresh domain Current RUC-13 CONUS domain HRRR domain

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

8 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 Purpose for the RUC/ Rapid Refresh “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 Time (UTC) Analysis Fields 3DVAR Obs 3DVAR Obs Back- ground Fields

10 RUC Hourly Assimilation Cycle 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 PBL – profiler/RASS ~25 Aircraft (V,temp) TAMDAR (V,T,RH) Surface/METAR Buoy/ship GOES cloud winds 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 Observations assimilated Time (UTC) 1-hr fcst Background Fields Analysis Fields 1-hr fcst RUC 3dvar Obs 1-hr fcst 3dvar Obs

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: 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 m/s / ~5.0 m/s = 7% better forecasts during weekdays due to more AMDAR reports

15 Study 2: Effect of 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 volume 2-15 Sept 01 (starting 00z 2 Sept) 2-8 Sept 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 Sep without AMDAR data, 3-h forecast are no better than 12-h

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. Graphs show the error increase when each observation type is removed. Observation types: Red: AMDAR, including TAMDAR Blue: Profiler Pink: NEXRAD VAD Brown: RAOB Blue: surface (inc. Mesonets) Green: GPS-IPW

22 Temperature relative impact (2) This shows relative AMDAR and TAMDAR impact for 3h Temperature forecasts valid at 0 UTC during winter 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 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.) Observation types: Red: AMDAR, including TAMDAR Blue: Profiler Pink: NEXRAD VAD Brown: RAOB Blue: surface (inc. Mesonets) Green: GPS-IPW

25 RH relative impact This shows relative AMDAR and TAMDAR impact for 3h Relative Humidity forecasts valid at 0 UTC during winter 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 ( 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) 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 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)

28 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) Direct forecaster use of AMDAR data (2)

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

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 – –Used by many weather service offices –Used by many research institutions –Soon to be transferred to operations Graphical data available over the web –

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

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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 JP9Z4Y55 took off at 2142 UTC Note strong wind direction shear in lowest levels

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

43 Zooming in on the sounding Note 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…

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47 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 –in plan view –as soundings

48 Thank you! William R. (Bill) Moninger NOAA/ESRL/GSD R/GSD1 325 Broadway Boulder, CO

49 ‘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)

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

51 RUC Wind forecast Accuracy - Sept-Dec 2002 Verification against RAOB 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* Analysis ~ ‘truth’ * this is an important accomplishment -- need to minimize model disturbances due to imperfect data (we use “DDFI”, next slide).

52 Forward integration, full physics RUC Diabatic Digital Filter Initialization (DDFI) -30 min -15 min Init +15 min RUC model forecast Backwards integration, no physics Obtain initial fields with improved balance Initial DFI in RUC model at NCEP adiabatic DFI Diabatic DFI introduced at NCEP

53 Forward integration, full physics -30 min -15 min Init +15 min RUC model forecast Backwards integration, no physics Obtain initial fields with improved balance Initial DFI in RUC model at NCEP adiabatic DFI Diabatic DFI introduced at NCEP Calculate digital-filter- weighted mean of 3-d fields from each time step over DFI period RUC Diabatic Digital Filter Initialization (DDFI)