Terminal Domain Decision Support. Jim Evans MIT Lincoln Lab. Tom Davis

Transcription

1 Terminal Domain Decision Support Jim Evans MIT Lincoln Lab on behalf of Tom Davis NASA Ames

2 Outline Review of Center-TRACON Advisory System (CTAS) Winds information to support trajectory calculations Convective storm information Current and expected positions of weather Pilot preferences on weather penetration Weather information to support terminal traffic flow management Winds Ceiling/visibility Convective activity Summary

3 CTAS

4

5 CTAS Concept E/DA advises conflict-free, fuel efficient descent to meet schedule Center Airspace TMA plans sequence and landing time FAST adjusts schedule and advises for accurate spacing on final approach TRACON Airspace

6 TMA: Graphical Displays

7 FAST Controller Display Passive FAST: Sequence & Runway Advisory - In operation at DFW Active FAST: Speed & Turn Advisory - Research focus Controller advisory instruction United 456, reduce speed to 190 knots, you are #2 for runway 18R Runway Advisory UAL456 18R B X x 260 Turn Advisory AAL123 17L MD80 3 Controller advisory instruction American 123, turn left to 260 degrees, you are #3 for runway 17L Sequence Advisory Speed Advisory 18R 17L Sequence Advisory

8 CTAS Software Architecture

9 Winds information for CTAS Winds information is needed to accomplish trajectory synthesis Required accuracy depends on: services provided by CTAS (e.g., active versus passive FAST) airport geometry and need for maximizing runway capacity of each runway An operationally useful CTAS capability at DFW and Denver has been provided by RUC Need for refinements (such as ITWS or plane augmented RUC) may hinge on cost/benefit considerations

10 Passive vs Active FAST Impact on Required Wind Accuracy Passive FAST is mainly sensitive to accumulated time of flight (TOF) error from arrival gates to key merge points Controllers may compensate for TOF errors Active FAST is a closed loop control system with delays (e.g., controller and pilot response times) in feedback loop Can help land more planes per hour in adverse winds Frequency response of control system must be considered in assessing wind requirements Conflict probe function Frequency of controller interactions and merging/sequencing performance

11 4 Merging and Sequencing

12 Studies to Date on Wind Accuracy NASA flight test results MIT/LL and NOAA/FSL wind error studies

13 Study data Domain 1300 km x 1300 km centered on Denver Approximately one year 1.2 million aircraft truth wind reports MDCRS distribution on May 1, reports, after United increased reporting frequency.

14 Simple error statistics 5.26 m/s vs 4.67 m/s RMS vector error for RUC-1 and RUC-2 FSL, over all 0-6 hour forecasts, corrected for MDCRS errors, 1 August August m/s vs 4.51 m/s RMS vector error for RUC-1 and AW-1 MIT, over RUC hour forecasts, corrected for MDCRS errors, 1 August, 96-1 August 97 Similar improvement of RUC-2 was demonstrated on a subset of data By these standard measures, each wind prediction system is accurate enough, on average

15 FSL Large error statistics Percent of Measured Errors > 10 m/s R U C-2 (1-2 h) R U C-1 (3-5 h) Aug S ep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug All Month 11% of RUC-1 vector errors > 10 m/s 4% of AW-1 vector errors > 10 m/s AW-1 RUC-1 % RMS vector error > 10 m/s Vector error in m/s MIT/LL

16 Large error statistics Number of hours with hourly Nth percentile vector errors above given thresholds. Results are for 7023 hours. Variable >7m/s >10m/s >15m/s RUC-1 25 th percentile AW-1 25 th percentile RUC-1 50 th percentile AW-1 50 th percentile RUC-1 75 th percentile MIT/LL AW-1 75 th percentile

17 CTAS ETAs with ITWS vs RUC Howdy Meter Fix to Rwy 17C Threshold winds 870 Time To Fly (sec) ITWS 2 km ITWS 10 km RUC 80 km No WX Time (Z)

18 Near term research on terminal winds requirements Interface ITWS terminal winds to CTAS Higher update rates and resolution to handle rapidly evolving winds and extreme shears Need to efficiently access wind data Complete studies of TRACON time of flight sensitivity to winds Assess afast ability to achieve higher landing rates per runway in highly adverse terminal wind conditions

19 Explicit Consideration of Convective Weather in CTAS Current CTAS logic permits user to close gates and runways Handling traffic as handled manually today requires ability to change routes on the fly Need to forecast where the storms will be versus time (=forecast problem discussed yesterday) what the pilots will do given the storm characteristics which routes will not be available as a function of time Need to create alternative routes Humans Automatic algorithms

20 Terminal Hazardous Weather Avoidance Study Objective was to develop a pilot deviation likelihood for determining route usability 60 hrs of DFW terminal area thunderstorm & flight track data from nine different days 1952 aircraft / storm cell encounters (642 deviations and 1310 penetrations) Results described in Lincoln Project Report No. NASA/A- 2, An Assessment of Thunderstorm Penetrations and Deviations by Commercial Aircraft on the Terminal Area, by D.A. Rhoda and M.L. Pawlak (available on WWW)

21 Weather Factors ASR-9 precipitation Lightning Microburst, gust front Hail, mesocyclone, tornado Doppler radar reflectivity, VIL, center of mass Echo top, bottom, and thickness

22 Example of a Storm Deviation

23 Example of a Storm Penetration

24 Aircraft Encounters With Thunderstorms in the Terminal Area Number of Encounters Range (km) Level 5 Level 4 Level 3 Level 2 Level 1 Contrary to conventional wisdom, aircraft do penetrate level 3+ weather, when close to the airport (200 cases observed at DFW) Some weather penetrations were followed by diversion to another airport (even though other aircraft landed), suggesting regret. (from Lincoln Project Report No. NASA/A-2)

25 Storm Flight Deviation Results Initial classifier was fairly successful in characterizing probability of deviation for storms in outer regions of TRACON Observed pilot behavior near runways was at variance with what was claimed to be practice in storm avoidance Additional studies are needed to better understand applicability to NAS as a whole Other airports and convective weather Plane weather radar equipage Need to also look at en route SFD altitude of plane versus storm altitude profile

26 Algorithms to Generate Terminal Routes Around Convective Weather Identify routes which will be impacted Lincoln interface to CTAS GUI/scheduling algorithm which identifies when various routes will be impacted (does not reschedule a/c) Create new routes Seagull (proprietary) algorithm using Snell s Law of Refraction (developed under Ames SBIR) Eby algorithm

27 Traffic Flow Management Interface Tactical traffic flow management coupling to terminal automation Automation tools (e.g., pfast) can help compensate for changes in runway capacity Precise trajectory computations of CTAS improve TFM Enhanced CTAS can help manage short term overloads due to imprecise TFM Key factors in changing terminal capacity Ceiling/visibility Wind changes Convective activity

28 Traffic Flow Management Interface Tactical traffic flow management coupling to terminal automation Automation tools (e.g., pfast) can help compensate for changes in runway capacity Precise trajectory computations of CTAS improve TFM Enhanced CTAS can help manage short term overloads due to imprecise TFM Key factors in changing terminal capacity Ceiling/visibility Wind changes Convective activity

29 C&V Impacts on Major Delay Airports PIT BOS DTW JFK LGA ORD EWR ATL PHL PRECIP RADIATION ADVECTION SNOW SEA CLT IAH DCA SFO MSP STL MEM DFW LAX AVERAGE ANNUAL AIRPORT IMPACTS

30 Approach to SFO POINT BONITA 10 km SAN FRANCISCO BEACH SAN FRANCISCO BAY SAN PEDRO POINT SFO SAN MATEO BRIDGE PILLAR POINT Alviso

31 Stratus Cloud Burnoff Automated Forecast Guidance M.I.T. Lincoln Laboratory Sattellite Summary 16:15 16:32 SURFACE OBSERVATIONS SQL Insolation Time Site T Td Wind Cloud Visib Pressure 1625 SFO OVC SMB OVC CONSENSUS FORECAST CLEARING AT 17:40 Time to Burnoff 1:08 SFO SODAR Inversion Base COMPONENT FORECASTS RunTime FCST conf weight 15:55 COBEL 18: :15 Local SFM 17: :00 Regional SFM 17: :15 Satellite SFM 16: ADDITIONAL DATA PAGES: Surface Observation Time Series Solar Radiation Plots SODAR Plots Satellite Products

32 Summary Achieving accurate, reliable trajectory synthesis has been principal CTAS concern to date RUC winds will suffice for most conditions Studies are underway to understand difficult conditions including using ITWS winds Operation during convective activity requires: predicting weather and pilot preferences algorithms for alternative routes considerable research needed in this area Coupling of automation to tactical flow management is just beginning Need to consider predicting winds and C & V How to use probabilistic forecasts