Opportunities to Leverage Aircraft-Derived Atmospheric Observation Data

Similar documents
Analysis and preprocessing of Czech Mode-S observations

Machine Learning Applied to Airspeed Prediction During Climb

Noise 101. Sources Metrics Noise Modeling Federal Statutes. O Hare Noise Compatibility Commission. June 16, 2017

Amsterdam Airport Schiphol. TP-Meteo Server. Paul de Kraker, LVNL. Progress report august 2008

VIAVI IFR6000 DME Mode Signal Generator Reply Pulse Width Output Frequency Echo Reply Output Level Reply Pulse Rise and Fall Times (all pulses)

Operational investigation of TCAS reports by pilots

Turbulence Accidents and NTSB Research Update. Nathan Doble Turbulence Impact Mitigation Workshop 3 McLean, Virginia September 6, 2018

Office of Aerospace Medicine

PDF created with FinePrint pdffactory Pro trial version

DIRECCION DE PERSONAL AERONAUTICO DPTO. DE INSTRUCCION PREGUNTAS Y OPCIONES POR TEMA

LEGEND 1989, 2002, & 2007 CNEL NOISE EXPOSURE CONTOUR COMPARISON

MANAGING FATIGUE AND SHIFT WORK. Prof Philippa Gander PhD, FRSNZ

TABLE OF CONTENTS 1.0 INTRODUCTION/INVENTORY OF EXISTING CONDITIONS A. Introduction

Portable Noise Monitoring Report August 15 - October 11, 2013 Woodland Park Elementary School. Vancouver Airport Authority

NITI MINI UNMANNED AIR VEHICLE. BULCOMERS KS Ltd

Data Collection Best Practices How to Manage Common Missteps

David Belton Geospatial World Forum May 9, 2014

SNOW/ICE ON GND(IN) WATER- EQUIV DEPTH

CONFORMANCE MONITORING APPROACHES IN CURRENT AND FUTURE AIR TRAFFIC CONTROL ENVIRONMENTS

SNOW/ICE ON GND(IN) WATER- EQUIV DEPTH

IMPROVING SAFETY: FATIGUE RISK MANAGEMENT

When communication breaks down. The case of flight AF 447

Implementation from an Airline Perspective: Challenges and Opportunities

SNOW/ICE ON GND(IN) WATER- EQUIV DEPTH

Port of Portland Hillsboro Airport Master Plan Update Planning Advisory Committee Charter

Ensemble based probabilistic forecasting of meteorology and air quality in Oslo, Norway

A Comparison of Baseline Hearing Thresholds Between Pilots and Non-Pilots and the Effects of Engine Noise

Fatigue. Minimising its Impact on Safety and Performance. Neil May Head of Human Factors

Elmendorf Aero Club May 2007 Rev 1 CESSNA 182 TEST. 2. The engine is a Continental O-470-U and rated at what horsepower?

AQRP Monthly Technical Report

FOOT-AND-MOUTH DISEASE (FMD) VIRUSES THE AIRBORNE TRANSMISSION OF. Elisabeth Schachner, Claudia Strele and Franz Rubel

Automated Probabilistic Analysis of Air Traffic Control Communications

INVESTIGATING ISSUES OF DISPLAY CONTENT VS. CLUTTER DURING AIR-TO-GROUND TARGETING MISSIONS

A model-based tool to predict the propagation of infectious disease via airports

Prevention and Control of Communicable Diseases at the Airport Hong Kong Experience

UCSF Benioff Children s Hospital Helipad Residential Sound Reduction Program. July 30, 2015

APPENDIX G NOISE TERMINOLOGY

Predictive fatigue risk management for construction. Scientifically-validated technology to predict and prevent fatiguerelated

Predictive fatigue risk management for fleets. Scientifically-validated technology to predict and prevent fatiguerelated

CANONICAL CORRELATION ANALYSIS OF DATA ON HUMAN-AUTOMATION INTERACTION

Measuring Distribution of Attention as a Part of Situational Awareness - a Different Approach

Real Time 2D Ultrasound Camera Imaging: A Higher Resolution Option to Phased Array Bob Lasser Randy Scheib Imperium, Inc.

Studying the psychology of decision making during unstable approaches and why go-around policies are ineffective.

Predictive fatigue risk management for mining. Scientifically-validated technology to predict and prevent fatiguerelated

Modeling climate change effects on air quality: Studies of allergenic tree pollen emission and transport

Cloud Condensation Nuclei Counter (CCN) Module

Appendix 44 Meadowbank and Whale Tail 2018 Noise Monitoring Program

Syar Napa Quarry Expansion EIR

Modified Cooper Harper Evaluation Tool for Unmanned Vehicle Displays

SNOW/ICE ON GND(IN) WATER- EQUIV DEPTH

GRIPEN, PROGRAM OVERVIEW AND FUTURE DEVELOPMENT.

Inspiring the Decision to Go Around

SNOW/ICE ON GND(IN) WATER- EQUIV DEPTH

SNOW/ICE ON GND(IN) WATER- EQUIV DEPTH

SNOW/ICE ON GND(IN) WATER- EQUIV DEPTH

Title:The self-reported health of U.S. flight attendants compared to the general population

Living in the Tail Pipe Pollution Dispersion and Transport David Waugh Air Quality Sciences Environment Canada Dartmouth, NS

Final Report: Aerial Surveys of Pinniped Haulout Sites in Pacific Northwest Inland Waters

Reexamination of Color Vision Standards, Part III: Analysis of the Effects of Color Vision Deficiencies in Using ATC Displays

DIRECCION DE PERSONAL AERONAUTICO DPTO. DE INSTRUCCION PREGUNTAS Y OPCIONES POR TEMA

DIRECCION DE PERSONAL AERONAUTICO DPTO. DE INSTRUCCION PREGUNTAS Y OPCIONES POR TEMA

Lecture 6. Human Factors in Engineering Design

Emergency Descent Plans, Procedures, and Context

Decision Document. Grid Point - CO Site - Rio Blanco Lake. Location - Meeker, Colorado

Perceptual Style and Air-to-Air Tracking Performance

ALOS-Indonesia POL-InSAR Experiment (AIPEX): Progress Update

Title 18 Chapter 86 Therapeutic Use of Cannabis 2013 Annual Report

Opioid Policy Steering Committee: Prescribing Intervention--Exploring a Strategy for

Risk Perception Among General Aviation Pilots

EVERY EAA CHAPER 643 MEMBER IS STRONGLY ENCOURAGED TO PARTICIPATE!

A Simple Regression Model for Estimating Actual Evapotranspiration in Various Types of Land Use, THAILAND

KINGSTON AIRPORT NOISE EXPOSURE TECHNICAL REPORT

Safe Injection Equipment

INDIVIDUAL PILOT FACTORS PREDICT DIVERSION MANAGEMENT DURING A SIMULATED CROSS- COUNTRY VFR FLIGHT

Shift Work, Sleep, Health, Safety, and Solutions. Prof Philippa Gander PhD, FRSNZ Sleep/Wake Research Centre Massey University

Cognitive Strategies and Eye Movements for Searching Hierarchical Displays

STPA-based Model of Threat and Error Management in Dual Flight Instruction

Epreuve commune à tous les candidats. Durée : 1 h

Managing Aerial Spray Drift. by Paul E. Sumner Extension Engineer

Advisory Circular. U.S. Department of Transportation Federal Aviation Administration

Laboratory Evaluation Aeroqual S-500 OZU Ozone Sensor

catapultsports.com THE MOST USED SECRET IN SPORT

A Descriptive Epidemiology of Skin Cancer in Military Aviators

Fatigue and Its Effect on Cabin Crew Member Performance

EpiDash A GI Case Study

Jitter-aware time-frequency resource allocation and packing algorithm

Tissue Strain Analytics Virtual Touch Tissue Imaging and Quantification

Job (4) Job group Job family Short description Typical CO grade

Guidelines: are they making us stupid?

The UCD community has made this article openly available. Please share how this access benefits you. Your story matters!

Preattentive Attributes in Visualization Design: Enhancing Combat Identification. Scott H. Summers Raytheon Solipsys Corporation Fulton, Maryland


BBB_company Brief_2018

The impact of the Wind guess, the Tracer size and the Temporal gap between images in the extraction of AMVs

Before the Department of Transportation, Office of the Secretary Washington, D.C

NIR Red Models for Estimation of Chlorophyll a Concentration Application to Aircraft and Satellite Data

E11(R1) Addendum to E11: Clinical Investigation of Medicinal Products in the Pediatric Population Step4

TITLE: Responsiveness of a Neuromuscular Recovery Scale for Spinal Cord Injury: Inpatient and Outpatient Rehabilitation

Embracing Complexity in System of Systems Analysis and Architecting

PREDICTING DISEASES. SAVING LIVES. ARTIFICIAL INTELLIGENCE IN MEDICAL EPIDEMIOLOGY

Transcription:

Opportunities to Leverage Aircraft-Derived Atmospheric Observation Data Michael McPartland 5 December 2017 Sponsor: Gary Pokodner, ANG-C6 DISTRIBUTION STATEMENT A: Approved for public release; distribution is unlimited.

Motivation Atmospheric observations are critical to aviation activities Sample Observation Systems Ground sensors (e.g., ASOS) Strategic Activities Sample applications Weather Models Flight Planning Traffic Management Tactical Activities Sample applications Balloon sensors (e.g., radiosondes) Aircraft-derived observations Real-time Tools ATC Decision- Making Pilot Decision- Making AdOs via Mode S EHS - 2 ASOS = Airport Surface Observation System

Aircraft-Derived Observation Usage in Forecasts Atmospheric observations used to best represent initial conditions in the forecast volume above the surface More important to forecast accuracy Winds (m/s) 0.5 0.4 0.3 0.2 0.1 0 Average reduction in wind RMS vector error From [1] -0.1 A B C D E F G H J Observation source A radiosonde obs B aircraft obs C profiler obs Exp. Profiler control D radar reflectivity E VAD wind F GPS-Met G GOES satellite obs H surface obs including METAR cloud J AMVs Exp. cloud drift Aircraft-derived weather observations are the most important input in wind and temperature forecast accuracy AdOs via Mode S EHS - 3 [1] James, E., and S. Benjamin, 2017: Observation system experiments with the hourly-updating Rapid Refresh model using GSI hybrid ensemble/variational data assimilation. Mon. Wea. Rev. doi:10.1175/mwr-d-16-0398.1, in press.

Aircraft-Derived Observations Aircraft measurements can be used for atmospheric observations Wind vector Meteorological Data Collection & Reporting System (MDCRS) is current airborne source Air referenced vector ADS-B Weather could enable greater access to aircraft-based observations, but not for foreseeable future Mode S Enhanced Surveillance (EHS) widely available now and can act as near-term surrogate for ADS-B Weather AdOs via Mode S EHS - 4 ADS-B = Automatic Dependent Surveillance Broadcast

Observation System Comparisons Observation Source Horizontal Coverage Vertical Range Update Period Latency Comment ASOS 900 sites many at airports Surface only 20 min/ 1 min <1 min Used primarily for airport operations Radiosondes 69 sites in CONUS Ground to >100 kft 12 hr < 2 hr Used primarily for forecast input data Aircraft-Derived Observations MDCRS Mode S EHS ADS-B Weather (future) Limited fleet coverage (<20% current US fleet) Growing fleet coverage (>50% current US fleet) None now, could be meaningful % in future Ground to typical cruise altitudes Ground to typical cruise altitudes Ground to typical cruise altitudes 6 sec ground, 1 min ascent/ descent 7 min cruise 7-60+ min, Average is 17 min 4.8-12 sec Seconds ~10 sec Seconds Used primarily for forecast input data Useful for forecast & real-time operations Specifications not planned until at least 2019 Opportunity to assess enhanced aircraft-derived observations via Mode S EHS to guide development of future applications and ADS-B Weather specifications AdOs via Mode S EHS - 5 ASOS = Airport Surface Observation System MDCRS = Meteorological Data Collection & Reporting System Mode S EHS = Mode S Enhanced Surveillance

Outline Background Current Aircraft-Derived Observation Systems Comparison of MDCRS & Mode S EHS Aircraft-Derived Observation Data Future Opportunities to Leverage Aircraft- Derived Atmospheric Observation Data AdOs via Mode S EHS - 6

Current System Characterization MDCRS: North American, 11 airlines reporting E-AMDAR: Europe, 14 airlines reporting Inertial reference unit Clock Air data computer ACARS GPS Typical Data Winds Temp RH% EDR Meteorological Community Ground stations ARINC Airlines AdOs via Mode S EHS - 7 E-AMDAR = European Aircraft Meteorological Data Relay ACARS = Aircraft Communications Addressing and Reporting System RH = Relative Humidity EDR = Eddy Dissipation Rate

MDCRS Spatial/Temporal Coverage Current sampling across country is varied and limited to a small percentage of commercial flights/routes 0 50-10 45 Latitude (degrees) 40 35 30-20 -30-40 Age at 3 PM EST (minutes) -50 25-120 -110-100 -90-80 -70 Longitude (degrees) -60 AdOs via Mode S EHS - 8

MDCRS Latency MDCRS data are delayed due to batching before transmitting Average observation delay = 17 minutes 5 10 4 MDCRS Observation Delay within CONUS Nov 1, 2017, 24-hrs (n=521648) 4.5 (x10000) 4 3.5 3 2.5 2 1.5 1 0.5 0 0 10 20 30 40 50 60 Delivery to Ground Station Delay (mins) Not appropriate for near real-time applications AdOs via Mode S EHS - 9

Sample MDCRS Coverage Around Boston At BOS, about 5% of scheduled flights report MDCRS data Nov 1, 2017, 2:00-3:00 PM EST Age at 3 PM EST (minutes) 0 120 observations 45 43-10 40 35 Latitude (degrees) 42-20 -30-40 30 25 20 15 10 Pressure Altitude (kft) -50 5-72 -71-70 Longitude (degrees) -60-72 -71-70 Longitude (degrees) 0 Limited MDCRS data available for forecasting and real-time applications: opportunity to leverage Mode S EHS observations AdOs via Mode S EHS - 10

Outline Background Current Aircraft-Derived Observation Systems Comparison of MDCRS & Mode S EHS Aircraft-Derived Observation Data Future Opportunities to Leverage Aircraft- Derived Atmospheric Observation Data AdOs via Mode S EHS - 11

Mode S EHS Based Observation System Aircraft collects data from its own sources: GPS and on-board sensors Mode S EHS enables interrogation of specific aircraft registers to extract or derive aircraft winds and temperature Register Content Comment 4.8 or 12 sec update rate Air referenced vector Wind vector 0x50 0x60 0x44 Ground speed True air speed Roll angle Track angle Mag heading Mach Altitude rate Wind speed/dir Temperature Used to estimate Wind speed Wind direction Temperature Only 5% of EHS A/C populate AdOs via Mode S EHS - 12

Lincoln Mode S EHS Aircraft-Derived Observation Evaluation FAA Mode S radars do not currently interrogate relevant EHS registers* Could do so with simple adaptation modifications Lincoln MODSEF has been adapted to interrogate aircraft within range (60 nmi radius) Data streaming started March 8, 2017 MIT/LL Mode S Experimental Facility (MODSEF) Engineering display 60 NM range 4.8s update rate Example EHS-derived wind vectors AdOs via Mode S EHS - 13 *Exception: Fremont Valley BI6 radar at Edwards AFB, which feeds High Desert TRACON (E10), conducts EHS interrogations

Comparison of MDCRS & Mode S EHS Observations Around Boston MDCRS 120 observations Nov 1, 2017 2:00-3:00 PM EST Age at 3 PM EST (minutes) 0 Mode S EHS 9809 observations 43-10 43 Latitude (degrees) 42-20 -30-40 42 Latitude (degrees) -50-60 -72-71 -70-72 -71-70 Longitude (degrees) Longitude (degrees) >80x increase in atmospheric observations with Mode S EHS vs MDCRS in this case AdOs via Mode S EHS - 14

Comparison of MDCRS & Mode S EHS Observations Around Boston Nov 1, 2017 Observations across 24 hours Pressure Altitude (kft) 45 40 35 30 25 20 15 10 5 Mode S EHS (n=200986) MDCRS (n=6465) Number of Observations ( 10,000) Number of Observations ( 10,000) (x10000) 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 Local Hour of Day Mode S EHS (n=200986) MDCRS (n=6465) 0 0 0 0.5 1 1.5 2 0 5 10 15 20 Significantly increased time and altitude coverage with Mode S EHS AdOs via Mode S EHS - 15

MDCRS & MODSEF Observations Around Boston Magenta points in figures are RAP model grid points initialized from observation data Age at 3 PM EST (minutes) MDCRS Mode S EHS 0-10 -20-30 -40-50 -60 Opportunity for forecast models to assimilate higher quantities of more recent data AdOs via Mode S EHS - 16 RAP = Rapid Refresh forecast model

Outline Background Current Aircraft-Derived Observation Systems Comparison of MDCRS & Mode S EHS Aircraft-Derived Observation Data Future Opportunities to Leverage Aircraft- Derived Atmospheric Observation Data Enhancing Numerical Weather Prediction Models Enhancing Real-time Operations AdOs via Mode S EHS - 17

Enhancing Numerical Weather Prediction Performance Quantify potential benefits of enhanced Aircraft-derived Observations (AdO) on forecast model performance Assess impacts of varying amount of data, assimilation period, etc. for aviation and other applications Atmospheric Observations Data Assimilation Numerical Weather Model Performance Assessment AdO Analytical Testbed Parameters: AdO equipage level AdO update rate Data quality Forecast update cycle Aircraft-derived Observation Requirements AdOs via Mode S EHS - 18

Enhancing Real-time Operations New procedure opportunities Enhanced wake vortex mitigation procedures, e.g., Heathrow Time-Based Separation procedures Improved RTA and IM procedures Better compression handling e.g., London-Heathrow Time-Based Separation procedure relies on real-time access to Mode S EHS winds Controller benefits Reduced radio calls Increased situational awareness AdOs via Mode S EHS - 19 RTA = Required Time of Arrival IM = Interval Management

Recommended Next Steps FAA Architectural considerations Conduct analysis on means to collect and disseminate aircraft-derived observations from Mode S EHS to end users 1030/1090 MHz spectrum analysis of increased interrogations Leverage knowledge gained through near-term Mode S EHS assessment to inform ADS-B weather requirements Operational considerations Identify and develop operational procedures to take advantage of real-time aircraft-derived observations Identify opportunities enabled by emerging UAS operations NOAA/FAA Perform benefits analysis on forecast improvements if large quantities of aircraft-derived observations were available AdOs via Mode S EHS - 20

Summary Leveraging aircraft-derived observations holds significant promise for improving weather forecasting and real-time operations Mode S EHS is a currently-available technology enabling immediate access to aircraft-derived observations Inform standards and opportunities for ADS-B Weather Potential to enhance forecasting performance Potential to enhance real-time operations AdOs via Mode S EHS - 21

Legal Notices Distribution Statement: Approved for public release; distribution is unlimited. This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8721-05-C-0002 and/or FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration. 2017 Massachusetts Institute of Technology. Delivered to the US Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work. AdOs via Mode S EHS - 22