Meteorological threat prevention systemfor airport near-field zones

DSc., prof. Mikhail KanevskiyCEO

Ekaterina LemishchenkoDirector for Foreign Economic Affairs

REQUIREMENTS TO THE NATIONAL AIR NAVIGATION SYSTEM (ANS)

ПРЯМАЯ МЕТЕОЗАВИСИМОСТЬ

ПРЯМАЯ МЕТЕОЗАВИСИМОСТЬ

АПОСРЕДОВАННАЯ МЕТЕОЗАВИСИМОСТЬ

• Acceptable flight safety level• Minimizing operators’ costs in

carrying out flights• Attractiveness of the ANS

Safety

Flight safety

DIRECT WEATHER DEPENDENCE

National security

ANS economics

National economy and environment

INDIRECT WEATHER DEPENDENCE

Optimal cost of air navigation service

Increased export of transport services

Positive effect of the ANS on other sectors

Minimized environmental harm

ANS economics

Impact of quality of service on operator

economics

Access conditions

Flight safety

National security

6

ANS management system

Management system effectivenessFlight path efficiency

Flexibility

Interoperability

Throughput

Predictability (delays)

Operator economics

DIRECT WEATHER DEPENDENCE

Access conditions

Equitable access to airspace

ПОРТФЕЛЬ СТРАТЕГИЧЕСКИХ ИНИЦИАТИВ

Development areasof the national ANS

Objectives

Ensuring optimal flight paths in terms of fuel use and time by adopting a PBN-based Airspace Concept

Optimizing taxi, increasing throughput and airfield safety

Ensuring the necessary throughput and optimal actual flight paths by introducing ATFM

Introduce throughput management and air traffic flow management (ATFM) measures

Introduce ATFM procedures based on 4D timespace trajectories

Create PBN-based routesInstitute PBN-based departure, arrival and approach patternsReduce the negative effect of restricted flight areasCreate conditions for unmanned aircraft operations

Introduce collaborative decision-making (CDM) proceduresDevelop and integrate takeoff and landing management, surface movement guidance and control systemsUse shorter separation intervals at takeoff/landing and offset takeoff/landing points

Introduce system-wide information management (SWIM)Create a network and develop downlink infrastructureDevelop services (aeronautical information, meteorological services, flight information, surveillance)

Efficient flight paths

Optimal throughput and flexibility

Interoperable systems and data

Efficient airport operations

Ensuring digital links between all ANS participants and quality data

Radar

ЛИДАР

Aerodrome sensors

SWIM

FIS serverAirfield condition

Information sharing ATC/AOC/AD applications

Sources of weather information

Optimized ramp handling

Safety Nets:warnings about wind shear and wake vortex

Hazardous phenomena in the

glide path

Detailed wind and temperature

forecast

Forecast and actual weather

phenomena along the airway

Weather around the aerodrome

D-MAN/A-MAN

TBO – trajectory-based operations

CDM – collaborative decision-making

Meteorological products

AMDAR

INNOVATIVE TECHNOLOGIES FOR AIR NAVIGATION WEATHER SUPPORT

Radar

LIDAR

Aerodrome sensors

SWIM

FIS serverAirfield condition

Information sharing

ATC/AOC/AD applications

Sources of weather information Optimized ramp

handling

Safety Nets:warnings about wind shear and wake vortex

Hazardous phenomena in the

glide path

Detailed wind and temperature

forecast

Forecast and actual weather

phenomena along the airway

Weather around the aerodrome

D-MAN/A-MAN

TBO – trajectory-based operations

CDM – collaborative decision-making

Meteorological products

AMDAR

INNOVATIVE TECHNOLOGIES FOR AIR NAVIGATION WEATHER SUPPORT

TBS – time-based separation

METEOROLOGICAL NEAR-FIELD RADAR STATION MONOCLE

MONOCLE ensures collection, processing and timely provision of radar weather information to

meteorological services and civil (state) aviation units and/or any other interested users.

Such information includes:

fields of cloudiness

precipitation and related weather hazards

rainfall intensity

wind conditions in the detected cloudiness

turbulence, vertical and horizontal wind shear in the given scanning sector

CHARACTERISTIC UNIT VALUE

Detection range km Up to 100

Hazardous

meteorological

phenomena

km

Up to 100;

Up to 150 km for weather

phenomena from showers

and higher

Wind shear areas km Up to 50

Dangerous turbulence

areaskm Up to 50

Transmitter power W Not less than 100

Emitted signals

frequencyMHz 9330–9375

SURVEILLANCE COVERAGE

in azimuth ° From 0 to 360

angularly ° From −1 to +90

Type of antenna Slot antenna array

Width of antenna

directional pattern Not more than 3.15° × 3.15°

Signal type Coherent, pulse

Dimensions mm 1100 × 1200 × 1200

Weight kg Not more than 65

METEOROLOGICAL NEAR-FIELD RADAR STATION MONOCLE

METEOROLOGICAL NEAR-FIELD RADAR STATION MONOCLE

IAC certification in 2018

Measurement height, m 1000

Profiling step, m

25 m at 0–100 m,

50 m at 100–1000 m

Measurement interval 5 min

Working frequency, GHz 56.6

Angle 2.5°

Measurement accuracy, SD 0.2–1.2°

Altitude detection accuracy 25%

Mass 20 kg

Power consumption

=12 V, max 100 W,

average 60 W

~220/110 V, 1A/2A,

50–60 Hz

Working temperatures −40 °C ... +50 °C

Calibration Self-calibrated

Specifications

MTP-5 temperature profiler is designed to measure temperature profiles up to 1000 m. It ensures statistically valid data representation of temperature profile up to 2000 m.

Measurements are carried out through the elevation sensing of atmospheric own thermal radiation at a fixed frequency.

MTP-5 is a maintenance-free, all-weather instrument.

This is an autonomous, all-weather measurement system that does not require radiosonde data for temperature profile restoration.

MTP-5 METEOROLOGICAL TEMPERATURE PROFILER

Zurich. Swiss Tomsk. RussiaBolzano.Italy.

JFK. USAPert. Australia

Rome. Italy

MAKS2013

Pulkovo, Russia

Parma, Italy

FORECASTING OF ICY RAIN OCCURRENCEAND DETECTION OF POSSIBLE ICING ZONES

FORECASTING OF FOG OCCURRENCE AND DISPERSION

1. FORECASTING (UP TO 4 HOURS) OF FOG OCCURRENCE AND DISPERSION

2. FORECASTING (UP TO 24 HOURS) OF ICY RAIN OCCURRENCE

3. FORECASTING (UP TO 24 HOURS) OF POSSIBLE ICING ZONES

MTP-5 METEOROLOGICAL TEMPERATURE PROFILER

MTP-5 with meteorologist workstation

Pulkovo

Trial operations

Carried out on the basis of:Decision of the meeting between Aviamettelekom and IANS on starting trial operations of the MTP-5 meteorological temperature profiler with a meteorologist workstation (Minutes dated 21 December 2016)

Intended use

WINDEX-5000 Doppler Pulse LIDAR is a reliable and high-precision tool for

automatic continuous remote monitoring of wind field parameters in the

surface layer of the atmosphere, detection of dangerous wind phenomena,

such as wind shear, areas of intense turbulence, wake vortex behind aircraft;

data transmission and automatic notification of users on hazardous

phenomena.

DOPPLER PULSE WIND LIDAR

15

2005–2007 2010–2013 2014–2015 2015–2016

Prototype WINDEX-300 PLV-300M PLV-300M2PLV-300-T WINDEX-5000

2017–2019

Development started

Certified by Rosstandartand the IAC

Trial operations at Pulkovo

Supplied to Sochi and Baikonur

Trial operations in Sochi (stage 1)

Trial operations in Sochi (stage 2)

Supplied to Vladivostok

Supplied to Korea, Belarus, Singapore

Certified by Rosstandart (Russia) and Gosstandart(Belarus)

Certified by the IAC

LIDAR EVOLUTION

WINDEX-5000 APPLICATION FOR ECOLOGICAL

MONITORING OF THE URBAN ENVIRONMENT

Supply of a set of three WINDEX-5000to Seoul, Republic of Korea (2017)

2018 – supply of low-level wind shear alerting systemto Belarus

IAC certification 2018–2019

Meteorologist workstation

WINDEX-5000

165.0 m

15.0 m

Runway No. 2 center

WINDEX-5000

Controltower

WINDEX-5000 layout relative to runway No. 2

WIND SHEAR IN MINSK NATIONAL AIRPORT

Scanning area

Wind shear

Microburst

WAKE VORTEX SEPARATION MINIMA MANAGEMENT

Внедрение базовых средств управления взлетами и посад-

ками (AMAN, DMAN)

Внедрение процедур совместного принятия решений (CDM)

С4

С3

С4

С2

0 мин

-2 мин

+3 мин

3 мин

С3

Переход к сокращенным интервалам эшелонирования при взлете/ посадке и смещенным точкам взлета/ посадки

С3

Внедрение систем управления наземным движением и контроля

за ним (A-SMGCS 1,2)

C1

Внедрение систем управления движением

по аэродрому (A-SMGCS 3,4)

C5 С1

С5

Динамическое изменение интервалов эшелонирования при взлете /

посадке за счет учета спутного следа

C6

Интегрированная система управления взлетами, посадками и операциями в

аэропорту (AMAN / DMAN / SMAN)

С6

С2

С1

С5

C7С4

С2

INITIATIVES ON AIRPORT OPERATIONAL EFFICIENCY

Weather support for operations: Wake, AMAN, DMAN, CDM

−2 min

+3 min

0 min

3 min

Use shorter separation intervals at takeoff/landing and offset

takeoff/landing points

Dynamic change of separation intervals at takeoff/landing using wake data

Introduce surface traffic control systems(A-SMGCS 3, 4)

Introduce advanced surface movement guidance and control systems

(A-SMGCS 1, 2)

Introduce basic arrival/departure management systems (AMAN,

DAMAN)

Integrated arrival, departure and surface operations management systems (AMAN,

DAMAN, SMAN)

Introduce collaborative decision-making procedures (CDM)

22

FIS server

AMDAR

BUFR

GF DATA

Wx

GF DATA – data from global or regional weather forecast centres, e.g. GFS, ICON, COSMO…

BUFR – location data from the Doppler locator on wind at flight levels transmitted in BUFR codes

AMDAR – flight wind measurements at flight levels transmitted using AMDAR or MeteoSquitter (S mode)

In-flight information for the crew (providing data to the FMS)about the wind situation (WIND UPLINK) at flight levels and around the airfield

ADC-BADS-C

FIS server

AMDAR BUFRGF DATA

Full information

FIS server

AMDAR GF DATA

Partial information

Blending GF DATAand local data from AMDAR

observations

Integrated processing ofobservation data with theuse of forecasts

FIS server

GF DATA

Incomplete information

Providing updates for forecast data

WIND DATA DEGRADATION LEVELS IN THE FLIGHT SUPPORT SYSTEM (WIND UPLINK)

23

Wind Uplink (VDLm2/SatCOM)

In-flight wind data update

(Boeing/Airbus)

Messages from the aircraft

communications addressing

and reporting system (ACARS)

Flight management system

(FMS)

FMD

AN N777BO

- PWI/DD290255090.200270080.

100285045.0100901876C4

Proposed solutions

FMS

Notes:1 – based on the analysis of 60 flight trajectories:

Moscow–St. Petersburg, Moscow–Yekaterinburg, Moscow–Samara

2 – in top 34 US and EU airports by passenger flow (~60% pass. flow), within 100 nautical miles from the landing strip

3 – capacity management and air traffic flow management4 – in top 10 US, EU and Russian airports by passenger flow

ASSESSING EFFECIENCY OF WIND UPLINK IMPLEMENTATION

Horizontal inefficiency in cruise Horizontal inefficiency in descent

Vertical inefficiency in descent

Mean deviation of actual trajectory from great circle route, %

Average extra flight time within 185 km of destination airport2, min

Average length of level flight within 185 km of destination airport4, min

Extra flight time, min

flight time at peakhours

flight time at non-peak

hours

Real trajectory (R)Optimal trajectory (O)

R PN

Destination airport

US

EU

Russia (SVO)

RUB ~5.5 billion

RUB ~5 billionRUB ~6 billion

Cruise

EU

US

Russia (SVO)

EU19

US

EU

Russia

Distance, km

Inefficient parts

Alt

itu

de

Source

Destination

Deviation from great circle

route, %

11International Aero

Navigation Systems Concern, JSC

Thank you for your attention

Address 15, 4-5, Dolgorukovskaya st., Moscow, 127006 Russia

Tel./fax +7 (495) 280 16 83E-mail info@ians.aeroWebsite www.ians.aero