Weather INformation Fusion and Correlation for weather and traffic situational awareness

Executive Summary:

The WINFC (Weather INformation Fusion and Correlation for weather and traffic situational awareness) project was aimed at the development of an adaptive data processing module that, once on board, collects and processes information incoming from different weather and traffic data providers. The module produces an awareness map by integrating weather and traffic changes data collected during the flight, and an updated weather map. The awareness map is a risk map available to the pilot for a prompt detection of hazard conditions, no matters whether they depend on weather or traffic changes.

The objectives of the WINFC project have been the following:

• The analysis of the information sources about un-forecasted events like weather change and traffic congestion in present and future ATM environment,

• The study of innovative on board measurements methods to provide real-time weather information,

• The development of a SW tool for simulating the information data flow coming from different sources in case of unexpected weather and traffic scenarios,

• The development of a data fusion tool for the adaptive integration and correlation of the information coming from heterogeneous information sources,

• The definition of an efficient and effective unbiased threat/importance factor(s) useful to the pilot and as input parameters to Q-AI trajectory and mission optimization algorithms,

• The validation of the data fusion tool by a test activity based on the data flow SW tool applied to selected realistic scenarios.

Project Context and Objectives:

2.1 Technical background

A Q-AI trajectory optimisation software has two types of inputs: meteorological data and a risk map, the first used to evaluate aircraft performances and emissions, the second to integrate weather related hazards and traffic constraints. Information on weather conditions can be obtained from multiple data sources and can be pre-loaded off-board, or collected during the flight from a ground base station, from other aircrafts or by the on-board instrumentations that include the weather radar processed by proper advanced weather radar algorithms. The data sources can include:

• Weather products provided by public or private institutions (NOAA, ECMWF, NHMS, etc..) for aviation: forecast charts (WINTEM, SIGWX), model outputs delivered every one to six hours, observation data including airport weather stations (METAR), ground weather radar, satellite observation, lightning strokes, specific risks assessments (icing, turbulence, convection, volcanic ash), PIREPS, etc.;

• On-board weather radar reflectivity data;

• Classification algorithm from on-board advanced weather radar, options for dual polarization upgrades the current single-polarization systems are considered in WINFC;

• On-board sensors (P, T, Wind, Humidity, Ice detection, etc.);

• Traffic information (Primary surveillance data, ADS-C, ADS-B, multilateration);

• Aeronautical Information Services (e.g. NOTAMs)

• Other sources: GPS, satellite receivers, etc.

The weather data used by the Q-AI (see http://klean.cnit.it/project) belong to two categories:

• Wind field: the direction and the intensity of the wind have a direct effect on the flight of the aircraft; moreover, it can also affect the evolution of a weather front in the near future and the influence of noise on a given environment;

• Air density, temperature, pressure, relative humidity: these quantity can affect the aircraft performances (i.e. thrust, fuel flow, etc.) and/or the pollution emissions model (i.e. CO2, NOx, etc., emission indexes).

A further source of information that can be exploited in the case a dual-polarisation option is adopted for the on-board weather radar is detection, along the route of hydrometeors related to risky conditions, such as hail.

Q-AI uses the risk map for updating the range of aircraft states that are feasible in trajectory planning. The risk map integrates problematic weather condition to be avoided during the aircraft trajectory with traffic data related to other nearby flights or regions to be avoided due to security and/or military reasons. The availability of an integrated risk map based data fusion of both weather and traffic information allows warning the pilot with the constraints in the proximity of the aircraft and can be used by the Q-AI to optimize the trajectory reducing the emissions.

2.2 Objectives

The objectives of the WINFC project are the following:

1 Analysis of the information sources about un-forecasted events like weather changes, traffic congestion in present and future ATM environment

2 Study of new measurements methods on board able to provide new weather real-time information.

3 Development of a SW tool for simulating the information data flow coming from different sources in case of unexpected weather and traffic scenarios

4 Development of a data fusion tool for the adaptive integration and correlation of the information coming from different information sources

5 Definition of an efficient and effective unbiased threat/importance factor(s) useful for the pilot and as input parameters to Q-AI trajectory and mission optimization algorithms.

6 Validation of the data fusion tool by a test activity based on the data flow SW tool applied to selected realistic scenarios

2.3 Innovative contributions of the project

Q-AI trajectory optimisation software has basically two types of inputs: meteorological data and risk map. The former are used to evaluate aircraft performances and emissions, the latter to integrate weather related hazards and traffic constraints. Weather conditions can be acquired from different data sources. These conditions can be pre-loaded off-board, or acquired during the flight from a ground entity base station, from other aircrafts or by on-board instrumentations, i.e. weather radar and advanced weather radar algorithms. The data sources can include:

• Weather data products for aviation provided by public or private institutions (NOAA, ECMWF, NHMS, etc.): forecast charts (WINTEM, SIGWX), model outputs every one to six hours, observation data including airport weather stations (METAR), ground weather radar, satellite observation, lightning strokes, specific risks assessments (icing, turbulence, convection, volcanic ash), PIREPS, etc.

• On-board weather radar, On-board advanced weather radar algorithm,

• On-board sensors (P, T, Wind, Humidity, Ice detection, etc.)

• Traffic information (Primary surveillance data, ADS-C, ADS-B, multilateration)

• Aeronautical Information Services (e.g. NOTAMs)

• Information coming from other sources: GPS, satellite receivers, etc.

The weather data used by Q-AI and affecting the flight of the aircraft can be divided in two main contributions:

• Wind field: the direction and the intensity of the wind have a direct effect on the flight of the aircraft; moreover, it can also affect the evolution of a weather front in the near future and the influence of noise on a given environment;

• Air density, temperature, pressure, relative humidity: these quantity can affect the aircraft performances (i.e. thrust, fuel flow, etc) and/or the pollution emissions model (i.e. CO2, NOx, etc emission indexes) of the aircraft

Q-AI uses the risk map for updating the range of aircraft states that are feasible in trajectory planning. The risk map integrates problematic weather condition to be avoided during the aircraft trajectory with traffic data related to other flights around the plane or regions to be avoided due to security and/or military reasons. The availability of an integrated risk map based on both weather and traffic data fusion allows to warn the pilot with the constraints in the proximity of the aircraft and can be used by the Q-AI to optimize the trajectory reducing the emissions.

2.4 Progress beyond the state of the art

The Data fusion approach

The data fusion approach proposed in WINFC aims at the delivery of two main results that can be used to be directly visualised to the pilot and to be input by the Q-AI software. The first is to build an updated weather representation that considers all the data available according to their spatial and temporal accuracy. The second is to provide an integrated risk map that integrates weather related threats with traffic related constraints, for example it can consider both convective clouds to be avoided and spatial regions temporarily closed to the air traffic. Both are typical inputs of the Q-AI trajectory planner that determine where the aircraft can go and at what environmental costs. These representations can be visualised to the pilot to show weather related constraints near the aircraft or an integrated representation of no flight zones. Notice that if provided data can be wide enough and some computational constraints can arise regarding real time update of situation awareness of flight conditions around the aircraft.

On-board Data Stream simulation tool

The innovation brought by the On-board Data Stream simulation tool have been:

1. to deliver a rich, coherent, and realistic dataset in simulated real-time or batch mode. The dataset covers a large spectrum of awareness information that can be available on-board today, and also in the future (with new on-board sensors, new information from nearby-aircraft or other ground or satellite systems).

2. to make available all the on-board information on a single "data bus". Thus, the complete evaluation bench is used for fusion algorithms (as in the project scope), but is also open to other type of uses in the future (coherent distribution of information to FMS, EFB, ND, etc.). The information distribution architecture can also be considered as first prototype of a future operational airborne implementation.

Satellite nowcasting services to detect and track aeronautical risk areas

Thanks to new satellite observation capabilities and ubiquitous communication facilities, it is possible to deliver on-board a worldwide short term forecast service (up to 1 hour), fast update (5 to 15 minutes), to prevent aircraft to encounter high risk areas. A typical benefit of such technique is to track deep convection over equatorial waters. Integration of such service in the simulation stream enriches the scenarios, and enables assessment of integration of such products in the information fusion process.

Polarimetric weather radar simulator

The main polarimetric observables commonly adopted in radar meteorology have been simulated, namely the reflectivity factor, the differential reflectivity, the linear depolarisation ratio, the co-polar correlation coefficient, and the specific differential propagation phase that can be used for the automatic identification of hydrometeor types. A Doppler radar is also capable of estimating the Doppler spectrum of weather targets: in particular, the first and the second spectral central moments, named radial velocity and spectrum width, are measurable. In addition, other two parameters that are indispensable for the risk assessment due to turbulence and wind shear have been simulated.

Real time Weather info by Satellite signals

In case the radar is blinded both due to strong attenuation and maximum distance, the missing information beyond the radar blind distance, in terms of 3D extension of the precipitation structure, could be estimated using the satellite signals that are sensitive to the liquid water and the water vapor content between airplane and the transmitting satellite. Some selected scenario in terms of precipitating structure and navigation and communication satellite constellations have been used to provide the feasibility of the proposed measurement system based on such satellite signals.

2.5 Project work plan

WINFC project has been implemented through the following six Work Packages, those numbered from 2 to 5 concerning research and development activities:

WP1 - Project management: This WP primarily aimed at providing a structured system for the full administrative and technical management of the project, addressing all methods of risk management, quality assurance and confidentiality including Intellectual Property Rights (IPR) handling.

WP2– Information sources, actual and future: This WP provided a clear view about all information sources (present and future), and their possible fusion for on-board trajectory optimization Decision Support System.

WP3 – Simulation environment: this WP developed a software based simulation environment that simulate the information delivery from the different on-board sources accounting for the output of the Weather and Traffic emulation and the output of a Polarimetric weather X-band radar in terms of time sequences of polarimetric parameters, hydrometeor classification and radar risk map.

WP4–Data fusion: this WP developed a data fusion tool of weather and traffic information to produce updated weather data and an awareness map of hazards that can be used as a risk map in the Q-AI optimization.

WP5 –Test and validation: this WP defined a set of test case assuming realistic weather and traffic scenario for analysing the performance of the data fusion applied to the simulation tool outputs

WP6 – Exploitation and dissemination: This WP outlined how to develop an exploitation and dissemination of the WINFC results in the SGO CLEANSKY program, in other European projects (FP7, H2020. etc), manufacturing industries and in the technical and scientific community, following Clean Sky JU directives.

Project Results:

The content of this section can be found in the attachment

Potential Impact:

4 Potential impact and the main dissemination activities and exploitation of results

4.1 Expected impact

The aim of the CleanSky system for Green Operations ITD, and specifically the Management of Trajectory and Mission (MTM) work package, is to demonstrate that the mitigation of external noise generated by the aircraft and the reduction of emissions (main environmental goals of ACARE, the European Technology Platform for Aeronautics and Air Transport) can be supported by the prediction of the new Green trajectory development.

The proposed WINFC module operates on board and collects information coming from weather data provider and traffic data provider. WINFC module can collect data and produces two main outputs: an awareness map that integrates weather changes and traffic changes during the aircraft flight, and an update weather map. The awareness map is a risk map that can be used by the pilot to immediately detect hazard conditions no matters whether they depends on weather or traffic changes. WINFC approach in unique, in its ability to merge data from on-board sensors with external sources, being conventional approaches based separate presentation of internal and external sources. WINFC architecture is sufficiently flexible to accommodate further products for flight safety that will be adopted by the relevant authorities.

4.2 Dissemination of the project

The dissemination of the project results has been carried out in two different ways: internal and external dissemination actions.

- Internal dissemination: the dissemination among the consortium partners has been done through the organization of internal meeting operated by audio or video conferences or held directly in the main sites of participants. Internal reports facilitated the divulgation of technical results among the project consortium staff.

- External dissemination: External dissemination has been carried out in four different ways.

(a) project web site (http://winfc.cnit.it)

(b) one open workshops addressed for the Cleansky community

(c) Participation to international scientific conferences

(d) Participation to Exhibition and DEMO sessions of international conference/events

4.3 Exploitation of the project

The results of the WINFC project provided useful benefits to existing correlated EU projects such as FP6 FLYSAFE (on flight safety), FP7 ALICIA (on operative conditions) SESAR (on overall air traffic optimization), SANDRA (on next generation of air-to-ground telecommunication systems) and CLEANSKY (on air traffic optimization to reduce emissions and noise pollution).

Specifically, WINFC will represent a useful procedure to verify how much the pilot decision support will have an impact in the flight green trajectory

4.4 Management of intellectual property rights

The project partners, CNIT and Atmosphere, agree on rules defining the access rights to the Intellectual Property Rights (IPR) on the Knowledge and on the pre-existing know-how, for the purpose of the achievement of the project on one side, and for further exploitation of those results on the other side.

4.5 Contribution to European Competitiveness

As explained in the impact section, WINFC can be considered as a reference model for aggregating and representing warnings to the pilot for what concerns all the main hazards affecting the aircraft flight. If the output of the WINFC is provided to a suited trajectory optimization module, a possible set of maneuvers to face such hazards are proposed.

European dissemination throughout the Europe also allows stakeholders to have a clear idea of the project results and show it to the air transport community, with a right level of new know how.

All these aspects are in the direction of providing significant gain in Europe, both individually and collectively, to have a significant impact for return of investment on Europe.

5.1 Relevant contact details

Person Role Email

Fabrizio Cuccoli Primary Project co-ordinator fabrizio.cuccoli@cnit.it

Luca Baldini Scientific co-ordinator l.baldini@isac.cnr.it

Paola Magri Administrative co-ordinator paola.magri@cnit.it

Jean Marc Gaubert WP leader jean-marc.gaubert@atmosphere.aero

Luca Facheris WP leader luca.facheris@unifi.it

Sandra Turner WP leader sandra.turner@atmosphere.aero

Massimiliano Nolich WP leader mnolich@units.it