Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios

Forecasting the weather to assist ATC and ATM operations

In EUROCONTROLS's recent summary report on Climate Changes Risks for European Aviation, several weather-related impacts were highlighted. There is a strong relation between highly impacting weather events and disruptions to the aviation network resulting in additional fuel consumption and CO2 emissions. In Europe, severe weather is responsible for up to 7.5% of the total en-route delays. In this respect, the H2020 Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project aims to demonstrate that very high-resolution and very short-range numerical weather forecasts, benefiting from the assimilation of radar data, in-situ weather stations, GNSS and lightning data, can improve the prediction of extreme weather events to the benefit of Air Traffic Management (ATM) and Air Traffic Control (ATC) operations. The assimilation of radar, GNSS, and lightning data shows a positive impact on the forecast of the convective cells for the four selected severe weather events. Moreover, two radar-based nowcasting strategies, PhaSt and RaNDeVIL, are tested to predict storm structures. Both methods are able to follow the more intense cells (VIL > 10 kg/m2) in all the case studies, as shown by the MODE results and the eye-ball verification The forecasts are used in an arrival management system (AMAN) to compute 4D trajectories around convective areas, integrate the affected aircraft into the arrival sequence, and assist air traffic controllers in implementing the approaches through just in time advisories and dynamic weather displays. With the help of real traffic scenarios and different weather models, diverse approach planning strategies are evaluated

Is an NWP-Based Nowcasting System Suitable for Aviation Operations?

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories

Sparse model identification of a 4x4 MIMO channel measurements in 5 GHz band

Channel data measurement and tap estimation with the LASSO estimator/ detector (Least Absolute Shrinkage and Selection Operator).The Least Absolute Shrinkage and Selection Operator has gained attention in the applied mathematics and signal processing communities. The thesis provides us theoretical expressions for solving Compressive Sensing by the LASSO algorithm for Direction of Arrival estimation. The central idea is highlight the fundamental concepts of the complex Least Absolute Shrinkage and Selection Operator (c-LASSO) and give an overview of its application to the Direction Of Arrival estimation.The role of the regularization parameter and suggestions on the selection are exposed. It is found that LASSO can be compared with Conventional beamforming and Least Squares. The presented results in the context of Direction of Arrival (DOA) single snapshot estimation using a 4 antenna linear sensor array

How can numerical weather prediction support the ATM activity during severe weather events?

Climate change is intensifying the water cycle, bringing more intense precipitation and flooding in some regions, as well as longer and stronger droughts in others. The number of short-term and highly localized phenomena, such as thunderstorms, hailstorms, wind gusts or tornadoes, is expected to grow in the coming years, with important repercussions in air traffic management activities (ATM). One of the challenges for meteorologists is to improve the location and timing of such events that develop on small spatial and temporal scales. In this regard, the H2020 Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project aims to demonstrate that numerical weather forecasts with high spatial and temporal resolution, benefiting from the assimilation of radar data, in situ weather stations, GNSS and lightning data, could improve the prediction of severe weather events for the benefit of air traffic control (ATC) and air traffic management (ATM). As part of the project, three severe weather events were identified on the Italian territory which resulted in the closure of the airport with heavy delays on arrivals and departures as well as numerous diversions. The data of the numerical simulations, carried out with the Weather Research and Forecasting (WRF) model and the 3D-VAR assimilation technique, will be integrated into air traffic control and management systems (Arrival Manager) in order to generate and optimize 4D trajectories avoiding areas affected by adverse phenomena with the objectives of increasing flight safety and predictability and reducing controllers' workload. In addition to the numerical simulations, a nowcasting technique called PHAse- diffusion model for STochastic nowcasting (PhaSt) has been investigated to further improve ATC supporting systems during severe weather. This work presents the results of the WRF and PhaSt experiments, for the Milan Malpensa case study of 11 May 2019, demonstrating that it is possible to improve the prediction of such events in line with expectations and ATM needs

Innovative integration of severe weather forecasts into an extended arrival manager

The impact of extreme weather on air traffic is a global challenge that results in varying delays to flights depending on climate zone, traffic, and available infrastructure. Due to an observed increase in severe weather events, this impact on air traffic is expected to grow in intensity in the coming years, making it increasingly important to organize airspaces safely and efficiently even under severe weather conditions. In the EU H2020 project "Satellite-borne and INsitu Observations to Predict The Initiation of Convection for ATM" (SINOPTICA), three new meteorological forecasting techniques - PhaSt, RaNDeVIL and WRF-RUC - were developed and tested to better nowcast severe weather events affecting tactical air traffic management operations. The nowcasts are used to organize the approaching traffic by an Extended Arrival Manager (E-AMAN) generating 4D trajectories to efficient detour around severe weather areas in the vicinity of airports. For this purpose, short-range severe weather forecasts with very high spatial resolution were elaborated starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) models and data assimilation. This compact nowcast information were integrated into an E-AMAN to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The combination of fast and reliable weather nowcasts with a guidance-support system enables on the one hand the 4D trajectory calculation for diversion coordination around severe weather areas, and on the other hand the visualization of dynamic weather information on the radar displays of controllers. A previous evaluation of the concept by ATCOs showed that the presentation of meteorological information must be compact and concise to not interfere with relevant traffic information on the display. Two severe weather events impacting different Italian airports were selected for validation of the E-AMAN. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds were defined for domains around the airports. The Weather Research and Forecasting model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations were assimilated by using a cycling three-dimensional variational technique. The validation of E-AMAN system in SINOPTICA project focused on the aspects of feasibility and efficiency and contained two phases. In the first phase, recorded weather data and realistic air traffic were combined and run in a traffic simulation, where the E-AMAN has to organize and to plan the aircraft depending on the measured and forecasted weather. For the evaluation, flight time, track miles and fuel consumption estimation KPIs were applied. In the second phase, various E-AMAN simulation runs were demonstrated to an international controller team for evaluation. The predictions of the three considered forecast models were surprisingly heterogeneous for the same period and area, so that a comparative statement regarding the support quality of the considered E-AMAN within the project is only possible to a limited extent. However, it is indicated that an E-AMAN is very helpful if there is a possibility for large-scale fly-around planning. For this purpose, longer-term and highly precise forecasts that are precisely tailored to Air Traffic Control requirements are essential. The forecast model must correspond to the safety perception of the air traffic controllers and pilots on site, so that they can manage the traffic as efficiently and safely as possible. However, with the help of sophisticated nowcast models and the E-AMAN, SINOPTICA was able to show that it is possible to support controllers and pilots in challenging meteorological situations to guide air traffic safely and efficiently, and thus to make planning more reliable and predictable for all stakeholders on ground and in the air. This contribution presents an overview of the final results of the SINOPTICA project

How can SINOPTICA support ATM and ATC during severe weather events?

The prediction of rapidly developing thunderstorms in small and localized areas is a challenge for the scientific community. Quickly developing but intense thunderstorms are usually characterized by large hail size, huge amount of rain in a short period, high lightning frequency and strong winds thus potentially capable to affect people and socio-economic activities/infrastructures. These phenomena affect also the flight safety, when aircrafts have to fly through or nearby storms, and the aviation management, or triggering flight re-routing, delays or cancellations. Weather-related flight cancellations and delays have increased over the past two decades in the US and Europe and this trend is going to increase due to the human-induced climate change. The objective of the H2020 SESAR Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project is to improve the performances of the numerical weather prediction model to nowcast severe weather events locally developed. In this work, we assimilate different ground based and satellite data into the Weather Research and Forecasting model, we nowcast the severe weather in the surrounding of four airports in Italy and we show the innovative approach to integrate the meteorological results with the Air Traffic Control procedures

Initial results of the project SINOPTICA (Satellite-borne and INsitu Observations to Predict The Initiation of Convection for ATM)

In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 – 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller. Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project

A nowcasting model for severe weather events at airport spatial scale: The case study of Milano Malpensa

One of the challenges for meteorologists is to forecast severe weather events developing at small spatial and temporal scales. The H2020 SESAR project "Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM" (SINOPTICA) aims at improving the performances of the numerical weather prediction model to nowcast severe weather events developing in the vicinity of airports. In the project, these new prediction technologies are used to integrate weather events into an Arrival Manager (AMAN) for approach controllers to visualize the actual meteorological development and to support arrival sequencing and target time calculation. We defined the users' requirements through a questionnaire distributed to air traffic controllers to find design solutions for additional controller support system functionalities. We are now developing a nowcasting model for air traffic controller support based on a dense network of ground-based sensors. The focus is on Milano Malpensa airport because it is located in a region with high risk of severe weather development and in which we have an easy availability of high-quality data. The results show that, for this specific case, the use of radar, lightning and Global Navigation Satellite System data greatly improve the prediction of the extremes while the weather stations alone are not essential for this purpose

A nowcasting algorithm of severe weather events at local spatial scale: The Venezia case study

Nowadays, predicting the exact location and timing of severe convective phenomena at small spatial and temporal scales is still a challenge. In this respect, the H2020 SESAR project "Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM" (SINOPTICA) aims to improve the forecast of severe weather events by using the numerical weather prediction models and the benefit of assimilating non-conventional observations, such as weather radar, GNSS and lightning, in combination with a nowcasting technique to predict the convective cells developing in the vicinity of airports to support air traffic control operations. In this work, we present the results related to the Venice case study pointing out the positive impact of assimilating radar data with lightning and GNSS for a very short-range forecast