SINOPTICA H2020

On 14 June 2022 Sandy Chkeir presented a collaboration between SINOPTICA and ALARM projects at the 1st workshop on data science for GNSS remote sensing (https://d4g-2022.de/programme.html)

In the H2020 projects SINOPTICA and ALARM, experiments were carried out on two different approaches to nowcast severe weather events capable of adversely affecting air traffic management operations. The SINOPTICA approach consists of the assimilation of GNSS ZTD in

combination with other weather-related measurements into the WRF model using a 3D-Var technique in rapid update cycle mode, while the ALARM approach is based on machine learning techniques. The objective was to evaluate the impact of GNSS ZTD on the nowcasting of the

weather parameters of interest, including rain, hail, and wind, for the hailstorm event that affected the Milano Malpensa airport on 13 July 2021.

The WRF simulations with data assimilation improve the prediction of the convective cell in terms of intensity: the forecasted hail diameter is consistent with the damage reported for an aircraft. However, the WRF simulations show a small error in the location of the convection. The machine learning algorithm is based on a Long Short-Term Memory (LSTM) network with encoder/decoder structure, providing in output wind speed and rain rate at each station location and over the Malpensa airport. The probability of detection of extreme wind within 1h around Malpensa is about 93% with about 1% of false alarms and it can predict the intensity of the wind with an RMSE of 0,081 m/s. The detection of extreme rain is more difficult because the algorithm underestimates the intensity, however with a post-processing procedure we reach about 97% of probability of detection with 2% of false alarms.

The 27th of May 2022 we presented this work at the annual EGU General Assembly within the session NH1.4 Extreme meteorological and hydrological events induced by severe weather and climate change.

This contribution has the main goal of identifying, characterizing, tracking and nowcasting severe thunderstorms using the Density of the Vertical Integrated Liquid (DVIL). The DVIL can synthesize all the volumetric information of a column of the weather radar in a 2D plane. This is, it estimates the quantity of precipitable liquid water in the column but, besides, it reduces the dependency on the height of the column. This point becomes crucial to give an appropriate weight of potential danger to thunderstorms that occurred out of the typical convective season. . This is particularly useful to improve the decision-making and early warning in critical environments and infrastructures, like airports and air traffic management (ATM). The usage of DVIL has multiple advantages, for instance, reducing the computational time consumed on the analysis of large areas. Also, to obtain a good and simple description of the potentially dangerous thunderstorms, and to have an easily integrating into other systems for ATM decision making. The main disadvantage is a less precise characterization of the atmospheric objects than with the whole radar volumetric data. Nevertheless, the differences are scarce and do not produce any significant inconvenience in the procedure. The algorithm first identifies those areas exceeding a DVIL threshold, which is established for thunderstorms with a certain probability of producing severe weather. The characterization module turns out simpler than in other methodologies because of the data type (2D instead of 3D reflectivity fields), but it can be combined with other data types if needed. The tracking and nowcasting procedure obtain the past trajectory of the thunderstorm and then use it to weather forecast from 5 to the next 60 minutes, with 5 minutes steps. Different convective episodes that have affected the proximity of Italian and Spanish airports have been analysed to evaluate the following points: (1) the performance of the correct identification of potentially dangerous thunderstorms, (2) the capability of tracking the path and characterizing the life cycle of those storms, and (3) the ability of the nowcasting to correctly forecast the time and the most dangerous area.

This project has received funding from the SESAR Joint Undertaking under grant agreement No 892362, SINOPTICA-H2020 (Satellite-borne and IN-situ Observations to Predict the Initiation of Convection for Air traffic management) project.

How to cite: Esbrí, L., Rigo, T., Llasat, M. C., and Parodi, A.: Using Vertical Integrated Liquid Density from a Weather Radar Network to Nowcast Severe Events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2532, https://doi.org/10.5194/egusphere-egu22-2532, 2022.

The 25th of May 2022 we presented this work at the annual EGU General Assembly within the session AS1.29 Aviation Meteorology and nowcasting: Observations and Models (AMANOM).

One of the main challenges for meteorologists is to improve the prediction of events that develop on small spatial and temporal scales, having important repercussions in air traffic activities. In this regard, the H2020 SESAR Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project, aims to demonstrate that the prediction of severe weather events with high spatial and temporal resolution, can benefit the ATM and aviation safety. SINOPTICA assimilates non-conventional observations such as Global Navigation Satellite System (GNSS), weather radar, and lightning data into numerical weather prediction model with a nowcasting technique called PHAse-diffusion model for STochastic nowcasting (PHAST) allowing to predict the highly localized convective events triggering in the vicinity of airports.

As part of the project, three severe weather events were identified on the Italian territory which caused the closure of the airports, delays on arrivals and departures, and numerous diversions. The results of the numerical simulations, carried out with the Weather Research and Forecasting (WRF) and nowcasting technique PHAST, were integrated into the Arrival Manager 4D-CARMA (4-Dimensional Cooperative Arrival Manager), an adaptive air traffic sequencing and management system for controllers, which generates and optimizes 4D trajectories to avoid areas affected by adverse phenomena and, under certain circumstances, reducing controllers’ and pilots’ workload. The results show that the nowcasting technique is able to predict the convective cells in shape, intensity and time. In addition, the assimilation of lightning and GNSS data improves the forecast accuracy of the above-mentioned events in line with expectations and ATM needs.

How to cite: Mazzarella, V., Milelli, M., Lagasio, M., Poletti, L., Biondi, R., Realini, E., Federico, S., Torcasio, R. C., Kerschbaum, M., Llasat, M. C., Rigo, T., Esbrí, L., Temme, M.-M., Gluchshenko, O., Temme, A., Nöhren, L., and Parodi, A.: Data assimilation and nowcasting of severe weather for air traffic management purposes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2823, https://doi.org/10.5194/egusphere-egu22-2823, 2022.

Presented at the 4th National Congress of the Associazione Italiana di Scienze dell'Atmosfera e Meteorologia, the 17th of February 2022.

A new output visualization presented.

The climatechange is intensifying the water cycle, leading to more intense rainfall and flooding in some regions and longer lasting droughts in others. The increase in short-lived and highly localised phenomena, such as thunderstorms, hailstorms, wind gusts and tornadoes, expected in the coming years, will also have important repercussions for airtraffic management activities. One of the challenges for meteorologists is to improve the localisation 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 H2020) project aims to demonstrate that numerical weather forecasting with high spatial and temporal resolution, benefiting from the assimilation of radar data, in-situ weather stations, GNSS and lightning data, can improve the prediction of severe weather events for the benefit of air traffic management (ATM) and air traffic control (ATC) operations.

The study presents the results of the assimilation of observations in the Weather Research and Forecasting (WRF) model and the related ATM implications, for the case study of the Milan Malpensa airport on 11 May 2019, blocked due to a violent thunderstorm with rain and hail, demonstrating that it is possible to improve the forecast of such events in line with ATM expectations and needs.

Italian version

Il cambiamento climatico sta intensificando il ciclo dell'acqua, portando così precipitazioni più intense e inondazioni in alcune regioni, nonché siccità più durature in altre. L’aumento dei fenomeni di breve durata e altamente localizzati, quali temporali, grandinate, raffiche di vento, tornado, atteso nei prossimi anni, avrà importanti ripercussioni anche nelle attività di gestione del traffico aereo. Una delle sfide per i meteorologi è migliorare la localizzazione ed il timing di tali eventi che si sviluppano su piccole scale spaziali e temporali. A questo proposito, il progetto H2020 Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) mira a dimostrare che le previsioni meteorologiche numeriche ad alta risoluzione spaziale e temporale, beneficiando dell'assimilazione di dati radar, stazioni meteorologiche in situ, GNSS e dati sui fulmini, possono migliorare la previsione degli eventi meteorologici severi a vantaggio delle operazioni di gestione (ATM) e di controllo del traffico aereo (ATC).

Nell’ambito del progetto, sono stati identificati quattro eventi di maltempo sul territorio italiano che hanno determinato la chiusura dello scalo aeroportuale con pesanti ritardi sui voli in arrivo/partenza e numerosi dirottamenti. I dati delle simulazioni numeriche, effettuate con il modello Weather Research and Forecasting (WRF) e tecnica di assimilazione 3D-VAR, verranno integrati nei sistemi di gestione e di controllo del traffico aereo al fine di ottimizzare e/o generare nuove traiettorie 4D per evitare le aree interessate dai fenomeni avversi con il duplice obiettivo di aumentare la sicurezza dei voli e di ridurre i disagi. Questo lavoro presenta i risultati dell'assimilazione delle suddette osservazioni nel modello WRF e le relative implicazioni ATM, per il caso studio di Milano Malpensa dell’11 maggio 2019, dimostrando che è possibile migliorare la previsione di tali eventi in linea con le aspettative e le esigenze di ATM.

Antonio Parodi Eugenio Realini Maria Carmen Llasat Tomeu Rigo Laura Esbrí Corbella Vincenzo Mazzarella Massimo Milelli Martina Lagasio Marco-Michael Temme Olga Gluchshenko

This paper was submitted at the SESAR Innovation Days with the results of our nowcasting system for the case study of Milano Malpensa airport.

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.

This is the poster we presented at the SESAR Innovation Days with the results of our nowcasting system for 4 case studies in Milano Malpensa, Venezia Marco Polo, Bergamo Orio al Serio and Palermo Falcone Borsellino airports.

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.

Poster presented at the European Meteorological Society (EMS) annual meeting 2021

Poster presented @EGU 2021

Today presentation of the preliminary results at the vEGU21.

Motivation of the study

Objectives

Data sources

Next steps

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 observation are assimilated by using a cycling three-dimensional variational

technique.

Results and details in the attached files.

Poster presentation at the 3rd European Hail Workshop

The H2020 SINOPTICA Project (2020-2022) aims at exploiting the untapped potential of assimilating remote sensing as well as GNSS-derived datasets and in-situ weather stations data. The main goal is getting very high-resolution, very short-range numerical weather forecasts to improve the prediction of extreme weather events to the benefit of Air Traffic Control (ATC) operations. SINOPTICA weather forecast results will be integrated into ATC decision-support tools, visualizing weather information on the controller's display, and generating 4D trajectories to avoid severe weather areas, including hailstorms.

Within this context, the preliminary results of the radar analysis on three hail events affecting Italian airports are presented. The cases have been selected for their relevant impact on the international airports of Milano-Malpensa, Marco Polo-Venice and Bergamo-Orio al Serio.

The analysis is based on the mosaic radar images provided by the Italian Civil Protection, which included relevant data such as the top of the clouds, vertically integrated liquid (VIL), and VIL density products. The proposed early warning system distinguish four periods: non-storm alert, pre-alert, alert level 1, alert level 2. The proposed domain to be monitored would have a radius of 75 km from the airport. The storm alert level 2 period would be considered when VIL radar echoes are above 1 mm within an area about 20 km from the airport, considering 1 km2 of spatial resolution and 5 min. of temporal resolution.

The storm alert level 1 period start two hours before the alert period, covering an area of 500 km2, with a spatial resolution of 3 km2 and temporal resolution of 15 min. The pre-alert period would correspond to the period between the first appearance of radar echoes on the Italian radar mosaic until the storm alert level 1 period starts. To monitor this period, the proposed spatial resolution is 5 km2 and temporal resolution would be 30 min. for the whole radar mosaic.

This procedure would help to identify and track convective storm structures responsible for ATM difficulties. VIL density variable is considered the most suitable candidate to compare the different episodes since they can occur in different seasons. The application of the proposed methodology to the selected cases has shown good ability to efficiently quantify the severity of the thunderstorms. Additionally, various VIL density thresholds have been tested as severity indicators. Maximum VIL density values in the affected region exceed 4 g/m3, however, on some occasions, they exceed 8 g/m3. VIL density showed a weak seasonal dependency with slightly higher values for summer events.