Severe weather events are constantly increasing over northern Italy impacting the air traffic of one of the major airports of Europe: Milano Malpensa. Monitoring and predicting extreme convection is very challenging especially when it develops locally in a short time range. This work is performed within two projects funded by the H2020 SESAR Programme, with the objective of nowcasting with high accuracy the strong weather events affecting the airport. We collected different types of data from 10 locations around the airport and developed an end-to-end nowcasting deep neural networks based model for each of these stations. We show in this paper the results that we obtained for Novara, the only station for which we have available weather stations, radar, GNSS and lightning.
<p>Extreme weather events in Europe have increased in frequency and intensity in the last decades, especially in some areas like Alpes and Balkans, and is expected to increase even more in the upcoming years due to the climate change. Monitoring and forecasting the severe weather events locally developed and in a short time range is very challenging but also very important for aviation safety. Several studies have been made for studying the pre-convective environment, however there are still gaps in the knowledge of the dynamical processes of regional and short duration deep convective systems.</p><p>This study is implemented within the SESAR ALARM project and focuses on the analysis of the pre-convective and convective environment in support to the air traffic management and air traffic control. The work focuses in the detection, analysis and nowcasting of severe weather events in a selected hotspot: the area of Milano Malpensa airport in Italy. We have used the data from 28 weather stations, 8 GNSS stations, radar and lightning detectors, in the period 2010-2020 to train a nowcasting algorithm and to characterize the pre-convective environment.</p><p>Our first results for different locations in the area of interest, show on average that the root mean square error of the rainfall prediction lie in the range 0.1029-0.2838 mm and 0.2720-0.7815 m/s for the wind speed prediction. Our algorithm shows the best rain predictive performance in the next 10 minutes higher than 90%, and higher than 80% in the next 30 minutes. Moreover, the pre-convective environment analysis shows that all the cases with wind field divergence never show an increasing trend of GNSS Zenith Total Delay before the event.</p>
<p>Extreme weather nowcasting has always been a challenging task in meteorology. Many research studies have been conducted to accurately forecast extreme weather events, related to rain rates and/or wind speed thresholds, in spatio-temporal scales. Over decades, this field gained attention in the artificial intelligence community which is aiming towards creating more accurate models using the latest algorithms and methods.&#160;&#160;</p><p>In this work, within the H2020 SESAR ALARM project, we aim to nowcast rain and wind speed as target features using different input configurations of the available sources such as weather stations, lightning detectors, radar, GNSS receivers, radiosonde and radio occultations data. This nowcasting task has been firstly conducted at 14 local stations around Milano Malpensa Airport as a short-term temporal multi-step forecasting. At a second step, all stations will be combined, meaning that the forecasting becomes a spatio-temporal problem. Concretely, we want to investigate the predicted rain and wind speed values using the different inputs for two case scenarios: for each station, and joining all stations together.&#160;</p><p>The chaotic nature of the atmosphere, e.g. non-stationarity of the driving series of each weather feature, makes the predictions unreliable and inaccurate and thus dealing with these data is a very delicate task. For this reason, we have devoted some work to cleaning, feature engineering and preparing the raw data before feeding them into the model architectures. We have managed to preprocess large amounts of data for local stations around the airport, and studied the feasibility of nowcasting rain and wind speed targets using different data sources altogether. The temporal multivariate driving series have high dimensionality and we&#8217;ve&#160; made multi-step predictions for the defined target functions.</p><p>We study and test different machine learning architectures starting from simple multi-layer perceptrons to convolutional models, and Recurrent Neural Networks (RNN) for temporal and spatio-temporal nowcasting. The Long Short-Term Memory (LSTM) encoder decoder architecture outperforms other models achieving more accurate predictions for each station separately.&#160; Furthermore, to predict the targets in a spatio-temporal scale, we will deploy a 2-layer spatio-temporal stacked LSTM model consisting of independent LSTM models per location in the first LSTM layer, and another LSTM layer to finally predict targets for multi-steps ahead. And the results obtained with different algorithm architectures applied to a dense network of sensors are to be reported.</p>
<p>Aviation safety can be jeopardised by multiple hazards arising from natural phenomena, e.g., severe weather, aerosols/gases from natural hazard, space weather. Furthermore, there is the anthropogenic emissions and climate impact of aviation, that could be reduced. The use of satellite sensors, ground-based networks, and model forecasts is essential to detect and mitigate the risk of airborne hazards for aviation, as flying through them can have a strong impact on engines (abrasion and damages caused by aerosols) and on the health of passengers (e.g. due to associated hazardous trace gases).</p><p>The goal of this work is to give an overview of the alert data products in development in the ALARM SESAR H2020 Exploratory Research project. The overall objective of ALARM (multi-hAzard monitoring and earLy wARning system; https://alarm-project.eu) is to develop a prototype global multi-hazard monitoring and Early Warning System (EWS), building upon SACS (Support to Aviation Control Service; https://sacs.aeronomie.be). This work presents the creation of alert data products, which have a potential use in geosciences (e.g. meteorology, climatology, volcanology). These products include observational data, alert flagging and tailored information (e.g., height of hazard and contamination of flight level &#8211; FL). We provide information about the threat to aviation, but also notifications for geoscience applications. Three different manners are produced, i.e., early warning (with geolocation, level of severity, quantification, &#8230;), nowcasting (up to 2 hours), and forecasting (from 2 to 48 hours) of hazard evolution at different FLs. Note that nowcasting and forecasting concerns SO<sub>2</sub> contamination at FL around selected airports and the risk of environmental hotspots. This study shows the detection of 4 types of risks and weather-related phenomena, for which our EWS generates homogenised NetCDF Alert Products (NCAP) data. The first type is the near real-time detection of recent volcanic plumes, smoke from wildfires, and desert dust clouds, and the interest of combining geostationary and polar orbiting satellite observations. For the second type, ALARM EWS uses satellite and ground-based (GB) observations, and model forecasts to create NCAP related to real-time space weather activity. Exploratory research is developed by ALARM partners to improve detection of a third type of risk, i.e., the initiation of small-scale deep convection (under 2 km) around airports. GNSS data (ground-based networks and radio-occultations), lightning and radar data, are used to implement NCAP data (designed with the objective of bringing relevant information for improving nowcasts around airports). The fourth type is related to the detection of environmental hotspots, which describe regions that are strongly sensitive to aviation emissions. ALARM partners investigate the climate impact of aviation emissions with respect to the actual atmospheric synoptical condition, by relying on algorithmic Climate Change Functions (a-CCFs). These a-CCFs describe the climate impact of individual non-CO<sub>2</sub> forcing compounds (contrails, nitrogen oxide and water vapour) as function of time, geographical location and cruise altitude.</p><p>Acknowledgements:</p><p>ALARM has received funding from the SESAR Joint Undertaking (JU) under grant agreement No 891467. The JU receives support from the European Union&#8217;s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union.</p>
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