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

Abstract

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.

A Wind Field Reconstruction from Numerical Weather Prediction Data Based on a Meteo Particle Model

“…Scientific and technological progress has led to an increase in the capabilities of weather forecasting over the past 40 years. Mazzarella et al [22] investigated whether NWP-based weather forecasts at a high-resolution (including data assimilation) could improve the capability of the prediction of extreme events in order to understand if such models could be suitable for supporting air traffic management. They found that, for a specific test case, the model was able to provide forecasts at least 1 h in advance, giving ATM operators sufficient time to manage air traffic and calculate new landing trajectories prior to an extreme event.…”

A Wind Field Reconstruction from Numerical Weather Prediction Data Based on a Meteo–Particle Model

“…Scientific and technological progresses have led to increasing the capabilities of weather forecasting over the past 40 years. Mazzarella et al [20] investigated if NWP-based weather forecasts at high-resolution (including data assimilation) can improve the capability of predicting extreme events, in order to understand if such models can be suitable to support air traffic management. They found that, for a specific test case, the model was able to provide forecasts at least 1 h in advance, giving ATM operators sufficient time to manage air traffic and calculate new landing trajectories prior to an extreme event.…”

A computer vision approach for satellite-driven wind nowcasting over complex terrains

“…A suggested solution to enhance forecast accuracy is the increase of numerical weather forecast model resolutions, to account for turbulent wind characteristics and wind shear in the lower troposphere over complex terrains [2]. However, these models show limitations, especially in small-scale patterns and physical processes, that are markedly pronounced in complex terrain and pose a risk to aviation [5,6]. Machine Learning (ML) techniques, exemplified by models like Convolutional Neural Networks (CNN) that use Deep Learning (DL) networks, emerge as an alternative approach.…”