A Review of Machine Learning for Convective Weather

Abstract

We present an overviewof recentwork on using artificial intelligence/machine learning techniques for forecasting convective weather and its associated hazards, including tornadoes, hail, wind, and lightning. These high-impact phenomena globally cause both massive property damage and loss of life yet they are quite challenging to forecast. Given the recent explosion in developing machine learning techniques across the weather spectrum and the fact that the skillful prediction of convective weather has immediate societal benefits, we present a thorough review of the current state of the art in artificial intelligence and machine learning techniques for convective hazards. Our review includes both traditional approaches, including support vector machines and decision trees as well as deep learning approaches. We highlight the challenges in developing machine learning approaches to forecast these phenomena across a variety of spatial and temporal scales. We end with a discussion of promising areas of future work for ML for convective weather, including a discussion of the need to create trustworthy AI forecasts that can be used for forecasters in real-time and the need for active cross-sector collaboration on testbeds to validate machine learning methods in operational situations.

Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models

“…In this study, eight different ML approaches are compared (Table 3), such as LR, RF, and Multi-layer perceptron (MLP), which are widely used in convective-hazard forecasting (e.g., [14,17,22,47,48]), along with ML approaches based on Ensemble Learning techniques (i.e., a group of predictors, called an ensemble, are trained together to improve predictive ability; Figure 3). All ML models, assembly strategies, metric estimation, and preprocessing methods were taken from scikit-learn, a free software ML library for the Python programming language (https://scikit-learn.org/stable/, accessed on 5 December 2023).…”

“…Convective initiation nowcasts utilize various observational data, such as radar imagery (e.g., the Thunderstorm Identification, Tracking, Analysis, and Nowcasting; TITAN [7]), satellite data [1,[8][9][10], atmospheric instability indices (e.g., [11]), numerical weather predictions (NWP, e.g., the Corridor Integrated Weather System; CIWS [12]), and other meteorological parameters to identify favorable conditions for the initiation of thunderstorms. For example, the Satellite Convection Analysis and Tracking (SATCAST [13]) is a CI nowcasting expert system that uses eight predictors, called "Interest Fields" based on infrared Geostationary Operational Environmental Satellite (GOES) data, to forecast CI with 0-1 h lead times [14]. In this work, CI is defined as the first detection of Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivities ≥ 35 dBZ produced by convective clouds and satellite-derived atmospheric motion vectors (AMVs) for tracking individual cumulus clouds.…”

“…Other forecasting systems based on satellite data were developed to aid in diagnosing the characteristics of the preconvective environment, and deep moist convection are the convective occurrence (CO) algorithms. CO approaches include modeling convective initiation/decay and advection of existing storms [14], the 0-9 h NearCast model, [9,20], object tracking techniques such as optical flow [21], and thunderstorm occurrence (TO [22][23][24]).…”

Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland

“…Existing satellite‐based detection of severe, hail‐producing convection in literature is based on proxy methods which link characteristics such as microwave brightness temperatures (e.g., Ferraro et al., 2015; Laviola et al., 2020) and overshooting cloud tops (e.g., Khlopenkov et al., 2021; Murillo, 2022; Punge et al., 2023) to storm severity and hail probability. In recent years, also machine learning‐based convection detection approaches have become more commonplace (e.g., Cintineo et al., 2020; McGovern et al., 2023; Zha et al., 2021). Direct detection methods, however, are rare and mostly rely on specific instrumentation, which is often specifically developed and installed for hail observation in rather small local networks.…”