most citedIncreasing NWP Thunderstorm Predictability Using Ensemble Data and Machine Learning

1 citations · 2 across the 4 of their papers we have counts for

collaborators

4 papers

physics.ao-ph2025

Physical Scales Matter: The Role of Receptive Fields and Advection in Satellite-Based Thunderstorm Nowcasting with Convolutional Neural Networks

Christoph Metzl, Kianusch Vahid Yousefnia, Richard Müller +3

The focus of nowcasting development is transitioning from physically motivated advection methods to purely data-driven Machine Learning (ML) approaches. Nevertheless, recent work i…

physics.ao-ph2025★ 1 cited

Increasing NWP Thunderstorm Predictability Using Ensemble Data and Machine Learning

Kianusch Vahid Yousefnia, Tobias Bölle, Christoph Metzl

While numerical weather prediction (NWP) models are essential for forecasting thunderstorms hours in advance, NWP uncertainty, which increases with lead time, limits the predictabi…

physics.ao-ph2024

A minimal model of the deep-convection lifecycle and its verification in remote-sensing observations

Tobias Bölle, Christoph Metzl, Kianusch Vahid Yousefnia

Deep convection is one of the most important atmospheric transport mechanisms and associated with various severe weather phenomena. Manifestations of deep convection in the atmosph…

physics.ao-ph2024★ 1 cited

Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model

Kianusch Vahid Yousefnia, Christoph Metzl, Tobias Bölle

Thunderstorms have significant social and economic impacts due to heavy precipitation, hail, lightning, and strong winds, necessitating reliable forecasts. Thunderstorm forecasts b…