1 citations · 1 across the 7 of their papers we have counts for
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Nowcasting aerosol particle size distributions from PM2.5 with physics-regularized flow matching
Zhi-Song Liu, Jenni Köykkä, Wenqing Peng +3
The mass concentration of particles smaller than 2.5um (PM2.5) is cheap to measure and widely monitored, whereas the particle number size distribution (PSD) requires costly instrum…
Climate downscaling with likelihood functional flow matching
Zhi-Song Liu, Helmi Toropainen, Michael Boy
Decisions about heat, frost and flooding are made locally, yet reanalyses and climate models resolve tens of kilometres. Generative downscalers add kilometre-scale detail with unce…
Evaluation of optimisation and Bayesian inference methods for reaction rates in atmospheric chemical mechanisms
Valery Ashu, Wenqing Peng, Zhi-Song Liu +7
Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxidation systems where many reac…
TailWeather: from tail to extremes, a global climatological dataset for machine-learning weather forecasting
Zhi-Song Liu, Michael Boy, Risto Makkonen
Weather forecasting models commonly use the average forecast skill for short-range forecasts. However, it does not necessarily imply skill in the tails of the weather distribution.…
Inverse Neural Operator for ODE Parameter Optimization
Zhi-Song Liu, Wenqing Peng, Helmi Toropainen +5
We propose the Inverse Neural Operator (INO), a two-stage framework for recovering hidden ODE parameters from sparse, partial observations. In Stage 1, a Conditional Fourier Neural…
Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction
Ammar Kheder, Helmi Toropainen, Wenqing Peng +3
Vision Transformers have achieved remarkable success in spatio-temporal prediction, but their scalability remains limited for ultra-high-resolution, continent-scale domains require…
TopoFlow: Topography-aware Pollutant Flow Learning for High-Resolution Air Quality Prediction
Ammar Kheder, Helmi Toropainen, Wenqing Peng +4
We propose TopoFlow (Topography-aware pollutant Flow learning), a physics-guided neural network for efficient, high-resolution air quality prediction. To explicitly embed physical…