Weather research and forecasting model (WRF) is widely used all over the world for operational forecasting. However the model’s computational costs increase faster than O(h-3), where h is the grid spacing, and the transition to the resolutions of the city building scale seems completely impractical. Also the classical numerical model forecast quality has fundamental limits. These limits are determined by the initial and boundary conditions and intrinsic model uncertainties and cannot be lifted by going to finer resolutions. In this talk we introduce methods of physically informed super resolution for weather forecasts. We have tried to adapt well known models for image super resolution, such as SRFBN and ESRGAN, by finetuning them on WRF data and adding some physically informed parts. We will describe our experiments with spectral loss and reanalysis loss. Our work is still in progress, so this talk is more an invitation to an open discussion than a classical presentation of the results.
CityAir and University of Arizona Air Quality Seminar
Physically informed super resolution models for regional weather forecasts. Work in progress
Streamed
at November 22, 2021
at November 22, 2021
Watch on Youtube
Vladimir Vanovskiy
Skoltech, ADASE group led by Prof. Evgeny Burnaev
Igor Labutin
Skoltech, ADASE group led by Prof. Evgeny Burnaev
Abstract
Links
Streamed at November 22, 2021
Watch on YoutubeArchive
Streamed at February 7, 2022
What kind of data and tools do customers need today for air quality monitoring and management?
Dmitry Trubitsyn
Founder of CityAir
Streamed at January 17, 2022
Data Science, A.I., Air Pollution, and Climate Change
Sam Silva
Head of Atmospheric Composition and Earth Data Science Group, Department of Earth Sciences, University of Southern California
Streamed at December 13, 2021
Simulation of Buoyancy Effects in Urban Microclimate
Mikhail Khrebtov
Laboratory of Power Plant Aerodynamics, Kutateladze Institute of Thermophysics, SB RAS
Streamed at November 29, 2021
A Survey of Atmospheric Sciences Research Conducted with Observational Methods Coupled to Machine Learning Tools
Armin Sorooshian
Department of Chemical & Environmental Engineering, The University of Arizona
Streamed at November 22, 2021
Physically informed super resolution models for regional weather forecasts. Work in progress
Vladimir Vanovskiy
Skoltech, ADASE group led by Prof. Evgeny Burnaev
Igor Labutin
Skoltech, ADASE group led by Prof. Evgeny Burnaev
Streamed at November 15, 2021
Towards Seamles Prediction of Chemical Weather and Air Quality
Ave Arellano
Hydrology and Atmospheric Sciences, GIDP Applied Mathematics, GIDP Remote Sensing and Spatial Analysis
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