9 papers in the archive.
UNet corrects TSMP European simulation mismatches against COSMO-REA6 for precipitation and pressure, improving RMSE by about 22%.
Post-processing & calibrationForschungszentrum Jülich / Jülich Supercomputing Centre
CLGAN, a GAN with U-Net and LSTM generator, improves heavy-precipitation nowcasts up to 2 h over PredRNN-v2 and optical flow baselines.
NowcastingNanjing University of Information Science & Technology (NUIST)Forschungszentrum Jülich / Jülich Supercomputing Centre
Conditional GAN and U-Net generate high-resolution short-range precipitation maps from full NWP variables, reducing over-forecast bias; CGAN gives the most realistic output.
Downscaling & super-resolutionDeutscher Wetterdienst (DWD)Forschungszentrum Jülich / Jülich Supercomputing Centre
Compares ConvLSTM and adversarial SAVP models for hourly 12-hour 2 m temperature forecasts over Europe; SAVP reaches MSE about 2.3 K² versus 3.6 K² for ConvLSTM.
NowcastingForschungszentrum Jülich / Jülich Supercomputing Centre
Provides a recipe for building atmospheric-science ML benchmark datasets and lists domain-specific challenges and needed benchmarks for weather, climate and air quality.
Benchmarks, datasets & evaluationECMWFForschungszentrum Jülich / Jülich Supercomputing CentreNSF NCAR / UCARNVIDIAUniversity of Oklahoma (AI2ES)
Conceptual proposal for FAIR, reproducible HPC-based workflows for ML weather and precipitation forecasting, using the DeepRain project as a case.
Software, scaling & compressionDeutscher Wetterdienst (DWD)Forschungszentrum Jülich / Jülich Supercomputing Centre
Discusses whether deep learning could fully replace numerical weather models and data assimilation, reviewing ML concepts and their fit for weather data.
Global medium-range NWPForschungszentrum Jülich / Jülich Supercomputing Centre
Random forest and SVM classify polar stratospheric clouds from MIPAS infrared limb spectra, with random forest the most promising and less prone to overfitting.
Observations & remote sensingKarlsruhe Institute of Technology (KIT)Forschungszentrum Jülich / Jülich Supercomputing Centre
A one-hidden-layer neural network replaces Monin–Obukhov stability functions for surface-layer fluxes, matching the standard method at seven flux sites.
Hybrid physics-ML & parameterizationKarlsruhe Institute of Technology (KIT)Forschungszentrum Jülich / Jülich Supercomputing Centre
