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Regional Reanalysis COSMO-REA6 - Standardised Parameters
The regional reanalysis system for Continental Europe matches the domain of the CORDEX EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6 km) instead... -
Backward Trajectories
Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period... -
Surface segregation in high-entropy alloys from alchemical machine learning: ...
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently,... -
Surface segregation in high-entropy alloys from alchemical machine learning: ...
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently,... -
Data for "A Lagrangian perspective on stable water isotopes during the West A...
This dataset contains backward trajectories analyzed in the study "A Lagrangian perspective on stable water isotopes during the West African Monsoon" (Diekmann et al., 2021).... -
HPSC Terrsys Fall School 2022 Training Material
These are sample output files of CORDEX simulations from the Community Land Model, version 3, and the COSMO meteorological model, kindly provided for training purposes by Dr.... -
Deep learning models for generation of precipitation maps based on NWP Code
Implementation of deep learning models for creating precipitation maps based on COSMO-DE-EPS forecast -
Deep learning models for generation of precipitation maps based on NWP Data
Numpy arrays used in the paper "Deep learning models for generation of precipitation maps based on NWP". trn = training set vld = validation set tst = test set x =... -
Post-processing of NWP precipitation forecasts using deep learning data
Train and test sets used for the ANNs post processing of NWP to predict precipitation
