ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.
Sungduk YuWalter M. HannahLiran PengMohamed Aziz BhouriRitwik GuptaJerry LinBjörn LütjensJustus C. WillTom BeuclerBryce E. HarropBenjamin R. HillmanAndrea M. JenneySavannah L. FerrettiNana LiuAnima AnandkumarNoah D. BrenowitzVeronika EyringPierre GentineStephan MandtJaideep PathakCarl VondrickRose YuLaure ZannaRyan P. AbernatheyFiaz AhmedDavid C. BaderPierre BaldiElizabeth A. BarnesGunnar BehrensChristopher S. BrethertonJulius J. M. BuseckePeter M. CaldwellWayne ChuangYilun HanYu HuangFernando Iglesias-SuarezSanket JantreKarthik KashinathMarat KhairoutdinovThorsten KurthNicholas J. LutskoPo-Lun MaGriffin MooersJ. David NeelinDavid A. RandallSara ShamekhAkshay SubramaniamMark A. Tayloret al.Published in: CoRR (2023)
Keyphrases
- multiscale
- high resolution
- training dataset
- low resolution
- image processing
- super resolution
- benchmark datasets
- small scale
- training set
- test set
- virtual machine
- training process
- edge detection
- real life
- scale space
- training phase
- multiple scales
- high quality
- classifier training
- artificial intelligence
- long term
- image analysis
- computer science
- remote sensing
- high frequency
- satellite images
- training examples
- natural images
- distributed systems
- database