Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Maksimālās kovariācijas analīze× | Modelis WRF× | |
|---|---|---|
| Nozare | Meteoroloģija | Meteoroloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1992 | 2000 |
| Autors≠ | Bretherton, Wallace | Skamarock and Klemp |
| Tips≠ | Covariance decomposition method | Atmospheric simulation system |
| Pirmavots≠ | Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., & Blade, I. (1992). The effective number of spatial degrees of freedom of a time-varying field. Journal of the Atmospheric Sciences, 49(11), 1063-1083. link ↗ | Skamarock, W. C., Klemp, J. B., Dudhia, J., et al. (2008). A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR. link ↗ |
| Citi nosaukumi | MCA, Singular value decomposition, SVD analysis, Covariance analysis | Weather Research and Forecasting, WRF, ARW, NMM |
| Saistītās≠ | 2 | 4 |
| Kopsavilkums≠ | Maximum covariance analysis (MCA) is a statistical technique that identifies coupled patterns of variability between two spatially distributed fields (e.g., sea surface temperature and precipitation). Unlike EOF analysis which focuses on variance in a single field, MCA identifies spatial patterns that are maximally correlated between two different fields. | The Weather Research and Forecasting (WRF) model is a mesoscale atmospheric simulation system used for weather forecasting, research, and climate applications. Developed cooperatively by NCAR, NOAA, and academic institutions, WRF became operational in 2004 and has become one of the most widely used atmospheric models worldwide. |
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