AI-GOMS: Large AI-Driven Global Ocean Modeling System

Yanai, M., Maruyama, T.: Stratospheric wave disturbances propagating over the equatorial pacific. Journal of the Meteorological Society of Japan. Ser. II 44

1966

Earlier work this paper cites.

Bjerknes, J.: Atmospheric teleconnections from the equatorial pacific. Monthly weather review 97

1969

Earlier work this paper cites.

Røed, L.P.: Modelling mesoscale features in the ocean. Waves and Nonlinear Processes in Hydrodynamics, 383–396 (1996)

1996

Earlier work this paper cites.

Chassignet, E.P., Hurlburt, H.E., Smedstad, O.M., Halliwell, G.R., Hogan, P.J., Wallcraft, A.J., Baraille, R., Bleck, R.: The hycom (hybrid coordinate ocean model) data assimilative system. Journal of Marine Systems 65

2007

Earlier work this paper cites.

Hurlburt, H.E., Chassignet, E.P., Cummings, J.A., Kara, A.B., Metzger, E.J., Shriver, J.F., Smedstad, O.M., Wallcraft, A.J., Barron, C.N.: Eddy-resolving global ocean prediction. Technical report, Naval Research Lab Stennis Space Center Ms Oceanography Div (2009)

2009

Earlier work this paper cites.

Gregg, W., Rousseaux, C.: Nasa ocean biogeo-chemical model assimilating satellite chlorophyll data global daily vr2017. Goddard Earth Sciences Data and Information Services Center (GES DISC)[data set], Greenbelt, MD, USA 10

2017

Earlier work this paper cites.

Fox-Kemper, B., Adcroft, A., Böning, C.W., Chassignet, E.P., Curchitser, E., Danabasoglu, G., Eden, C., England, M.H., Gerdes, R., Greatbatch, R.J., et al

2019

Earlier work this paper cites.

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat, f.: Deep learning and process understanding for data-driven earth system science. Nature 566

2019

Earlier work this paper cites.

Levin, L.A., Bett, B.J., Gates, A.R., Heimbach, P., Howe, B.M., Janssen, F., McCurdy, A., Ruhl, H.A., Snelgrove, P., Stocks, K.I., et al

2019

Earlier work this paper cites.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

Original

2020

Cited alongside, same era.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al

2020

Cited alongside, same era.

Rasp, S., Dueben, P.D., Scher, S., Weyn, J.A., Mouatadid, S., Thuerey, N.: Weatherbench: a benchmark data set for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems 12

2020

Cited alongside, same era.

Sonnewald, M., Lguensat, R., Jones, D.C., Dueben, P.D., Brajard, J., Balaji, V.: Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters 16

2021

Cited alongside, same era.

Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., Tian, Q.: Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv preprint arXiv:2211.02556 (2022)

Original

2022

Later among the works it cites.

Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Pritzel, A., Ravuri, S., Ewalds, T., Alet, F., Eaton-Rosen, Z., et al.: Graphcast: Learning skillful medium-range global weather forecasting. arXiv preprint arXiv:2212.12794 (2022)

Original

2022

Later among the works it cites.

He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)

2022

Later among the works it cites.

Environmental Information, N.N.C.: ETOPO 2022 15 Arc-Second Global Relief Model. NOAA Boulder, CO, USA (2022)

2022

Later among the works it cites.