Deep Learning Models Ratify ENSO's Substantial Impact on Antarctic Sea Ice Subseasonal Predictability: supplemental data ...

• Main Author: Wang, Yunhe
• Format: Dataset
• Language: unknown
• Published: figshare 2024
• Subjects: Physical oceanography; Antarctic; Antarc*; Sea ice
• Online Access: https://dx.doi.org/10.6084/m9.figshare.24572866.v3; https://figshare.com/articles/dataset/AI_Model_Affirms_ENSO_s_Boost_to_Subseasonal_Predictability_of_Antarctic_Sea_Ice_supplemental_data/24572866/3

cor_spatial38c.hdf contains SIPNet skill information, comprising four subsets: 'mid', 'nino', 'nina', and 'all', representing model skill under neutral conditions, El Niño, La Niña, and the entire time range, respectively.acc_per_spatial38.hdf is similar to cor_spatial38c but represents the model skill for anomaly persistence.cor_spatial38_linear.hdf, like cor_spatial38c, represents the model skill but specifically for the linear SIPNet model.sic_stddev.hdf contains sea ice variability information with three subsets: 'nino', 'mid', and 'nina', denoting sea ice variability under El Niño, neutral conditions, and La Niña, respectively.SIPN_ENSO_ice_model1979_2022trian1980_2022test8_8_2236.h5: Antarctic weekly SIC prediction from SIPNet model. The data has two sub-datasets: 'predicted_test' and 'y_test'. 'predicted_test' refers to the predicted sea ice value, while 'y_test' is the observed value corresponding to 'predicted_test'. SIPN_ENSO_ice_model1979_2022trian1980_2022test8_8linear_2236.h5: Similar to ...