Bias correction for spatially interpolated daily mean air temperature during winter in eastern Hokkaido using multimodal machine learning

Earth and Planetary Sciences

• Model 85%
• Bias 57%
• Temperature 42%
• Region 42%
• Air Temperature 42%
• Root-Mean-Square Error 42%
• Datum 28%
• Value 28%
• Utilization 28%
• Correction 28%
• Data Set 28%
• Interest 28%
• Prediction 28%
• Temperature Distribution 28%
• Winter 28%
• Landscape 28%
• Machine Learning 28%
• Wind 14%
• Cooling 14%
• Observation 14%
• Shape 14%
• Event 14%
• Size 14%
• Approach 14%
• Accumulation 14%
• Heterogeneity 14%
• Estimating 14%
• Quality 14%
• Surface Layer 14%
• Improvement 14%
• Cold Air 14%
• Service 14%
• Farmland 14%
• Formation Mechanism 14%
• Geography 14%
• Snow 14%

Agricultural and Biological Sciences

• Temperature 100%
• Bias 57%
• Roots 42%
• Winter 28%
• Topography 28%
• Learning 28%
• Meteorological Data 28%
• Surfaces 14%
• Variance 14%
• Wind 14%
• Snow 14%
• Geography 14%