Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Addressing Deep Learning Model Uncertainty in Long-Range Climate
  Forecasting with Late Fusion   Global warming leads to the increase in frequency and intensity of climate
extremes that cause tremendous loss of lives and property. Accurate long-range
climate prediction allows more time for preparation and disaster risk
management for such extreme events. Although machine learning approaches have
shown promising results in long-range climate forecasting, the associated model
uncertainties may reduce their reliability. To address this issue, we propose a
late fusion approach that systematically combines the predictions from multiple
models to reduce the expected errors of the fused results. We also propose a
network architecture with the novel denormalization layer to gain the benefits
of data normalization without actually normalizing the data. The experimental
results on long-range 2m temperature forecasting show that the framework
outperforms the 30-year climate normals, and the accuracy can be improved by
increasing the number of models.

26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga