Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling   Spatial Precipitation Downscaling is one of the most important problems in
the geo-science community. However, it still remains an unaddressed issue. Deep
learning is a promising potential solution for downscaling. In order to
facilitate the research on precipitation downscaling for deep learning, we
present the first REAL (non-simulated) Large-Scale Spatial Precipitation
Downscaling Dataset, RainNet, which contains 62,424 pairs of low-resolution and
high-resolution precipitation maps for 17 years. Contrary to simulated data,
this real dataset covers various types of real meteorological phenomena (e.g.,
Hurricane, Squall, etc.), and shows the physical characters - Temporal
Misalignment, Temporal Sparse and Fluid Properties - that challenge the
downscaling algorithms. In order to fully explore potential downscaling
solutions, we propose an implicit physical estimation framework to learn the
above characteristics. Eight metrics specifically considering the physical
property of the data set are raised, while fourteen models are evaluated on the
proposed dataset. Finally, we analyze the effectiveness and feasibility of
these models on precipitation downscaling task. The Dataset and Code will be
available at https://neuralchen.github.io/RainNet/.

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The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
2
The categories of this paper are: math
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The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: bayes-an