Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-resolution Probabilistic Precipitation Prediction for use in
  Climate Simulations   The accurate prediction of precipitation is important to allow for reliable
warnings of flood or drought risk in a changing climate. However, to make
trust-worthy predictions of precipitation, at a local scale, is one of the most
difficult challenges for today's weather and climate models. This is because
important features, such as individual clouds and high-resolution topography,
cannot be resolved explicitly within simulations due to the significant
computational cost of high-resolution simulations. Climate models are typically
run at $\sim$50-100 km resolution which is insufficient to represent local
precipitation events in satisfying detail. Here, we develop a method to make
probabilistic precipitation predictions based on features that climate models
can resolve well and that is not highly sensitive to the approximations used in
individual models. To predict, we will use a temporal compound Poisson
distribution dependent on the output of climate models at a location. We use
the output of Earth System models at coarse resolution $\sim$50 km as input and
train the statistical models towards precipitation observations over Wales at
$\sim$10 km resolution. A Bayesian inferential scheme is provided so that the
compound-Poisson model can be inferred using a
Gibbs-within-Metropolis-Elliptic-Slice sampling scheme which enables us to
quantify the uncertainty of our predictions. In addition, we use a Gaussian
process regressor on the posterior samples of the model parameters, to infer a
spatially coherent model and hence to produce spatially coherent rainfall
prediction. We illustrate the prediction performance of our model by training
over 5 years of the data up to 31st December 1999 and predicting precipitation
for 20 years afterwards for Cardiff and Wales.

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The categories of this paper are: cs
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: physics