Statistical Downscaling of Temperature Distributions from the Synoptic
Scale to the Mesoscale Using Deep Convolutional Neural Networks
- URL: http://arxiv.org/abs/2007.10839v1
- Date: Mon, 20 Jul 2020 06:24:08 GMT
- Title: Statistical Downscaling of Temperature Distributions from the Synoptic
Scale to the Mesoscale Using Deep Convolutional Neural Networks
- Authors: Tsuyoshi Thomas Sekiyama
- Abstract summary: One of the promising applications is developing a statistical surrogate model that converts the output images of low-resolution dynamic models to high-resolution images.
Our study evaluates a surrogate model that downscales synoptic temperature fields to mesoscale temperature fields every 6 hours.
If the surrogate models are implemented at short time intervals, they will provide high-resolution weather forecast guidance or environment emergency alerts at low cost.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning, particularly convolutional neural networks for image
recognition, has been recently used in meteorology. One of the promising
applications is developing a statistical surrogate model that converts the
output images of low-resolution dynamic models to high-resolution images. Our
study exhibits a preliminary experiment that evaluates the performance of a
model that downscales synoptic temperature fields to mesoscale temperature
fields every 6 hours. The deep learning model was trained with operational
22-km gridded global analysis surface winds and temperatures as the input,
operational 5-km gridded regional analysis surface temperatures as the desired
output, and a target domain covering central Japan. The results confirm that
our deep convolutional neural network (DCNN) is capable of estimating the
locations of coastlines and mountain ridges in great detail, which are not
retained in the inputs, and providing high-resolution surface temperature
distributions. For instance, while the average root-mean-square error (RMSE) is
2.7 K between the global and regional analyses at altitudes greater than 1000
m, the RMSE is reduced to 1.0 K, and the correlation coefficient is improved
from 0.6 to 0.9 by the surrogate model. Although this study evaluates a
surrogate model only for surface temperature, it probably can be improved by
augmenting the downscaling variables and vertical profiles. Surrogate models of
DCNNs require only a small amount of computational power once their training is
finished. Therefore, if the surrogate models are implemented at short time
intervals, they will provide high-resolution weather forecast guidance or
environment emergency alerts at low cost.
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