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TRU-NET : a deep learning approach to high resolution prediction of rainfall
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Adewoyin, Rilwan A., Dueben, Peter, Watson, Peter, He, Yulan and Dutta, Ritabrata (2022) TRU-NET : a deep learning approach to high resolution prediction of rainfall. Machine Learning, 110 (8). pp. 2035-2062. doi:10.1007/s10994-021-06022-6
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Official URL: http://dx.doi.org/10.1007/s10994-021-06022-6
Abstract
Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and heavy precipitation events. However, these numerical simulators produce outputs with low spatial resolution that exhibit difficulties representing precipitation events accurately. This is mainly due to computational limitations on the spatial resolution used when simulating multi-scale weather dynamics in the atmosphere. To improve the prediction of high resolution precipitation we apply a Deep Learning (DL) approach using input data from a reanalysis product, that is comparable to a climate model’s output, but can be directly related to precipitation observations at a given time and location. Further, our input excludes local precipitation, but includes model fields (weather variables) that are more predictable and generalizable than local precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an encoderdecoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers to effectively model multi-scale spatio-temporal weather processes. We also propose a non-stochastic variant of the conditionalcontinuous (CC) loss function to capture the zero-skewed patterns of rainfall. Experiments show that our models, trained with our CC loss, consistently attain lower RMSE and MAE scores than a DL model prevalent in precipitation downscaling and outperform a state-of-the-art dynamical weather model. Moreover, by evaluating the performance of our model under various data formulation strategies, for the training and test sets, we show that there is enough data for our deep learning approach to output robust, high-quality results across seasons and varying regions.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Statistics |
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| Library of Congress Subject Headings (LCSH): | Climatology -- Mathematical models, Climatology -- Simulation methods, Precipitation forecasting , Rainfall probabilities , Neural networks (Computer science), Deep learning (Machine learning) | ||||||||
| Journal or Publication Title: | Machine Learning | ||||||||
| Publisher: | Springer | ||||||||
| ISSN: | 2632-2153 | ||||||||
| Official Date: | August 2022 | ||||||||
| Dates: |
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| Volume: | 110 | ||||||||
| Number: | 8 | ||||||||
| Page Range: | pp. 2035-2062 | ||||||||
| DOI: | 10.1007/s10994-021-06022-6 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access |
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