2020
DOI: 10.1002/met.1961
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A comparative study of convolutional neural network models for wind field downscaling

Abstract: We analyze the applicability of convolutional neural network (CNN) architectures for downscaling of short‐range forecasts of near‐surface winds on extended spatial domains. Short‐range wind forecasts (at the 100 m level) from European Centre for Medium Range Weather Forecasts ERA5 reanalysis initial conditions at 31 km horizontal resolution are downscaled to mimic high resolution (HRES) (deterministic) short‐range forecasts at 9 km resolution. We evaluate the downscaling quality of four exemplary CNN architect… Show more

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Cited by 67 publications
(55 citation statements)
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“…A possible solution is the use of DL techniques as described in [63]. Examples of the use of DL for the downscaling of wind fields were given in [64,65]. An example of the use of DL for the downscaling of temperature was given in [66].…”
Section: Downscalingmentioning
confidence: 99%
“…A possible solution is the use of DL techniques as described in [63]. Examples of the use of DL for the downscaling of wind fields were given in [64,65]. An example of the use of DL for the downscaling of temperature was given in [66].…”
Section: Downscalingmentioning
confidence: 99%
“…A difference to the conventional U-Net is that we experimented with additional data channels that provide more information to resolve ambiguities in the inverse map f −1 . The effectiveness of additional data channels was already demonstrated by Vandal et al (2017) and Höhlein et al (2020) for downscaling. These additional channels include latitude and longitude such that the network can learn regional weather patterns, and altitude to include dependencies on the topography.…”
Section: Inputs and Outputsmentioning
confidence: 89%
“…In computer vision terms, this approach can be considered a multi-view super-resolution problem, whereas we investigate the more challenging single-image super-resolution. Höhlein et al (2020) studied multiple architectures for spatial downscaling of wind velocity data, including a U-Net based architecture and deep networks with residual learning. The latter resulted in the best performance on the wind velocity fields that they studied.…”
Section: Introductionmentioning
confidence: 99%
“…In this sense, some enhancements of CNN have been proposed for specific duties in recent times, to obtain greater accuracy in predicting visual recognition in data science, such as subpixel displacement measures [36], defect identification in high-speed trains [37], correlating image-like data out of quantum systems [38], modeling wind field downscaling [39], designing a zero knowledge proof scheme [40], classifying satellite image time series [41], working with ensembles [42], dealing with osteoporosis diagnoses [43], screening and staging diabetic retinopathy [44], analyzing cloud particles [45], inspecting diffraction data [46], or examining x-ray images [47].…”
Section: Convolutional Neural Networkmentioning
confidence: 99%