Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

Reconstructing High Resolution ESM Data Through a Novel Fast Super Resolution Convolutional Neural Network (FSRCNN)

(2022) Linsey S. Passarella et al.   GEOPHYSICAL RESEARCH LETTERS

Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach

(2022) Antoine Doury et al.   CLIMATE DYNAMICS

Wildfire Danger Prediction and Understanding With Deep Learning

(2022) Spyros Kondylatos et al.   GEOPHYSICAL RESEARCH LETTERS

A container-based workflow for distributed training of deep learning algorithms in HPC clusters

(2022) Jose González-Abad et al.   Cluster Computing-The Journal of Networks Software Tools and Applications

On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections

(2021) Jorge Baño-Medina et al.   CLIMATE DYNAMICS

Assessing Decadal Predictability in an Earth‐System Model Using Explainable Neural Networks

(2021) Benjamin A. Toms et al.   GEOPHYSICAL RESEARCH LETTERS

Statistical downscaling of daily temperature and precipitation over China using Deep Learning Neural Models: Localization and Comparison with other methods

(2020) Lei Sun et al.   INTERNATIONAL JOURNAL OF CLIMATOLOGY

Evaluation, Tuning and Interpretation of Neural Networks for Working with Images in Meteorological Applications

(2020) Imme Ebert-Uphoff et al.   BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY

Deep Learning for Image Super-Resolution: A Survey

(2020) Zhihao Wang et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Improving Precipitation Estimation Using Convolutional Neural Network

(2019) Baoxiang Pan et al.   WATER RESOURCES RESEARCH

Making the black box more transparent: Understanding the physical implications of machine learning

(2019) Amy McGovern et al.   BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY

Statistical downscaling of precipitation using machine learning techniques

(2018) D.A. Sachindra et al.   ATMOSPHERIC RESEARCH

An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment

(2018) J. M. Gutiérrez et al.   INTERNATIONAL JOURNAL OF CLIMATOLOGY

An R-based open framework for reproducible climate data access and post-processing

(2018) M. Iturbide et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation

(2018) Thomas Vandal et al.   THEORETICAL AND APPLIED CLIMATOLOGY

Spatial downscaling of precipitation using adaptable random forests

(2016) Xiaogang He et al.   WATER RESOURCES RESEARCH

VALUE: A framework to validate downscaling approaches for climate change studies

(2015) Douglas Maraun et al.   Earths Future

Reassessing Statistical Downscaling Techniques for Their Robust Application under Climate Change Conditions

(2012) J. M. Gutiérrez et al.   JOURNAL OF CLIMATE

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

(2011) D. P. Dee et al.   QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada

(2011) Manish Kumar Goyal et al.   THEORETICAL AND APPLIED CLIMATOLOGY

Statistical Downscaling of Wind Variability from Meteorological Fields

(2010) Robert J. Davy et al.   BOUNDARY-LAYER METEOROLOGY