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Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction.
Weather prediction by numerical process
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Guidelines for assessing the suitability of spatial climate data sets
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The data assimilation research testbed: A community facility
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
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Which polarimetric variables are important for weather/no-weather discrimination?
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Probabilistic 0–1-h convective initiation nowcasts that combine geostationary satellite observations and numerical weather prediction model data
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
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A unified approach to interpreting model predictions
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Data assimilation by artificial neural networks for an atmospheric general circulation model
Rosangela Saher Cintra and Haroldo F de Campos Velho · 2018
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Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres
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“dendrology” in numerical weather prediction: What random forests and logistic regression tell us about forecasting extreme precipitation
Gregory R Herman and Russ S Schumacher · 2018
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The ncep/ncar 40-year reanalysis project
Eugenia Kalnay, Masao Kanamitsu, Robert Kistler, William Collins, Dennis Deaven, Lev Gandin, Mark Iredell, Suranjana Saha, Glenn White, John Woollen, et al · 2018
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Neural networks for postprocessing ensemble weather forecasts
Stephan Rasp and Sebastian Lerch · 2018
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Deep learning to represent subgrid processes in climate models
Stephan Rasp, Michael S Pritchard, and Pierre Gentine · 2018
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Statistical downscaling of precipitation using machine learning techniques
DA Sachindra, Khandakar Ahmed, Md Mamunur Rashid, S Shahid, and BJC Perera · 2018
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Predicting weather forecast uncertainty with machine learning
Sebastian Scher and Gabriele Messori · 2018
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Machine learning for precipitation nowcasting from radar images
Shreya Agrawal, Luke Barrington, Carla Bromberg, John Burge, Cenk Gazen, and Jason Hickey · 2019
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Techniques for interpretable machine learning
Mengnan Du, Ninghao Liu, and Xia Hu · 2019
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Using a 10-year radar archive for nowcasting precipitation growth and decay: A probabilistic machine learning approach
Loris Foresti, Ioannis V Sideris, Daniele Nerini, Lea Beusch, and Urs Germann · 2019
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Interpretable deep learning for spatial analysis of severe hailstorms
David John Gagne II, Sue Ellen Haupt, Douglas W Nychka, and Gregory Thompson · 2019
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Making the black box more transparent: Understanding the physical implications of machine learning
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Interpretable machine learning: definitions, methods, and applications
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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Improving precipitation estimation using convolutional neural network
Baoxiang Pan, Kuolin Hsu, Amir AghaKouchak, and Soroosh Sorooshian · 2019
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An open source software suite for multi-dimensional meteorological data computation and visualisation
YaQiang Wang · 2019
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Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction
Peter AG Watson · 2019
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Can machines learn to predict weather? using deep learning to predict gridded 500-hpa geopotential height from historical weather data
Jonathan A Weyn, Dale R Durran, and Rich Caruana · 2019
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Prediction of tropical cyclone genesis from mesoscale convective systems using machine learning
Tao Zhang, Wuyin Lin, Yanluan Lin, Minghua Zhang, Haiyang Yu, Kathy Cao, and Wei Xue · 2019
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Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks
Ismail Alaoui Abdellaoui and Siamak Mehrkanoon · 2020
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A machine learning-based global atmospheric forecast model
Troy Arcomano, Istvan Szunyogh, Jaideep Pathak, Alexander Wikner, Brian R Hunt, and Edward Ott · 2020
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al · 2020
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Rainnet v1. 0: a convolutional neural network for radar-based precipitation nowcasting
Georgy Ayzel, Tobias Scheffer, and Maik Heistermann · 2020
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Indicator patterns of forced change learned by an artificial neural network
Elizabeth A Barnes, Benjamin Toms, James W Hurrell, Imme Ebert-Uphoff, Chuck Anderson, and David Anderson · 2020
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Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?
Nicola Bodini, Julie K Lundquist, and Mike Optis · 2020
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Advances of four machine learning methods for spatial data handling: A review
Peijun Du, Xuyu Bai, Kun Tan, Zhaohui Xue, Alim Samat, Junshi Xia, Erzhu Li, Hongjun Su, and Wei Liu · 2020
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Evaluation, tuning and interpretation of neural networks for working with images in meteorological applications
Imme Ebert-Uphoff and Kyle Hilburn · 2020
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Predicting rapid intensification in north atlantic and eastern north pacific tropical cyclones using a convolutional neural network
Sarah M Griffin, Anthony Wimmers, and Christopher S Velden · 2022
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Improving predictions of land-atmosphere interactions based on a hybrid data assimilation and machine learning method
Xinlei He, Yanping Li, Shaomin Liu, Tongren Xu, Fei Chen, Zhenhua Li, Zhe Zhang, Rui Liu, Lisheng Song, Ziwei Xu, et al · 2022
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Graphcast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, et al · 2022
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Combining data assimilation and machine learning to estimate parameters of a convective-scale model
Stefanie Legler and Tijana Janjić · 2022
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Comparing and interpreting differently designed random forests for next-day severe weather hazard prediction
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The era5 global reanalysis
Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, et al · 2020
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Development and interpretation of a neural-network-based synthetic radar reflectivity estimator using goes-r satellite observations
Kyle A Hilburn, Imme Ebert-Uphoff, and Steven D Miller · 2020
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A machine learning model for predicting silica concentrations through time series analysis of mining data
Seung Hoon Lee, Yeon Ah Yoon, Jin Hyeong Jung, Tai-Woo Chang, Yong Soo Kim, et al · 2020
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Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network
Jussi Leinonen, Daniele Nerini, and Alexis Berne · 2020
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Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
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Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes
Axel Seifert and Stephan Rasp · 2020
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Physically interpretable neural networks for the geosciences: Applications to earth system variability
Benjamin A Toms, Elizabeth A Barnes, and Imme Ebert-Uphoff · 2020
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Eric D Loken, Adam J Clark, and Amy McGovern · 2022
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Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
Antonios Mamalakis, Elizabeth A Barnes, and Imme Ebert-Uphoff · 2022
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Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al · 2022
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High-resolution downscaling with interpretable deep learning: Rainfall extremes over new zealand
Neelesh Rampal, Peter B Gibson, Abha Sood, Stephen Stuart, Nicolas C Fauchereau, Chris Brandolino, Ben Noll, and Tristan Meyers · 2022
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Identifying relations between deep convection and the large-scale atmosphere using explainable artificial intelligence
MH Retsch, C Jakob, and MS Singh · 2022
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Diagnosing supercell environments: A machine learning approach
Stephen A Shield and Adam L Houston · 2022
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Using an explainable machine learning approach to characterize earth system model errors: Application of shap analysis to modeling lightning flash occurrence
Sam J Silva, Christoph A Keller, and Joseph Hardin · 2022
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Short-term weather forecasting using spatial feature attention based lstm model
Masooma Ali Raza Suleman and S Shridevi · 2022
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Deep learning augmented data assimilation: Reconstructing missing information with convolutional autoencoders
Yueya Wang, Xiaoming Shi, Lili Lei, and Jimmy Chi-Hung Fung · 2022
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Hourly rolling correction of precipitation forecast via convolutional and long short-term memory networks
Ruyi Yang, Jianli Mu, Shudong Wang, and Lijuan Wang · 2022
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Terrain-guided flatten memory network for deep spatial wind downscaling
Tingzhao Yu, Ruyi Yang, Yan Huang, Jinbing Gao, and Qiuming Kuang · 2022
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Philine Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, and Marina M-C Höhne · 2023
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Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets
Sandy Chkeir, Aikaterini Anesiadou, Alessandra Mascitelli, and Riccardo Biondi · 2023
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Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine N Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2023
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Using explainability to inform statistical downscaling based on deep learning beyond standard validation approaches
Jose González-Abad, Jorge Baño-Medina, and José Manuel Gutiérrez · 2023
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Deep learning forecast uncertainty for precipitation over the western united states
Weiming Hu, Mohammadvaghef Ghazvinian, William E Chapman, Agniv Sengupta, Fred Martin Ralph, and Luca Delle Monache · 2023
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A survey on explainability of graph neural networks
Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, and Sourav Medya · 2023
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Neural general circulation models
Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, James Lottes, Stephan Rasp, Peter Düben, Milan Klöwer, et al · 2023
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Changes in united states summer temperatures revealed by explainable neural networks
Zachary M Labe, Nathaniel Johnson, and Thomas L Delworth · 2023
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Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, et al · 2023
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Thunderstorm nowcasting with deep learning: A multi-hazard data fusion model
Jussi Leinonen, Ulrich Hamann, Ioannis V Sideris, and Urs Germann · 2023
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Deep-learning post-processing of short-term station precipitation based on nwp forecasts
Qi Liu, Xiao Lou, Zhongwei Yan, Yajie Qi, Yuchao Jin, Shuang Yu, Xiaoliang Yang, Deming Zhao, and Jiangjiang Xia · 2023
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Climax: A foundation model for weather and climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover · 2023
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Gencast: Diffusion-based ensemble forecasting for medium-range weather
Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Timo Ewalds, Andrew El-Kadi, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, and Matthew Willson · 2023
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Seasonal forecast of winter precipitation over china using machine learning models
QiFeng Qian and XiaoJing Jia · 2023
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Hybrid explainable srnn-lstm architecture for irradiance, temperature and wind speed forecasting
Umamaheswari Rajasekaran, GK Sriram, A Malini, and Vandana Sharma · 2023
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Sar-unet: Small attention residual unet for explainable nowcasting tasks
Mathieu Renault and Siamak Mehrkanoon · 2023
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A deep learning model for estimating tropical cyclone wind radius from geostationary satellite infrared imagery
Chong Wang and Xiaofeng Li · 2023
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Temporal dynamic network with learnable coupled adjacent matrix for wind forecasting
Tingzhao Yu and Ruyi Yang · 2023
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Skilful nowcasting of extreme precipitation with nowcastnet
Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I Jordan, and Jianmin Wang · 2023
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Unifying fourteen post-hoc attribution methods with taylor interactions
Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, and Quanshi Zhang · 2024
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