Cover Image

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Papacharalampous, Georgia, Tyralis, Hristos, Doulamis, Nikolaos, Doulamis, Anastasios
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Atmospheric and Oceanic Physics Applications Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products
by: Papacharalampous, Georgia, et al.
Published: (2024)

Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products
by: Papacharalampous, Georgia, et al.
Published: (2024)

Ocean-DC: An analysis ready data cube framework for environmental and climate change monitoring over the port areas
by: Kavouras, Ioannis, et al.
Published: (2024)

Loss functions arising from the index of agreement
by: Tyralis, Hristos, et al.
Published: (2025)

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
by: Berlinghieri, Renato, et al.
Published: (2023)

Uncertainty quantification for data-driven weather models
by: Bülte, Christopher, et al.
Published: (2024)

Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents
by: Papacharalampous, Georgia, et al.
Published: (2022)

Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments
by: Chandra, Rohitash, et al.
Published: (2024)

Generative machine learning methods for multivariate ensemble post-processing
by: Chen, Jieyu, et al.
Published: (2022)

Learning with the Nash-Sutcliffe loss
by: Tyralis, Hristos, et al.
Published: (2026)

A review of predictive uncertainty estimation with machine learning
by: Tyralis, Hristos, et al.
Published: (2022)

covXtreme : MATLAB software for non-stationary penalised piecewise constant marginal and conditional extreme value models
by: Towe, Ross, et al.
Published: (2023)

Extending the blended generalized extreme value distribution
by: Krakauer, Nir Y.
Published: (2024)

Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data
by: Lehmann, Nils, et al.
Published: (2024)

Long-term foehn reconstruction combining unsupervised and supervised learning
by: Stauffer, Reto, et al.
Published: (2024)

Probabilistic storyline attribution using machine learning
by: Loer, Frieder, et al.
Published: (2026)

Neural general circulation models optimized to predict satellite-based precipitation observations
by: Yuval, Janni, et al.
Published: (2024)

A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models
by: Ning, Ding, et al.
Published: (2025)

Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
by: Ning, Ding, et al.
Published: (2025)

Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
by: Keisler, Julie, et al.
Published: (2026)

A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)
by: Nakamura, Jennifer, et al.
Published: (2026)

Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies
by: Kutsuna, Takuro, et al.
Published: (2026)

Spatiotemporal Forecasting in Climate Data Using EOFs and Machine Learning Models: A Case Study in Chile
by: Herrera, Mauricio, et al.
Published: (2025)

MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
by: Nath, Shruti, et al.
Published: (2024)

Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
by: Horat, Nina, et al.
Published: (2024)

Data-Driven Probabilistic Air-Sea Flux Parameterization
by: Wu, Jiarong, et al.
Published: (2025)

Global Climate Model Bias Correction Using Deep Learning
by: Pasula, Abhishek, et al.
Published: (2025)

Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification
by: Hart, J., et al.
Published: (2024)

Probabilistic measures afford fair comparisons of AIWP and NWP model output
by: Gneiting, Tilmann, et al.
Published: (2025)

Validating Climate Models with Spherical Convolutional Wasserstein Distance
by: Garrett, Robert C., et al.
Published: (2024)

Reconstructing Global Daily CO2 Emissions via Machine Learning
by: Li, Tao, et al.
Published: (2024)

Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks
by: Zanetta, Francesco, et al.
Published: (2024)

Score-based generative emulation of impact-relevant Earth system model outputs
by: Bouabid, Shahine, et al.
Published: (2025)

Physics-based vs. data-driven 24-hour probabilistic forecasts of precipitation for northern tropical Africa
by: Walz, Eva-Maria, et al.
Published: (2024)

Functional data decomposition reveals unexpectedly strong soil moisture-precipitation coupling over the Great Plains
by: Gao, Yifu, et al.
Published: (2025)

Regularization of the ensemble Kalman filter using a non-parametric, non-stationary spatial model
by: Tsyrulnikov, Michael, et al.
Published: (2023)

Pattern change of precipitation extremes in Bear Island
by: Ray, Arnob, et al.
Published: (2023)

The impact of the spatial resolution of wind data on multi-decadal wind power forecasts in Germany
by: Morelli, Sofia
Published: (2024)

Arnoldi Singular Vector perturbations for machine learning weather prediction
by: Winkler, Jens, et al.
Published: (2025)

Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
by: Wu, Jingyang, et al.
Published: (2025)