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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting

As of 24 July 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.11457.

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measured 31 of 31 reference resolution

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Outbound references

Observation 9cef47cc-331f-4bbd-a1f0-4e04dbefe6e0 · outbound

This paper cites Machine learning for precipitation nowcasting from radar im- ages, 2019.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Machine learning for precipitation nowcasting from radar im- ages, 2019

Reference 1

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Observation 5aa8de73-eacf-4888-b5be-b30d2811e759 · outbound

This paper cites The rise of data-driven weather forecasting: A first statisti- cal assessment of machine learning–based weather forecasts in an operational-like context.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting The rise of data-driven weather forecasting: A first statisti- cal assessment of machine learning–based weather forecasts in an operational-like context

Reference 2

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This paper cites Pangu-weather: A 3d high- resolution model for fast and accurate global weather forecast, 2022.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Pangu-weather: A 3d high- resolution model for fast and accurate global weather forecast, 2022

Reference 3

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Observation 5552f40b-5afe-452a-b98c-eb661ce0c20f · outbound

This paper cites FuXi: a cascade machine learning forecasting system for 15-day global weather forecast.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting FuXi: a cascade machine learning forecasting system for 15-day global weather forecast

Reference 4

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Observation 57b71ae2-64fd-41b6-ae2d-87d140fb4d19 · outbound

This paper cites URL http s://www.nature.com/articles/s41612-023 -00512-1.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting URL http s://www.nature.com/articles/s41612-023 -00512-1

Reference 5

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This paper cites URL https://www.ecmwf.int/ en/about/media-centre/news/2024/anemoi -new-framework-weather-forecasting-bas ed-machine-learning.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting URL https://www.ecmwf.int/ en/about/media-centre/news/2024/anemoi -new-framework-weather-forecasting-bas ed-machine-learning

Reference 6

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Observation 4de064df-2f7f-40a4-9719-5a1e84f704e0 · outbound

This paper cites IFS Documentation CY49R1 - Part III: Dynamics and Numerical Procedures, chapter 3.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting IFS Documentation CY49R1 - Part III: Dynamics and Numerical Procedures, chapter 3

Reference 7

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This paper cites IFS Documentation CY49R1 - Part IV: Physical Processes, chapter 4.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting IFS Documentation CY49R1 - Part IV: Physical Processes, chapter 4

Reference 8

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This paper cites The operational forecast process at metcoop.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting The operational forecast process at metcoop

Reference 9

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This paper cites HarmonEPS—The HARMONIE En- semble Prediction System.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting HarmonEPS—The HARMONIE En- semble Prediction System

Reference 10

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This paper cites Singleton, Morten Ø.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Singleton, Morten Ø

Reference 11

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Unresolved cited work

Reference 12

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This paper cites The ERA5 global reanaly- sis.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting The ERA5 global reanaly- sis

Reference 13

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This paper cites Forecasting Global Weather with Graph Neural Networks.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Forecasting Global Weather with Graph Neural Networks

Reference 14

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This paper cites GraphCast: Learning skillful medium-range global weather forecasting.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting GraphCast: Learning skillful medium-range global weather forecasting

Reference 15

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Observation 109287ff-27b1-4ae4-ae45-28292e18b382 · outbound

This paper cites AIFS -- ECMWF's data-driven forecasting system.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting AIFS -- ECMWF's data-driven forecasting system

Reference 16

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This paper cites A multi-scale loss formulation for learning a probabilistic model with proper score optimisation.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting A multi-scale loss formulation for learning a probabilistic model with proper score optimisation

Reference 17

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Aifs-crps: en- semble forecasting using a model trained with a loss function based on the continuous ranked probability score

Reference 18

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Modulated adaptive fourier neural oper- ators for temporal interpolation of weather forecasts,

Reference 19

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Unresolved cited work

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Modulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts

Reference 21

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting An update to ECMWF’s machine-learned weather forecast model AIFS, 2025

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Re- gional data-driven weather modeling with a global stretched-grid

Reference 23

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting URL https: //journals.ametsoc.org/view/journals/a ies/aop/AIES-D-25-0001.1/AIES-D-25-000 1.1.xml

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting High-resolution probabilistic data- driven weather modeling with a stretched-grid

Reference 25

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Graph-based Neural Weather Prediction for Limited Area Modeling

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting GenCast: Diffusion-based ensemble forecasting for medium-range weather

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This paper cites The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1.Geoscientific Model Development, 11(1):257–281, January 2018.

HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1.Geoscientific Model Development, 11(1):257–281, January 2018

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting Multi-radar multi-sensor (mrms) quanti- tative precipitation estimation: Initial operating ca- pabilities

Reference 30

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HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting FuXi-2.0: Advancing machine learning weather forecasting model for practical applications

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