Geophysical Research Letters
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Key Points

  • The Subseasonal-to-Seasonal prediction of summer precipitation over southern China is improved with a U-Net based deep learning method

  • The U-Net demonstrated promising performance in both general statistics and extreme events and shows superiority to the quantile mapping benchmark

  • The model skills arise from precipitation itself at the early stage, while atmospheric factors play important roles at longer lead times

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Earth and Space Science VOL.12 ISSUE 3 | 6 March 2025

Geophysics Indicator of Sandstone‐Type Uranium Mineralization in the Northern Ordos Basin, China: Analysis From Gravity and Magnetic Data

M L Xu + Y B Yang + Y M Deng ...

Ordos Basin, one of the largest uranium resource areas in China, holds significant potential due to its favorable metallogenic geological conditions and promising potential....

Geophysical Research Letters VOL.52 ISSUE 6 | 26 March 2025

Deformation Controlled Fluid Mass‐Transfer Processes in Ancient Orogens

Graham Hill + Andrew J Calvert + Ben Frieman ...

Despite abundant empirical evidence, the details of coupled deformation and mass transfer processes within a framework of the crustal architecture of ancient orogens remains...

Earth and Space Science VOL.12 ISSUE 2 | 17 February 2025

PyReX: A Recursion Based Crossover Detection Algorithm in Python for Along‐Track Geophysical Measurements

K V N G Vikram + D V P Krishna + K M Sreejith

A crossover point is the location of intersection of any two ground tracks charted by multiple platforms (ships, satellite radar and laser altimeters etc.). Detection of crossovers...

AGU24 | 12 December 2024

Fast Convolutions for Geophysical Fluid Dynamics

Anthony Chen + Robert Krasny

Convolutions on the sphere are useful in many Geophysical Fluid Dynamics (GFD) problems, and many unstructured numerical methods make heavy use of them. However, when discretized,...

AGU24 | 12 December 2024

Efficient algorithm for computing long-time statistics for a class of geophysical models

Xiaoming Wang + Daozhi Han + Jack Coleman

We present a family of highly efficient, second-order-in-time numerical algorithms for approximating the long-term statistics and climate of a family of damped-forced geophysical...