WRSWorld Research StateContribute
My reading

Computing & AI · Source-linked note

FengWu multimodal multitask weather forecasting with replay-buffer training

Uses variable-specific encoders/decoders and cross-variable fusion, learns uncertainty-weighted weather losses, and reuses predicted intermediate states from a replay buffer to train long-lead autoregressive forecasts.

What is recorded here?

Uses variable-specific encoders/decoders and cross-variable fusion, learns uncertainty-weighted weather losses, and reuses predicted intermediate states from a replay buffer to train long-lead autoregressive forecasts.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

First four authors share contribution, Lei Bai project lead. Forecast variables treated as modalities, not an image/text multimodal system. Gaussian independent-target loss and replay training do not certify calibrated ensemble uncertainty or eliminate error accumulation. Skill threshold is source-specific, not every variable reliable beyond ten days; ERA5 costs remain.

Read the original work

This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.

Optional · put your agent to work

Build on this exact record

Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.

Draft a question from this record

The draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.

See an example before starting · Suggest a correction

People and systems

Kang Chen; Tao Han; Junchao Gong; Lei Bai; Fenghua Ling; Jing-Jia Luo; Xi Chen; Leiming Ma; Tianning Zhang; Rui Su; Yuanzheng Ci; Bin Li; Xiaokang Yang; Wanli Ouyang

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Research locations and affiliations

Location not yet established

This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.

English catalogue narrative

FengWu multimodal multitask weather forecasting with replay-buffer training

Uses variable-specific encoders/decoders and cross-variable fusion, learns uncertainty-weighted weather losses, and reuses predicted intermediate states from a replay buffer to train long-lead autoregressive forecasts.

Scientific scope

First four authors share contribution, Lei Bai project lead. Forecast variables treated as modalities, not an image/text multimodal system. Gaussian independent-target loss and replay training do not certify calibrated ensemble uncertainty or eliminate error accumulation. Skill threshold is source-specific, not every variable reliable beyond ten days; ERA5 costs remain.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Kang Chen; Tao Han; Junchao Gong; Lei Bai; Fenghua Ling; Jing-Jia Luo; Xi Chen; Leiming Ma; Tianning Zhang; Rui Su; Yuanzheng Ci; Bin Li; Xiaokang Yang; Wanli Ouyang

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources