English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Traditional weather forecasting relies on domain expertise andcomputationally intensive numerical simulation systems. Recently, with thedevelopment of a data-driven approach, weather forecasting based on deeplearning has been receiving attention. Deep learning-based weather forecastinghas made stunning progress, from various backbone studies using CNN, RNN, andTransformer to training strategies using weather observations datasets withauxiliary inputs. All of this progress has contributed to the field of weatherforecasting; however, many elements and complex structures of deep learningmodels prevent us from reaching physical interpretations. This paper proposes aSImple baseline with a spatiotemporal context Aggregation Network (SIANet) thatachieved state-of-the-art in 4 parts of 5 benchmarks of W4C22. This simple butefficient structure uses only satellite images and CNNs in an end-to-endfashion without using a multi-model ensemble or fine-tuning. This simplicity ofSIANet can be used as a solid baseline that can be easily applied in weatherforecasting using deep learning.

Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network