← Back to all news
arXiv Machine Learning By Zhixing Ruan, Lixin Lu

A Sequence-to-Sequence ConvLSTM Approach for Leaf Area Index Forecasting over the South-Central United States

Read the original on arXiv Machine Learning →

arXiv:2608. 00879v1 Announce Type: cross Abstract: Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
2d ago

An Input-Frugal Deep Learning Framework for Weather-Driven National Crop-Yield Forecasting: A Case Study of Brazilian Soybean

The paper introduces a lightweight deep learning framework that forecasts Brazilian soybean yields using only routine weather data and two simple static inputs (crop year and agro-environmental label). Across 20 seasons, transformer-based models achieved the highest accuracy, outperforming traditional ridge regression and a moving‑average baseline by nearly 48%. Ablation studies show that the static inputs and spatial expansion improve performance without adding complexity, and SHAP analysis highlights the importance of crop year and weather variables in driving yield variations.

By Fernando Dupin da Cunha Mello (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil), Prashant Kumar (Global Centre for Clean Air Research), Erick G. Sperandio Nascimento (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil)
llmsbenchmarks
More like this →
arXiv AI
Jun 18

A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors

arXiv:2606. 19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena.

By David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Nick P. Bassill, Chris D. Thorncroft
llmscomputer-visionsafety
More like this →
arXiv Machine Learning
Aug 28

Bridging short- and medium-range weather forecasting with machine learning

The paper introduces Nested‑EAGLE, a 0.25° global weather model with a 6 km refinement over the contiguous United States, designed to merge short‑ and medium‑range forecasts into a single system. It shows lower mean‑squared error for near‑surface and low‑level variables over the U.S. compared to NOAA’s GFS and HRRR, while remaining competitive globally. Although precipitation forecasts are less skillful than HRRR’s deterministic training, Nested‑EAGLE delivers the most accurate storm‑location predictions at longer lead times, with blurred extrema.

By Timothy A. Smith, Mariah Pope, Sergey Frolov, Brett Basarab, Daniel Abdi, Paul Madden, Isidora Jankov
efficiency
More like this →