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Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region

Muhammad Akhtar Munir, Fahad Shahbaz Khan, Salman Khan

TL;DR

The paper tackles the challenge of producing accurate, computationally efficient regional weather forecasts by adapting a pre-trained transformer-based climate model to the MENA region. It employs parameter-efficient fine-tuning (LoRA and variants) and flash attention within the ClimaX framework to tailor regional forecasting while reducing training cost. Experiments on ERA5 data show regional models outperform global baselines and that LoRA can drastically cut trainable parameters (e.g., from 108M to 16.2M) and memory usage by about 38.8%, with strong ACC/RMSE performance. The results suggest a practical path toward scalable, region-specific weather forecasting and downstream climate services, with potential applicability beyond MENA.

Abstract

Accurate weather and climate modeling is critical for both scientific advancement and safeguarding communities against environmental risks. Traditional approaches rely heavily on Numerical Weather Prediction (NWP) models, which simulate energy and matter flow across Earth's systems. However, heavy computational requirements and low efficiency restrict the suitability of NWP, leading to a pressing need for enhanced modeling techniques. Neural network-based models have emerged as promising alternatives, leveraging data-driven approaches to forecast atmospheric variables. In this work, we focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. As a case study, we consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. This targeted approach allows us to tailor the model's capabilities to the unique conditions of the region of interest. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.

Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region

TL;DR

The paper tackles the challenge of producing accurate, computationally efficient regional weather forecasts by adapting a pre-trained transformer-based climate model to the MENA region. It employs parameter-efficient fine-tuning (LoRA and variants) and flash attention within the ClimaX framework to tailor regional forecasting while reducing training cost. Experiments on ERA5 data show regional models outperform global baselines and that LoRA can drastically cut trainable parameters (e.g., from 108M to 16.2M) and memory usage by about 38.8%, with strong ACC/RMSE performance. The results suggest a practical path toward scalable, region-specific weather forecasting and downstream climate services, with potential applicability beyond MENA.

Abstract

Accurate weather and climate modeling is critical for both scientific advancement and safeguarding communities against environmental risks. Traditional approaches rely heavily on Numerical Weather Prediction (NWP) models, which simulate energy and matter flow across Earth's systems. However, heavy computational requirements and low efficiency restrict the suitability of NWP, leading to a pressing need for enhanced modeling techniques. Neural network-based models have emerged as promising alternatives, leveraging data-driven approaches to forecast atmospheric variables. In this work, we focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. As a case study, we consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. This targeted approach allows us to tailor the model's capabilities to the unique conditions of the region of interest. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
Paper Structure (13 sections, 2 equations, 4 figures, 6 tables)

This paper contains 13 sections, 2 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Preliminaries
  4. Fine tuning using PEFT
  5. Experiments and Results
  6. Experiments with Global and Regional Modeling
  7. Conclusion
  8. More details on GLoRA
  9. Dataset
  10. Implementation Details
  11. More Results
  12. Ablations
  13. Qualitative

Figures (4)

  • Figure 1: Main architecture: Integration of LoRA involves trainable layers while transformer blocks are frozen. The architecture modifies the main ViT by dealing with each channel separately for tokenization.
  • Figure 2: Qualitative: Error/Bias in Predictions and Actual measurements for temperature_2m (K). Dated, 11$^{th}$ April 2017, lead time 3 days.
  • Figure 3: Qualitative: Error/Bias in Predictions and Actual measurements for temperature_2m (K), with reference to independentKuwaitSwelters. Dated, 22$^{nd}$ June 2017.
  • Figure 4: Qualitative: Error/Bias in Predictions and Actual measurements for temperature_2m (K), on Non-MENA region. Dated, 20$^{th}$ May 2017.