AI in Weather Forecasting
An artificial intelligence-based limited area model for forecasting of surface meteorological variables
This research introduces YingLong, a novel AI-based limited area weather forecasting model with 3 km resolution, designed to predict surface meteorological variables with high accuracy and speed. Utilizing a parallel global-local architecture and trained on high-resolution regional analysis data, YingLong outperforms traditional dynamical models in surface wind speed forecasting. While its performance on surface temperature and pressure is slightly less, improvements are shown to be achievable with better lateral boundary conditions. This study also explores optimal lateral boundary strategies, demonstrating YingLong's potential for real-time, high-resolution weather predictions crucial for various enterprise applications.
Key Metrics at a Glance
YingLong's AI-driven approach offers significant advantages for enterprises reliant on accurate, real-time weather data, from logistics to energy management.
3
KM Resolution
24
Forecast Variables
0.5s
Sec/48-hr Forecast
1500x
Faster than NWP
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Model Architecture
YingLong features a parallel global-local structure leveraging both SWIN transformers for local features and AFNO for global features, enabling multiscale meteorological feature capture. This architecture is designed for optimal feature extraction and robust prediction. The model's computational efficiency allows for rapid forecasting outputs.
Training & Data
The model is trained on 7 years (2015-2021) of hourly HRRR analysis data, ensuring a rich dataset for learning. HRRR data assimilates observations into NWP forecasts, providing high-quality labels. The model is validated on 2023 data and tested on 2022 data, with careful consideration of data inhomogeneity.
Lateral Boundary Conditions (LBCs)
A crucial aspect for limited area models, LBCs are provided by global AI models (Pangu-Weather) or coarse-resolution HRRR.F data. The study demonstrates the effectiveness of a smoothing boundary condition strategy to ensure continuity between inner and lateral boundary regions, enhancing forecast accuracy.
Performance & Skills
YingLong exhibits superior performance in surface wind speed forecasting compared to dynamical models, a critical factor for wind energy and logistics. While surface temperature and pressure show comparable or slightly lower skills, the research identifies paths for improvement through optimized LBCs.
3 KM
Spatial Resolution for Fine-Grained Weather Forecasts
Enhancing Wind Power Forecasting with AI
Accurate wind speed prediction is paramount for the wind power industry. YingLong's superior performance in forecasting U10 and V10 (10m wind speed components) at high resolution offers significant benefits for wind farm planning, scheduling, operation, and maintenance. By providing hourly, high-resolution wind forecasts more promptly and accurately than traditional NWPs, YingLong enables better resource management and grid stability.
Enterprise Process Flow
Input HRRR Analysis Data (2015-2021)
→
Integrate Topography & Forcing Elevation
→
Embedding Layer (Patch, Position, Time)
→
Spatial Mixing (Local SWIN & Global AFNO)
→
Linear Decoder (Output 24 Weather Variables)
→
Rolling Forecast with LBCs (Pangu-Weather/HRRR.F24)
0.5s
To Generate a 48-Hour Forecast on A100 GPU
| Forecast Model | YingLong-Pangu | YingLong-HRRR.F24 | HRRR.F |
|---|---|---|---|
| U10 RMSE (ED Inner Area) |
|
|
|
| V10 RMSE (ED Inner Area) |
|
|
|
| U10 RMSE (WD Inner Area) |
|
|
|
| V10 RMSE (WD Inner Area) |
|
|
|
Optimizing Logistics and Operations
High-resolution, accurate weather forecasts are vital for sectors like transportation and supply chain management. YingLong's ability to predict surface variables with high granularity allows enterprises to optimize routing, mitigate weather-related delays, and enhance operational efficiency. This proactive approach leads to significant cost savings and improved service delivery.
Advanced ROI Calculator
Estimate your potential return on investment by integrating AI-driven weather forecasting into your operations.
Potential Annual Savings
Annual Hours Reclaimed
Our AI Implementation Roadmap
A clear, phased approach to integrating advanced AI weather forecasting into your existing infrastructure.
Phase 1: Discovery & Strategy
In-depth analysis of your current weather-dependent operations, data infrastructure, and business objectives. We define KPIs and tailor an AI forecasting strategy unique to your needs.
Phase 2: Data Integration & Model Customization
Seamlessly integrate YingLong with your enterprise data sources. Customization of model parameters and training to optimize performance for your specific geographical regions and variables.
Phase 3: Deployment & Training
Secure and scalable deployment of the AI model. Comprehensive training for your team to ensure maximum adoption and utilization of the new forecasting capabilities.
Phase 4: Monitoring & Optimization
Continuous monitoring of model performance, data pipelines, and system health. Ongoing recalibration and updates to ensure sustained accuracy and ROI.
Ready to Transform Your Enterprise with AI?
Our experts are ready to discuss how AI-driven weather forecasting can provide a competitive edge for your business.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat