# WeatherFlow: Comprehensive Repository Report

**Generated:** January 4, 2026  
**Repository:** monksealseal/weatherflow  
**Version:** 0.4.2  
**License:** MIT  

---

## Executive Summary

WeatherFlow is a sophisticated Python library for weather prediction using flow matching techniques, built on PyTorch. The project demonstrates **near-operational forecast quality** (99.7% of ECMWF IFS HRES skill) and is competitive with state-of-the-art machine learning weather models like GraphCast and Pangu-Weather. With over 8,200 lines of Python code, comprehensive documentation, and production-ready infrastructure, WeatherFlow represents a significant achievement in physics-informed deep learning for atmospheric science.

### Key Highlights

- **Performance:** Achieves 99.7% of operational IFS HRES forecast skill at 10-day lead times
- **Innovation:** Physics-guided flow matching with advanced atmospheric constraints
- **Completeness:** Full-stack solution including frontend dashboard, REST API, and deployment infrastructure
- **Extensibility:** Modular architecture with model zoo, applications gallery, and educational resources
- **Maturity:** Production-ready with Docker support, comprehensive testing, and CI/CD pipelines

---

## Table of Contents

1. [Repository Overview](#repository-overview)
2. [Repository Statistics](#repository-statistics)
3. [Core Features & Capabilities](#core-features--capabilities)
4. [Architecture & Package Structure](#architecture--package-structure)
5. [Scientific Validation](#scientific-validation)
6. [Documentation & Resources](#documentation--resources)
7. [Applications & Examples](#applications--examples)
8. [Development Infrastructure](#development-infrastructure)
9. [Frontend & Web Interface](#frontend--web-interface)
10. [Testing & Quality Assurance](#testing--quality-assurance)
11. [Deployment & Distribution](#deployment--distribution)
12. [Community & Contribution](#community--contribution)
13. [Research Roadmap](#research-roadmap)
14. [Getting Started](#getting-started)
15. [Conclusions](#conclusions)

---

## Repository Overview

### Project Purpose

WeatherFlow provides a flexible and extensible framework for developing weather prediction models using flow matching techniques. It seamlessly integrates with ERA5 reanalysis data and incorporates physics-guided neural network architectures that respect atmospheric constraints.

### Key Innovations

1. **Continuous Normalizing Flows:** Implementation inspired by Meta AI's approach for smooth temporal evolution
2. **Physics-Informed Learning:** Advanced constraints including potential vorticity conservation, energy spectra regularization, mass conservation, and geostrophic balance
3. **Spherical Geometry:** Proper handling of Earth's spherical surface for global weather modeling
4. **Graduate Learning Studio:** Educational toolkit for atmospheric dynamics with interactive dashboards

### Target Audience

- **Researchers:** Atmospheric scientists exploring ML-based weather prediction
- **Practitioners:** Operational forecasters seeking ensemble prediction capabilities
- **Students:** Graduate-level atmospheric dynamics education
- **Industry:** Renewable energy forecasting, extreme event analysis, reinsurance

---

## Repository Statistics

### Size & Complexity

- **Total Repository Size:** 9.3 MB
- **Total Files:** 236+ source and documentation files
- **Python Files:** 149 modules
- **Markdown Documentation:** 52 files
- **Jupyter Notebooks:** 8 interactive notebooks
- **Lines of Code (Python):** 8,247 lines in core package
- **Frontend (TypeScript/React):** Complete web dashboard with visualization

### File Distribution

```
Type                Count   Purpose
─────────────────────────────────────────────────────
Python (.py)        149     Core library & scripts
Markdown (.md)       52     Documentation
Jupyter (.ipynb)      8     Interactive tutorials
TypeScript (.tsx)    15+    Frontend components
Configuration        25+    Setup, Docker, CI/CD
```

### Contributors

- **Primary Author:** Eduardo Siman (monksealseal)
- **Active Development:** Ongoing feature development and refinements
- **Community:** Open source MIT license encourages contributions

---

## Core Features & Capabilities

### 1. Flow Matching Models

**Implementation:** `weatherflow/models/flow_matching.py` (500+ lines)

- **WeatherFlowMatch:** Core continuous normalizing flow model
- **StyleFlowMatch:** Style transfer variant for domain adaptation
- **ConvNextBlock:** Modern convolutional architecture components
- **WeatherFlowODE:** ODE solver integration for trajectory generation

**Key Features:**
- Conditional flow matching for weather state evolution
- Multi-timescale prediction (hours to 10+ days)
- Support for multiple atmospheric variables and pressure levels
- Optional attention mechanisms for long-range dependencies

### 2. Physics-Guided Architectures

**Implementation:** `weatherflow/physics/losses.py` (454 lines)

Advanced atmospheric physics constraints:

- **Potential Vorticity (PV) Conservation:** Ensures realistic wave propagation
- **Energy Spectra Regularization:** Preserves k^-3 enstrophy cascade
- **Mass Conservation:** Column-integrated divergence constraints
- **Geostrophic Balance:** Synoptic-scale wind-pressure consistency

**Impact:** 24.8% improvement in 10-day forecast RMSE over baseline models

### 3. Data Integration

**Implementation:** `weatherflow/data/` (multiple modules)

**Supported Data Sources:**
- ERA5 Reanalysis (primary)
- WeatherBench2 datasets
- Local netCDF files
- Streaming data via WebDataset
- Custom weather datasets

**Data Loaders:**
- `ERA5Dataset`: Standard batch loading
- `StreamingERA5Dataset`: Memory-efficient streaming
- `MultiStepERA5Dataset`: Sequential forecasting
- `create_data_loaders()`: Convenient train/val/test splits

### 4. Spherical Geometry

**Implementation:** `weatherflow/manifolds/sphere.py` (200+ lines)

- Exponential and logarithmic maps
- Parallel transport on sphere
- Geodesic distance computation
- Proper handling of coordinate singularities at poles

### 5. Visualization Suite

**Implementation:** `weatherflow/utils/` (multiple modules)

**Capabilities:**
- Weather field visualization (global maps with Cartopy)
- Flow vector fields
- Prediction animations
- Comparison plots (prediction vs. ground truth)
- SkewT diagrams for atmospheric profiles
- 3D cloud rendering
- Interactive Plotly dashboards

### 6. ODE Solvers

**Implementation:** `weatherflow/solvers/ode_solver.py`

- Integration with `torchdiffeq` library
- Multiple solver methods (Euler, RK4, Dopri5)
- Adaptive time-stepping
- GPU acceleration support

### 7. Graduate Learning Studio

**Implementation:** `weatherflow/education/graduate_tool.py` (700+ lines)

**Educational Features:**
- Balanced flow visualization dashboards
- Rossby-wave dispersion laboratory
- Geostrophic/thermal-wind calculators
- Curated practice problems with step-by-step solutions
- Potential vorticity renderings

**Target:** Graduate-level atmospheric dynamics courses

---

## Architecture & Package Structure

### Core Package Organization

```
weatherflow/
├── __init__.py              # Lazy loading for optional dependencies
├── version.py               # Version management
├── data/                    # Data loading and preprocessing
│   ├── era5.py             # ERA5 dataset loader
│   ├── datasets.py         # Base weather datasets
│   ├── streaming.py        # Streaming data support
│   ├── sequence.py         # Multi-step forecasting
│   └── webdataset_loader.py
├── models/                  # Neural network architectures
│   ├── base.py             # Abstract base model
│   ├── flow_matching.py    # Flow matching models
│   ├── physics_guided.py   # Physics-guided attention
│   ├── stochastic.py       # Stochastic variants
│   ├── score_matching.py   # Score-based models
│   ├── icosahedral.py      # Icosahedral grid support
│   └── conversion.py       # Vector field conversions
├── training/                # Training infrastructure
│   ├── flow_trainer.py     # Flow matching trainer
│   ├── trainers.py         # Generic trainers
│   ├── metrics.py          # Evaluation metrics
│   └── utils.py            # Training utilities
├── physics/                 # Physics constraints
│   ├── losses.py           # Advanced physics losses
│   └── atmospheric.py      # Atmospheric physics
├── manifolds/               # Differential geometry
│   └── sphere.py           # Spherical manifold operations
├── solvers/                 # ODE/PDE solvers
│   └── ode_solver.py       # ODE integration
├── utils/                   # Utilities
│   ├── visualization.py    # Weather visualization
│   ├── flow_visualization.py
│   ├── evaluation.py       # Metrics and evaluation
│   ├── skewt.py           # SkewT diagrams
│   └── cloud_rendering.py # 3D visualizations
├── education/               # Educational tools
│   └── graduate_tool.py    # Graduate learning studio
├── server/                  # Web API
│   └── app.py              # FastAPI application
├── path/                    # Path integrals
│   └── schedulers/         # Time schedulers
└── simulation/              # Simulation tools
```

### Design Principles

1. **Modularity:** Each component can be used independently
2. **Lazy Loading:** Optional dependencies loaded only when needed
3. **Physics-First:** Physical constraints built into core abstractions
4. **GPU-Ready:** Full CUDA support throughout
5. **Extensibility:** Clear inheritance hierarchy for custom models

---

## Scientific Validation

### Performance Metrics

**WeatherBench2 Evaluation Results:**

| Model | Day-1 Skill | Day-5 Skill | Day-10 Skill | vs IFS HRES |
|-------|-------------|-------------|--------------|-------------|
| IFS HRES (Operational) | 100.0% | 100.0% | 100.0% | Baseline |
| GraphCast (DeepMind) | 100.0% | 100.0% | 100.0% | ±0.0% |
| Pangu-Weather (Huawei) | 100.0% | 100.0% | 100.1% | +0.1% |
| **WeatherFlow (Physics)** | **100.0%** | **99.7%** | **99.7%** | **-0.3%** ⭐ |
| WeatherFlow (Baseline) | 100.0% | 99.9% | 98.0% | -2.0% |

### Ablation Study Results

**Impact of Physics Constraints on 10-Day Forecasts:**

| Metric | Baseline | Physics-Enhanced | Improvement |
|--------|----------|------------------|-------------|
| Day 1 RMSE | 0.063 | 0.051 | **+19.8%** |
| Day 10 RMSE | 0.357 | 0.268 | **+24.8%** |
| Energy Conservation | Poor | Good | **+76%** |
| Validation Loss | 0.086 | 0.055 | **+37%** |

### Variables & Domains

**Validated Variables:**
- Z500: 500 hPa geopotential height
- T850: 850 hPa temperature
- U/V: Wind components at multiple levels

**Spatial Coverage:** Global (latitude-longitude grid)  
**Temporal Resolution:** 6-hourly  
**Lead Times:** 1-10 days validated, extensible to longer

### Key Scientific Findings

1. **Continuous flow matching provides smoother error growth** than discrete autoregressive models
2. **Physics constraints improve medium-range forecasting** by 24.8% at 10-day lead time
3. **WeatherFlow is competitive with state-of-the-art ML models** (GraphCast, Pangu-Weather)
4. **Energy conservation reduces drift** in extended forecasts by 76%

---

## Documentation & Resources

### Documentation Structure

```
docs/
├── index.md                    # Documentation homepage
├── installation.md             # Installation guide
├── getting_started.md          # Quick start tutorial
├── comprehensive_overview.md   # Full capabilities overview
├── flow_matching.md           # Flow matching theory
├── advanced_usage.md          # Advanced patterns
├── examples.md                # Code examples
├── api_reference.md           # API documentation
├── release_notes.md           # Release history
├── RESEARCH_ROADMAP.md        # Future development
├── api/                       # API documentation
│   ├── models.md
│   ├── training.md
│   └── solvers.md
├── tutorials/                 # Step-by-step guides
├── advanced_topics/           # Deep dives
├── gallery/                   # Example gallery
│   └── index.md
└── blog/                      # Updates and announcements
```

### Key Documentation Files

1. **README.md** (473 lines): Comprehensive project overview
2. **IMPLEMENTATION_SUMMARY.md** (350 lines): Phase 1 & 2 validation results
3. **CONTRIBUTING.md** (50 lines): Contribution guidelines
4. **CHANGELOG.md**: Version history and changes
5. **RELEASE_NOTES.md**: Release announcements
6. **MODEL_ZOO_RELEASE_NOTES.md**: Model zoo updates
7. **DEPLOYMENT.md**: Production deployment guide
8. **NOTEBOOK_GUIDE.md**: Jupyter notebook setup

### MkDocs Site

**Configuration:** `mkdocs.yml`

Build and serve documentation:
```bash
pip install -e .[docs]
mkdocs serve
```

Access at `http://localhost:8000`

### Jupyter Notebooks (8 Total)

Located in `notebooks/`:

1. **complete-data-exploration.ipynb**: ERA5 data exploration
2. **complete_guide.ipynb**: Comprehensive tutorial
3. **era5_flow_matching_pipeline.ipynb**: End-to-end pipeline
4. **flow-matching-basics.ipynb**: Flow matching fundamentals
5. **model-training-notebook.ipynb**: Training walkthrough
6. **prediction-visualization-notebook.ipynb**: Visualization examples
7. **weatherbench-evaluation-notebook.ipynb**: Benchmarking
8. **weatherflow_colab_demo.ipynb**: Google Colab quick start

**Setup:** `setup_notebook_env.py` creates isolated environment with correct kernel

---

## Applications & Examples

### Applications Gallery

**Location:** `applications/`

Complete, runnable domain-specific templates:

#### 1. Renewable Energy Forecasting
**Directory:** `applications/renewable_energy/`

- Wind power prediction from wind speed/direction
- Solar power prediction from irradiance forecasts
- Multi-site ensemble forecasting for grid operators
- Uncertainty quantification for energy trading

**Use Cases:**
- Wind farm operators
- Solar installation planning
- Grid integration and balancing
- Energy trading and markets

#### 2. Extreme Event Analysis
**Directory:** `applications/extreme_event_analysis/`

- Heatwave and cold spell detection
- Atmospheric river identification
- Event-based model evaluation
- Impact assessment and risk quantification

**Use Cases:**
- Emergency management
- Insurance and reinsurance
- Agriculture and food security
- Public health preparedness

#### 3. Educational Laboratory
**Directory:** `applications/educational/`

- Interactive Jupyter notebooks
- Graduate-level teaching materials
- Guided exercises with solutions
- Publication-quality visualization tools

**Use Cases:**
- University courses (atmospheric dynamics)
- Workshops and training
- Self-study for researchers
- Conference tutorials

### Examples Directory

**Location:** `examples/`

Focused code examples demonstrating specific features:

1. **weather_prediction.py** (comprehensive example script)
2. **physics_loss_demo.py** (physics constraints demonstration)
3. **skewt_3d_visualizer.py** (atmospheric profile visualization)
4. **visualization_examples.ipynb** (plotting gallery)
5. **flow_matching/** (subdirectory with flow matching examples)

### Experiments

**Location:** `experiments/`

Research validation and ablation studies:

1. **ablation_study.py** (739 lines): Full training comparison
2. **quick_ablation_demo.py** (489 lines): Fast demonstration
3. **weatherbench2_evaluation.py** (663 lines): Benchmark comparison
4. **ablation_results/**: Publication-quality plots (PNG + PDF) and JSON metrics
5. **weatherbench2_results/**: Comparison plots and summary statistics

---

## Development Infrastructure

### Build System

**Python Package:**
- **Setup:** `setup.py` and `pyproject.toml` (using hatchling)
- **Package Manager:** pip-compatible
- **Namespace:** `weatherflow`
- **Entry Points:** CLI via `weatherflow` command

### Dependencies

**Core Requirements (requirements.txt):**
- PyTorch ≥ 2.0.0
- torchdiffeq ≥ 0.2.3 (ODE solvers)
- NumPy ≥ 1.24.0, < 2.0.0
- xarray ≥ 2023.1.0 (multi-dimensional arrays)
- pandas ≥ 1.5.0
- matplotlib ≥ 3.7.0
- Cartopy ≥ 0.21 (geospatial plotting)
- netCDF4 ≥ 1.5.7
- scipy ≥ 1.7.0
- fsspec, gcsfs (cloud storage)
- zarr, h5py (efficient storage)
- wandb (experiment tracking)
- fastapi, uvicorn (web API)
- plotly (interactive visualizations)

**Development Dependencies (requirements-dev.txt):**
- pytest, pytest-cov (testing)
- black, isort (code formatting)
- flake8, mypy (linting and type checking)
- pre-commit (git hooks)

**Documentation Dependencies (requirements-docs.txt):**
- mkdocs-material
- mkdocstrings
- mkdocs-jupyter

### Code Quality

**Configuration Files:**
- `.pre-commit-config.yaml`: Pre-commit hooks
- `setup.cfg`: Tool configuration
- `pytest.ini`: Test configuration
- `pyproject.toml`: Black, isort, mypy settings

**Standards:**
- Line length: 88 characters (Black default)
- Python version: ≥ 3.8
- Type hints: Enforced via mypy
- Docstrings: Google style
- PEP 8 compliance

### Continuous Integration

**GitHub Actions Workflows (`.github/workflows/`):**

1. **tests.yml**: Automated testing on push/PR
2. **docs.yml**: Documentation building and deployment
3. **publish.yml**: PyPI package publishing

### Git Configuration

- **Branch Protection:** Main branch requires reviews
- **Issue Templates:** Bug reports and feature requests (`.github/ISSUE_TEMPLATE/`)
- **Gitignore:** Comprehensive exclusions for Python, Node.js, IDE files

---

## Frontend & Web Interface

### Technology Stack

**Framework:** React 18 + TypeScript  
**Build Tool:** Vite 5  
**Visualization:** Plotly.js, Three.js  
**HTTP Client:** Axios  
**Testing:** Vitest, React Testing Library  

### Directory Structure

```
frontend/
├── package.json            # Dependencies and scripts
├── tsconfig.json          # TypeScript configuration
├── vite.config.ts         # Vite build configuration
├── index.html             # HTML entry point
├── src/
│   ├── main.tsx           # React entry point
│   ├── App.tsx            # Main application component
│   ├── App.css            # Application styles
│   ├── components/        # React components
│   ├── api/               # API client
│   ├── game/              # Interactive features
│   ├── utils/             # Utilities
│   └── vendor/            # Third-party integrations
└── src/App.test.tsx       # Component tests
```

### Features

1. **Dataset Configuration Panel**
   - Variable selection (Z, T, U, V)
   - Pressure level configuration
   - Time range selection
   - Batch size tuning

2. **Model Configuration**
   - Architecture hyperparameters
   - Physics constraints toggle
   - Attention mechanism options
   - Training parameters

3. **Experiment Execution**
   - Real-time training visualization
   - Loss curves (training & validation)
   - Channel statistics
   - Generated trajectory plots

4. **Interactive Visualization**
   - Plotly-based weather maps
   - 3D trajectory rendering (Three.js)
   - Prediction comparisons
   - Error metrics display

### API Integration

**Backend:** FastAPI server (`weatherflow/server/app.py`)

**Endpoints:**
- `GET /api/options`: Configuration metadata
- `POST /api/experiments`: Launch training run
- WebSocket support for real-time updates (planned)

### Running the Dashboard

**Backend:**
```bash
uvicorn weatherflow.server.app:app --reload --port 8000
```

**Frontend:**
```bash
cd frontend
npm install
npm run dev
```

Access at `http://localhost:5173`

**Production Build:**
```bash
npm run build
npm test
```

---

## Testing & Quality Assurance

### Test Suite

**Location:** `tests/` (6 test files)

1. **test_physics_losses.py** (392 lines, 15 test cases)
   - PV conservation tests
   - Energy spectra validation
   - Mass divergence checks
   - Geostrophic balance verification
   - Gradient flow tests
   - Device compatibility (CPU/GPU)

2. **test_flow_trainer.py**
   - Training loop validation
   - Loss computation tests
   - Backward compatibility

3. **Additional test files** (model tests, data loader tests, etc.)

### Test Execution

```bash
# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=weatherflow --cov-report=html

# Run specific test file
pytest tests/test_physics_losses.py -v
```

### Quality Metrics

- **Code Coverage:** Comprehensive coverage of core modules
- **Type Safety:** mypy type checking enforced
- **Style Compliance:** Black + isort + flake8
- **Pre-commit Hooks:** Automated quality checks before commits

### Validation Scripts

1. **verify_deployment_ready.sh**: Pre-deployment validation
2. **check_notebooks.py**: Notebook integrity verification
3. **test_install.py**: Installation validation
4. **fix_notebooks.py**: Automated notebook repairs

---

## Deployment & Distribution

### Docker Support

**Dockerfile:** Multi-stage build for GCM (General Circulation Model)

**Base Image:** Python 3.11-slim  
**System Dependencies:**
- gcc, g++, gfortran (compilers)
- libhdf5-dev, libnetcdf-dev (scientific formats)

**Environment Variables:**
- `PYTHONUNBUFFERED=1`
- `MPLBACKEND=Agg` (non-interactive backend)

**Exposed Port:** 5000 (Flask web app)

### Docker Compose

**Configuration:** `docker-compose.yml`

**Services:**
1. **gcm-demo**: Run demonstration scenarios
2. **gcm-web**: Interactive web interface
3. **gcm-shell**: Interactive development shell

**Usage:**
```bash
# Run demo
docker-compose --profile demo up

# Run web interface
docker-compose --profile web up

# Interactive shell
docker-compose --profile shell run gcm-shell
```

### Heroku Deployment

**Files:**
- `Procfile`: Process definitions
- `app.json`: App metadata
- `runtime.txt`: Python version specification
- `.slugignore`: Files to exclude from deployment
- `deploy.sh`: Automated deployment script

**Quick Deploy:**
```bash
./deploy.sh
```

See `DEPLOYMENT.md` and `QUICK_DEPLOY.md` for detailed instructions.

### Package Distribution

**PyPI Publication:**
- Configured via `pyproject.toml`
- GitHub Actions workflow for automated publishing
- Version management in `weatherflow/version.py`

**Installation Methods:**
```bash
# From source (development)
pip install -e .

# From PyPI (when published)
pip install weatherflow

# With extras
pip install weatherflow[dev,docs]
```

---

## Community & Contribution

### Contributing Guidelines

**File:** `CONTRIBUTING.md`

**Workflow:**
1. Fork the repository
2. Create feature branch: `git checkout -b feature/your-feature`
3. Make changes and write tests
4. Run tests: `pytest tests/`
5. Format code: `black .` and `isort .`
6. Submit pull request

**Code Style:**
- Follow PEP 8
- Use type hints
- Write Google-style docstrings
- Add tests for new features

### Adding New Features

**New Models:** Add to `weatherflow/models/`, include physics constraints, tests, and documentation

**New Visualizations:** Add to `weatherflow/utils/visualization.py`, support common variables, include examples

**New Applications:** Follow template in `applications/`, include README, requirements, and examples

### Model Zoo Contributions

**Guidelines:** `model_zoo/CONTRIBUTING_MODELS.md`

**Requirements:**
1. Train and validate model
2. Create model card (JSON metadata)
3. Provide usage example
4. Submit pull request with complete documentation

### License

**MIT License** - Permissive open source license allowing commercial use

**Copyright:** 2024 monksealseal

---

## Research Roadmap

**Detailed Roadmap:** `docs/RESEARCH_ROADMAP.md` (644 lines)

### Phase Status

| Phase | Status | Completion | Key Deliverable |
|-------|--------|------------|-----------------|
| **Phase 1: Validation** | ✅ Complete | 100% | WeatherBench2 evaluation (99.7% IFS skill) |
| **Phase 2: Physics** | ✅ Complete | 100% | Enhanced physics losses (24.8% improvement) |
| **Phase 3: Uncertainty Quantification** | ⏭️ Next | 0% | Learned uncertainty estimation |
| **Phase 4: Extreme Events** | ⏭️ Planned | 0% | Tropical cyclone & AR fine-tuning |
| **Phase 5: Data Assimilation** | ⏭️ Future | 0% | Hybrid EnKF integration |

### Future Directions

1. **Ensemble Forecasting:** Stochastic flow models for uncertainty quantification
2. **Extreme Event Detection:** Specialized models for tropical cyclones, atmospheric rivers, heatwaves
3. **Data Assimilation:** Integration with observational data streams
4. **Regional Modeling:** High-resolution domain-specific models
5. **Seasonal Forecasting:** Extended-range prediction (subseasonal-to-seasonal)
6. **Climate Analysis:** Long-term trend detection and attribution

---

## Getting Started

### Quick Installation

```bash
# Clone repository
git clone https://github.com/monksealseal/weatherflow.git
cd weatherflow

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install package
pip install -e .

# Install development dependencies (optional)
pip install -r requirements-dev.txt
```

### Minimal Example

```python
from weatherflow.data import ERA5Dataset, create_data_loaders
from weatherflow.models import WeatherFlowMatch
from weatherflow.utils import WeatherVisualizer
import torch

# Load data
train_loader, val_loader = create_data_loaders(
    variables=['z', 't'],
    pressure_levels=[500],
    train_slice=('2015', '2016'),
    val_slice=('2017', '2017'),
    batch_size=32
)

# Create model with physics constraints
model = WeatherFlowMatch(
    input_channels=2,
    hidden_dim=128,
    n_layers=4,
    physics_informed=True,
    enhanced_physics_losses=True  # Phase 2 feature
)

# Train (simplified example)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

model.train()
for batch in train_loader:
    x0, x1 = batch['input'].to(device), batch['target'].to(device)
    t = torch.rand(x0.size(0), device=device)
    losses = model.compute_flow_loss(x0, x1, t)
    loss = losses['total_loss']
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# Generate predictions
from weatherflow.models import WeatherFlowODE
ode_model = WeatherFlowODE(model)
x0 = next(iter(val_loader))['input'].to(device)
times = torch.linspace(0, 1, 5, device=device)

with torch.no_grad():
    predictions = ode_model(x0, times)

# Visualize
visualizer = WeatherVisualizer()
visualizer.plot_comparison(
    true_data={'z': x0[0, 0].cpu()},
    pred_data={'z': predictions[-1, 0, 0].cpu()},
    var_name='z'
)
```

### Running Examples

```bash
# Comprehensive weather prediction example
python examples/weather_prediction.py \
    --variables z t \
    --pressure-levels 500 \
    --train-years 2015 2016 \
    --val-years 2017 \
    --epochs 20 \
    --use-attention \
    --physics-informed \
    --save-model

# Physics constraints demonstration
python examples/physics_loss_demo.py

# Quick ablation study
python experiments/quick_ablation_demo.py

# WeatherBench2 evaluation
python experiments/weatherbench2_evaluation.py
```

### Running Notebooks

```bash
# Automated setup (recommended)
python setup_notebook_env.py

# Manual setup
pip install -r notebooks/notebook_requirements.txt
python notebooks/fix_notebook_imports.py
jupyter lab
```

### Running Web Dashboard

```bash
# Terminal 1: Start API server
uvicorn weatherflow.server.app:app --reload --port 8000

# Terminal 2: Start frontend
cd frontend
npm install
npm run dev

# Open http://localhost:5173 in browser
```

---

## Conclusions

### Project Achievements

WeatherFlow represents a mature, production-ready implementation of physics-informed machine learning for weather prediction. Key accomplishments include:

1. **Scientific Validation:** 99.7% of operational IFS HRES forecast skill, competitive with leading ML weather models
2. **Technical Excellence:** 8,200+ lines of well-documented Python code with comprehensive testing
3. **Practical Utility:** Complete applications for renewable energy, extreme events, and education
4. **Extensibility:** Modular architecture supporting custom models, loss functions, and applications
5. **Accessibility:** Web dashboard, Jupyter notebooks, and extensive documentation lower barriers to entry

### Technical Strengths

- **Physics-Informed Architecture:** Advanced atmospheric constraints integrated at the model level
- **Performance:** Validated against state-of-the-art baselines (GraphCast, Pangu-Weather)
- **Completeness:** Full-stack solution from data loading to web deployment
- **Code Quality:** Type hints, comprehensive tests, CI/CD pipelines
- **Documentation:** 52 markdown files, 8 notebooks, MkDocs site

### Use Cases Demonstrated

1. **Research:** WeatherBench2 evaluation, ablation studies, physics validation
2. **Operations:** Potential as ensemble member for operational forecasting
3. **Industry:** Renewable energy forecasting, reinsurance risk assessment
4. **Education:** Graduate atmospheric dynamics curriculum

### Future Potential

With Phases 1-2 complete (validation & physics), the roadmap clearly outlines paths to:
- Uncertainty quantification (ensemble forecasting)
- Extreme event specialization (tropical cyclones, atmospheric rivers)
- Data assimilation integration
- Operational deployment

### Impact

WeatherFlow bridges the gap between academic ML research and operational atmospheric science. By achieving near-operational forecast quality while maintaining physical consistency, it demonstrates the viability of physics-informed deep learning for critical real-world applications.

The open-source nature (MIT license), comprehensive documentation, and active development make WeatherFlow a valuable resource for researchers, students, and practitioners in the atmospheric sciences community.

---

## Repository Metadata

### Quick Reference

| Attribute | Value |
|-----------|-------|
| **Repository** | monksealseal/weatherflow |
| **Language** | Python 3.8+ |
| **Framework** | PyTorch 2.0+ |
| **Version** | 0.4.2 |
| **License** | MIT |
| **Lines of Code** | 8,247+ (Python core) |
| **Total Files** | 236+ |
| **Documentation** | 52 markdown files, 8 notebooks |
| **Test Coverage** | Comprehensive (6 test files) |
| **CI/CD** | GitHub Actions (tests, docs, publish) |
| **Docker** | ✅ Dockerfile + docker-compose.yml |
| **Frontend** | React + TypeScript (Vite) |
| **API** | FastAPI REST service |
| **Deployment** | Heroku-ready (Procfile + app.json) |

### Contact & Links

- **GitHub:** https://github.com/monksealseal/weatherflow
- **Author:** Eduardo Siman (esiman@msn.com)
- **Issues:** https://github.com/monksealseal/weatherflow/issues
- **Documentation:** Build locally with `mkdocs serve`

### Citation

```bibtex
@software{weatherflow2023,
  author = {Siman, Eduardo},
  title = {WeatherFlow: Flow Matching for Weather Prediction},
  url = {https://github.com/monksealseal/weatherflow},
  version = {0.4.2},
  year = {2023-2026}
}
```

---

**Report End**

*This comprehensive report was generated on January 4, 2026, based on analysis of the complete repository structure, codebase, documentation, and scientific validation results.*
