Machine Learning for Weather and Climate Prediction

Module: Sustainable Tech | Difficulty: Premium

Atmospheric Dynamics

The Navier-Stokes equations for atmospheric flow:

Forecast Skill Scores

Comparison

Python Implementation

import torch
import torch.nn as nn

class ClimateTransformer(nn.Module):
    def __init__(self, n_variables=69, embed_dim=256, n_heads=8, n_layers=6):
        super().__init__()
        self.embedding = nn.Conv2d(n_variables, embed_dim, kernel_size=1)
        encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=n_heads, dim_feedforward=1024, batch_first=True)
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=n_layers)
        self.output = nn.Conv2d(embed_dim, n_variables, kernel_size=1)

    def forward(self, x):
        B, C, H, W = x.shape
        emb = self.embedding(x).flatten(2).permute(0, 2, 1)
        out = self.transformer(emb)
        out = out.permute(0, 2, 1).reshape(B, -1, H, W)
        return self.output(out)

Research Insight: Foundation models like Pangu-Weather and GraphCast have surpassed traditional NWP models at medium range, running 10,000x faster on single GPUs.