Bridging Meteorology and Analytics

Just got back from a great week in New Orleans for #AMS25. Three observations about where things are headed with weather data in 2025. 1. The private-sector presence in atmospheric science will continue to grow. On the “data provider” side, there is an incredible amount of private-sector innovation happening around AI-driven weather forecasting, from big tech companies (Google DeepMind, NVIDIA, Microsoft) to startups (Brightband, Excarta, Silurian AI, Salient, Zeus AI, Jua.ai, etc) to more established companies breaking into this field (e.g. Spire, Tomorrow.io). On the “end user” side, we’re seeing growing sophistication by commodities and energy traders in their use of weather data, with many companies building significant in-house capacity for analytics and modeling by building data infrastructure and hiring meteorologists. 2. AI is changing the requirements for data systems. Whereas the main infrastructure requirement for weather forecasting used to be a hard-core HPC system for compute-bound workloads, AI requires smaller, GPU-based clusters, plus a massive trove of clean, AI-ready data upon which to train. I/O bottlenecks are surpassing compute bottlenecks for many teams. Ingestion, curation, and optimization of training and evaluation datasets is becoming a major priority. In this new world, modeling is starting to look a lot more like data analytics and visualization in terms of infrastructure requirements. 3. The Pangeo Community stack, built on the foundation of Xarray and Zarr, continues to expand its impact in this new AI-centric world. Nearly every team training AI weather models is doing so from Zarr data. However, practices vary widely in terms of on-disk data layout and data loader architecture. The coming year will likely see some convergence. European Centre for Medium-Range Weather Forecasts - ECMWF's Anemoi framework caught my eye as an interesting new approach. A talk by Alfonso Ladino-Rincon about analysis-ready Zarr-based radar data was another highlight. Meanwhile, the centrality of GRIB files in operational meteorology systems will continue to create friction for practitioners by requiring slow and costly data transformations. Will 2025 be the year we see operational forecasts in Zarr from a public-sector data provider? Or will the VirtualiZarr approach make this question moot? Overall, this conference was extremely fun and stimulating for me. We had a ton of traffic at the Earthmover booth. I’m more convinced than ever that solving “boring” data infrastructure problems can help accelerate work across the weather and climate enterprise. Feeling fired up about the opportunities ahead!

Quantum-Inspired Algorithm Revolutionizes Weather Forecasting and Turbulence Simulations Overview • Researchers at the University of Oxford have developed a quantum-inspired algorithm that dramatically speeds up weather forecasting and fluid turbulence simulations. • This algorithm can reduce computation times from several days on a supercomputer to just hours on a regular laptop. • Unlike traditional quantum computers, this method runs on classical machines while borrowing key quantum principles. Why It Matters • Improved Weather Forecasting • More efficient simulations could lead to faster and more accurate weather predictions, helping governments and businesses prepare for extreme weather events. • Advancements in Industrial Efficiency • Turbulence simulations are critical in aerospace, automotive, and energy industries to optimize fluid dynamics, reduce fuel consumption, and enhance design processes. • Bridging Classical and Quantum Computing • The approach mimics quantum computing advantages without requiring fully developed quantum hardware. • It simplifies turbulence modeling by using tensor networks, an advanced mathematical framework used in quantum mechanics. The Bigger Picture • Accelerating Computational Science: This breakthrough aligns with the broader trend of quantum-inspired computing driving innovation in science, engineering, and meteorology. • Future of Quantum Algorithms: While true quantum computing is still developing, quantum-inspired techniques are already proving useful for real-world applications. • AI and Quantum Synergy: Combining this algorithm with AI-driven climate models could further enhance weather prediction and environmental modeling. Bottom Line This quantum-inspired algorithm marks a major leap in computational efficiency, enabling faster weather forecasts and fluid simulations on classical computers. By applying quantum principles to classical computing, researchers are bridging the gap between today’s technology and future quantum breakthroughs.

Hello Friends, continuing our AI & ML Journey in Atmospheric Sciences. Previously, we introduced the core methods of AI and machine learning for weather and climate prediction, laying the groundwork for transforming raw atmospheric data into actionable insights. Today, let's dive deeper into the data preprocessing stage—a crucial step that bridges raw data and robust ML models. In weather and climate research, raw data from stations and satellites is noisy, incomplete, and heterogeneous. That’s why building a robust machine learning model starts with solid data preprocessing. Starting Simple: • Data Cleaning:Remove duplicates and flag outliers using methods like Z-score analysis (e.g., flag values if |(x – mean)/std| > 3). • Missing Data Imputation: Replace missing values with the mean or median. • Scaling & Encoding: Use basic methods like min-max scaling to map data into [0,1] and one-hot encoding for categorical variables. While these simple methods are easy to implement, they have limitations. They often fail when data is highly skewed, when outliers distort averages, or when relationships between variables matter. Advancing Your Approach: • Robust Outlier Detection: Use median absolute deviation (MAD) or Isolation Forests for more resilient outlier handling. • Model-Based Imputation: Go beyond simple averages with kNN, MICE, or even autoencoder-based methods to capture complex relationships. • Advanced Scaling/Transformation: Apply robust scaling, Box–Cox or Yeo–Johnson transformations to better stabilize variance. • Sophisticated Feature Extraction: Use PCA, ICA, t-SNE, or UMAP to reduce dimensionality while preserving key patterns. • Enhanced Categorical Encoding & Imbalance Handling: Employ target encoding or frequency encoding, and use techniques like SMOTE or ADASYN to balance rare events. • Time Series & Data Fusion: For time series, decompose data seasonally or use differencing; combine multiple data sources using Kalman filters. How do you handle complex atmospheric data? Let’s discuss! please check out the PDF for more detailed discussion and classification. #MachineLearning #DataPreprocessing #ClimateScience #Meteorology #DataScience