| Term | Definition |
|---|---|
| Backtesting | The process of evaluating a forecast model against historical data (using past initial conditions) to assess its performance and accuracy over past events. |
| BigQuery | A serverless, highly scalable data warehouse on Google Cloud. WeatherNext datasets are available here for large-scale SQL-based analysis. |
| CRPS (Continuous Ranked Probability Score) | A primary metric for evaluating the accuracy of a probabilistic (ensemble) forecast. It measures both the accuracy and the "spread" (confidence) of the forecast. Lower is better. |
| Deterministic Forecast | A single "best guess" forecast that predicts one specific future weather state. This contrasts with a probabilistic forecast. |
| Diffusion Model | A type of generative AI model used in WeatherNext Gen (GenCast) that works by adding "noise" to data and then learning how to reverse the process iteratively to generate new, realistic outputs. |
| Earth Engine | A Google Cloud platform for petabyte-scale scientific analysis and visualization of geospatial datasets. WeatherNext data is available here for raster-based analysis. |
| Ensemble / Ensemble Forecast | A probabilistic forecasting method that runs a model multiple times (or runs multiple models) to generate a collection of possible future weather scenarios, rather than just one. |
| ECMWF ENS (Ensemble Prediction System) | The primary ensemble operational forecast model from the ECMWF. WeatherNext Gen and WeatherNext 2 are often benchmarked against it. |
| ECMWF ERA5 | A widely used global "reanalysis" dataset from the ECMWF. It provides a comprehensive, high-quality historical record of the Earth's climate and is often used to train AI weather models. |
| ECMWF HRES (High Resolution Forecast) | The primary deterministic operational forecast model from the ECMWF, a leading traditional NWP model. WeatherNext Graph is often benchmarked against it. |
| Geopotential (Height) | A measure of atmospheric pressure defined as the altitude at which a specific pressure value (e.g., the 500 hPa level) is reached in the atmosphere. It is fundamental to tracking large-scale weather patterns. |
| GFS (Global Forecast System) | The primary global weather forecast model run by NOAA in the United States. |
| GNN (Graph Neural Network) | A type of AI architecture used in WeatherNext Graph (GraphCast) that represents the Earth as a graph and models the connections between different points. |
| Google Cloud Storage (GCS) | Google Cloud's object storage service. WeatherNext data is stored here in the Zarr format for direct file access. |
| hPa (hectopascal) | A standard unit of atmospheric pressure. Used to define "pressure levels" in the atmosphere, which are standard altitudes for weather analysis. A pressure of "one atmosphere" is equivalent to 1013.25 HPa. |
| HRES-fc0 | Refers to the initial step used to generate the HRES forecasts, which are provided by ECMWF. It is a form of operational analysis, but differs slightly from the HRES Analysis, which is the main operational analysis product from ECMWF. |
| HRES-fc1to5 | Refers to the first 5 hourly steps produced by the ECMWF HRES model. |
| Inference | The process of using a trained AI model to make a prediction given some input data (e.g., running the WeatherNext model to generate a forecast). |
| Initial Conditions | The set of atmospheric data (like temperature, pressure, and wind) at a specific starting time, used to initialize a weather model run. |
| Lead Time | The duration a forecast predicts into the future measured from the time of the initial condition (e.g., "10-day lead time"). |
| MetNet | A specialized Google AI model designed for nowcasting, focusing on high-resolution precipitation forecasts for the immediate future (e.g., 0-12 hours). |
| NeuralGCM | A Google AI model that combines a General Circulation Model (GCM) with AI models. It learns the fundamental physics of the atmosphere to run long-range global weather simulations very quickly. |
| Nowcasting | The practice of forecasting weather for the very short term, typically 0-6 hours ahead, often with a focus on precipitation. |
| NWP (Numerical Weather Prediction) | The traditional method of weather forecasting that uses complex mathematical models of the atmosphere and ocean. Typically NWP models run on supercomputers. (e.g., HRES, GFS). |
| Operational Analysis | A scientific method that blends real-time observations with an operational NWP to create a comprehensive and consistent "best guess" of the current state of the atmosphere. (related to Reanalysis). |
| Pressure Level | A standard surface in the atmosphere defined by its pressure (e.g., 500 hPa), used for analyzing weather patterns at different altitudes. |
| Probabilistic Forecasting | A type of forecast that provides a range of possible outcomes and the likelihood (probability) of each one occurring, often by means of an ensemble of forecasts. |
| Reanalysis | A scientific method that blends historical observations with a modern forecast model (like ERA5) to create a comprehensive and consistent "best guess" dataset of past weather (related to Operational Analysis). |
| RMSE (Root Mean Squared Error) | A common metric for evaluating the accuracy of a deterministic forecast. It measures the average magnitude of the errors. Lower is better. |
| Spatial Resolution | The size of the grid cells in a forecast (e.g., 0.25 degrees, which corresponds to a cell of about 27 km x 27 km at the equator). A smaller grid size (higher resolution) provides more geographic detail. |
| Temporal Resolution | The frequency of the forecast's time steps (e.g., 1-hour, 6-hour). A 1-hour resolution provides a more detailed view of how weather evolves. |
| TPU (Tensor Processing Unit) | Google's custom-designed accelerator chip, optimized for machine learning workloads. |
| Vertex AI Model Garden | A Google Cloud service where developers can discover, deploy, and manage AI models, including WeatherNext 2, for custom, on-demand inference. |
| WeatherNext | A family of advanced, AI-powered weather forecasting models developed by Google. |
| WeatherNext 2 (FGN) | The next generation of Google's AI weather model, standing for Functional Generative Network. It's an advanced ensemble model that improves upon GenCast with higher accuracy and resolution. |
| WeatherNext Gen (GenCast) | An AI model that uses a diffusion-based approach to generate an ensemble forecast. Instead of one single prediction, it produces many possible weather futures to capture uncertainty. |
| WeatherNext Graph (GraphCast) | An AI model that uses Graph Neural Networks (GNNs) to produce a single, high-resolution deterministic forecast. It is known for its high accuracy, outperforming traditional models in many metrics. |
| Zarr | A data format for storing "chunked," N-dimensional arrays. It is highly efficient for cloud-based scientific datasets like weather forecasts, allowing users to read small pieces without downloading the entire file. |
Glossary
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-11-14 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-11-14 UTC."],[],[]]