ClimRISE Lab

GRAMI · GWTC-v3 · Coming soon

Global Reanalysis Air Mass Index (GRAMI)

An extension of the GWTC-v2, GRAMI is a global reanalysis-based classification of daily near-surface air-mass conditions, built from hourly ERA5 data and designed for climate, health, ecological, and applied environmental research.

Data access How it works How to cite
Overview What GRAMI measures and why it exists. Download GRAMI Choose full archive, point, region, or time-subset downloads. Methods ERA5 inputs, local relativity, and classification logic. GWTC legacy How v3 builds on GWTC, GWTC-2, and prior publications.

Overview

GRAMI is a gridded, globally consistent way to describe the multivariate character of daily weather as an air mass condition, rather than as a single meteorological variable. Each location-day is classified relative to what is typical for that location and time of year. One major enchancement is in the spatial smoothing... GRAMI is available without smoothing, with a meso-scale (200km) smoother, or a synoptic-scale (500km) smoother.

Local and seasonal context

A warm day in Cleveland in January, a cool day in Miami in August, and a dry day in the tropics are each evaluated relative to their own local climatology.

Multivariate weather

The index is designed to capture combinations of near-surface conditions that matter for human health, ecosystems, hazards, and climate impacts.

Global reanalysis backbone

GWTC-v3 moves the system to an ERA5-based framework, supporting a more stable, research-grade, global historical record.

Data access

GRAMI data products are currently being prepared. This page will serve as the landing page for data downloads, documentation, metadata, update notes, and example workflows.

Historical archive

Monthly or annual gridded files for the full historical ERA5 period. Add the final download link when the archive is ready.

Historical data coming soon
Global Daily classifications ERA5-based

Documentation and examples

Planned documentation will include file structure, variable names, code meanings, suggested citation language, and example scripts.

Documentation coming soon
Metadata Methods Reproducible use

GWTC-2 updates were suspended because CFS data availability ended in February 2025. GWTC-v3 is intended to provide the next-generation reanalysis-based pathway forward.

Choose your data download method...

GRAMI can be accessed as complete archive files or as customized subsets by location, region, and time period.

Full archive

Download the complete GRAMI dataset by year or month. Recommended for users doing large-scale global analysis.

Browse archive files

Single-point time series

Enter a latitude and longitude to download the full GRAMI history for the nearest grid cell.

Create point download

Bounding-box subset

Select a latitude/longitude box and optional date range to download a regional GRAMI subset.

Create regional subset

Global time subset

Download the whole world for a selected month, year, or custom time range.

Browse time subsets

Programmatic access

Access GRAMI directly from Python, R, command-line tools, ERDDAP, OPeNDAP, or Zarr.

View code examples

Documentation

Review class codes, metadata, file structure, citation guidance, and version history.

Read documentation

How the index works

The index classifies daily air-mass conditions using a geographically and seasonally relative framework. Rather than labeling weather only by raw temperature, humidity, or pressure, each day is interpreted relative to what is normal for that grid cell and time of year.

1. Reanalysis inputs

Hourly ERA5 fields provide a consistent global atmospheric record. Near-surface variables are summarized into daily air-mass conditions.

2. Local anomalies

Conditions are standardized relative to local seasonal expectations, so the same raw weather can mean different things in different places and seasons.

3. Air-mass classes

Each location-day is assigned an air-mass category that summarizes the combined thermal, moisture, pressure, cloud, and wind character of the day.

Air Mass Categories

The table below shows what each Air Masses code and category represents.

Code Category Abbreviation Description
0 Missing N/A No valid classification for the location-day.
1 Humid Cool HC Cooler-than-normal and more humid air-mass conditions.
2 Humid H Near-seasonal temperature with higher moisture.
3 Humid Warm HW Warmer-than-normal and more humid air-mass conditions.
4 Cool C Cooler-than-normal conditions without strong humidity or dryness dominance.
5 Seasonal S Conditions close to local seasonal expectations.
6 Warm W Warmer-than-normal conditions without strong humidity or dryness dominance.
7 Dry Cool DC Cooler-than-normal and drier air-mass conditions.
8 Dry D Near-seasonal temperature with lower moisture.
9 Dry Warm DW Warmer-than-normal and drier air-mass conditions.
10 Cold Front Passage CFP Transitional conditions consistent with cold-frontal passage.
11 Warm Front Passage WFP Transitional conditions consistent with warm-frontal passage.

Potential applications

Climate change indicators

Track changes in the frequency, persistence, and seasonality of air-mass types over time.

Health and biometeorology

Link multivariate air-mass conditions to heat stress, respiratory outcomes, mortality, or other weather-sensitive endpoints.

Ecology and impacts

Study biological, agricultural, hydrological, or hazard responses to changing daily air-mass regimes.

Relationship to GWTC and GWTC-2

GRAMI is considered the next generation (v3) of the Gridded Weather Typing Classification. The original GWTC established an automated, gridded weather-typing framework. GWTC-2 expanded that framework to the global domain using CFS-based data. GRAMI extends the approach again by moving toward an ERA5 reanalysis foundation and a clearer air-mass-index identity.

The rebranding emphasizes the scientific purpose of the dataset: a globally consistent index of relative air-mass character, suitable for climate-scale analysis and interdisciplinary applications.

How to cite

Citation guidance for GRAMI/GWTC-v3 will be updated when the dataset and methods documentation are finalized. Until then, users should cite the source webpage and the foundational GWTC/GWTC-2 publications as appropriate.

  1. Lee, C. C. 2020. Trends and variability in airmass frequencies: indicators of a changing climate. Journal of Climate, 33(19), 8603–8617.
  2. Lee, C. C. 2019. The Gridded Weather Typing Classification version 2: a global-scale expansion. International Journal of Climatology, 40(2), 1178–1196.
  3. Lee, C. C. 2014. The development of a gridded weather typing classification scheme. International Journal of Climatology, 35(5), 641–659.