AWS Bucket Name and Region: s3://openmeteo; us-west-2; AWS Registry
Open-Meteo integrates weather models from well-known national weather services, delivering a rapid weather API. Real-time weather forecasts are unified within a time-series database that covers both historical and future weather data. Open-Meteo is designed to analyse long time-series of weather data any place on earth.
This database is made available through the AWS Open Data Sponsorship program.
Weather datasets are sourced from the following national weather services:
- Forecast: NOAA NCEP, DWD, ECMWF, Environment Canada, MeteoFrance, JMA, BOM, CMA, Met Norway, DMI, KNMI
- Historical data: Copernicus, ECMWF
- Climate data: CMIPS
This open-data distribution is managed by Open-Meteo and is not directly affiliated with national weather services such as ECMWF or NOAA NCEP. Open-Meteo does not guarantee the accuracy, completeness, or uninterrupted provision of the data products, and they are provided without any warranty. For support inquiries, please contact Open-Meteo by creating issues or discussions in this repository.
All available data can be explored using the S3 explorer.
Weather models can be broadly categories by their coverage:
- Global models run at lower resolution (11-50 km) but offer 7 to 16 days of forecast
- Local models use higher resolution (1-7 km) but offer only 2-5 days of weather forecast
Local models are nested into global models and rely on boundary conditions that drive large scale weather patterns. The Open-Meteo API seamlessly combines local and global weather models. Depending on your use-case, you may want to use different weather models. E.g. If you only need 2 days of forecast for North America, use ncep_hrrr_conus, but for more than 2 days, you have to add ncep_gfs013. Further more, you can select only temperature_2m to more fine grained of how much data is being transferred.
Ideally, familiarise yourself with the Weather Forecast API and explore the S3 explorer to select the right weather models.
| Model | Region | Resolution | Timeinterval | Forecast length | Updates | # Surface Variables | # Pressure Variables | Available since |
|---|---|---|---|---|---|---|---|---|
| dwd_icon | Global | 0.1° (~11 km) | Hourly | 7.5 days | Every 6 hours | 49 | 5 (18 levels) | 2023-12-15 |
| dwd_icon_eu | Europe | 0.0625° (~7 km) | Hourly | 5 days | Every 3 hours | 42 | 5 (17 levels) | 2023-12-15 |
| dwd_icon_d2 | Central Europe | 0.02° (~2 km) | Hourly | 2 days | Every 3 hours | 44 | 5 (11 levels) | 2023-12-15 |
| dwd_icon_d2_15min | " | " | 15-Minutely | " | " | 8 | - | 2023-12-15 |
| ncep_gfs013 | Global | 0.11° (~13 km) | Hourly | 16 days | Every 6 hours | 27 | - | 2023-12-15 |
| ncep_gfs025 | Global | 0.25° (~25 km) | Hourly | 16 days | Every 6 hours | 11 | 7 (38 levels) | 2023-12-15 |
| ncep_nbm_conus | U.S. Conus | 2.5 km | Hourly | 11 days | Every hour | 20 | - | 2024-10-03 |
| ncep_hrrr_conus | U.S. Conus | 3 km | Hourly | 2 days | Every hour | 23 | 7 (39 levels) | 2023-12-15 |
| ncep_hrrr_conus_15min | " | " | 15-Minutely | " | " | 12 | - | 2023-12-15 |
| meteofrance_arpege_world025 | Global | 0.25° (~25 km) | Hourly | 4 days | Every 6 hours | 29 | 6 (23 levels) | 2023-12-15 |
| meteofrance_arpege_europe | Europe | 0.1° (~11 km) | Hourly | 4 days | Every 6 hours | 29 | 6 (23 levels) | 2023-12-15 |
| meteofrance_arome_france0025 | France | 0.025° (~2.5 km) | Hourly | 51 hours | Every 3 hours | 29 | 6 (24 levels) | 2023-12-15 |
| meteofrance_arome_france_hd | " | 0.01° (~1.5 km) | " | " | " | 12 | - | 2023-12-15 |
| ecmwf_ifs04 | Global | 0.4 (~44 km) | 3-Hourly | 10 days | Every 6 hours | 14 | 7 (9 levels) | 2023-12-15 |
| ecmwf_ifs025 | Global | 0.25 (~25 km) | 3-Hourly | 10 days | Every 6 hours | 14 | 7 (9 levels) | 2024-02-03 |
| ukmo_global_deterministic_10km | Global | 0.09 (~10 km) | Hourly | 7 days | Every 6 hours | 19 | 5 (59 levels) | 2022-03-01 |
| ukmo_uk_deterministic_2km | UK, Ireland | 2 km | Hourly | 2 days | Every hour | 24 | 5 (59 levels) | 2022-03-01 |
| cmc_gem_gdps | Global | 0.15° (~15 km) | 3-Hourly | 10 days | Every 12 hours | 24 | 5 (31 levels) | 2023-12-15 |
| cmc_gem_rdps | North America, North Pole | 10 km | Hourly | 3.5 days | Every 6 hours | 24 | 5 (31 levels) | 2023-12-15 |
| cmc_gem_hrdps | Canada, Northern US | 2.5 km | Hourly | 2 days | Every 6 hours | 24 | 5 (28 levels) | 2023-12-15 |
| jma_gsm | Global | 0.5° (~55 km) | 6-Hourly | 11 days | Every 6 hours | 8 | 6 (11 levels) | 2023-12-15 |
| jma_msm | Japan, Korea | 0.05° (~5 km) | Hourly | 4 days | Every 3 hours | 11 | - | 2023-12-15 |
| metno_nordic_pp | Norway, Denmark, Sweden, Finland | 1 km | Hourly | 2.5 days | Every hour | 9 | - | 2023-12-15 |
| cma_grapes_global | Global | 0.125° (~13 km) | 3-Hourly | 10 days | Every 6 hours | 48 | 8 | 2024-01-01 |
| bom_access_global | Global | 0.175°/0.117° (~15 km) | Hourly | 10 days | Every 6 hours | 33 | - | 2024-01-01 |
| arpae_cosmo_5m | Europe | 5 km | Hourly | 3 days | Every 12 hours | 9 | - | 2024-02-01 |
| arpae_cosmo_2i | Italy | 2.2 km | Hourly | 2 days | Every 12 hours | 9 | - | 2024-02-01 |
| arpae_cosmo_2i_ruc | Italy | 2.2 km | Hourly | 18 hours | Every 3 hours | 9 | - | 2024-02-01 |
| dmi_harmonie_arome_europe | Central & Northern Europe | 2 km | Hourly | 60 hours | Every 3 hours | 39 | - | 2024-07-01 |
| knmi_harmonie_arome_europe | Central & Northern Europe | 5.5 km | Hourly | 60 hours | Every hour | 22 | 5 (5 levels) | 2024-07-01 |
| knmi_harmonie_arome_netherlands | Netherlands, Belgium | 2 km | Hourly | 60 hours | Every hour | 28 | - | 2024-07-01 |
The following ocean wave models are integrated into the Marine Wave API.
| Model | Region | Resolution | Timeinterval | Forecast length | Updates | # Surface Variables | # Pressure Variables | Available since |
|---|---|---|---|---|---|---|---|---|
| ecmwf_wam025 | Global | 0.25° (~25 km) | 3-Hourly | 10 days | Every 6 hours | 4 | - | 2024-03-01 |
| meteofrance_currents | Global | 0.08° (~8 km) | Hourly | 10 days | Every 24 hours | 1 | - | 2022-01-01 |
| meteofrance_wave | Global | 0.08° (~8 km) | 3-Hourly | 10 days | Every 12 hours | 9 | - | 2021-10-01 |
| ncep_gfswave025 | Global | 0.25° (~25 km) | Hourly | 16 days | Every 6 hours | 9 | - | 2024-06-20 |
| dwd_gwam | Global | 0.25° (~25 km) | Hourly | 7.5 days | Every 12 hours | 11 | - | 2023-12-15 |
| dwd_ewam | Europe | 0.05° (~5 km) | Hourly | 4 days | Every 12 hours | 11 | - | 2023-12-15 |
| copernicus_era5_ocean | Global | 0.5° (~50 km) | Hourly | 5 days delay | Every 24 hours | 5 | - | 2023-12-15 |
The following models are used in the Air Quality API
| Model | Region | Resolution | Timeinterval | Forecast length | Updates | # Surface Variables | # Pressure Variables | Available since |
|---|---|---|---|---|---|---|---|---|
| cams_global | Global | 0.4° (~44 km) | Hourly | 4.5 days | Every 12 hours | 10 | - | 2023-12-15 |
| cams_europe | Europe | 0.1° (~11 km) | Hourly | 4 days | Every 12 hours | 14 | - | 2023-12-15 |
The following models are used in the Historical Weather API.
| Model | Region | Resolution | Timeinterval | Delay to realtime | Updates | # Surface Variables | # Pressure Variables | Available since |
|---|---|---|---|---|---|---|---|---|
| copernicus_era5 | Global | 0.25° (~25 km) | Hourly | 5 days | Every 24 hours | 23 | - | 1940-01-01 |
| copernicus_era5_land | Global | 0.1° (~11 km) | Hourly | 5 days | Every 24 hours | 11 | - | 1950-01-01 |
| ecmwf_ifs | Global | 9 km | Hourly | 2 days | Every 24 hours | 24 | - | 2017-01-01 |
TBA
TBA
Based on the GLO-90 digital elevation model (DEM) from Copernicus, the weather API uses terrain information to optimise and downscale weather data. Although the Open-Meteo weather API works without elevation information, forecasts in mountainous terrain is less accurate.
| Model | Region | Resolution | Timeinterval | Forecast length | Updates | # Surface Variables | # Pressure Variables | Available since |
|---|---|---|---|---|---|---|---|---|
| copernicus_dem90 | Global | 90 m | - | - | - | - | - | 2023-12-15 |
Data is structured by weather models, variable and time. This allows to retrieve only a small subset of required data. In many cases only a limited number of weather variables like temperature is required significantly reducing the required data size.
Bucket contents:
data/<model>/<weather-variable>/<time>.omREADME.md
URL components:
model: All data is grouped by weather model. E.g.ncep_gfs013ordwd_icon_euweather-variable. Each model contains multiple weather variables. E.g.temperature_2morrelative_humidity_2m. Some weather variables likewind_speed_10mare calculated by the API, butwind_u_component_10mandwind_v_component_10mare stored.time: For each variable, data is split by time. This can be an entire year for historical data, or chunks of 1-2 weeks of data. An entire year is specified likeyear_2010.omwhile chunks of varying size use arbitrary indices likechunk_927382.om.omfile extension: All data is stored in compressed multi-dimensional arrays. For an optimal compression, a custom file format is used. See below.
Real-time weather models refresh every 1, 3, 6, or 12 hours. Upon completion of a new weather model run, Open-Meteo retrieves the original GRIB files from national server services, updates the database, and publishes the revised database on AWS S3.
Data is structured into chunks covering 3 to 14 days per file, resulting in existing files being overwritten with the most recent data. The length of each time-chunk is manually picked for each model for a good balance between file size, compression ratio and read performance.
Typically, historical weather data doesn't undergo updates. However, in the case of ERA5, daily updates are applied with a 5-7 day delay. Older historical data spanning the past 80 years remains unaltered, of course.
Open-Meteo provides a free API for quick data retrieval without the need to download from the AWS bucket.
However, there are two primary scenarios where downloading data locally is advantageous:
- Research with Historical Weather Data: Conducting intensive analyses on millions of events with varying locations and time steps is facilitated by having data available locally or on a dedicated high-performance VM instance. With Open-Meteo on AWS Open Data, you can download temperature data for the past 80 years using the Copernicus ERA5-Land dataset. Basic steps include:
- Installing the Open-Meteo Docker image
docker pull ghcr.io/open-meteo/open-meteo - Download archived ERA5 data for temperature from AWS
docker run open-meteo sync copernicus_era5_land temperature_2m --past-days 730(roughly 8 GB) - Launch your local API endpoint
docker run -p 8080:8080 open-meteo serve - Get data for individual coordinates
curl "http://127.0.0.1:8080/v1/archive?latitude=47.1&longitude=8.4&hourly=temperature_2m&start_date=20220101&end_date=20231031"
Note: Docker commands are exemplary. Follow the Tutorial for downloading historical weather data to get it working quickly.
- Running Your Own Weather API: If you require an extensive amount of weather data through an API daily and wish to run your own weather API, you can obtain current weather data from Open-Meteo on AWS Open Data. The Open-Meteo Docker container can listen for newly published data and keep your local database up-to-date. Similar to using past weather data:
- Install the Open-Meteo Docker image
- Start the data synchronization for a given weather model
docker run open-meteo sync ncep_gfs013 temperature_2m,relative_humidity_2m,wind_u_component_10m,wind_v_component_10m --past_days 3 --repeat-interval 5 - Launch the API instance and get the latest forecast from your new API endpoint
To help you in setting up your own weather API you can follow this tutorial to setup your own weather API.
Data is stored in a customized file format featuring a simple header and compressed data chunks. Files use the extension om and contain a 2-dimensional array for data, encompassing spatial and temporal dimensions. For global high-resolution domains like GFS at 0.125°, each file can consist of 4 to 6 million grid cells. With one value per hour over a year, the array dimensions reach 6,000,000 x 8,760.
To efficiently compress large arrays, data is chunked into blocks ranging from 6 to 20 locations and 2 weeks of data, resulting in chunk dimensions of 20 x 336. Specific lengths of dimensions and chunks are outlined in the meta-data header. Data published is optimised for time-series access. Accessing larger areas for single time steps is less efficient.
Meta data includes:
- Length of time and space dimensions (denoted as
dim0anddim1) - Chunk dimensions (denoted as
chunk0andchunk1) - Compression type and data scale-factor
Compressing 2-dimensional chunks achieves high compression ratios due to the homogeneity of gridded weather data in space and time. Compared to GRIB files, compression ratios are approximately 5 times higher on average. The compression scheme involves scaling weather data to reasonable accuracy (e.g., 0.05K for temperature), 2-dimensional delta coding, outlier filtering, and bit-length encoding. The reference implementation can be found in the Open-Meteo GitHub repository. Libraries in other programming languages may follow based on public interest.
Each individual chunk varies in size. The format includes a lookup table addressing each chunk individually, enabling random reads to specific portions of a file. Thus, the overall format comprises meta-data, a lookup table, and a data blob to store compressed chunks.
Binary Format:
2 byte: magic number "OM"1 byte: version1 byte: compression type with filter4 byte: float scalefactor8 byte: dim0 dim (slow)8 byte: dom0 dim1 (fast)8 byte: chunk dim08 byte: chunk dim1Array of 64-bit Integer: Offset lookup tableBlob: Data for each chunk, offset but the lookup table
This format draws inspiration from formats such as Zarr or HDF5 but is designed to encompass only a single 2-dimensional array. Opting for a custom format was a deliberate choice to seamlessly integrate a specialized compression scheme, interact directly with data, and eliminate any abstraction present in HDF5 or other libraries. Previous file formats and compression methods fell short of achieving the necessary compression performance.
The reference implementation is also designed to be thread-safe, minimize dynamic allocations, leverage high queue depth in SSDs, and deliver high throughput by harnessing the capabilities of modern CPUs with vector instructions.
A typical scenario involves retrieving one week of hourly weather data for a specific location. To accomplish this, an application must:
- Calculate the grid-cell position, for example,
1,345,434out of6,000,000grid cells. For a global 0.125° regular grid, the calculation islongitude / 0.125 + (latitude / 0.125) * (360/0.125). - Open the
omfile and read the first 56 bytes to decode the header. - Utilize the array and chunk dimensions to determine the chunk number.
- Access the offset through the lookup table.
- Read data from the specified offset.
- Decompress the data.
All these decoding steps are seamlessly integrated into the Open-Meteo API. There's no need for you to implement them. It is highly recommended to use the provided Docker files for reading from om files.
The om file format enables reading small subsections of data without the need to download the entire file. Only three read operations are necessary to obtain the data, and the first two reads can be efficiently cached.
As of now, the Open-Meteo API does not directly support partial reads from S3, but this functionality will be implemented in the future.
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