Global Weather Forecasting Models | European and Regional Weather Models | Atmospheric Pollutants and other Chemical Compounds | AI Models | Oceanic Models | Global Reanalysis | Oceanic Models | Radar, Satellite and Remote Sensing | Station Observations & MOS Prognoses | Climate Scenarios
Example:
https://api.meteomatics.com/2024-12-03T00ZP2D:PT1H/t_2m:C/47.43,9.4/csv?source=mix-obs&on_invalid=fill_with_invalid
Models and Data Sources
The data described in the API is based on different models and combined in an intelligent mix so that the best data source is chosen for each time and location. The available data sources are:
Global Weather Forecasting Models
| Identifier | Description |
|---|---|
mix |
The Meteomatics Mix combines different models and sources into an intelligent blend, such that the best data source is chosen for each time and location. The Mix consists of radar data, satellite data, deterministic and ensemble-based predictions, trend predictions, climate projections and reanalysis data. The length of the forecasting period as well as the spatial resolution depends on the model from which the requested parameters originate.
spatial resolution: up to 0.0012° (~90 m) temporal resolution: up to 5 minutes lead time: depending on variable — days, weeks and even years ahead updates per day: depending on variable — refresh time down to every minute |
ecmwf-ifs |
Enhanced downscaled model data based on the European Center for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecasting System (IFS), which is the world's leading atmospheric global circulation model that describes the dynamical evolution of the atmosphere worldwide and is used for medium-range forecasts. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: up to 1 hour lead time: 15 days (6 and 18 UTC up to 90 hours) updates per day: 4 |
ecmwf-ens |
Enhanced downscaled model data based on the Ensemble Prediction System (EPS) of ECMWF. EPS is a system that is used to predict forecast confidence. Uncertainties in the initial conditions are represented by creating a set of 50 forecasts (ensemble members) with slightly different initial conditions. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources. See here for information about the ensemble member selection. If nothing else is specified, the control run is returned. spatial resolution: 0.0012° (~90 m) temporal resolution: up to 3 hours lead time: 15 days (6 and 18 UTC up to 144 hours) updates per day: 4 |
ecmwf-ens-cluster |
Daily clustering data of the forecast fields based on the ECMWF ensemble data. The clustering is performed according to a set of fixed climatological regimes for each season, computed using 29 years of reanalysis data. See here for information about the cluster selection. spatial resolution: 1.5° (~114 km) temporal resolution: 12 hours lead time: 15 days updates per day: 2 |
ecmwf-ens-tc |
Ensemble model data for tropical cyclones (tropical depressions, tropical storms, hurricanes and typhoons) based on data provided by ECMWF.
spatial resolution: 0.1° (~7.6 km) temporal resolution: 6 hours lead time: 10 days updates per day: 2 |
ecmwf-vareps |
Enhanced downscaled long-range ensemble forecast based on data provided by ECMWF. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources. The forecast consists of an ensemble of 100 members .
spatial resolution: 0.0012° (~90 m) temporal resolution: up to 3 hours lead time: 46 days updates per day: 1 |
ecmwf-mmsf |
Downscaled long-range seasonal forecast based on data provided by ECMWF. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 6 hours lead time: 7 months updates per month: 1 |
cmc-gem |
Enhanced downscaled model data based on the Global Environmental Multiscale model operated by the Canadian Meteorological Center. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 3 hours lead time: 6 days updates per day: 2 |
ncep-gfs |
Enhanced downscaled model data based on the Global Forecasting System by the National Centers for Environmental Prediction (NCEP). The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 3 hours lead time: 16 days updates per day: 4 |
ncep-gfs-ens |
Ensemble model data of the Global Forecasting System by NCEP. The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of 30 members, each perturbed from the original observations. See here for information about the ensemble member selection. If nothing else is specified, the control run is returned. spatial resolution: 0.5° (~38 km) temporal resolution: 3 hours lead time: 16 days updates per day: 4 |
ukmo-um10 |
Enhanced downscaled model data based on the 10km Global model by the UK MetOffice. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 1 hour lead time: 7 days (6 and 18 UTC up to 60 hours) updates per day: 4 |
dwd-icon-global |
Enhanced downscaled model data based on the 13km Global model by the DWD. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 1 hour lead time: 7.5 days (6 and 18 UTC up to 120 hours) updates per day: 4 |
mm-tides |
Tidal amplitude simulation by Meteomatics.
spatial resolution: 0.125° (~9.5 km) temporal resolution: 1 minute |
| Examples | https://api.meteomatics.com/2024-12-03T00ZP2D:PT1H/t_2m:C/50,10/html?source=ecmwf-ifs https://api.meteomatics.com/2024-12-03T00Z/t_2m:C/switzerland:0.01,0.01/html?source=ncep-gfs |
AI Models
| Identifier | Description | ai-fourcast-ecmwf-ifs |
Enhanced downscaled model data based on FourCastNet. FourCastNet is a Fourier forecasting neural network model which is trained on historical weather data from ECMWF's ERA5 reanalysis archive. It generates predictions based on ECMWF's IFS initial state.
spatial resolution: 0.0012° (~90 m) temporal resolution: 6 hours lead time: 20 days updates per day: 4 |
|---|---|
ai-graphcast-ecmwf-ifs |
Enhanced downscaled model data based on GraphCast. GraphCast is a multi-scale graph neural network-based autoregressive model. It is trained on historical weather data from ECMWF's ERA5 reanalysis archive and generates predictions based on ECMWF's IFS initial state.
spatial resolution: 0.0012° (~90 m) temporal resolution: 6 hours lead time: 20 days updates per day: 4 |
ecmwf-aifs |
Enhanced downscaled model data based on ECMWF Artificial Intelligence/Integrated Forecasting System (AIFS). ECMWF-AIFS is trained on a subset of the ERA5 reanalysis data for 1979–2018 and fine-tuned on operational IFS data from 2019 to 2020.
spatial resolution: 0.0012° (~90 m) temporal resolution: 6 hours lead time: 15 days updates per day: 4 |
European and Regional Weather Models
| Identifier | Description |
|---|---|
mm-euro1k |
High-resolution model for Europe designed by Meteomatics. The downscaling improves the native grid resolution of 1 km to a resolution of 90 m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 20 minutes lead time: 24 hours updates per day: 24 |
mm-swiss1k |
High-resolution model for Switzerland designed by Meteomatics. The downscaling improves the native grid resolution of 1 km to a resolution of 90 m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 20 minutes lead time: 3 days updates per day: 4 |
mm-nd1k |
High-resolution model for North Dakota designed by Meteomatics. The downscaling improves the native grid resolution of 1 km to a resolution of 90 m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 20 minutes lead time: 26 hours updates per day: 24 |
mf-arome |
Regional model by Meteo France. The resolution of the model depends on the parameter.
spatial resolution: 0.01° - 0.025° (~760 m - 1900 m) temporal resolution: 1 hour lead time: 42 hours updates per day: 5 |
dwd-icon-eu |
Enhanced downscaled model data based on forecast data provided by the ICON model by the DWD. The downscaling increases the native grid resolution of 0.0625° up to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 1 hour (first 78 hours of leadtime) | 3 hours lead time: 120 hours updates per day: 4 |
ncep-hrrr |
Enhanced downscaled model data based on the real-time High-Resolution Rapid Refresh model (HRRR) by the National Centers for Environmental Prediction (NCEP). The downscaling increases the native grid resolution of 3 km up to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 15 min (for some parameters during first 18 hours) | 1 hour lead time: 48 hours (0, 6, 12, 18 UTC) | 18 hours updates per day: 24 |
Atmospheric Pollutants and other Chemical Compounds
| Identifier | Description |
|---|---|
ecmwf-cams |
Atmospheric data based on Copernicus Atmosphere Monitoring Service by the ECMWF. CAMS is one of six services that form Copernicus (Earth observation program of the European Union). Copernicus offers information services based on satellite Earth observation, in situ (non-satellite) data and modeling. CAMS provides global forecasts of aerosols, atmospheric pollutants, greenhouse gases, stratospheric ozone and the UV-Index.
spatial resolution: 0.4° (~30.4 km) temporal resolution: up to 1 hour lead time: 5 days updates per day: 2 |
fmi-silam |
System for Integrated Modeling of Atmospheric Composition by the Finnish Meteorological Institute for Europe.
spatial resolution: 0.1° (~7.6 km) temporal resolution: up to 1 hour lead time: 2 days updates per day: 4 |
Oceanic Models
| Identifier | Description |
|---|---|
ecmwf-wam |
Enhanced oceanic data based on the ECMWF Ocean Wave Model, which is a global model that describes the development and evolution of wind generated surface waves and their height, direction and period. Since it does not dynamically model the ocean itself, it is coupled to the atmospheric forecast model and to the ocean model. The output is enhanced by incorporating a more detailed bathymetry as well as highly resolved coastlines (resolution of 90 - 200 m).
spatial resolution: 0.125° (~9.5 km) temporal resolution: 3 hours lead time: 10 days updates per day: 2 |
ecmwf-cmems |
Enhanced oceanic data based on Copernicus Marine Environment Monitoring Service (CMEMS), which provides information about ocean currents, ocean temperatures, salinity and sea ice thickness. The output is enhanced by incorporating a more detailed bathymetry as well as highly resolved coastlines (resolution of 90 - 200 m).
spatial resolution: 0.08333° (~9.25 km) temporal resolution: 1 hour for ocean currents and temperatures | 24 hours for salinity and sea ice concentration lead time: 10 days updates per day: 1 |
noaa-hycom |
The NOAA Hybrid Coordinate Ocean Model provides forecasts for several wave parameters. The hybrid coordinate approach proved to be feasible for handling deep and shallow water regions throughout the annual heating/cooling cycle.
spatial resolution: 0.08° (~6084 m) temporal resolution: 3 hours lead time: 7 days updates per day: 1 |
nasa-ghrsst |
Retrospective and near-realtime analysis data set for sea surface temperature and sea ice conentration by the Group for High Resolution Sea Surface Temperature (GHRSST). The latency of the data set is one day.
spatial resolution: 0.01° (~760 m) temporal resolution: 1 hour updates per day: 1 delay: 1 day |
Global Reanalysis
| Identifier | Description |
|---|---|
ecmwf-era5 |
Enhanced downscaled reanalysis data based on ERA5, which is a global atmospheric reanalysis from 1940 to present, continuously updated in real time. ERA5 combines vast amounts of historical observations into global estimates using advanced modeling and data assimilation systems. The downscaling improves the coarse grid native representation down to a resolution of 90m. This is achieved by applying high-resolution land usage data, soil, terrain data, astronomical computations & other sources.
spatial resolution: 0.0012° (~90 m) temporal resolution: 1 hour |
chc-chirps2 |
Rainfall hindcast from rain gauge and satellite observations from 1981 until present.
spatial resolution: 0.05° (~5 km) temporal resolution: 24 hours |
Radar, Satellite and Remote Sensing
| Identifier | Description |
|---|---|
mix-radar |
Composite of precipitation radar from various sources (e.g. NOAA, DWD, ...) including nowcasting.
spatial resolution: 0.014° (~1 km) temporal resolution: 5 minutes lead time: 2 hours |
mm-heliosat |
Satellite-based cloud and radiation forecast for Europe by Meteomatics.
spatial resolution: 0.014° (~1 km) temporal resolution: 5 minutes lead time: 3 hours |
noaa-swpc |
Geomagnetic activity observation and forecast by NOAA.
temporal resolution: 3 hours lead time: 2 days updates per day: 2 |
mix-satellite |
Meteomatics satellite composite comprising geostationary satellite images of GOES 16, GOES 17, Himawari8, Meteosat 8, Meteosat 11 and Meteosat MSG. RGB and IR channels are available.
temporal resolution: 5 - 15 minutes spatial resolution: 1 - 3 km |
eumetsat-h60b |
Based on IR satellite images from the SEVIRI instrument, instantaneous precipitation charts are generated.
temporal resolution: 15 minutes spatial resolution: 3 km near sub-satellite point | 8 km on average over Europe |
dlr-corine |
CORINE land cover (CLC) is a data set for land usage in Europe. There are 44 land usage classes.
spatial resolution: 10 ha minimum mapping unit spatial extent: Europe |
| Examples | Satellite image https://api.meteomatics.com/2024-12-03T00Z/sat_ir_039:idx/switzerland:0.01,0.01/html?source=mix-satellite Radar image https://api.meteomatics.com/2024-12-03T00Z/precip_5min:mm/switzerland:0.01,0.01/html?source=mix-radar |
Station Observations & MOS Prognoses
| Identifier | Description |
|---|---|
mix-obs |
Observational data from weather stations (some restrictions to formats might apply). Present data and historical data are available. Further details concerning the selection of stations can be found at Weather Station Identifiers. It is also recommended to specify the treatment of missing data (Behavior on missing or invalid Data). |
mm-mos |
MOS (Model Output Statistics) based on observational data from weather stations.
temporal resolution: 1 hour lead time: 15 days updates per hour: 2 |
| Examples | Station query http://api.meteomatics.com/2024-12-02T00Z--2024-12-03T00Z:PT1H/t_2m:C,wind_speed_10m:ms/wmo_066810/html?source=mix-obs&on_invalid=fill_with_invalid MOS query http://api.meteomatics.com/2024-12-03T00ZP5D:PT1H/t_2m:C,wind_speed_10m:ms/wmo_066810/html?source=mm-mos |
Climate Scenarios
| Identifier | Description |
|---|---|
mri-esm2-ssp126 |
Downscaled climate data for Scenario 1 covering the period from 2015 until 2100 provided by the Meteorological Research Institute Earth System Model Version 2.0.
spatial resolution: 0.0012° (~90 m) temporal resolution: 3 hours |
mri-esm2-ssp245 |
Downscaled climate data for Scenario 2 covering the period from 2015 until 2100 provided by the Meteorological Research Institute Earth System Model Version 2.0.
spatial resolution: 0.0012° (~90 m) temporal resolution: 3 hours |
mri-esm2-ssp370 |
Downscaled climate data for Scenario 3 covering the period from 2015 until 2100 provided by the Meteorological Research Institute Earth System Model Version 2.0.
spatial resolution: 0.0012° (~90 m) temporal resolution: 3 hours |
mri-esm2-ssp460 |
Downscaled climate data for Scenario 4 covering the period from 2015 until 2100 provided by the Meteorological Research Institute Earth System Model Version 2.0.
spatial resolution: 0.0012° (~90 m) temporal resolution: 3 hours |
mri-esm2-ssp585 |
Downscaled climate data for Scenario 5 covering the period from 2015 until 2100 provided by the Meteorological Research Institute Earth System Model Version 2.0.
spatial resolution: 0.0012° (~90 m) temporal resolution: 3 hours |
Behavior on missing or invalid Data
This section describes the options on what to do if data is missing (this currently only applies to sourcemix-obs):
on_invalid parameter:
| Identifier | Description |
|---|---|
fail |
Send an error message as soon as data is missing, instead of the incomplete data. (default) |
fill_with_invalid |
Replace invalid data by -999 and still send the whole time series. |