Introduction
This block provides historic, current and forecast data for agriculture through the Meteomatics Weather API. These datasets are available worldwide starting with 1979.
Technical Information
Compatible blocks
Compatible blocks |
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Export Data (Raster) |
NetCDF → GeoTIFF Conversion |
Geographic coverage
The geographic coverage is global.
Dataset Information
Specifications | Description |
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Spatial Resolution | To avoid reaching the query limit provided by the Meteomatics Weather API, the spatial resolution (in decimal degrees) for retrieving the time series of a weather parameter will be set based on the AOI size. The minimum resolution is 90 m (0.001 decimal degrees): 0.001 decimal degrees (AOI < 100 sq. km) 0.01 decimal degrees (AOI 100 - 10000 sq. km) 0.1 decimal degrees (AOI 10000 - 100000 sq. km) 1 decimal degree (AOI > 100000 sq. km) |
Dataset Type | Optical and radar |
Satellite Sensors | The dataset is a satellite composite that merges images acquired by the following geostationary sensors: GOES 16 GOES 17 Himawari 8 Meteosat 8 Meteosat 11 Meteosat MSG For more information, please refer to Global Satellite Images. |
Revisit Frequency | sub-hourly |
Data Availability | Historical data: starting with 1979 Forecast data: up until 2100 For more information, please refer to Forecast and historical data. |
Global Weather Forecasting Model | mix |
File Format | NetCDF |
Bit Depth | 64 bits per pixel (float) |
Coordinate System | EPSG 4326 |
Can be reused in an UP42 workflow | No |
Limitations
The AOI must be minimum 0.01 square kilometers.
Weather and climate parameters
Weather and Climate Parameters | Meteomatics parameter name | Example |
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Amount of precipitation in the previous interval [mm] | precip_<interval>:mm | precip_5min:mm |
Max. diameter of hail in the previous interval [cm] | hail_<interval>:cm | hail_6h:cm |
Instantaneous temperature AGL | t_<level>:<unit> | t_1000hPa:K |
Frost depth [cm] | frost_depth:cm | frost_depth:cm |
Chance for soil frost occurrence [%] | soil_frost:p | soil_frost:p |
Soil moisture deficit - a measure for droughts - deviation from long year average [mm] | soil_moisture_deficit:mm | soil_moisture_deficit:mm |
Soil moisture index at a certain depth [cm] | soil_moisture_index_<depth>:idx | soil_moisture_index_-15cm:idx |
Evapotranspiration of previous interval [mm] | evapotranspiration_<interval>:mm | evapotranspiration_1h:mm |
Accumulated growing degree days [gdd] | growing_degree_days_accumulated:gdd | growing_degree_days_accumulated:gdd |
Sum of grass land temperature [°C] | grass_land_temperature_sum:<unit> | grass_land_temperature_sum:C |
Leaf wetness index | leaf_wetness:idx | leaf_wetness:idx |
Phytophthora negative prognosis (index for safety measures against potato blight) | phytophthora_negative:idx | phytophthora_negative:idx |
Most similar year [y] | most_similar_year:y | most_similar_year:y |
For a complete list, please refer to the Meteomatics Agricultural Parameter List.
How it works
Supported JSON parameters | Default value | Min | Max | Examples |
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time | "2020-01-01T00:00:00+00:00/2021-12-31T23:59:59+00:00" | 1979 | 2100 | "time": "2020-01-01T00:00:00+00:00/2021-12-31T23:59:59+00:00" |
variables | [ "precip_3h:mm", "soil_moisture_index_-15cm:idx", "grass_land_temperature_sum:C" ] | n.a. | n.a. | "variables": [ "frost_depth:cm", "soil_moisture_index_-15cm:idx","soil_moisture_deficit:mm", "grass_land_temperature_sum:C", "soil_frost:p", "precip_5min:mm"] |
time_series | null | 1979 | 2100 | "time_series": ["2021-03-31T00:00:00+00:00/2021-04-01T00:00:00+00:00", "2021-04-30T00:00:00+00:00/2021-05-01T00:00:00+00:00" ] |
time_interval | 3 | 1 | - | "time_interval": 6 |
bbox /intersects | null | 0.01 sq. km | n.a. | Please check the examples from the JSON parameters. |
Disclaimer
It is not possible to run a test query in the job configuration window. The length of the forecasting period and the spatial resolution depend on the model from which the requested parameters originate.
Examples
Example of a workflow created with the data block Weather Data for Agriculture, based on a set of weather parameters:
Example of a workflow created with the data block Weather Data for Agriculture, based on a set of weather parameters:
Capabilities
Output
raster
custom | |
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variables | ${variables} |
up42_standard | |
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dtype | float |
format | NetCDF |
sensor | Meteomatics |
processing_level | l3 |