WindBorne Systems API Reference

Introduction

WeatherMesh is the world’s most accurate AI weather model, and is used operationally to guide the largest balloon constellation in the world.

WeatherMesh currently predicts surface temperature, pressure, winds, precipitation, and radiation; and then geopotential, temperature, winds, and moisture at 28 pressure levels. It has both a deterministic and and ensemble forecast system, each with a resolution of 0.25 degrees. It also has a specialized point-forecasting model for arbitrary locations in between grid points.

The WindBorne API is designed to be predictable, performant, and easy to use.

Need access?

Email data@windbornesystems.com

Base URL

https://forecasts.windbornesystems.com/api/v1

Python Library

The easiest way to access data is through our command line interface. This allows querying of all endpoints and saving to files.

You can also use the python library directly. See the python library documentation for more information.

Installation

Bash

pip3 install windborne

Sample usage

Bash

windborne points '37.77,-122.42'

Authentication

WindBorne uses API keys to authenticate API requests. If an API request is not properly authenticated, it will fail. To get an API key, email data@windbornesystems.com.

You should have an API key and a client id. Conceptually, you can think of your client id as a username and your API key as your password. You should make sure to keep your API key safe and secure.

To authenticate, pass HTTP basic auth headers in the GET request with username YOUR_CLIENT_ID and password YOUR_API_KEY. This follows RFC 7617; ie, as is standard, the credentials are colon-separated then base-64 encoded; many HTTP libraries will do this for you.

Using JWTs from a key server ▸

Example

import os
import time
# install dependencies with `pip3 install requests`
import requests


def wb_get_request(url):
    """
    Make a GET request to WindBorne, authorizing with WindBorne correctly
    """

    client_id = os.environ['WB_CLIENT_ID']  # Make sure to set this!
    api_key = os.environ['WB_API_KEY']   # Make sure to set this!

    # make the request, checking the status code to make sure it succeeded
    response = requests.get(url, auth=(client_id, api_key))
    response.raise_for_status()

    # return the response 
    return response

Point forecasts

This endpoint allows getting the forecast at a given point or set of points. This runs WindBorne's custom point forecasting model, making it more accurate than simple interpolation of the gridded output.

These forecasts are at hourly resolution out to 5 days and then 6-hourly beyond that.

It accepts the following query string parameters:

Name	Type	Description
coordinates	string	A semi-colon separated list of latitude,longitude tuples, eg `37,-121;40.3,-100`
min_forecast_time	string	Optional. An ISO 8601 date string, supporting formats YYYYMMDDHH, YYYY-MM-DDTHH, and YYYY-MM-DDTHH:mm:ss., representing the minimum forecast time to calculate point forecasts for.
max_forecast_time	string	Optional. An ISO 8601 date string, supporting formats YYYYMMDDHH, YYYY-MM-DDTHH, and YYYY-MM-DDTHH:mm:ss., representing the maximum forecast time to calculate point forecasts for.
min_forecast_hour	integer	Optional. The minimum forecast hour to calculate point forecasts for.
max_forecast_hour	integer	Optional. The maximum forecast hour to calculate point forecasts for.
initialization_time	string	Optional. An ISO 8601 date string, supporting formats YYYYMMDDHH, YYYY-MM-DDTHH, and YYYY-MM-DDTHH:mm:ss., representing the time at which the forecast was made. This looks solely at the date and the hour; minutes and seconds are discarded. If nothing is provided, the latest forecast is used.

It returns the following:

Name	Type	Description
forecasts	[Forecast]	An array of forecasts, in the same order as the input coordinates
initialization_time	string	The ISO 8601 date string of the forecast initialization time

A forecast is an array of the form:

Name	Type	Description
temperature_2m	number	The temperature at 2m above ground level, in degrees Celsius
dewpoint_2m	number	The dewpoint at 2m above ground level, in degrees Celsius
wind_u_10m	number	The U component of wind speed at 10m above ground level, in m/s
wind_v_10m	number	The V component of wind speed at 10m above ground level, in m/s
pressure_msl	number	The pressure as converted to MSL pressure, in hPa
latitude	number	The latitude of the point, between -90 and 90
longitude	number	The longitude of the point, between -180 and 180
time	string	The ISO8601 timestamp of when this forecast point is valid

Endpoints

    GET /points.json
    GET https://forecasts.windbornesystems.com/api/v1/points.json

Sample Response

{
  "forecasts": [
    [
      {
        "temperature_2m": 15.5,
        "dewpoint_2m": 10.5,
        "wind_u_10m": 5.5,
        "wind_v_10m": 5.5,
        "pressure_msl": 1013.5,
        "latitude": 37.819754,
        "longitude": -122.477847,
        "time": "2024-04-01T00:00:00Z"
      },
      {
        "temperature_2m": 15.5,
        "dewpoint_2m": 10.5,
        "wind_u_10m": 5.5,
        "wind_v_10m": 5.5,
        "pressure_msl": 1013.5,
        "latitude": 37.819754,
        "longitude": -122.477847,
        "time": "2024-04-01T01:00:00Z"
      },
      {
        "temperature_2m": 15.5,
        "dewpoint_2m": 10.5,
        "wind_u_10m": 5.5,
        "wind_v_10m": 5.5,
        "pressure_msl": 1013.5,
        "latitude": 37.819754,
        "longitude": -122.477847,
        "time": "2024-04-01T02:00:00Z"
      }
    ]
  ],
  "initialization_time": "2024-04-01T00:00:00Z"
}

Example: Using the point forecasts API

In this example, we request forecasts for two points (the Golden Gate Bridge and the Statue of Liberty) using the wb_get_request function from the Authentication section above. We request forecasts for 0-2 hours, starting from the latest initialization time.

You may consider instead using the get_point_forecasts function from the pip library.

The response groups forecasts first by point, then by forecast hour. Each forecast hour contains the forecasted values for various weather variables.

Example

Python

import os
import requests

def wb_get_point_forecast(params):
    """
    Make a GET request to the WindBorne point forecast API using wb_get_request
    """
    base_url = "https://forecasts.windbornesystems.com/api/v1/points.json"
    
    # Convert the parameters dictionary to query string using requests' utility
    response = wb_get_request(f"{base_url}?{requests.compat.urlencode(params)}")
    return response.json()

params = {
    "coordinates": "37.819754,-122.477847;40.689672,-74.045847", # Golden Gate Bridge and Statue of Liberty
    "min_forecast_hour": 0,
    "max_forecast_hour": 2
}

result = wb_get_point_forecast(params)
print(result)

Example response

{'forecasts': [[{'dewpoint_2m': 8.3, 'latitude': 37.819754, 'longitude': -122.477847, 'precipitation': 0.0, 'pressure_msl': 1019.8, 'temperature_2m': 12.1, 'time': '2024-12-03T12:00:00+00:00', 'wind_u_10m': -1.1, 'wind_v_10m': -3.4}, {'dewpoint_2m': 7.9, 'latitude': 37.819754, 'longitude': -122.477847, 'precipitation': 0.0, 'pressure_msl': 1020.1, 'temperature_2m': 11.7, 'time': '2024-12-03T13:00:00+00:00', 'wind_u_10m': -0.8, 'wind_v_10m': -5.5}, {'dewpoint_2m': 7.6, 'latitude': 37.819754, 'longitude': -122.477847, 'precipitation': 0.0, 'pressure_msl': 1020.2, 'temperature_2m': 11.7, 'time': '2024-12-03T14:00:00+00:00', 'wind_u_10m': -0.7, 'wind_v_10m': -5.7}], [{'dewpoint_2m': -5.8, 'latitude': 40.689672, 'longitude': -74.045847, 'precipitation': 0.0, 'pressure_msl': 1024.4, 'temperature_2m': -0.6, 'time': '2024-12-03T12:00:00+00:00', 'wind_u_10m': 4.7, 'wind_v_10m': -3.3}, {'dewpoint_2m': -5.1, 'latitude': 40.689672, 'longitude': -74.045847, 'precipitation': 0.0, 'pressure_msl': 1024.6, 'temperature_2m': -0.0, 'time': '2024-12-03T13:00:00+00:00', 'wind_u_10m': 4.6, 'wind_v_10m': -2.5}, {'dewpoint_2m': -4.8, 'latitude': 40.689672, 'longitude': -74.045847, 'precipitation': 0.0, 'pressure_msl': 1025.1, 'temperature_2m': 1.1, 'time': '2024-12-03T14:00:00+00:00', 'wind_u_10m': 4.5, 'wind_v_10m': -2.6}]], 'initialization_time': '2024-12-03T12:00:00+00:00'}

Gridded forecast, single variable

This endpoint allows getting the gridded output of global forecasts for a single variable. The variable is set in the url; for a surface variable, the endpoint is /gridded/{variable} whereas for an upper-level variable, the endpoint is /gridded/{level}/{variable}. These outputs are returned as a netCDF (.nc) file.

This endpoint can be used for both the latest forecast (by specifying time) or for fetching historical forecasts (by specifying initialization_time and forecast_hour). Available initialization times and forecast hours can be found from the forecast hours endpoint.

Valid variables are:

Name	Description	Type
temperature_2m	2m temperature	surface
wind_u_10m	10m u-component of wind	surface
wind_v_10m	10m v-component of wind	surface
pressure_msl	Mean sea level pressure	surface
geopotential	Geopotential (not geopotential height)	upper-level
temperature	Temperature	upper-level
wind_u	U-component of wind	upper-level
wind_v	V-component of wind	upper-level

This will generally be a redirect to a presigned URL on R2, so you will need to follow the redirect to get the file. Only in the short time it takes for the file to be uploaded will it be served directly.

It accepts the following query string parameters:

Name	Type	Description
time	string	The time at which the forecast is valid. This may be of format ISO 8601, YYYYMMDDHH, or YYYY-MM-DDTHH. This looks solely at the date and the hour; minutes and seconds are discarded. If there is no forecast available within an hour of the requested time, it will return a 404. Alternatively, you can specify `initialization_time` and `forecast_hour`.
initialization_time	string	The initialization time for the forecast. This may be of format ISO 8601, YYYYMMDDHH, or YYYY-MM-DDTHH. This looks solely at the date and the hour; minutes and seconds are discarded. Must be used in conjunction with `forecast_hour`. Cannot be used with `time`.
forecast_hour	number	An integer representing the number of hours from the `initialization_time` for which to get the forecast. For example, if `initialization_time` is `2023-01-01T00:00:00` and `forecast_hour` is `6`, the forecast will be for `2023-01-01T06:00:00`. Must be used in conjunction with `initialization_time`. Cannot be used with `time`.
ens_member	string	The ensemble member to get initialization times for. Can be `mean` or a number between 1 and 25. Cannot be used with `intracycle`.
intracycle	boolean	Whether to return initialization times for our intracycle model. This model comes out several hours before the main model or other models like GFS and ECMWF, and can give a directional signal on how the forecast is likely to evolve. Defaults to `false`. Cannot be used with `ens_member`.

Surface Endpoints

    GET /gridded/{variable}
    GET https://forecasts.windbornesystems.com/api/v1/gridded/{variable}

Upper-level Endpoints

    GET /gridded/{level}/{variable}
    GET https://forecasts.windbornesystems.com/api/v1/gridded/{level}/{variable}

Example: 2m temperature endpoints

    GET /gridded/temperature_2m
    GET https://forecasts.windbornesystems.com/api/v1/gridded/temperature_2m

Example: 500hPa geopotential endpoints

    GET /gridded/500/geopotential
    GET https://forecasts.windbornesystems.com/api/v1/gridded/500/geopotential

Gridded forecast, all variables at all levels

This command allows getting the full gridded output of global forecasts, including data for all variables at all levels. These outputs are returned as a netCDF (.nc) file.

It accepts the following query string parameters:

Name	Type	Description
time	string	The time at which the forecast is valid. This may be of format ISO 8601, YYYYMMDDHH, or YYYY-MM-DDTHH. This looks solely at the date and the hour; minutes and seconds are discarded. If there is no forecast available within an hour of the requested time, it will return a 404. Alternatively, you can specify `initialization_time` and `forecast_hour`.
initialization_time	string	The initialization time for the forecast. This may be of format ISO 8601, YYYYMMDDHH, or YYYY-MM-DDTHH. This looks solely at the date and the hour; minutes and seconds are discarded. Must be used in conjunction with `forecast_hour`. Cannot be used with `time`.
forecast_hour	number	An integer representing the number of hours from the `initialization_time` for which to get the forecast. For example, if `initialization_time` is `2023-01-01T00:00:00` and `forecast_hour` is `6`, the forecast will be for `2023-01-01T06:00:00`. Must be used in conjunction with `initialization_time`. Cannot be used with `time`.
ens_member	string	The ensemble member to get initialization times for. Can be `mean` or a number between 1 and 25. Cannot be used with `intracycle`.
intracycle	boolean	Whether to return initialization times for our intracycle model. This model comes out several hours before the main model or other models like GFS and ECMWF, and can give a directional signal on how the forecast is likely to evolve. Defaults to `false`. Cannot be used with `ens_member`.

Endpoints

    GET /gridded/FULL
    GET https://forecasts.windbornesystems.com/api/v1/gridded/FULL

Example: Using the gridded forecast API

In this example, we request the 2m temperature forecast one week from the time that the script is run, at 00:00 UTC. We use the wb_get_request function from the Authentication section above.

You may consider instead using the get_gridded_forecast function from the pip library.

This example queries for 2m temperature and uses the io and xarray libraries to process the response into an xarray dataset. The returned dataset has three variables: latitude, longitude, and temperature_2m. The latitude and longitude variables are 1D arrays of the latitudes and longitudes of the grid points, respectively. The temperature_2m variable is a 2D array of the forecasted 2 meter temperatures at each grid point. The dataset contains additional metadata in the form of attributes.

Example

Python

from datetime import datetime, timedelta, timezone
import io
import os
import requests
import xarray as xr

def wb_get_gridded_2t(params):
    """
    Make a GET request to the WindBorne gridded forecast API for 2m temperature using wb_get_request
    """
    base_url = "https://forecasts.windbornesystems.com/api/v1/gridded/temperature_2m"

    # Convert the parameters dictionary to query string and make the request
    req = f"{base_url}?{requests.compat.urlencode(params)}"
    response = wb_get_request(f"{base_url}?{requests.compat.urlencode(params)}")

    # Read the content as a netCDF dataset
    nc_file = io.BytesIO(response.content)
    return xr.open_dataset(nc_file)

now = datetime.now(tz=timezone.utc)
one_week_from_now = now + timedelta(days=7)
params = {"time": one_week_from_now.strftime("%Y-%m-%dT00:00:00Z")}
result = wb_get_gridded_2t(params)
print("Dataset: ", result, "\n")
print("Units: ", result["temperature_2m"].units, "\n")
print("Values: ", result["temperature_2m"].values)

Example response

Dataset:  <xarray.Dataset> Size: 4MB
Dimensions:         (lat: 720, lon: 1440)
Dimensions without coordinates: lat, lon
Data variables:
    latitude        (lat) float32 3kB ...
    longitude       (lon) float32 6kB ...
    temperature_2m  (lat, lon) float32 4MB ...
Attributes:
    initialization_time:  2024-12-11T18:00:00+00:00
    forecast_hour:        174
    description:          Gridded forecast data for temperature_2m
    source:               WindBorne WeatherMesh Qfiz 

Units:  K 

Values:  [[250.7576  250.72754 250.7576  ... 251.08824 251.20847 250.93794]
 [250.6073  250.69748 250.6073  ... 250.54718 250.6073  250.51712]
 [250.2466  250.457   250.39688 ... 250.27666 250.2466  250.15642]
 ...
 [245.5274  245.5274  245.49734 ... 245.55746 245.5274  245.70776]
 [245.5274  245.49734 245.3771  ... 245.49734 245.49734 245.58752]
 [245.55746 245.5274  245.40717 ... 245.49734 245.55746 245.58752]]

Example: Fetching a historical gridded forecast

In this example, we request the 2m temperature forecast for a prior initialization time. In this specific case, we request the forecast initialized the previous day at 00:00 UTC and forecasted for 48 hours beyond that. We use the wb_get_request function from the Authentication section above.

You may consider instead using the get_gridded_forecast function from the pip library.

Example

Python

from datetime import datetime, timedelta, timezone
import io
import os
import requests
import xarray as xr

def wb_get_gridded_2t(params):
    """
    Make a GET request to the WindBorne gridded forecast API for 2m temperature using wb_get_request
    """
    base_url = "https://forecasts.windbornesystems.com/api/v1/gridded/temperature_2m"

    # Convert the parameters dictionary to query string and make the request
    req = f"{base_url}?{requests.compat.urlencode(params)}"
    response = wb_get_request(f"{base_url}?{requests.compat.urlencode(params)}")

    # Read the content as a netCDF dataset
    nc_file = io.BytesIO(response.content)
    return xr.open_dataset(nc_file)

now = datetime.now(tz=timezone.utc)
one_day_ago = now - timedelta(days=1)
params = {"initialization_time": one_day_ago.strftime("%Y-%m-%dT00:00:00Z"), "forecast_hour": 48}
result = wb_get_gridded_2t(params)
print("Dataset: ", result, "\n")
print("Units: ", result["temperature_2m"].units, "\n")
print("Values: ", result["temperature_2m"].values)

Example response

Dataset:  <xarray.Dataset> Size: 4MB
Dimensions:         (lat: 720, lon: 1440)
Dimensions without coordinates: lat, lon
Data variables:
    latitude        (lat) float32 3kB ...
    longitude       (lon) float32 6kB ...
    temperature_2m  (lat, lon) float32 4MB ...
Attributes:
    initialization_time:  2024-12-11T18:00:00+00:00
    forecast_hour:        174
    description:          Gridded forecast data for temperature_2m
    source:               WindBorne WeatherMesh Qfiz 

Units:  K 

Values:  [[250.7576  250.72754 250.7576  ... 251.08824 251.20847 250.93794]
 [250.6073  250.69748 250.6073  ... 250.54718 250.6073  250.51712]
 [250.2466  250.457   250.39688 ... 250.27666 250.2466  250.15642]
 ...
 [245.5274  245.5274  245.49734 ... 245.55746 245.5274  245.70776]
 [245.5274  245.49734 245.3771  ... 245.49734 245.49734 245.58752]
 [245.55746 245.5274  245.40717 ... 245.49734 245.55746 245.58752]]

Initialization Times

This endpoint allows getting the available initialization times for point forecasts and other endpoints.

It can optionally accept the following query string parameters:

Name	Type	Description
ens_member	string	The ensemble member to get initialization times for. Can be `mean` or a number between 1 and 25. Cannot be used with `intracycle`.
intracycle	boolean	Whether to return initialization times for our intracycle model. This model comes out several hours before the main model or other models like GFS and ECMWF, and can give a directional signal on how the forecast is likely to evolve. Defaults to `false`. Cannot be used with `ens_member`.

It returns the following:

Name	Type	Description
available	[string]	An array of ISO8601 timestamps of available initialization times for point forecasts, ordered from newest to oldest.
latest	string	The ISO8601 timestamp of latest available initialization time for point forecasts.

Endpoints

    GET /initialization_times.json
    GET https://forecasts.windbornesystems.com/api/v1/initialization_times.json

Sample Response

{
    "available": [
        "2025-05-17T18:00:00+00:00",
        "2025-05-17T12:00:00+00:00",
         ...
        "2025-05-16T00:00:00+00:00"
    ],
    "latest": "2025-05-17T18:00:00+00:00"
}

Example: Using the initialization times API

In this example, we request the available initialization times for point forecasts using the wb_get_request function from the Authentication section above.

You may consider instead using the get_initialization_times function from the pip library.

The response includes a list of available initialization times formatted as ISO8601 timestamps. Initialization times are ordered from newest to oldest, with an additional entry indicating the latest initialization time.

Example

Python

import os
import requests

def wb_get_initialization_times():
    """
    Make a GET request to the WindBorne initialization times forecast API using wb_get_request
    """
    base_url = "https://forecasts.windbornesystems.com/api/v1/initialization_times.json"
    
    response = wb_get_request(base_url)
    return response.json()

result = wb_get_initialization_times()
print(result)

Example response

{
  "available": [
    "2025-05-17T18:00:00+00:00",
    "2025-05-17T12:00:00+00:00",
    ...
    "2025-05-16T00:00:00+00:00"
  ],
  "latest": "2025-05-17T18:00:00+00:00"
}

Generation Times

This endpoint provides the creation times for each forecast hour output file, allowing you to track when specific forecast data became available.

It can optionally accept the following query string parameters:

Name	Type	Description
ens_member	string	The ensemble member to get generation times for. Can be `mean` or a number between 1 and 25. Cannot be used with `intracycle`.
intracycle	boolean	Whether to return generation times for our intracycle model. This model comes out several hours before the main model or other models like GFS and ECMWF, and can give a directional signal on how the forecast is likely to evolve. Defaults to `false`. Cannot be used with `ens_member`.

It returns a nested dictionary structure:

Name	Type	Description
initialization_time	object	The top-level keys are ISO8601 timestamps of initialization times, ordered from newest to oldest. Each initialization time maps to an object containing forecast hour generation times.
forecast_hour	string	Within each initialization time object, the keys are forecast hours (as strings), and the values are ISO8601 timestamps indicating when that forecast hour's output file was created.

This endpoint is useful for:

Monitoring the progress of forecast generation
Determining data freshness and latency
Identifying when specific forecast hours become available
Tracking the temporal pattern of model output generation

Endpoints

    GET /generation_times.json
    GET https://forecasts.windbornesystems.com/api/v1/generation_times.json

Sample Response

{
    "2025-05-17T18:00:00+00:00": {
        "6": "2025-05-17T19:23:45+00:00",
        "12": "2025-05-17T19:45:12+00:00",
        "18": "2025-05-17T20:01:33+00:00",
        "24": "2025-05-17T20:15:47+00:00",
        ...
        "336": "2025-05-17T23:58:21+00:00"
    },
    "2025-05-17T12:00:00+00:00": {
        "6": "2025-05-17T13:22:18+00:00",
        "12": "2025-05-17T13:44:29+00:00",
        ...
        "336": "2025-05-17T17:45:03+00:00"
    },
    ...
}

Forecast Hours

This endpoint allows getting the available forecast hours for different model initialization times. This may include initialization times that are not included in the initialization times API that represent forecasts that are still in the process of being generated.

It can optionally accept the following query string parameters:

Name	Type	Description
ens_member	string	The ensemble member to get forecast hours for. Can be `mean` or a number between 1 and 25. Cannot be used with `intracycle`.
intracycle	boolean	Whether to return forecast hours for our intracycle model. This model comes out several hours before the main model or other models like GFS and ECMWF, and can give a directional signal on how the forecast is likely to evolve. Defaults to `false`. Cannot be used with `ens_member`.

It returns a dictionary where:

Name	Type	Description
key	string (ISO8601 timestamp)	An initialization time for which forecast hours are available.
value	[integer]	An array of available forecast hours for that specific initialization time.

Endpoints

    GET /forecast_hours.json
    GET https://forecasts.windbornesystems.com/api/v1/forecast_hours.json

Sample Response

{
    "2025-05-16T00:00:00+00:00": [0, 1, 2, ..., 360],
    "2025-05-16T06:00:00+00:00": [0, 1, 2, ..., 360],
    "2025-05-16T12:00:00+00:00": [0, 1, 2, ..., 360],
    "2025-05-16T18:00:00+00:00": [0, 1, 2, ..., 360],
    "2025-05-17T00:00:00+00:00": [0, 1, 2, ..., 360],
    // ... more initialization times
}

Tropical Cyclones

This endpoint allows getting the predicted ground track of active tropical cyclones.

It accepts the following query string parameters:

Name	Type	Description
initialization_time	string	Optional. An ISO 8601 date string, supporting formats YYYYMMDDHH, YYYY-MM-DDTHH, and YYYY-MM-DDTHH:mm:ss., representing the time at which the forecast was made. This looks solely at the date and the hour; minutes and seconds are discarded. If nothing is provided, the latest forecast is used.
basin	string	Optional. A string indicating the tropical cyclones basins to be filtered in the response. If not set, all tropical cyclones will be included.

The basin parameter defines specific geographical regions where tropical cyclones are present:

Name	Type	Description
NA	string	North Atlantic Basin spanning from African Coast (20°E) to 140°W and Equator to 90°N.
EP	string	Eastern Pacific Basin spanning from 140°W to 180°W and Equator to 90°N.
WP	string	Western Pacific Basin spanning from 100°E to 180°E and Equator to 60°N.
NI	string	North Indian Ocean Basin spanning from 45°E to 100°E and Equator to 90°N.
SI	string	South-West Indian Ocean Basin spanning from 20°E to 90°E and 40°S to Equator.
AU	string	Australian Region Basin spanning from 90°E to 160°E and 36°S to Equator.
SP	string	South Pacific Basin spanning from 160°E to 120°W and 40°S to Equator.

It returns an object (dictionary) with keys being tropical cyclone IDs and values being arrays of ground track points. Each ground track point is of the form:

Name	Type	Description
latitude	number	The latitude where the tropical cyclone is predicted to be
longitude	number	The longitude where the tropical cyclone is predicted to be
time	string	The ISO8601 timestamp of when this forecast point is valid

Endpoints

    GET /tropical_cyclones
    GET https://forecasts.windbornesystems.com/api/v1/tropical_cyclones

Sample Response

{
    "2024121118": [
        {
            "latitude": 55.0,
            "longitude": 163.5,
            "time": "2024-12-12T00:00:00"
        },
        {
            "latitude": 56.0,
            "longitude": 163.75,
            "time": "2024-12-12T06:00:00"
        },
                    . . .
        {
            "latitude": 57.75,
            "longitude": 167.25,
            "time": "2024-12-15T00:00:00"
        },
        {
            "latitude": 58.0,
            "longitude": 165.25,
            "time": "2024-12-15T06:00:00"
        }
    ]
}

Example: Using the tropical cyclones forecast API

Here is an example of making a request to the tropical cyclones forecast API using Python, again using the wb_get_request function from the Authentication section above. This example queries for tropical cyclones and saves the response in a json file.

In this example, we request the tropical cyclones within Eastern Pacific basin for an initialization time .

The response is an object (dictionary) with keys being tropical cyclone IDs and values being arrays of ground track points.

Example

Python

import os
import requests

def wb_get_tropical_cyclones(params):
    """
    Make a GET request to the WindBorne gridded forecast API for tropical cyclones using wb_get_request
    """
    base_url = "https://forecasts.windbornesystems.com/api/v1/tropical_cyclones"

    # Convert the parameters dictionary to query string using requests' utility
    response = wb_get_request(f"{base_url}?{requests.compat.urlencode(params)}")

    return response.json()

params = {
    "initialization_time": "2024-11-18T06:00:00",
    "basin": "EP"
}

result = wb_get_tropical_cyclones(params)
print(result)

Example response

{
    "CyNH00032024": [
        {
            "latitude": 42.0,
            "longitude": -150.25,
            "time": "2024-11-19T00:00:00"
        },
        {
            "latitude": 41.5,
            "longitude": -143.25,
            "time": "2024-11-19T06:00:00"
        },
                    . . .
        {
            "latitude": 47.0,
            "longitude": -140.25,
            "time": "2024-11-22T06:00:00"
        },
        {
            "latitude": 45.0,
            "longitude": -141.25,
            "time": "2024-11-22T12:00:00"
        }
    ]
}