AI-generated Key Takeaways
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This document describes the NetCDF data format for contrail forecast data from the Contrail Forecast API
getGridsmethod. -
The forecast data is returned in NetCDF4 format with dimensions for longitude, latitude, flight level, time, and forecast reference time.
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The primary variable is "contrails", a float32 array representing continuous contrail forcing index values ranging from 0 to 4.
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Contrail forcing index values are interpreted as levels of contrail severity from None (0) to Extreme (4).
This document specifies the
NetCDF data contract for
contrail forecast data as returned by the Contrail Forecast API method
getGrids.
Data format
Forecast grids cover the whole globe and are returned in the netCDF4 format.
Dimensions
| Name | Type | Description |
|---|---|---|
longitude |
float32 |
-180 to +180 (inclusive), as configured in the request. Grid resolution is 0.25 degrees but may change in the future. |
latitude |
float32 |
-90 to +90 (inclusive), as configured in the request. Grid resolution is 0.25 degrees but may change in the future. |
flight_level |
int16 |
[270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440], as configured in the request, in hectofeet. Flight levels are derived from pressure levels. |
time |
datetime64[ns] |
The requested real world forecast date and time, in UTC. |
forecast_reference_time |
datetime64[ns] |
Date and time at which the forecast was computed, rather than the time of forecast itself, in UTC. |
Variables
contrails (float32): (longitude, latitude, flight_level, time) continuous
contrail forcing index values from [0 - 4].
Contrail forcing index values are inspired by turbulence forecast values.
Severity values can be interpreted as:
| Value | Interpretation |
|---|---|
| 0 | None |
| 1 | Low (light) |
| 2 | Moderate |
| 3 | High (severe) |
| 4 | Extreme |
applied_erf_over_rf_ratio = 0.42 radiative forcing over effective radiative
forcing ratio (Lee et al., 2021)
Example
Example scaling translates ef_per_m to contrails index:
ds["contrails"] = ds["ef_per_m"].clip(min=2e7, max=2e8)
ds["contrails"] = ((ds["contrails"] - 2e7) / (2e8 - 2e7)) * 4
For more information on background informing example mapping from ef_per_m to
contrails index, see Energy Forcing Interpretation.