Slot: Ensemble Member Count (exposure_model_ensemble_member_count)
For ensemble products, the number of members. Null with reason not_provided_by_source for single-realisation products.
Tier: conditionally_core
Why this slot matters
Mandatory for ensemble products: the member count determines how the ensemble spread can be interpreted as an uncertainty estimate. Without it, per-value spread statistics cannot be reproduced or sanity-checked.
In plain terms
Some products run the same model many times with slightly different settings and combine the results — each run is a "member". Knowing how many members there were (say, 100) tells you how much the spread between runs can be trusted as a measure of uncertainty.
URI: envar:slot/exposure_model_ensemble_member_count
Applicable Classes
| Name |
Description |
Modifies Slot |
| ExposureModel |
The model class that produced the values (interpolation, reanalysis, ML, stat... |
no |
Properties
Type and Range
Cardinality and Requirements
Examples
See Also
Annotations
| property |
value |
| tier |
conditionally_core |
| justification |
Mandatory for ensemble products: the member count determines how the ensemble spread can be interpreted as an uncertainty estimate. Without it, per-value spread statistics cannot be reproduced or sanity-checked. |
| explanation |
Some products run the same model many times with slightly different settings and combine the results — each run is a "member". Knowing how many members there were (say, 100) tells you how much the spread between runs can be trusted as a measure of uncertainty. |
Schema Source
Mappings
| Mapping Type |
Mapped Value |
| self |
envar:exposure_model_ensemble_member_count |
| native |
envar:exposure_model_ensemble_member_count |
LinkML Source
name: exposure_model_ensemble_member_count
annotations:
tier:
tag: tier
value: conditionally_core
justification:
tag: justification
value: 'Mandatory for ensemble products: the member count determines how the ensemble
spread can be interpreted as an uncertainty estimate. Without it, per-value
spread statistics cannot be reproduced or sanity-checked.'
explanation:
tag: explanation
value: Some products run the same model many times with slightly different settings
and combine the results — each run is a "member". Knowing how many members there
were (say, 100) tells you how much the spread between runs can be trusted as
a measure of uncertainty.
description: For ensemble products, the number of members. Null with reason `not_provided_by_source`
for single-realisation products.
title: Ensemble Member Count
examples:
- value: '100'
description: e.g. an ensemble ML product with 100 members; single-realisation products
such as Daymet leave this null
from_schema: https://w3id.org/linkml/microschemas/envar
see_also:
- https://en.wikipedia.org/wiki/Ensemble_forecasting
rank: 1000
domain_of:
- ExposureModel
range: integer