SLOSH model
E237736
The SLOSH model is a computer simulation tool developed by the U.S. National Weather Service to estimate and predict storm surge heights and coastal flooding from hurricanes and other tropical cyclones.
All labels observed (1)
| Label | Occurrences |
|---|---|
| SLOSH model canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T2121305 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SLOSH model Context triple: [Storm Surge Unit, usesModel, SLOSH model]
-
A.
Storm Surge Unit
The Storm Surge Unit is a specialized division of the National Hurricane Center that analyzes and forecasts coastal flooding risks associated with tropical cyclones and other storm events.
-
B.
Einstein bed-load function
The Einstein bed-load function is a seminal hydraulic engineering formula developed by Hans Albert Einstein to predict the transport rate of sediment particles rolling and sliding along a riverbed under flowing water.
-
C.
Ekman layer
The Ekman layer is the thin region of fluid near a boundary (such as the ocean surface or seafloor) where the balance between friction and the Coriolis effect causes the flow to spiral with depth.
-
D.
Flood
Flood is a renowned British record producer and audio engineer known for his work with influential rock and alternative artists such as U2, Depeche Mode, and Nine Inch Nails.
-
E.
Loop Current
The Loop Current is a warm ocean current in the Gulf of Mexico that flows northward from the Caribbean, loops east and south, and then exits through the Florida Straits to feed the Gulf Stream.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SLOSH model Target entity description: The SLOSH model is a computer simulation tool developed by the U.S. National Weather Service to estimate and predict storm surge heights and coastal flooding from hurricanes and other tropical cyclones.
-
A.
Storm Surge Unit
The Storm Surge Unit is a specialized division of the National Hurricane Center that analyzes and forecasts coastal flooding risks associated with tropical cyclones and other storm events.
-
B.
Einstein bed-load function
The Einstein bed-load function is a seminal hydraulic engineering formula developed by Hans Albert Einstein to predict the transport rate of sediment particles rolling and sliding along a riverbed under flowing water.
-
C.
Ekman layer
The Ekman layer is the thin region of fluid near a boundary (such as the ocean surface or seafloor) where the balance between friction and the Coriolis effect causes the flow to spiral with depth.
-
D.
Flood
Flood is a renowned British record producer and audio engineer known for his work with influential rock and alternative artists such as U2, Depeche Mode, and Nine Inch Nails.
-
E.
Loop Current
The Loop Current is a warm ocean current in the Gulf of Mexico that flows northward from the Caribbean, loops east and south, and then exits through the Florida Straits to feed the Gulf Stream.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
computer simulation tool
ⓘ
numerical model ⓘ storm surge model ⓘ |
| appliesTo |
coastal regions
ⓘ
hurricanes ⓘ tropical cyclones ⓘ |
| basedOn |
numerical hydrodynamics
ⓘ
shallow-water equations ⓘ |
| countryOfOrigin |
United States of America
ⓘ
surface form:
United States
|
| developer |
National Oceanic and Atmospheric Administration
ⓘ
National Weather Service ⓘ National Weather Service ⓘ
surface form:
U.S. National Weather Service
|
| domain |
coastal engineering
ⓘ
disaster management ⓘ meteorology ⓘ oceanography ⓘ |
| hasComponent |
basin-specific domains
ⓘ
computational grid ⓘ |
| input |
bathymetry
ⓘ
coastline geometry ⓘ forward speed ⓘ landfall location ⓘ storm intensity ⓘ storm size ⓘ storm track ⓘ topography ⓘ |
| operatedBy | National Weather Service ⓘ |
| operationalStatus | operational forecasting tool ⓘ |
| output |
extent of coastal flooding
ⓘ
inundation depth ⓘ storm surge height ⓘ |
| purpose |
support evacuation planning
ⓘ
support hazard mitigation planning ⓘ support risk assessment ⓘ |
| use |
estimate coastal flooding
ⓘ
estimate storm surge heights ⓘ hurricane storm surge forecasting ⓘ predict coastal flooding ⓘ predict storm surge heights ⓘ tropical cyclone storm surge forecasting ⓘ |
| usedBy |
National Hurricane Center
ⓘ
emergency managers ⓘ federal agencies ⓘ local emergency planners ⓘ state agencies ⓘ |
| usedFor |
deterministic storm surge guidance
ⓘ
evacuation zone delineation ⓘ hazard mapping ⓘ probabilistic storm surge guidance ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: SLOSH model Description of subject: The SLOSH model is a computer simulation tool developed by the U.S. National Weather Service to estimate and predict storm surge heights and coastal flooding from hurricanes and other tropical cyclones.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.