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New Use Case: construct use case verifying GFS cloud forecasts vs. GFS cloud analyses #2743
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GFS data are here: Using this command:
I see relevant variables:
In addition, there is an isobaric "total cloud cover" variables ( |
@CPKalb you and I may need to tackle this one; A GFS-to-GFS gridded comparison seems...too basic. We have plenty of examples where this type of verification is completed, including some of the cloud verification use cases (some of which are more complex and still adaptable). I think in the last NRL meeting we discussed looping in a MODE analysis/DMAP approach on this; I'd like for both of us to do some research and see if there's any cloud-identification research that could shed a light on a path forward. I'll try to find some time to meet up on Fri-early Mon to discuss our findings and settle on an approach. |
We have a use case that uses distance maps on brightness temperatures. I can't remember if there was a paper or anything associated with this though. I'll have to poke around. |
Adding notes here so direction of use case is recorded: It sounds like (for the time being) we should just focus on a cloud fraction comparison between the two and exhibit the difference between traditional statistics and an object based approach. So the use case would be a GridStat call and a MODE call, looking at 1 (or more) of the fractional cloud coverage heights (low, mid, high, or convective). |
Is there a date I should start with for this verification or should I just pick something? |
Dates are discussed here: https://docs.google.com/document/d/1SJb2ZBrLG9jqH_Tu_ssB8lqeDxnPx1Xp-FHFRdj7HM0/edit?tab=t.0#heading=h.76lq03tjjtx6 3/7/2024 00 UTC initialization with forecasts leads of 6,12,18h and valid times of 06, 12, and 18 UTC on 3/7/2024. |
Describe the New Use Case
This use case will demonstrate verifying forecasts of cloud information using the GFS global 0.25 degree grid using
GridStat
.The fields to verify will be cloud fields we identify in GFS output such as:
We will need to search for and identify the proper field names and levels in the GFS files.
The measures of skill that should be included are:
The end goal is for the user to be able to substitute the GFS analysis which we will use as "truth", with a separate GFS-based AI/ML cloud forecast product on the same GFS 0.25 degree grid. This framework is to support them to be able to do this. We may get some sample data of their truth, however due to restrictions on releasing the data we may need to leave the GFS analysis in place.
The user would also like to be able to stratify forecast performance based on categories of cloud types. These cloud types will be provided later on, but we should brainstorm another type of stratification we can perform using an external classification (maybe weather regimes? precipitation type?), or, implement some simple post-processing, for example stratify performance by all clouds e.g. >= 8 km ("high clouds").
Checklist to get working:
Use Case Name and Category
`model_applications/clouds/GridStat_fcstGFS_obsGFS_cloudFracBaseTop
Input Data
GFS 0.25 degree forecasts and analyses.
List input data types and sources.
Provide a total input file size, keeping necessary data to a minimum.
Acceptance Testing
Describe tests required for new functionality.
As use case develops, provide a run time here
Time Estimate
Estimate the amount of work required here.
Issues should represent approximately 1 to 3 days of work.
Sub-Issues
Consider breaking the new feature down into sub-issues.
Relevant Deadlines
Must be completed by 12/31/2024
Funding Source
7730022
Define the Metadata
Assignee
Labels
Milestone and Projects
Define Related Issue(s)
Consider the impact to the other METplus components.
New Use Case Checklist
See the METplus Workflow for details.
Branch name:
feature_<Issue Number>_<Description>
Pull request:
feature <Issue Number> <Description>
Select: Reviewer(s) and Development issue
Select: Milestone as the next official version
Select: METplus-Wrappers-X.Y.Z Development project for development toward the next official release
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