Research SMHI endpoints #939
Labels
No labels
_CRITICAL_
API
app
backEnd
Blocked, waiting for further changes
bug
cleanup
close
design
duplicate
enhancement
feature request
frontEnd
help wanted
invalid
low priority
needs input
needs review
project documentation
question
research
reviewed
script
security
SQL
style
testing
topLevel
wontfix
No milestone
No project
No assignees
5 participants
Notifications
Due date
No due date set.
Dependencies
No dependencies set.
Reference
Andras/BoundlessFlowCampus2K#939
Loading…
Add table
Add a link
Reference in a new issue
No description provided.
Delete branch "%!s()"
Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?
Currently we use SMHI to get prognosis and weather conditions.
This issue involves researching what else we could get from SMHI that would be of intrest.
If there is no other interesting data then that is the conclusion otherwise make a list of the data you believe we want.
SMHI api
What I found interesting
Warning- Data
Additional
But warning data appears to provide the highest additional value since it would integrate good with the current forecast functionality.
Requesting review from @b24krila
Review
The research has identified warning-data as the most valuable addition and that I can agree with and it can work together with our already existing SMHI cards. The information is clear and aligns well with our current cards. And the description of the severity level (red, orange, yellow), event types and the affected area is relevant and makes it easier to understand how it would fit into our current system.
The other suggestions for additions are all valid parameters and could complement our already existing cards, however they are just presented at a high level so its hard to see how they would be used, in that regard it might be beneficial to go a little more into detail.
Overall the research is good and have pointed out some important additions. and the current research is good if we are to only add the warning-data but if we are to implement others it might need some additional information.
@b24krila wrote in #939 (comment):