Use Cases
How real people use the dashboard
KLIMAT-P is built for people whose decisions carry real weight and whose planning horizon is seasonal, not daily. Below are five real scenarios showing exactly which part of the dashboard to check and how to read what it's telling you.
🌾 Agriculture
"Is it safe to plant before a late frost catches my crop?"
1
Select your region from the dropdown so every card reflects your local stations, not a national average.
2
Open the Late Frost Risk card. Check the percentage, the trend arrow, and the confidence rating together — a falling trend with high confidence is a very different situation from a rising trend with low confidence.
3
Read the driver text underneath the number — it explains what's pushing the probability up or down, not just the number itself.
4
Ask the chat directly: "Is it safe to plant tomatoes this week?" — it reasons from the same live data, in plain language.
A low, falling, high-confidence frost risk plus a chat confirmation is about as much certainty as an honest seasonal tool can offer — still a probability, not a guarantee.
Illustrative example — not live data
Late Frost Risk
After 15 May
↓ Falling
Warm air mass dominance firmly established; overnight lows staying well above freezing across all stations.
Confidence: High
🚛 Logistics
"Which weeks this quarter are most likely to disrupt delivery schedules?"
1
Check Precipitation Deficit and Heat Wave Window together — extreme readings on either can mean disrupted routes or restricted delivery windows.
2
Scroll to Flagged Event Windows in the Dynamic Prediction panel — it names specific date ranges (e.g. "Jun–Aug") where a signal is elevated, not just a single seasonal number.
3
Cross-check with Historical Analogs — past seasons with similar signals show what actually happened, which is often more useful for planning than the raw probability alone.
Use the flagged windows as a calendar overlay for your own routing schedule — treat them as "elevated risk," not as a guaranteed disruption.
Illustrative example — not live data
Sustained Heat Wave
Jun – Aug
61%
↑ Medium confidence
Drought Persistence
Jun – Aug
44%
→ Medium confidence
⚡ Energy
"How should I expect wind and solar output to trend this quarter?"
1
Go straight to Teleconnection Indices — NAO, AO, and ENSO are the large-scale signals that actually drive seasonal wind and storm-track patterns over Europe.
2
A strongly positive NAO tends to mean stronger, more consistent westerlies; a negative phase tends to mean weaker, more variable wind — check the current sign and trend.
3
Cross-reference with the Climate Scenarios panel (Hot & Dry / Mixed / Near Normal) to see which broader pattern the model currently favours.
Teleconnection indices are the same signals meteorologists use informally for this exact purpose — KLIMAT-P just surfaces them directly instead of making you dig for them.
Illustrative example — not live data
Teleconnection Indices
NAO
+0.7
↑ Positive
AO
+0.6
↑ Positive
ENSO
−0.2
↓ Negative
🎪 Events
"Should I book this outdoor venue for a date three months out?"
1
Select the region and the forecast horizon that matches your event's season (e.g. Summer 2026).
2
Check Precipitation Deficit for the dry/wet outlook, and read the Current Conditions Summary — a plain-language paragraph synthesising what all the live signals mean together.
3
For a date-specific gut check, ask the chat: "What's the chance of a dry weekend in mid-August?" — it can also pull the day-ahead Open-Meteo forecast once you're within about 10 days.
Three months out, treat this as directional risk for your contingency planning (tents, indoor backup) — not as a booking go/no-go signal on its own.
Illustrative example — not live data
Current Conditions — Summary
This week has been drier and warmer than the seasonal norm across most stations, with the Atlantic pattern showing early signs of a shift toward more typical conditions by late summer…
🏛️ Public Sector
"Should our municipality start drought contingency planning this season?"
1
Check Drought Risk and River Flood Risk together — municipalities usually need to plan for both ends of the water-management spectrum, not just one.
2
Watch the Model Change Log — a jump of 5% or more between updates is flagged automatically, which is often the earliest signal that a situation is developing before it's obvious on the ground.
3
Use Historical Analogs to see what past comparable seasons required in terms of actual response, not just the abstract percentage.
The Verification page shows exactly how accurate this model has been in your region historically — useful context before using it to justify a public resourcing decision.
Illustrative example — not live data
Model Change Log
14 Jul
+7%
Drought Risk — reduced Atlantic moisture advection persisting longer than prior estimate
Ready to check your own region?
All five scenarios above use real, live features — nothing here is hypothetical.
Open the dashboard →