Differences

This shows you the differences between two versions of the page.

--- verify [2025/02/06 16:04] – created admin
+++ verify [2025/02/07 15:19] (current) – admin
@@ Line 1: / Line 1: @@
 ==== Python for Probability of Precipitation Verification ====
+//from 2014//
 This page shows my successful attempt to reproduce
@@ Line 16: / Line 18: @@
 accuracy than the forecast product, is indicated with a blue dot.
-{{attachment:reliability.png}}
+{{:reliability.png}}
 === Receiver Operating Characteristic ===
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  * False Alarm Rate: false alarms/(true negatives + false alarms)
-{{attachment:ROC.png}}
+{{:ROC.png}}
 == Relative Value Curves ==
-This shows the economic value of your binary decisions, based on the thresholds that were also used for ROC.  For example, if your cost is C=$6 to bring an umbrella when you expect rain, and your loss is L=$10 when it rains on you without your umbrella, your C/L ratio is 0.6. At 0.6, you can see that your maximum Relative Value is about 0.2. The red envelope curve is coincident with the curve labelled 0.750. So the maximum economic benefit occurs if you use a PoP of 75% for your threshold.  This means you should bring your umbrella only when the PoP is forecasted to be 80%, 90% or 100%.  The Relative Value of 20% means the economic benefit of using these imperfect forecasts is 20% of the value of perfect forecasts.
+This shows the economic value of your binary decisions, based on the thresholds that were also used for ROC.  For example, if your cost is C=$6 to bring an umbrella when you expect rain, and your loss is L=\$10 when it rains on you without your umbrella, your C/L ratio is 0.6. At 0.6, you can see that your maximum Relative Value is about 0.2. The red envelope curve is coincident with the curve labelled 0.750. So the maximum economic benefit occurs if you use a PoP of 75% for your threshold.  This means you should bring your umbrella only when the PoP is forecasted to be 80%, 90% or 100%.  The Relative Value of 20% means the economic benefit of using these imperfect forecasts is 20% of the value of perfect forecasts.
-{{attachment:RelaVal.png}}
+{{:RelaVal.png}}
 === Python Program: verify.py ===
-[[attachment:verify.py]]
+{{:verify.py.gz}}
 === other data sets ===
-''Dear website reader, please share with me links to alternative data sets to which {{{verify.py}}} can be applied.''
+//Dear website reader, please share with me links to alternative data sets to which ''verify.py'' can be applied.//
 Here is one that I know about: