# Presentation: 832827

## Verifications of CPC Seasonal Forecasts from 1995-2007

**Arun Kumar **
**Peitao Peng**
**Climate Prediction Center**
**32****nd**** CDPW, Tallahassee, FL**

## CPC’s Seasonal Forecasts

**Have been issued in their current format since January 1995**
**Are issued with a lead time of ~15 days, and are made for next 13 overlapping seasons**
**Are for surface temperature and precipitation (over the contiguous U.S. & Alaska)**
**Are based on a combination of empirical tools (CCA, OCN, SMLR) and a dynamical coupled model (currently the CFS)**
**Major changes have been**
***2 upgrades in the dynamical prediction system since 1995***
***Introduction of an objective consolidation technique in January 2006***

**Notes:**
3-dynamical system since 1995

Dave Unger 

## Outline

**Format of Seasonal Forecasts**
**Verifications of seasonal forecasts**
***Different skill measures***
***Temporal and spatial distribution of skill measures***
***Attributing skill to different predictors***
***Cost-Loss analysis of forecasts***
**Future plans**

## Format of Seasonal Forecasts

## Today’s  Special at the Café Verification are...

**Surface temperature**** over the contiguous U.S.**
**Various Skill Measures**
***Heidke***
***RPSS***
***Reliability***
***ROC***
**Sample size: All (145 seasons); ****Warm and cold season (60 seasons);**** ****individual season (12 seasons)**

## Heidke Skill Score (HSS): Temporal Variations

**11 MRM**

**Seasonal Cycle (HSS; % Area Covered)**

**Notes:**
All time high for Non-EC vs. all scores... more consistent with the RPSS

The gap between EC and non-EC is proportional to how low this number is... On the positive side EC score < non-EC but on the negative side EC > non-EC

Sample size for the seasonal time-series...

Cosine Weighted

## Heidke Skill Score: Spatial Variations

_**Heidke Skill Score: Spatial Variations**_

## HSS vs. Number of Times Forecast was Issued

**Not sure what kind of relationship to expect? **
**Forecast made more frequently (always higher confidence in the signal) ******** Higher skill? **
**Forecast made less frequently (but made only when there is signal)******** Higher skill?**

**?**

**Notes:**
Scatter plot of % of time forecast of issued at a grid point and Heidke skill score.

Not quite sure how to interpret this.  A quasi-linear relationship (i.e., better skill for places where forecast was issued more frequently) is good but there is considerable spread?  

## Heidke Skill Score: Seasonal Contrast

_**Heidke Skill Score: Seasonal Contrast**_

## RPSS: Spatial and Temporal Variations

**Notes:**
Have not crossed the score we had in 2000... note the scale

## Predicted and Observed Frequency Distribution for the Above Normal Category

| Bin
Number | Forecast Probability (Frequency) | Observed Occurrences | Observed Non-Occurrences | Observed Frequency of Occurrence |
| --- | --- | --- | --- | --- |
| 1 |   0-10(%) | 6 | 25 | 0.19 |
| 2 | 10-20(%) | 29 | 30 | 0.49 |
| 3 | 20-30(%) | 684 | 851 | 0.45 |
| 4 | 30-40(%) | 9880 | 10766 | 0.48 |
| 5 | 40-50(%) | 1729 | 1241 | 0.58 |
| 6 | 50-60(%) | 474 | 205 | 0.70 |
| 7 | 60-70(%) | 115 | 43 | 0.73 |
| 8 | 70-80(%) | 8 | 6 | 0.57 |
| 9 | 80-90(%) | 0 | 0 | NA |
| 10 | 90-100(%) | 0 | 0 | NA |

- Frequency Distribution:  Surface Temp; Upper Tercile (above normal)

_**Predicted and Observed Frequency Distribution for the Above Normal Category**_

**Notes:**
Contingency table for Reliability diagram:  Above Normal category

Biased cold... did not predict enough occurrence of warm events... warm events occurred more frequently than were predicted

## Surface Temperature: Reliability

## Heidke Skill Score: Official vs. Consolidated

**60% Improvement**

**11 MRM**

**Notes:**
Have not crossed the score we had in 2000

## Heidke Skill Score: Official vs. Consolidated

**Notes:**
Could not get the consolidated forecast in the appropriate format

Which season it is coming from?

## Future Plans

**Complete the verification summary (precipitation; verification  for individual tools;...)**
**Update the verification summary after the calendar year is over**
**Error bars on skill scores**
**Cost-Loss Analysis for different (decision) probability thresholds**
**Consider how to intelligently use this information from the **_**user’s**_** and forecaster’s perspective...**

**Notes:**
Not a simple task

## Farmer Jones’ Dilemma

**Farmer Jones’ Dilemma**

**What is the relevance of past skill information in the context of ****me**** using this ****SPECIFIC**** forecast? **

**Notes:**
Why even use it if the past skill was low?

If the forecasters have access to this information then why are they issuing this forecast with this probability?

Cost loss models... non-linear impacts

Awareness vs. basing decisions

## Solution to Farmer Jones’ Dilemma? Conditional Skill Estimates and Reliable Forecasts, but...

**Skill conditional to the expected state of predictors!!**** **

## Backup Slides

## Surface Temperature: ROC Score