Improvements in Skill of CPC Outlooks Ed O’Lenic and Ken Pelman, NOAA-NWS-Climate Prediction Center 33rd Climate Diagnostics and Prediction Workshop, October.

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Presentation transcript:

Improvements in Skill of CPC Outlooks Ed O’Lenic and Ken Pelman, NOAA-NWS-Climate Prediction Center 33rd Climate Diagnostics and Prediction Workshop, October 21-24, 2008, Lincoln, Nebraska

Introduction This paper discusses recent improvements in the skill and coverage of CPC T, P Outlooks. The heidke skill score and the percentage of non-EC probabilities are the performance measures. s = ((c-e)/(t-e))*100, -50 < s < 100 s is the percent improvement over random forecasts

CPC 3-Month Outlook map lines show the probability that the indicated category, B, N, or A, will occur. In blank regions, the probabilities of B, N, A are equal at 1/3 each (EC), and give no forecast. Lines show Non-EC (potentially useful) forecast regions. On a line, probabilities of B and A vary simultaneously and inversely above and below 33.33%, while that of N usually stays at 33.33%. The 3 sum to 100% at every point on the map.

How CPC Outlooks are Made CPC 3-month outlooks are currently made using a combination of at least 5 tools, in consultation with partners. From these tools were weighted subjectively. In 2006, an objective consolidation (CON) was introduced, which weights the tools by skill history and spread (Unger et al, 2008). Retrospective verification of CON forecasts shows them to be much more skillful than official (OFF) outlooks (O’Lenic et al, 2008), in both categorical U.S. average skill, and in coverage by non-equal-chances (non-EC) forecasts, properties users want.

NEW OTLK

½ MO LEAD PRECIPITATION RESULTS Heidke Skill Score (HSS, lines) and Percent Non-EC (colors), Map average % Non-EC. A. OFFICIAL FORECAST (OFF) B. CONSOLIDATION (CON) C. DIFFERENCE, CON-minus-OFF, US average% Non-EC CON raises US annual average HSS from 9 to 12 compared with OFF A B C Area non-ec=53% +20% Area non-ec=35% Area non-ec=27% +8% Area non-ec=14% Area non-ec=32% +18% Area non-ec=20% Area non-ec=36% +16% Map color legend, % HSS DIF OFF CON Area non-ec=33%

A B C ½ MO LEAD TEMPERATURE RESULTS Heidke Skill Score (HSS, lines) and Percent Non-EC (colors), Map average % Non-EC. A. OFFICIAL FORECAST (OFF) B. CONSOLIDATION (CON) C. DIFFERENCE CON – OFF US average% Non-EC CON raises US annual average HSS from 22 to 26 compared with OFF Area non-ec=41 Area non-ec=96% Area non-ec=46 Area non-ec=57 Area non-ec=47 Area non-ec=78 Area non-ec=27 Area non-ec=67 +11% +31% +40% +55% Map color legend, % HSS DIF OFF CON

GPRA Score Official Skill Metric: 48-Mo. Running Mean U.S. Average T HSS

SUMMARY - Outlook prepared subjectively Objective consolidation begun Retrospective verification shows significant increase in CON skill over OFF - Western and Eastern P forecasts better than many areas - Forecasts are better, more objective - Higher categorical skill - Far fewer “EC” forecasts - P HSS rose from 9 (OFF) to 12 (CON) (US ann. mean) - T HSS rose from 22 (OFF) to 26 (CON) (US ann. mean) - % Non-EC rises in all seasons, >30% for P, >50% for T - Official T skill rose starting in 2006 due to use of CON.

Forecast Evaluation Tool: Example of a Means to Address Gaps What FET and CLIDDSS provide: User-centric forecast evaluation and data access and display capability. Leveraging of community software development capabilities. Opportunity to DISCOVER, collect, and invest in user requirements.

FUTURE: Implement FET at CPC

FET: A Wide Variety of Skill Renderings P T A A A A A A B B B B B B

FUTURE of the FET Next 6 months: Finalize and implement FET project plan at CPC. Ellen Lay (CLIMAS) to train CPC personnel on FET version control and bug tracking at CPC, November 12-14, Necessary software (APACHE TOMCAT, JAVA, Desktop View) acquired and installed at CPC. Forecast, observations datasets in-place at CPC. FET code ported to CPC, installed, tested. FET installed to NWS Web Operations Center (WOC) servers