Advancing Seasonal Forecast Skill for Western Watersheds
Explore knowledge gaps and priority research questions to enhance seasonal precipitation forecast skill for western watersheds, highlighting the importance of ENSO and antecedent conditions. Understand strengths and weaknesses to improve accuracy in seasonal predictions of precipitation over the United States.
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Presentation Transcript
NOAA/WSWC Meeting on advancing a Seasonal Precipitation Forecast Improvement Project Name: Andy Hoell Organization: NOAA/ESRL/PSD
1. What are the knowledge gaps to advance seasonal forecast skill for western watersheds? Identify conditional skill to not just provide a forecast, but a forecast of the skill: a. Better utilize the state of ENSO Weather forecast skill has progressively increased Seasonal forecast skill has not increased b. Utilize the antecedent conditions Half Month Lead Seasonal Precipitation Heidke Skill Score Atmospheric Models Empirical CCA Coupled Models Many Coupled Source: National Research Council Source: CPC
2. What are the priority research questions that need to be answered to advance seasonal forecast skill for western watersheds? ENSO is a key driver. Is it the only one? For N. CA Strongest El Ni o events drive a shift to wet conditions whereas moderate events do not Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field that associated with the linear atmospheric signal of El Nin o Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. Diagnosing Sources of U.S. Seasonal Forecast Skill (2006), J. Climate
3. What are the strengths and weaknesses of the white paper as currently presented? Accurate seasonal prediction of precipitation over the United States is an extremely difficult problem. Improving these predictions will require a substantial and sustained commitment of resources focusing on gaps in the end to end chain from increased understanding of sources of predictability through operational implementation of new and improved tools. Powerful statements that can be explored further Are forecasters not sufficiently knowledgeable? No, they are. Do we have accurate tools to make seasonal forecasts? No, we don t. Different models exhibit different accuracy and precision, as do the practices that we use to optimally combine the various tools Do we have accurate data from which to begin models? No, we don t. Example: Monitoring ENSO by infrastructure like TOGA/TAO
