The potential value of the new North American Multi-Model Ensemble (NMME) system for sub-seasonal to seasonal (S2S) prediction has not yet been realized by stakeholders in the water management applications sector. Key hurdles include: (1) products are not aligned with users space-time analysis needs (which typically follow specific watershed boundaries or sets of point location): (2) products are in formats that users cannot easily process into their analysis tools; (3) products based on raw model outputs are biased relative to user climatologies, or have only a simple, model-scale bias-correction; and (4) product verification is tailored toward forecast producer diagnostic needs but is unusable by water sector users. In each of these areas, more work can be done to bridge the gap to potential stakeholders and enhance quality, specificity, and accessibility – hence usability – of NMME predictions. Furthermore, new products can be developed to extend the current CPC climate product suite toward user decision needs. Stakeholder impacts often arise most from short-range extremes (such as 2-3 day storms or 1-week heat waves) for which few products exist at S2S time-scales. While weather-scale events are inherently uncertain at S2S lead times, there is value for stakeholders in probabilistic products describing the potential for short-term extremes (an analogy is the seasonal prediction of the number of landfalling hurricanes) to occur at these lead times.
The proposed project will both address the hurdles to water stakeholder adoption described above and explore opportunities for extremes prediction at S2S time scales. The project will use NMME reforecasts and real-time forecasts to apply various post-processing approaches to enhance the skill and reliability of the raw NMME climate outputs. It will develop data products characterizing the predictability of surface precipitation and temperature predictions at bi-weekly, monthly and seasonal time steps over the CONUS domain, at watershed-focused USGS Hydrology Unit Code (HUC) 4 and 2 spatial units. The benefits of statistical post-processing will be assessed against benchmarks (or baselines) from raw model outputs. For the prediction of extremes potential, the project will apply spatial extremes models using hierarchical Bayesian framework with climate system covariates. Project techniques will be transitioned to a test operational implementation at CPC in Year 2, reaching a Technical Readiness Level of 8.