The Subseasonal to Seasonal (S2S) Program had three primary focus areas in its FY25 NOFO:
- Improve data assimilation (DA) for different parts of the Earth (such as ice, oceans, waves, land, and atmosphere). This includes using new types of observations to improve predictions for S2S time scales, ensuring the processes work well with existing global Unified Forecast System (UFS) applications.
- Improve Earth system models by developing and evaluating individual model parts and their sub-elements, simple models, models covering small areas, and more. This will be done within the framework of the community-based UFS. Improvements may focus on what happens within one part of the Earth system or how different parts interact with each other.
- Improve existing forecast ensembles and how they initialize by applying new methods to all parts of the Earth system model. Determining the best way to set up those ensembles should lead to better forecast skill and clearer assessments of uncertainty, especially for precipitation and the conditions that lead to unusual precipitation events.
| Project Title | Principal Investigators | Project Description | Hazard Focus |
|---|---|---|---|
| Know, Explore, Improve: Hypothesis-Driven Development of the UFS | Benjamin Cash, George Mason University | The Unified Forecast System (UFS) Seasonal Forecast System (SFSv1) is being developed by NOAA to replace the aging CFSv2 system for operational seasonal predictions. This research will perform and analyze hierarchical suites of experiments with SFSv1, increasingly constrained by observations: (1) four 30-year ensembles focused on characterizing the model climatology and the impact of ENSO errors, and (2) four sets of reforecasts of selected high-impact droughts and floods. This will better characterize the climatological behavior of SFSv1 to guide further development, increase model performance, and better identify sources of predictability for precipitation and temperature. | All Hazards |
| Predictability of the Summer Precipitation Extremes in the Southern Great Plains through the Remote Effects of Snow Dynamics in the U.S. West | Guo-Yue Niu, University of Arizona | This project will investigate the link between March snow cover in the Western U.S. and early summer precipitation in the Southern Great Plains, which has implications on the agricultural sector. It will incorporate snow, soil moisture, and vegetation observations to improve how the UFS represents these variables. These improvements will be tested by hindcasting past early summer precipitation events in the Southern Great Plains. | Flooding and Precipitation |
| Utilizing Water Isotopic Composition in the Unified Forecast System to Constrain Precipitation and Hydrologic Processes | Cheng-Hsuan (Sarah) Lu, SUNY Albany | The ratio of different forms of water molecules — known as isotopes — acts as a unique identifier in the water cycle, offering valuable insight into atmospheric processes like precipitation, runoff, evaporation, and recharge. This project will integrate this isotope-derived data into the UFS to advance forecasting. | Severe Weather, Flooding and Precipitation, Winter Weather, Fire Weather |
| Improving NOAA UFS S2S prediction via implementing data assimilation of vegetation characteristics in JEDI and enhanced plant hydraulics physics | Cenlin He, UCAR | The current UFS uses climatological vegetation representations, which do not explicitly represent crucial plant hydrologic processes. This causes models to have uncertainties in land conditions, land-atmosphere interactions, and S2S predictions. This project addresses these issues by integrating Leaf Area Index (LAI) and Solar-Induced Fluorescence (SIF) into JEDI, and a new plant hydraulics scheme in the UFS. The resulting improved vegetation representation will enhance land surface process modeling and S2S forecasts. | All Hazards |
