Project goal: our analysis indicates that the western US snow anomaly in march has the same prediction power as the NIO 3.4 SST anomaly for the southern great plains (SGP) precipitation anomaly in…

The University of Arizona received $871,998 in funding from the National Oceanic and Atmospheric Administration (NOAA) under the Weather and Air Quality Research program (CFDA 11.459) for a three-year project running from August 1, 2025, through July 31, 2028. The cooperative agreement focuses on improving subseasonal-to-seasonal (S2S) precipitation prediction skill for the Southern Great Plains by enhancing the Unified Forecast System (UFS) and NOAH-MP land surface model. The project will deliver improved snow/rain partitioning modules, soil hydrology and hydraulics representations, and vegetation dynamics capabilities, along with associated data products including snow climatology, soil hydraulic properties, and bedrock depth information. All model code and datasets will be released through the UFS Common Community Physics Package (CCPP) repository for operational implementation.

The research addresses critical limitations in current UFS/NOAH-MP performance by analyzing snow, soil moisture, and vegetation interactions, assimilating snow data using the Joint Effort for Data Integration (JEDI) system, and conducting ensemble hindcasts to validate improvements. Expected outcomes include enhanced land memory representations in the UFS/NOAH-MP model and broader benefits to weather, climate, and water prediction communities, as NOAH-MP is widely used across the UFS, Weather Research and Forecasting (WRF) model, and National Water Model. The project will facilitate collaboration with the Environmental Modeling Center (EMC) at the National Center for Environmental Prediction (NCEP) to transition research findings into operational forecasting infrastructure.

Project goal: our analysis indicates that the western US snow anomaly in march has the same prediction power as the NIO 3.4 SST anomaly for the southern great plains (SGP) precipitation anomaly in early summer. We aim to improve the ufss predictability of this remote effect by improving the UFS/noah-MP land model. Problem/opportunity statement: persistency of initial snow conditions in spring through soil/vegetation responses can alter the heating of local atmosphere, propagating to downstream regions through atmospheric waves. However, UFS/noah-MP produces a low subseasonal predictability due to 1) underestimation of SWE 2) inadequate soil hydrology/hydraulics, and 3) lack of vegetation adaptation mechanisms. Methodology/activities to be performed: we will 1) analyze snow, soil moisture, and vegetation data and atmospheric reanalysis to reveal the mechanism, 2) use jedi to assimilate snow data and modify the UFS/noah-MPS representations of snow, soil moisture, and vegetation dynamics, and 3) conduct ensemble hindcasts using UFS coupled with the updated versions of noah-MP. Primary project outputs/products: 1) improved S2S prediction skill of precipitation extremes in SGP, 2) modules of well-tested snow/rain partitioning, soil hydrology/hydraulics, and vegetation dynamics/hydraulics; and 3) data products of snow, soil hydraulic properties and bedrock depth. We will release the model code and datasets through the UFS common community physics package (ccpp) repository. Expected results, outcomes, and benefits: through collaborations with EMC/ncep, this project will improve representations of processes controlling the land memory of UFS/noah-MP and hence benefit its implementation into operation. This project will also impact broader communities of weather, climate, and water predictions, because noah-MP has been widely used in UFS, WRF, and national water model. Intended beneficiaries and recipients: 1) the land team at EMC/ncep through our collaborator (helin WEI), who has been testing approaches to reduce the UFS dry/warm model biases in the central US but with limited success, 2) earth prediction innovation center (epic) land data assimilation system team, and 3) the ncar RAL land team.