The UFS forms the core of NOAA’s operational global modeling system for global weather, including the Global Forecast system (GFS) for medium range weather out to 16 days and the Global Ensemble Forecast system (GEFS) for subseasonal ensemble forecasts out to 45 days.  Currently, the UFS consists of the FV3 dynamical core with the Common Community Physics Package (CCPP) for the atmosphere,  MOM6 for the ocean, GOCART for aerosols, CICE6 for sea ice and WW3 for ocean waves.  NOAH, NOAH-MP and RUC land models are currently available as options within the CCPP framework. The components are coupled using the NOAA Environmental Modeling System (NEMS), which is being superseded by the Community Mediator for Earth Prediction Systems (CMEPS). The Joint Effort for Data Assimilation Integration (JEDI) project will provide the data assimilation system to initialize the model forecasts using observations spanning all components of the coupled system.

The Medium-Range and Subseasonal-Seasonal (MRW/S2S) component of the UFS-R2O project is focused on transitioning cutting-edge research into the operational GFS and GEFS.  The primary development foci for advancing the skill of NOAA’s operational predictions of MRW/S2S timescales are:

• Diagnose the sources of and mitigate atmospheric model biases associated with parameterized physics deficiencies, especially those associated with layers of high static stability in the lower atmosphere that affect the initiation of convection and the representation of land-atmosphere interactions.

• Improved representation of tropical and stratospheric variability, including equatorially trapped tropical modes such as the Madden Julian Oscillation (MJO) and the quasi-biennial oscillation (QBO) in the stratosphere.

• Implement a coupled (atmosphere/aerosols/land/ocean/sea-ice/waves) ensemble prediction system in NOAA operations, including data assimilation and reforecast capabilities.

• Improve quantification of model uncertainty in ensemble forecasts, particularly near model component interfaces.

• Improve initialization of (coupled) model forecasts, through improved use of observations and advances in data assimilation algorithms.

These objectives are the driving force for advancing the GFS and GEFS over the next five years, and the plan for addressing them is organized into four sub-projects. Briefly, the sub-projects and their primary deliverables over the next two years are as follows:

1. Coupled Model, learn more

2. Atmospheric composition, learn more

3. Data Assimilation and Reanalysis & Reforecast, learn more

4. Atmospheric physics, learn more

The UFS R2O coupled system sub-project is aimed to develop a coupled ensemble prediction system, which consists of GFS, MOM6, CICE6, CMEPS and WWIII, for medium-range weather and subseasonal to seasonal forecasts for operational implementation at NCEP in 2024. The project focuses on a) improved couplings between components, performing testing and evaluation of prototypes; and, b) developing and refining stochastic parameterizations in the land, atmosphere, and ocean to improve coupled data assimilation and ensemble prediction.

To build a state-of-the-art coupled forecast system requires the participation of all SIP working groups to develop model physics, dynamics, coupling infrastructure, coupled data assimilation and verification and validation strategies. This sub-project is focused on the application of the coupled system for medium-range weather and S2S forecasting.  The goal is to develop a coupled model suitable for medium-range and S2S forecasting, to incorporate and test new and improved science and technology presented by the WGs in the coupled system, to investigate model forecast biases and feedback to the developers, to set up and test the system in the NCEP operational environment, and ultimately to put together a best performing coupled GFS-MOM6-CICE6-WWIII system for operational implementations of GFS v17 and GEFS v13 in 2024.

Ensemble prediction is an essential part of this coupled system. This sub-project will also develop and refine parameterizations in the coupled UFS to provide more physically based, process-level stochastic representations of model uncertainty.  This is expected to improve ensemble spread and reduce systematic error, both in coupled data assimilation and free forecasts. In coordination with the physics development application team, a long-term vision is to include a capability of modeling the stochasticity that is intrinsic to physical processes on subgrid scales in the advanced UFS physics suite for operations