ufs-land-da-v1.2.0

The Earth Prediction Innovation Center (EPIC) and the Unified Forecast System (UFS) community are proud to announce the public release of the Land Data Assimilation (DA) System version 1.2.0. This release is an update to the version 1.1.0 release from May 2023 and reflects a number of changes currently available in the land-DA_workflow development branch. In the Land DA System, the Noah-MP land surface model (LSM) in the UFS Weather Model (WM) and the Joint Effort for Data assimilation Integration (JEDI) system are used to assimilate snow depth data via the Local Ensemble Transform Kalman Filter-Optimal Interpolation (LETKF-OI) algorithm. The new release includes the following updates:

• Integration of the UFS Noah-MP land component into the Land DA System for use as an alternative to the Common Community Physics Package (CCPP) Noah-MP LSM land driver. The coupling layer of the land component is developed using the Earth System Modeling Framework (ESMF) and the National Unified Operational Prediction Capability (NUOPC) interoperability layer.

• Updates to model forcing options for use of the UFS land component

  • Provided a new analysis option in the cubed-sphere native grid using GSWP3 forcing

  • Established global land grid-point consistency with the head of the UFS WM baseline test cases

  • Added a new sample configuration file (settings_DA_cycle_gswp3)

  • Included an additional ECMWF ERA5 reanalysis forcing option in the existing vector-to-tile conversion analysis process

• CTest suite upgrades—the ERA5 CTests now test the operability of seven major components of Land DA: vector2tile, create_ens, letkfoi_snowda, apply_jediincr, tile2vector, land_driver, and ufs_datm_land

• Upgrade of the JEDI DA system to JEDI Skylab v4.0

• Updates to sample datasets for the release (see the Land DA data bucket)

• Singularity container (ubuntu20.04-intel-landda-release-public-v1.2.0.img) updates to support the changes described above

• Documentation updates to reflect the changes for this release

The v1.2.0 User's Guide has been updated to reflect these improvements to the Land DA System, and test cases with all associated data files are also available to the public in the Land DA data bucket). Interested users can get support by submitting a question through the GitHub Discussions Q & A page.

Known Issues:

• For the GSWP3 forcing option, an artificial GHCN snow depth observation file is provided for a single-cycle analysis test for 2000-01-03. The GHCN observation database will be extended in the near future.

ufs-land-da-v1.1.0

Since the release of the UFS Land Data Assimilation (DA) System v1.0.0, the land DA workflow code base is charting a path forward to unify the Noah-MP forecast model with the UFS Weather Model. JEDI analysis code bases have been updated to use the JEDI Skylab v3.0 release. This is a minor release to include several new features.

Added changes are:

• Migrated the original land-offline_workflow and Land-DA_update GitHub repositories to the land-DA_workfow (https://github.com/ufs-community/land-DA_workflow) and land-DA (https://github.com/ufs-community/land-DA) repositories, respectively.
• Replaced fv3-bundle and ioda-bundle with jedi-bundle release/skylab-v3: https://github.com/JCSDA/jedi-bundle
• Updated spack-stack and modulefiles to support UFS Weather Model build
• Updated CMakeLists.txt to add the option to build the UFS Weather Model (UFS WM) land application as an external project
• Updated Singularity container to support the changes described above: ubuntu20.04-intel-ue-landda.img

Known issue:

• Git Action Workflow yaml for the Docker-based build and ctest was turned off due to the limited disk space provided in the Git Action free runner. Alternatives under investigation are binary library containerization approach and Jenkins CI/CD workflow pipeline.

A User’s Guide is available here to provide further information about the Land DA System and instructions for running and testing the system. Data files required to run Land DA are available to the public in the Land DA data bucket. Interested users can get support by submitting a question through GitHub Discussions GitHub Discussions Q & A page.