1.1. Introduction

This User’s Guide provides guidance for running the Unified Forecast System (UFS) offline Land Data Assimilation (DA) System. Land DA is an offline version of the Noah Multi-Physics (Noah-MP) land surface model (LSM) used in the UFS Weather Model (WM). Its data assimilation framework uses the Joint Effort for Data assimilation Integration (JEDI) software. The offline UFS Land DA System currently only works with snow data. Thus, this User’s Guide focuses primarily on the snow DA process.

Since the last release, developers have added a variety of features:

  • 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

  • Model forcing options for use with 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 (New global land grid point is changed from 18360 to 18322.)

    • Added a new sample configuration file (settings_DA_cycle_gswp3)

    • Included a new 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 JEDI DA framework to use JEDI Skylab v4.0 (PR #28)

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

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

  • Documentation updates to reflect the changes above

The Land DA System citation is as follows and should be used when presenting results based on research conducted with the Land DA System:

UFS Development Team. (2023, December 11). Unified Forecast System (UFS) Land Data Assimilation (DA) System (Version v1.2.0). Zenodo. https://doi.org/10.5281/zenodo.7675721

1.1.1. Organization

This User’s Guide is organized into four sections: (1) Background Information; (2) Building, Running, and Testing the Land DA System; (3) Customizing the Workflow; and (4) Reference.

1.1.1.1. Background Information

  • This chapter (Introduction) provides background information on the Unified Forecast System (UFS) and the NoahMP model.

  • Chapter 1.2 (Technical Overview) outlines prerequisites, user support levels, and directory structure.

1.1.1.2. Building, Running, and Testing the Land DA System

1.1.1.3. Customizing the Workflow

  • Chapter 3.1: Model provides information on input data and configuration parameters in the Noah-MP LSM and its Vector-to-Tile Converter.

  • Chapter 3.2: DA Framework provides information on the DA system, required data, and configuration parameters.

1.1.1.4. Reference

1.1.2. User Support and Documentation

1.1.2.1. Questions

The Land DA System’s GitHub Discussions forum provides online support for UFS users and developers to post questions and exchange information. When users encounter difficulties running the Land DA System, this is the place to post. Users can expect an initial response within two business days.

When posting a question, it is recommended that users provide the following information:

  • The platform or system being used (e.g., Hera, Orion, container, MacOS, Linux)

  • The version of the Land DA System being used (e.g., develop, release/public-v1.1.0). (To determine this, users can run git branch, and the name of the branch with an asterisk * in front of it is the name of the branch or tag they are working with.) Note that the Land DA version being used and the version of the documentation being used should match, or users will run into difficulties.

  • Stage of the application when the issue appeared (i.e., build/compilation, configuration, or forecast run)

  • Contents of relevant configuration files

  • Full error message (preferably in text form rather than a screenshot)

  • Current shell (e.g., bash, csh) and modules loaded

  • Compiler + MPI combination being used

  • Run directory and code directory, if available on supported platforms

1.1.2.2. Bug Reports

If users (especially new users) believe they have identified a bug in the system, it is recommended that they first ask about the problem in GitHub Discussions, since many “bugs” do not require a code change/fix — instead, the user may be unfamiliar with the system and/or may have misunderstood some component of the system or the instructions, which is causing the problem. Asking for assistance in a GitHub Discussion post can help clarify whether there is a simple adjustment to fix the problem or whether there is a genuine bug in the code. Users are also encouraged to search open issues to see if their bug has already been identified. If there is a genuine bug, and there is no open issue to address it, users can report the bug by filing a GitHub Issue.

1.1.2.3. Feature Requests and Enhancements

Users who want to request a feature enhancement or the addition of a new feature have two options:

  1. File a GitHub Issue and add (or request that a code manager add) the EPIC Support Requested label.

  2. Post a request for a feature or enhancement in the Enhancements category of GitHub Discussions. These feature requests will be forwarded to the Earth Prediction Innovation Center (EPIC) management team for prioritization and eventual addition to the Land DA System.

1.1.3. Background Information

1.1.3.1. Unified Forecast System (UFS)

The UFS is a community-based, coupled, comprehensive Earth modeling system. It includes multiple applications that support different forecast durations and spatial domains. NOAA’s operational model suite for numerical weather prediction (NWP) is quickly transitioning to the UFS from many different modeling systems. The UFS is designed to enable research, development, and contribution opportunities within the broader Weather Enterprise (including government, industry, and academia). For more information about the UFS, visit the UFS Portal.

1.1.3.2. Noah-MP

The offline Noah-MP LSM is a stand-alone, uncoupled model used to execute land surface simulations. In this traditional uncoupled mode, near-surface atmospheric forcing data are required as input forcing. This LSM simulates soil moisture (both liquid and frozen), soil temperature, skin temperature, snow depth, snow water equivalent (SWE), snow density, canopy water content, and the energy flux and water flux terms of the surface energy balance and surface water balance.

Noah-MP uses a big-leaf approach with a separated vegetation canopy accounting for vegetation effects on surface energy and water balances, a modified two-stream approximation scheme to include the effects of vegetation canopy gaps that vary with solar zenith angle and the canopy 3-D structure on radiation transfer, a 3-layer physically-based snow model, a more permeable frozen soil by separating a grid cell into a permeable fraction and impermeable fraction, a simple groundwater model with a TOPMODEL-based runoff scheme, and a short-term leaf phenology model. Noah-MP LSM enables a modular framework for diagnosing differences in process representation, facilitating ensemble forecasts and uncertainty quantification, and choosing process presentations appropriate for the application. Noah-MP developers designed multiple parameterization options for leaf dynamics, radiation transfer, stomatal resistance, soil moisture stress factor for stomatal resistance, aerodynamic resistance, runoff, snowfall, snow surface albedo, supercooled liquid water in frozen soil, and frozen soil permeability.

The Noah-MP LSM has evolved through community efforts to pursue and refine a modern-era LSM suitable for use in the National Centers for Environmental Prediction (NCEP) operational weather and climate prediction models. This collaborative effort continues with participation from entities such as NCAR, NCEP, NASA, and university groups.

Noah-MP has been implemented in the UFS via the CCPP physics package and is currently being tested for operational use in GFSv17 and RRFS v2. Additionally, the UFS Weather Model now contains a Noah-MP land component. Noah-MP has also been used operationally in the NOAA National Water Model (NWM) since 2016. Details about the model’s physical parameterizations can be found in Niu et al. [NYM+11] (2011).

1.1.4. Disclaimer

The United States Department of Commerce (DOC) GitHub project code is provided on an “as is” basis and the user assumes responsibility for its use. DOC has relinquished control of the information and no longer has a responsibility to protect the integrity, confidentiality, or availability of the information. Any claims against the Department of Commerce stemming from the use of its GitHub project will be governed by all applicable Federal laws. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation, or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

1.1.5. References

[Bra99]

B. Brasnett. A global analysis of snow depth for numerical weather prediction. J. Appl. Meteorol., 38(6):726–740, 1999. doi:10.1175/1520-0450(1999)038%3C0726:AGAOSD%3E2.0.CO;2.

[CZ09]

F. Chen and Y. Zhang. On the coupling strength between the land surface and the atmosphere: from viewpoint of surface exchange coefficients. Geophys. Res. Lett., 2009. doi:10.1029/2009GL037980.

[FMMW+07]

Y. Fan, G. Miguez-Macho, C.P. Weaver, R. Walko, and A. Robock. Incorporating water table dynamics in climate modeling: 1. water table observations and equilibrium water table simulations. Journal of Geophysical Research, 112(D10):19569–19585, 2007. doi:10.1029/2006JD008111.

[FJSWD22]

S. Frolov, J.S. Whitaker, and C. Draper. Including parameterized error covariance in local ensemble solvers: experiments in a 1d model with balance constraints. Quarterly Journal of the Royal Meteorological Society, 2022. doi:10.1002/qj.4289.

[HVMWK20]

D. Holdaway, G. Vernieres, M. Wlasak, and S. King. Status of model interfacing in the joint effort for data assimilation integration (jedi). JCSDA Quarterly Newsletter, 66:15–24, 2020. doi:10.25923/RB19-0Q26.

[HHZ+20]

R. Honeyager, S. Herbener, X. Zhang, A. Shlyaeva, and Y. Trémolet. Observations in the joint effort for data assimilation integration (jedi) — unified forward operator (ufo) and interface for observation data access (ioda). JCSDA Quarterly Newsletter, 66:24–33, 2020. doi:10.25923/RB19-0Q26.

[HEJKS07]

B.R. Hunt, E.J. Kostelich, and I. Szunyogh. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform kalman filter. Physica D: Nonlinear Phenomena, 230(1–2):112–126, 2007. doi:10.1016/j.physd.2006.11.008.

[Jor91]

R. Jordan. A one-dimensional temperature model for a snow cover: technical documentation for sntherm.89. Technical Report Special Rep. 91-16, U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH, October 1991.

[KSM+99]

V. Koren, J. Schaake, K. Mitchell, Q.Y. Duan, F. Chen, and J.M. Baker. A parameterization of snowpack and frozen ground intended for ncep weather and climate models. Journal of Geophysical Research, 104(D16):19569–19585, 1999. doi:10.1029/1999JD900232.

[MMFW+07]

G. Miguez-Macho, Y. Fan, C.P. Weaver, R. Walko, and A. Robock. Incorporating water table dynamics in climate modeling: 2. formulation, validation, and soil moisture simulation. Journal of Geophysical Research, 2007. doi:10.1029/2006JD008112.

[NY06]

G.-Y. Niu and Z.-L. Yang. Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale. Journal of Hydrometeorology, 7(5):937–952, 2006. doi:10.1175/JHM538.1.

[NYD+07]

G.-Y. Niu, Z.-L. Yang, R.E. Dickinson, L.E. Gulden, and H. Su. Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data. Journal of Geophysical Research, 2007. doi:10.1029/2006JD007522.

[NYM+11]

G.-Y. Niu, Z.-L. Yang, K.E. Mitchell, F. Chen, M.B. Ek, M. Barlage, A. Kumar, K. Manning, D. Niyogi, E. Rosero, M. Tewari, and Y. Xia. Simple water balance model for estimating runoff at different spatial and temporal scales. Journal of Geophysical Research, 2011. doi:10.1029/2010JD015139.

[NYREDG05]

G.-Y. Niu, Z.-L. Yang, R.E. Dickinson, and L.E. Gulden. A simple topmodel-based runoff parameterization (simtop) for use in gcms. Journal of Geophysical Research, 2005. doi:10.1029/2005JD006111.

[SZ09]

K. Sakaguchi and X. Zeng. Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the community land model (clm3.5). Journal of Geophysical Research, 2009. doi:10.1029/2008JD010834.

[SKD+96]

J.C. Schaake, V.I. Koren, Q.-Y. Duan, K.E. Mitchell, and F. Chen. Simple water balance model for estimating runoff at different spatial and temporal scales. Journal of Geophysical Research, 101(D3):7461–7475, 1996. doi:10.1029/95JD02892.

[SMDHH92]

P.J. Sellers, M.D. Heiser, and F.G. Hall. Relations between surface conductance and spectral vegetation indices at intermediate (100 m2 to 15 km2) length scales. Journal of Geophysical Research, 97(D17):19033–19059, 1992. doi:10.1029/92JD01096.

[TA20]

Y. Trémolet and T. Auligné. The joint effort for data assimilation integration (jedi). JCSDA Quarterly Newsletter, 66:1–5, 2020. doi:10.25923/RB19-0Q26.

[WH12]

J.S. Whitaker and T.M. Hamill. Evaluating methods to account for system errors in ensemble data assimilation. Monthly Weather Review, 140(9):3078–3089, 2012. doi:10.1175/MWR-D-11-00276.1.

[ZCSS04]

F. Zhang, C. Snyder, and J. Sun. Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble kalman filter. Monthly Weather Review, 132(5):1238–1253, 2004. doi:10.1175/1520-0493(2004)132%3C1238:IOIEAO%3E2.0.CO;2.