The Earth Prediction Innovation Center (EPIC) and the Unified Forecast System (UFS) community are proud to announce the public release of the Short-Range Weather (SRW) Application (App) v3.0.0, more information is available via the EPIC SRW Get Code page. This release is an update to the v2.2.0 release from October 2023 and incorporates a number of changes currently available in the SRW App develop branch, including significant enhancements to the SRW App’s ability to to simulate 
smoke, dust, and two-way coupled fire-atmosphere interactions. The new release includes the following updates:
- Integration of Community Fire Behavior Model, developed by the Community Fire Behavior Model (CFBM) group at National Science Foundation (NSF) – National Center for Atmospheric Research (NCAR):
- Enabled fire simulation on a very high-resolution fire modeling grid nested within the atmospheric domain
- Updated documentation and workflow end-to-end (WE2E) test to reflect the two-way coupling feature
- Integration of Smoke and Dust (SD) Capabilities to the SRW App develop branch
- Added a new SD WE2E test case, a sample configuration YAML file (config.smoke_dust.yaml), and a separate Conda environment (sd_environment.yml)
- Added the FV3_HRRR_gf physics suite for SD capabilities
- Ported SD features to all Tier-1 platforms
- Completed various SD-related script updates and bug fixes
- Integrated verification options for smoke and dust forecast fields
- Miscellaneous enhancements, including:
- Physics parameterizations: UFS Weather Model (WM) updated with the latest developments in physical parameterizations. Includes the RRFS_sas physics suite for the Rapid Refresh Forecast System (RRFS) v1. RRFS v1 is a high-resolution limited-area NOAA developmental model being proposed for operational implementation at the National Weather Service.
Newly added/supported grids: The SUBCONUS_CO_3km (3km grid over Colorado) and SUBCONUS_CO_1km (1km grid over Colorado) predefined grids were created for UFS Fire but can be used by any experiments
- Verification (VX): Simplified verification tasks, improved METplus configuration, and added the MET_ensemble_verification_winter_wx test
- Workflow: Added option to install Miniforge for SRW environments (srw_app, srw_graphics, srw_aqm); replaced Unified Workflow (UW) Command Line Interface (CLI) with Application Programming Interface (API) calls in Python scripts.
- Data: Reorganized Amazon Web Service (AWS) S3 data bucket structure for improved versioning and effective data management
- Graphics: Updated plotting scripts for improved visibility of ensemble outputs
- Testing & Continuous Integration and Continuous Deployment (CI/CD): Improved Jenkinsfile handling, skill-score metrics, and platform node skipping; added remaining UFS case studies as WE2E tests.
- Dependencies: Upgraded SRW App to spack-stack 1.6.0
- Updated UFS WM and component hashes
- Miscellaneous bug fixes since the SRW App v2.2.0 release, including issues related to verification, CI/CD, cloud instances, and dependencies.
- Documentation updates and enhancements:
- Documentation for fire and SD capabilities
- Addition of technical documentation and documentation testing via GitHub Actions
- New tutorials:
- Updated container for the v3.0.0 release
View the full changelog on GitHub: v2.2.0…v3.0.0.
Enhancements to the SRW App’s ability to simulate smoke, dust, and two-way coupled fire-atmosphere interactions improve the model’s capacity for high-resolution forecasting in scenarios involving wildfires, air quality concerns, and particulate matter dispersion. The integration of these capabilities into the SRW App allows for a more detailed representation of atmospheric feedback mechanisms, thereby improving predictive capabilities to support both research and operational weather forecasting efforts.
The reconciliation of the RRFS production branch with the UFS WM develop branch laid the foundation for integration of smoke and dust modeling into the SRW App. The smoke and dust features include a new WE2E test designed specifically for smoke and dust, as well as a sample configuration file (config.smoke_dust.yaml) to guide users in setting up and running these simulations. To ensure smooth functionality across multiple platforms, a dedicated Conda environment (srw_sd) was introduced, resolving conflicts related to Message Passing Interface (MPI) and Earth System Modeling Framework (ESMF) installations. Extensive testing confirmed the successful implementation of this capability, with fixes applied to forecast execution scripts, post-processing, and machine-specific compatibility issues.
In addition to smoke and dust modeling, v3.0.0 marks the introduction of a two-way coupled fire-atmosphere feedback mechanism. The CFBM has been integrated into the SRW App, allowing for simulation of interactions between fire events and weather patterns. This newly introduced capability enables users to simulate fires on a very high-resolution fire modeling grid nested within the atmospheric domain, enhancing model accuracy in simulating wildfire spread, smoke emissions, and their subsequent meteorological impacts.
Beyond these core enhancements, the release incorporates several workflow improvements, bug fixes, and documentation updates to streamline performance and usability. The SRW App now benefits from expanded verification capabilities, improved automation in its CI/CD pipeline, and refinements to its AWS S3 data bucket structure. These upgrades contribute to a more robust and efficient modeling system, reinforcing the SRW App’s role as a cutting-edge tool for short-range weather forecasting, environmental monitoring, and hazard assessment.
User Support Information: The SRW App v3.0.0 User’s Guide has been updated to reflect these improvements to the Application, and numerous test cases with all associated data files are also available to the public in the SRW App data bucket. Interested users can get additional support by submitting a question through the GitHub Discussions forums.
Platforms: The SRW App is highly portable and runs on a wide variety of pre-configured platforms. Additionally, Docker images containing the full software stack and pre-built SRW App are available for download or conversion to Singularity containers. The containers can be used to run through the full workflow on any high-performance computing system that has a Linux operating system, a job scheduler/workload management software (e.g., Slurm, PBS), and Intel compilers and MPI from the OneAPI (year 2021 or newer).
Contributors [in alphabetic order]: The SRW Application Release Working Group includes members from NOAA laboratories, centers, cooperative institutes, and community partners — notably the Air Resources Laboratory (ARL), the Cooperative Institute for Earth System Research and Data Science (CIESRDS), the Cooperative Institute for Research in the Atmosphere (CIRA), Cooperative Institute for Research in Environmental Sciences (CIRES), the Cooperative Institute for Satellite Earth System Studies (CISESS), the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), the Developmental Testbed Center (DTC), the Earth Prediction Innovation Center (EPIC), the Geophysical Fluid Dynamics Laboratory (GFDL), the Global Systems Laboratory (GSL), the National Center for Atmospheric Research’s (NCAR’s) Climate and Global Dynamics (CGD) Laboratory and Research Application Laboratory (RAL), the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC), the National Severe Storms Laboratory (NSSL), and the Physical Sciences Laboratory (PSL).
The development of the spack-stack software used by the UFS Applications is a collaborative effort among the Joint Center for Satellite Data Assimilation (JCSDA), EMC, The U.S. Naval Research Laboratory (NRL) and EPIC. The code is hosted on GitHub. Publicly available data is provided via an AWS S3 bucket established as part of the NOAA Open Data Dissemination (NODD) Program. Computing resources used for preparation of this release were provided by NOAA High Performance Computing and Communications (HPCC) Program and NCAR Computational and Information System Laboratory (CISL).
This release was funded by the NOAA Weather Program Office’s Earth Prediction Innovation Center (EPIC), Fire Weather and Air Quality (FWAQ) and Joint Technology Transfer Initiative (JTTI) programs, the National Weather Service Office of Science and Technology Integration (OSTI) modeling programs; and the NOAA Disaster Supplemental Program. A portion of this work was funded by Infrastructure Investment and Jobs Act/Bipartisan Infrastructure Law, Public Law 117-58.
