Regional Modeling Systems

BACKGROUND

Regional modeling activities in the Joint Numerical Testbed (JNT; http://www.ral.ucar.edu/jnt) are focused primarily on the Developmental Testbed Center (DTC; http://www.dtcenter.org) activities, and real-time systems. The DTC is a distributed facility with components in the JNT at NCAR's Research Applications Laboratory (RAL), and the Global Systems Division (GSD) of NOAA's Earth System Research Laboratory (ESRL). It facilitates the transfer of research results into operations by providing the research community with Numerical Weather Prediction (NWP) system components for research. One of the DTC's focal points is regional forecasting systems, with a goal of accelerating the rate at which new technology is infused into operational weather forecasting. The DTC meets its goals by maintaining and supporting community codes that represent the latest NWP technology, performing extensive testing and evaluation of new NWP technology, maintaining a state-of-the-art verification package, and connecting the NWP research and operational communities through its visitor program. In addition to DTC activities, JNT staff have been working to transfer technologies in support of mesoscale weather prediction for the Colombian Civil Aviation Authority, Saudi Arabia weather service (General Authority for Meteorology and Environmental Protection: GAMEP), and sparsely observed regions of the world.

FY2019 ACCOMPLISHMENTS

Community Codes

Community code is a free and shared resource with distributed development and centralized support. The DTC's community code efforts are collaborative activities with developers at NCEP's Environmental Model Center (EMC), NCAR's Mesoscale and Microscale Meteorology (MMM) Division, NOAA/ESRL/GSD, NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), National Aeronautics and Space Administration’s (NASA) Global Modeling and Assimilation Office (GMAO), National Environmental Satellite, Data and Information Service (NESDIS), the University of Rhode Island (URI), and NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Research Division (HRD). During 2019, the DTC worked with the following software packages:

The DTC contributes to the software management and user support for publicly released versions of these systems, which include the latest developments of new capabilities and techniques. Prior to each official release to the user community, the DTC ensures the integrity of all community code software components through a broad range of testing. The DTC also strives for system evolution, in particular through increased interoperability of existing system components, as well as adding new capabilities or techniques. In addition, the DTC provides user support for these packages in the form of Users' Guides, webpages, email helpdesks, and online and on–site tutorials.

As NCEP’s Environmental Modeling Center (EMC) moves toward a unified modeling suite across both spatial (regional and global) and temporal (weather, sub-seasonal and seasonal) scales the DTC has also begun pivoting to the new modeling suite built around GFDL’s Finite-Volume Cubed-Sphere (FV3) dynamical core. While the Unified Forecast System (UFS) has not yet been officially released to the community, the DTC has been working to establish robust code management practices, regression testing, documentation, workflow infrastructure, and support forums to prepare for the upcoming release.

Testing and Evaluation (T&E)

The DTC provides a trusted facility that developers and the operational community can rely on for unbiased assessments of the operational prediction systems and potential new additions to those systems. Testing and evaluation undertaken by the developers of new NWP techniques from the research community are generally focused on case studies. However, in order to adequately assess these new technologies, extensive testing and evaluation must be performed to ensure they are indeed ready for operational consideration. Testing and evaluation by the DTC focuses on either extended retrospective time periods or real–time forecast experiments. These forecasts can be generated by the DTC or provided by external modeling groups. The DTC's evaluations include the use of standard verification techniques, as well as new verification techniques. All verification statistics include a statistical significance (SS) and practical significance (PS) assessment when appropriate.

During 2019, the focus within the regional modeling group was on ensemble systems. In most existing regional ensemble systems, model-related uncertainty is addressed by using multiple dynamic cores, multiple physics suites, or a combination of these two approaches. While these approaches have demonstrated potential, it is time-consuming and costly to maintain such systems, especially in operations. In order to move toward a more sustainable and unified system, stochastic parameter perturbations within the High-resolution Rapid Refresh (HRRR) physics suite are being investigated. Focus has been placed on adding stochasticity into planetary boundary layer (PBL) and land surface model (LSM) processes, along with microphysical processes.

To investigating the stochastic perturbation approach, the DTC partnered with the Hazardous Weather Testbed to address the merits of different approaches to representing model-related uncertainty by conducting an evaluation of a subset of the members of the Community Leveraged Unified Ensemble (CLUE) from the Spring Forecast Experiments (SFEs) held annually. The CLUE evaluation activity is focused on addressing the question of whether there is an advantage to using an ensemble composed of multiple microphysics and PBL schemes over a single physics suite ensemble, using initial and lateral boundary condition and stochastic perturbations. Prior studies of multi-physics, convective-allowing ensemble systems have focused on composite reflectivity, whereas this study is considering multiple verification metrics and methods. This approach demonstrates the need to consider a broader perspective when evaluating the merits of various ensemble approaches before reaching any conclusions as to which approach provides the best overall forecast skill.

Containers

Many times the biggest hurdle when running a new software system is getting it set up and compiled on the intended computer platform. Building complex systems that require a number of external libraries can be a prohibitive hurdle for users to overcome.  In order to reduce some of this difficulty, software containers are being exploited to ship complete software systems to users. The containers have everything that is needed to run a software application, including the necessary operating system components (tools and libraries) and compiled executable (or code and compiler), thus, allowing for the user to quickly produce output without being delayed by technical issues. DTC staff members created GSI, UPP, MET and METViewer (the database and display system for viewing MET statistical output) containers to supplement those containers that had already been established by others in the community (including, WPS, WRF, and NCL) so that an end-to-end NWP system can be fully employed through containers. Along with the software containers, datasets for several cases were bundled in a data container. In 2019, explicit instruction on using the established end-to-end NWP containers on a cloud server was detailed. In addition, DTC staff members coordinated with Metropolitan State University of Denver to teach a forecasting lab that included hands on instruction for running the NWP system on the cloud. By establishing these additional containers, the DTC is assisting the user community (especially students) with efficiently running NWP components and making connections with future collaborators.  To further assist the community, the DTC is offering an AMS short course in January 2020 geared toward raising awareness of these tools for testing and evaluation of NWP innovations.

Real-time modeling systems

JNT staff have participated in technology transfer activities in support of the Colombian Civil Aviation Authority’s weather prediction needs. A prediction system based on the WRF model and GSI has been developed and deployed in collaboration with a private partner called Sutron/Meteostar, which is responsible for providing operational support and visualization. The work has leveraged JNT capability developed under DTC funding. Workflows based on Rocoto provide a stable and modular deployment environment.  The focus in FY2019 has been on implementing the latest, stable version GSI data assimilation system for data assimilation of standard and local surface observations along with satellite fields and to develop a 10-member multi-physics ensemble system in an effort to improve forecast capabilities.  The final operational system was installed at Sutron/Meteostar in December 2018 and training on how to use and maintain the system is planned with Civil Aviation Authority.

The JNT is leading an effort to modernize Saudi Arabian’s weather service (GAMEP: General Authority for Meteorology and the Environment) forecast and display capabilities.  A new regional forecast system based on WRF has been developed with a 3-nest domain design with a high-resolution 2-km inner nest that covers the Saudi Arabian Peninsula.  The forecast system also includes real-time data assimilation of GAMEP’s local observations.  A multi-member ensemble is also being developed that utilizes GFS and ECMWF global models.  A dust-forecast system has also been developed using WRF-Chem.  The modeling team has also developed new forecast projects and a mobile application that interfaces with the forecast products.  An extensive training is being developed for GAMEP staff.

In addition, JNT staff supported the evaluation of observation data quality from a USAID-funded project to develop and deploy low-cost weather instrumentation in sparsely observed regions of the world.  The project uses innovative new technologies such as 3D printers, Raspberry Pi computing systems, and wireless communications to develop a sustainable system that can be built locally in under-developed countries.  The study evaluated data quality of temperature, pressure, humidity, wind, and precipitation observations collected for the NCAR and NOAA testbed sites in Boulder, CO and Sterling, VA. Results indicate the low-cost sensors provide high quality data that could be used for applications for agriculture, water resource monitoring, health, and monitoring of hazardous weather conditions.

FY2020 PLANS

In the coming year, the JNT through the DTC will continue to support various community codes, including NWP systems that will move to a UFS focus, GSI and MET. The DTC will also help organize and support tutorials on the community codes that it supports, as well as on regional models, data assimilation, hurricanes, and forecast verification.  Relevant workshops will be offered to stimulate discussion among the research and operational modeling communities on future directions of development. In addition, efforts will continue related to evaluating deterministic and ensemble-based probabilistic model output. Efforts to further broaden the usage of the NWP containers, both on local machines and on the cloud, will be undertaken.