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Home » Focus Areas » Weather Model Improvements

Weather Model Improvements

Unified Forecast System (UFS) Physics Suite Component:

Weather models provide one way to predict the precipitation, general sensible and severe weather, winds and solar energy which are important for decision-making within the public and private sectors. Improvements to the moist-physics parameterizations is the most effective pathway to provide general forecast skill improvement. The project will focus on improving all moist-physics schemes, which may include the boundary layer parameterization, treatment of shallow convection, subgrid scale clouds, interaction with radiation schemes, surface layer schemes, and gravity wave drag schemes for the Unified Forecast System (UFS). For the representation of boundary-layer physics, GSL will test a combination of EDMF and higher-order closures for turbulence, including various representations of subgrid-scale precipitation processes, and convective momentum transport. GSL will perform testing in single-column, limited-area, and global frameworks. The new/updated moist-physics parameterization schemes and related physics will be candidates for the future Rapid Refresh Forecast System (RRFS) and GFS, which will both be FV3/CCPP-based systems. This will provide the foundation for an advanced unified (CCPP-compliant) moist physics suite for the coupled UFS to incorporate innovations, state-of-the-science and software-engineering capabilities, and optimal performance targeted UFS applications for all scales.

Forecast Impact and Quality Assessment Services (FIQAS)

The Forecast Impact and Quality Assessment Services (FIQAS) Branch develops state-of-the-art environmental forecast, decision support, visualization, and evaluation capabilities to provide scientifically robust forecast assessment tools, concepts, and analyses to support the weather decision-making process. Our mission is to advance the understanding and use of weather information through impact-based forecast assessments and targeted real-time information delivery to benefit decision-making in response to high impact weather events.

Atmospheric Science for Renewable Energy (ASRE)

GSL leads the ASRE program with the vision to improve solar and wind renewable energy forecasts by improving the representation of boundary layer and other processes in numerical weather prediction models.

Developmental Testbed Center

GSL is a partner in the Developmental Testbed Center (DTC), a distributed facility where the NWP community can test and evaluate new models and techniques for use in research and operations.

Joint Effort for Data assimilation Integration (JEDI)

Data assimilation (DA) algorithms themselves are also evolving and progressing to better exploit available data. The JEDI framework includes existing operational DA algorithms and facilitates the exploration of new DA science across domains and applications. It is important to note that a unified system does not mean a single configuration to be imposed on all partners, as each agency can use or develop different applications within the framework. The Joint Center for Satellite Data Assimilation (JCSDA) partners with GSL and the wider community to continuously work together with the JEDI through collaborative tools and workshops to provide feedback and guidance for ongoing developments.

GSL has added parallelization to the JEDI infrastructure and develops tests to ensure the capabilities work in JEDI as well as making a number of software contributions. GSL will assess and improve the performance of JEDI beginning with its initial release of JEDI-FV3 v1.0.0. GPU parallelization of the CRTM (radiation) code used to process satellite data will allow testing for performance optimizations using CPU and GPU computing. GSL will experiment with other tuning optimizations to improve the accuracy and performance of the JEDI data assimilation code.


The Real-Time Mesoscale Analysis (RTMA) was developed to provide forecasters with frequent high-resolution meteorological displays over the continental U.S. (CONUS) and, more recently, other regions of interest such as Alaska, Hawaii, and the Caribbean. An operational three-dimensional RTMA (3D-RTMA) is being developed, building upon the current RTMA surface-only analysis. The overarching goals of this work include: 1) improving tools for situational awareness and nowcasting, 2) providing a 3D analysis of record (AOR) for verification, and bias-correction and 3) accelerating improvement of numerical weather prediction (NWP) models.

Objective and subjective feedback from users, primarily facilitated by NWS regional centers, NCEP prediction centers, and via testbeds, will be used to iteratively advance the 3D-RTMA.

The new product suite will ultimately improve forecast guidance provided to the public by NWS forecast offices and national centers, with the potential to mitigate losses of life and property during hazardous weather events.

Model Analysis Tool Suite (MATS)

A verification application for models