The Three-Dimensional Real-Time Mesoscale Analysis System (3D-RTMA) - Latest Developments

NOAA/OAR/GSL and NOAA/NCEP/EMC have been developing a hybrid-3DEnVar-based Three-Dimensional Real-Time Mesoscale Analysis (3D-RTMA) that integrates a non-variational cloud analysis, since late 2017. The goal of the 3D-RTMA is to extend the operational RTMA, a 2DVar-based surface analysis, to three dimensions for “full atmosphere” situational awareness and for a 3-D analysis of record. The system is envisioned to run with a 15-min cadence at a 2.5 -or 3 km horizontal resolution. Besides providing a full-column representation of standard meteorological fields such as temperature, water vapor, and wind, as well as hydrometeors (i.e., clouds, precipitation of all forms), and eventually aerosols, the 3D-RTMA will also include land-surface diagnostics (e.g., soil moisture, snow state from multi-level land-surface fields), and convective (e.g., hail size, supercell rotation tracks) fields. The3-D cloud hydrometeor analysis in 3D-RTMA has the potential to provide critical “nowcast” information for identifying hazards to aviation (e.g., in-flight icing, and ceiling and visibility restrictions) and ground transportation (e.g., highway snow and ice accumulation in complex terrain). With all these components, the NOAA 3D-RTMA will 1) improve tools for situational awareness and nowcasting, and ultimately improve forecast guidance provided to the public by NWS forecast offices and national centers, 2) provide a three-dimensional analysis of record (AOR) suitable for forecast verification and bias-correction, and 3) help accelerate the improvement of numerical weather prediction (NWP) models.

The presentation will provide an overview of the latest 3D-RTMA developments over the past year. These advancements encompass several key areas:

1. Enhanced Background Error Covariance: We have refined the background error covariance specification based on the forecast error statistics from the regional FV3 model. This adjustment ensures a more accurate alignment between the analysis and surface observations, improving the overall quality of the analysis.
2. Adaptive Automatic Quality Control: Our approach now includes an adaptive automatic quality control mechanism. This filtering process effectively identifies and excludes erroneous observations, ensuring that only reliable and representative data are assimilated into the system. It also dispenses with the use of static observation reject and accept lists which are difficult to update in an operational setting.
3. 3D-RTMA in the cloud: To ensure seamless and uninterrupted product delivery for evaluation during the Hazardous Weather Testbed 2023 Spring Forecast Experiment, we successfully ported the 3D-RTMA system to the Amazon Cloud. This not only enabled consistent product availability but also allowed us to explore new developments in the computation-intensive North American 3 km runs. Additionally, a prototype unified graphics portal was established in the cloud for enhanced user accessibility.
4. Dynamic Downscaling and Machine Learning: We've begun to test the use of a dynamic downscaling and machine learning approaches to refine the accuracy of the background fields. This has the potential to further improve the precision of our analyses.
5. Uncertainty Estimation: We are exploring methods to compute estimations of the analysis uncertainty. This addition provides users with valuable insights into the reliability of the produced analyses.
6. Multigrid Beta Filter (MGBF): Work on the implementation of a highly scalable filter for background error covariance modeling is at an advanced stage. The new approach uses finite-support beta filters embedded in a multigrid structure for computational efficiency, and will replace the use of recursive filters.
7. Prototyping Unrestricted Real-time Mesoscale Analysis (3D-URMA): A prototype of the 3D Unrestricted Real-time Mesoscale analysis over the North American 3 km domain has been established.
8. Expansion to Marine Applications: Collaborating with the University of Connecticut, we are extending the capabilities of 3D-RTMA to marine applications. This expansion opens up new avenues for enhancing our understanding and prediction of marine weather phenomena.
9. Collaboration with RRFS Team: In collaboration with the Rapid Refresh Forecast System (RRFS) team, we are actively advancing the ufs-srweather-app and the associated regional workflow. This joint effort aims to enhance the overall efficiency and effectiveness of both systems.