Activities
| Activity | Estimated Level of Effort |
|---|
HEC-MetVue |
| Manage FY26 Contracts | $20,000 (labor) |
| Implement Ensemble Features | $120,000 (contract) |
| Add New Functions to MetCalculator | $50,000 (contract) |
| Enhance MetCalculator GUI | $30,000 (contract) |
| Improve McCormick Method Performance | $25,000 (contract) |
HEC-MetVue
Manage FY26 Contracts
The team will design, scope, and award contracts for HEC-MetVue enhancements as described below.
Implement Ensemble Features
This work will enable support for ensemble datasets in HEC-MetVue and HEC-RTS, including data input/output, processing, and visualization. This includes updates to HEC-RTS extracts and HEC-MetVue distributed processing capabilities for ensemble workflows.
Add New Functions to MetCalculator
The HEC-MetVue compute engine is built on MetCalculator functions, which are accessed through both the HEC-MetVue interface and scripting tools. Core MetCalculator capabilities include reading and writing gridded data across various file formats, applying spatial and temporal constraints, calculating basin average statistics, editing data and filling gaps, and performing temporal transformations.
This task focuses on expanding MetCalculator functionality by adding tools to support bias correction and introducing new statistical operations—particularly exceedance probability calculations—to enhance analysis of gridded ensemble datasets. Additionally, it will implement functions to support storm transposition for “what-if” scenario simulations.
Enhance MetCalculator GUI
A prototype Graphical User Interface (GUI) for MetCalculator was developed to provide a user-friendly alternative to manual script writing. While the GUI is functional, it requires enhancements to improve interaction with the functions tree and definitions panel, support integration of Python scripts, and streamline workflow management.
Refining the MetCalculator GUI will significantly reduce the burden on users by simplifying the creation of HEC-MetVue scripts for real-time gridded data processing, as well as for custom or backup HEC-RTS applications that rely on publicly available meteorological data.
The McCormick Method is one of the most effective techniques currently supported in MetInterp for generating interpolated gridded precipitation data from station-based time series. It is the only spatial interpolation method that incorporates the predominant direction and movement of storms. However, this added sophistication results in high computational demands. This task will focus on improving performance by optimizing the compute code and implementing data caching strategies.