Leveraging METcalcpy and METplotpy for Enhanced Ensemble Weather Forecasting Verification

The evolution of weather forecasting relies heavily on the accurate evaluation and visualization of meteorological models. The Model Evaluation Tool (MET), a comprehensive suite developed by the Developmental Testbed Center (DTC), serves as the backbone for this essential task, comprising components such as METviewer, METdataio, METplotpy, METcalcpy, and more.

The introduction of METplotpy marks a significant advancement, particularly in its capacity to eliminate the need for unwieldy SQL-based database setups associated with METdataio and METviewer instances. This traditional configuration process has often proved infeasible for certain users and groups, creating barriers to accessibility. METplotpy offers a more streamlined verification plotting option, allowing for command line-only operations that directly translate model output into graphics. This feature not only simplifies the process but also broadens the reach of MET, making it a more viable solution for various groups involved in both research and operational model development.

Certain statistics represent not merely mathematical transformations but crucial steps in understanding and interpreting meteorological phenomena. The implementations in METcalcpy and METplotpy developed in this work provide a pathway to harness these statistics without the complex environment associated with the setup of a database, bringing a more intuitive and effective evaluation process.

In the context of a growing need for direct and efficient statistical analyses, this work presents the implementation of vital capabilities within METcalcpy and METplotpy. By bypassing the constraints tied to database systems like METdataio, these implementations foster a more accessible and automated process, crucial for modern verification processes.

This shift not only alleviates the setup burdens associated with METviewer but also provides a tailored solution that aligns with the evolving requirements of ensemble weather forecasting. The synergy of METcalcpy and METplotpy with current workflows highlights a promising direction for meteorological model evaluation, streamlining both the access to complex weather data and the extraction of meaningful insights.