Challenges with Winter Weather Products in the National Weather Service National Blend of Models

The National Blend of Models (NBM) is a nationally consistent and skillful set of calibrated forecast guidance based on a blend of both National Weather Service (NWS) and non-NWS numerical weather prediction data and post-processed model guidance. The goal of the NBM is to create a highly accurate starting point for the National Digital Forecast Database (NDFD) grids. Improvements to the NBM are considered a critical component of the increased NWS focus on Impact-Based Decision Support Services (IDSS).

Numerous changes to the winter output fields were made in the January 2023 implementation of NBMv4.1. Most notably are the increase in the number of model inputs (18 to 100) and the inclusion of the direct model output dominant precipitation type (p-type). The latter modifications now define the p-type product as non-conditional as opposed to conditional, meaning p-type forecasts are only produced where models produce precipitation. Expert weighting of the individual model p-types is used to generate a probability of each p-type occurring and identifying a categorical p-type. A brief overview of these v4.1 improvements will be presented.

While NBMv4.1 Winter products demonstrate an overall noteworthy improvement over the previous version, some challenges remain: (1) removing the inconsistencies noted between the expected deterministic and probabilistic NBM winter accumulations; (2) improving the downscaled quantitative precipitation forecast for lower resolution ensemble members used in calculating snow amounts. (3) improving the accuracy of snowfall accumulation products in marginal thermodynamic environments.