RTMA/URMA Observation Changes to Enhance NBM Forecasts

The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on numerical weather prediction model data. The NBM is instrumental in the National Weather Service’s (NWS) efforts to provide decision support services to protect life and property by minimizing time spent on forecast-generation and maximizing time spent on communicating potential weather hazards and their associated uncertainties with customers and core partners.

It has recently become apparent that wind forecasts from NBM often do not serve the needs of NWS forecasters or core partners. This is often due to observations used in the UnRestricted Mesoscale Analysis (URMA), a two-dimensional gridded surface analysis used for local bias correction in NBM. Quality control of wind observations can be a considerable challenge for URMA, since wind observations from non-standard sites are used, and these observations are more sensitive to siting issues or subgrid scale effects than observations of other variables. Therefore, more coordination between NBM and URMA developers and NWS field offices is necessary to improve wind forecasts from NBM.

This presentation will go over case studies of poor NBM wind forecasts presented from several NWS field offices from different parts of the country to the NBM development team. We will show how URMA has impacted NBM wind forecasts, and how data denial experiments, testing, and subsequent changes to the URMA have resulted in improved operational NBM wind forecasts. In some cases, the solution is simply to collaborate with field offices to identify and remove offending wind observations from URMA. We will also discuss potential alternatives to using URMA for bias correction for winds, especially in the nearshore marine environment.