Using Mesonet Observation Metadata to Improve the RTMA Wind Analysis

Stakeholder feedback on the Real Time and UnRestricted Mesoscale Analysis (RTMA and URMA) systems indicates that it has a low wind speed bias when compared to available, high quality METAR observations. Furthermore, this low wind bias has also carried over into low wind speed forecasts from the National Blend of Models owing to its use of the URMA for calibration.

The low wind speed bias is partially due to the assimilation of observations from mesonet stations, which are often sited in poorly-exposed environments. In addition, it has been assumed that mesonet wind observations are taken at a standard height of 10 meters above ground level, owing to a lack of available station metadata. Many mesonet networks, especially those consisting primarily of home weather stations, contain observations taken much closer to the ground. Given the large dependency of wind speed on height above the ground, accounting for varying height of wind observations is important in the RTMA, and can help correct the low wind speed bias.

In order to properly account for observations from these stations an initial metadata database was created with help from external stakeholders. The database contains wind sensor height where possible; assumptions are made for other large networks where wind observation quality is suspect. The Monin-Obukhov similarity theory is used in the RTMA to account for wind observations taken near the ground when computing a 10 m AGL wind speed analysis. Impacts of the improved metadata and similarity theory on the RTMA analysis will be presented.