Intercomparison of New York State Mesonet Wind Data: Propeller Wind Monitor versus Sonic Anemometer

The New York State Mesonet (NYSM) consists of 126 stations across the state with an average spacing of ~20 miles. All stations make measurements of standard meteorological variables plus soil moisture at three levels, solar radiation, snow depth and take camera image. In addition, in a first for a state mesonet, NYSM has three sub-networks (“Profiler”, “Flux”, and “Snow”) comprised of 17, 17, and 20 sites to provide atmospheric vertical profiles, the surface energy budget, and snow water equivalent, respectively.

NYSM is the first network operationally making 10-m wind measurements from two independent instruments: propeller wind monitor and sonic anemometer. The inter-comparison between propeller and sonic instruments provides a unique opportunity (1) to quality-control the data from both instruments, (2) to fill up the data gaps when one instrument fails, (3) to understand the strength and weakness of each instrument and potentially combine two measurements to create the best wind data, and more importantly (4) to potentially detect freezing rain events and study their characteristics. The comparison shows that the propeller systematically underestimates the wind speed, and the underestimate increases with wind speed and is generally less than 1 m/s. More detailed comparisons will be presented. Sonic data have been used routinely during winter to identify the times when the propeller significantly underestimates wind speed or completely stops working due to freezing rain or snow and flag the data. Prior studies have found that the northeast US experiences the greatest number of freezing rain days per year on average. During last winter we found 28 such events based on wind comparisons with the longest duration of 68 hours for an event on December 18, 2016 and the largest number of affected sites (33) for an event on January 10-11, 2017. However, the NOAA/NCEI Storm Events database only reported one event (2/7/2017) during the period. For this event, the affected area qualitatively agrees with each other from two datasets. Majority of those events occurred when the surface temperature was within -3°C to 1°C. More work is required to validate and improve our approach to detect freezing rain events and understand their characteristics.