Abstract/Summary

Meteorological observations are very important for weather forecasting; one potential source of observations is commercial aircraft. Commercial aircraft transmit Mode Selective Enhanced Surveillance (Mode-S) reports, which include data about the aircraft’s ground speed and magnetic heading, but the reports do not include temperature readings. Instead, the Mach number and airspeed can be used to derive the temperature. However, to transmit the Mode-S report the precision in the Mach number and airspeed is reduced from 16 bits to 10 bits. This reduction in precision creates errors in the Mach number and airspeed, which in turn translate to errors between 4–7 K and 3–5 K respectively, in the derived temperature. These large errors may limit the use of these observations for weather forecasting. This paper investigates if optimal interpolation, a type of data assimilation, can be used to combine aircraft data with atmospheric model data to provide a better estimate of the temperature-derived observations. We find that optimal interpolation can reduce the standard deviation of the derived temperature observations to 1.12 K; however, this is too large to be used with numerical weather prediction where a standard deviation of 1.0K is required.