Abstract:In order to more effectively apply the globally shared AMDAR (Aircraft Meteorological Data Relay) data in local meteorological operations and address the challenge stemming from the uneven spatio-temporal distribution of AMDAR data, this paper initially conducts quality control processing using AMDAR data from April 2019 to May 2020 around Hangzhou. This is carried out with reference to the aircraft meteorological observation quality control scheme of the NOAA in the United States and the National Meteorological Information Centre. Following this, a new method is proposed for extracting AMDAR profile data, taking into consideration the determination of the temporal, spatial representation, and vertical resolution of AMDAR data. This method views AMDAR data within a specific temporal and spatial range around the airport as analogous to the observations of a weather balloon drifting to different positions, thereby extracting temperature and wind vertical profiles based on specified temporal and spatial representativeness. In the vertical direction, the interpolation algorithm is utilised to achieve a uniform distribution of the profile, and median filtering algorithm is carried out on the obtained profile data for additional quality control. Our results from comparing the AMDAR profile data with Hangzhou radiosonde data demonstrate that the overall average differences in temperature, wind speed, and wind direction between the AMDAR data and radiosonde data in Hangzhou are -0.83 ℃, 0.02 m/s, and 0.47°respectively. The root mean square errors amount to 1.93 ℃ for temperature, 2.02 m/s for wind speed, and 25.05° for wind direction. There is a trend toward the AMDAR temperature profile data being smaller than the radiosonde temperature data, as a result of the systematic error of aircraft detection. It should be noted that this is more evident in relatively warm and wet seasons compared to relatively dry and cold seasons. Notably, the AMDAR wind profile data do not exhibit clear systematic error, which leaves the data quality in a satisfactory state. The comparison errors of temperature and wind speed are slightly realigned in the boundary layer height range of 0-1000 m compared to 1000-2000 m, which increase with the increase in height in the 2000 m and above range. However, the comparison error of wind direction drastically diminishes with the increase in height in the whole comparison height range. Furthermore, the higher the ambient wind speed, the greater the comparison error of wind speed, but the smaller the comparison error of wind direction. The AMDAR profile data and radiosonde data show a good level of agreement, although in terms of data integrity, there appear to be numerous missing measurements in 02:00-06:00 and above 5000 m. This is attributed to the limitations of aircraft detection influenced by specific flight times and routes. In conclusion, the AMDAR profile extraction method proposed in this paper elucidates the temporal and spatial representation of AMDAR profile data. Furthermore, by ensuring it is evenly distributed in time and height, this contributes to convenience in meteorological operations. This new AMDAR profile extraction method indeed holds certain application value and can offer a reference point for local application of AMDAR data in different regions.
