A Generalized Navigation Correction Method for Airborne Doppler Radar Data

作者：Cai, Huaqing^*; Lee, Wen-Chau; Bell, Michael M.; Wolff, Cory A.; Tang, Xiaowen; Roux, Frank

来源：Journal of Atmospheric and Oceanic Technology, 2018, 35(10): 1999-2017.

DOI：10.1175/JTECH-D-18-0028.1

• Aircraft observations
• Algorithms
• Global positioning systems (GPS)
• In situ atmospheric observations
• Quality assurance
• control
• Radars
• Radar observations

Uncertainties in aircraft inertial navigation system and radar-pointing angles can have a large impact on the accuracy of airborne dual-Doppler analyses. The Testud et al. (THL) method has been routinely applied to data collected by airborne tail Doppler radars over flat and nonmoving terrain. The navigation correction method proposed in Georgis et al. (GRH) extended the THL method over complex terrain and moving ocean surfaces by using a variational formulation but its capability over ocean has yet to be tested. Recognizing the limitations of the THL method, Bosart et al. (BLW) proposed to derive ground speed, tilt, and drift errors by statistically comparing aircraft in situ wind with dual-Doppler wind at the flight level. When combined with the THL method, the BLW method can retrieve all navigation errors accurately; however, it can be applied only to flat surfaces, and it is rather difficult to automate. This paper presents a generalized navigation correction method (GNCM) based on the GRH method that will serve as a single algorithm for airborne tail Doppler radar navigation correction for all possible surface conditions. The GNCM includes all possible corrections in the cost function and implements a new closure assumption by taking advantage of an accurate aircraft ground speed derived from GPS technology. The GNCM is tested extensively using synthetic airborne Doppler radar data with known navigation errors and published datasets from previous field campaigns. Both tests show the GNCM is able to correct the navigation errors associated with airborne tail Doppler radar data with adequate accuracy.

• 出版日期2018-10
• 单位南京大学