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Comparison of aircraft-derived observations with in situ research aircraft measurements

Mirza, A. K., Ballard, S. P., Dance, S. L., Maisey, P., Rooney, G. G. and Stone, E. K. (2016) Comparison of aircraft-derived observations with in situ research aircraft measurements. Quarterly Journal of the Royal Meteorological Society, 142 (701). pp. 2949-2967. ISSN 1477-870X

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To link to this item DOI: 10.1002/qj.2864

Abstract/Summary

Mode Selective Enhanced Surveillance (Mode-S EHS) reports are aircraft-based observations that have value in numerical weather prediction (NWP). These reports contain the aircraft's state vector in terms of its speed, direction, altitude and Mach number. Using the state vector, meteorological observations of temperature and horizontal wind can be derived. However, Mode-S EHS processing reduces the precision of the state vector from 16-bit to 10-bit binary representation. We use full precision data from research grade instruments, on-board the United Kingdom's Facility for Atmospheric Airborne Measurements, to emulate Mode-S EHS reports and to compare with derived observations. We aim to understand the observation errors due to the reduced precision of Mode-S EHS reports. We derive error models to estimate these observation errors. The temperature error increases from 1.25 K to 2.5 K between an altitude of 10 km and the surface due to its dependency on Mach number and also Mode-S EHS precision. For the cases studied, the zonal wind error is around 0.50 ms− 1 and the meridional wind error is 0.25 ms− 1. The wind is also subject to systematic errors that are directionally dependent. We conclude that Mode-S EHS derived horizontal winds are suitable for data assimilation in high-resolution NWP. Temperature reports may be usable when aggregated from multiple aircraft. While these reduced precision, high frequency data provide useful, albeit noisy, observations; direct reports of the higher precision data would be preferable.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:65960
Publisher:Royal Meteorological Society

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