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 UK'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 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 m s−1 and the meridional wind error is 0.25 m s−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.

Mode‐Selective Enhanced Surveillance (Mode‐S EHS) aircraft reports can be collected at a low cost and are readily available around busy airports. The new work presented here demonstrates that observations derived from Mode‐S EHS reports can be used to study the evolution of temperature inversions since the data have a high spatial and temporal frequency. This is illustrated by a case study centred around London Heathrow airport for the period January 4–5, 2015. Using Mode‐S EHS reports from multiple aircraft and after applying quality control criteria, vertical temperature profiles are constructed by aggregating these reports at discrete intervals between the surface and 3,000 m. To improve these derived temperatures, four smoothing methods using low‐pass filters are evaluated. The effect of smoothing reduces the variance in the aircraft derived temperature by approximately half. After smoothing, the temperature variance between the altitudes 3,000 and 1,000 m is 1–2 K; below 1,000 m, it is 2–4 K. Although the differences between the four smoothing methods are small, exponential smoothing is favoured because it uses all available Mode‐S EHS reports. The resulting vertical profiles may be useful in operational meteorology for identifying elevated temperature inversions above 1,000 m. However, below 1,000 m they are less useful because of the reduced precision of the reported Mach number. A better source of in situ temperature observations would be for aircraft to use the meteorological reporting function of their automatic dependent surveillance system.

Aircraft can report in situ observations of the ambient temperature by using aircraft meteorological data relay (AMDAR) or these can be derived using modeselect enhanced tracking data (Mode-S EHS). These observations may be assimilated into numerical weather prediction models to improve the initial conditions for forecasts. The assimilation process weights the observation according to the expected uncertainty in its measurement and representation. The goal of this paper is to compare observation uncertainties diagnosed from data assimilation statistics with independent estimates. To quantify these independent estimates, we use metrological comparisons, made with in-situ research-grade instruments, as well as previous studies using collocation methods between aircraft (mostly AMDAR reports) and other observing systems such as radiosondes. In this study we diagnose a new estimate of the vertical structure of the uncertainty variances using observation-minus-background and observation-minus-analysis statistics from a Met Office limited area three-dimensional variational data assimilation system (3 km horizontal grid-length, 3-hourly cycle). This approach for uncertainty estimation is simple to compute but has several limitations. Nevertheless, the resulting diagnosed variances have a vertical structure that is like that provided by the independent estimates of uncertainty. This provides confidence in the uncertainty estimation method, and in the diagnosed uncertainty estimates themselves. In the future our methodology, along with other results, could provide ways to estimate the uncertainty for the assimilation of aircraft-based temperature observations.

A study has been undertaken to determine whether there are useable incremental benefits from providing upper air wind data at high horizontal resolution to the process of airline flight planning. Currently winds are provided on a horizontal resolution of approximately 140 km. The study looked at resolutions varying between 160 and 40 km. A theoretical calculation was undertaken using published variance power spectra, which quantify the wind variability as a function of horizontal scale. This calculation also used a published formula for the time saving in flying across an area of constant vorticity. The results of the theoretical calculation were expressed in terms of the time saving on a transatlantic flight lasting typically 8 h stemming from a resolution change from 160 to 80 km. In this case the answer was well under one second. It is thought that such a small incremental benefit could not be used to justify the practical steps needed to exploit high resolution data. The more practical part of the study involved running an optimum routes diagnosis package with variable resolution input data. Input data at resolutions of 160 and 40 km were considered. Although this approach was only applied to a restricted number of cases and routes, it confirmed the theoretical result.  Crown