Comparison of aircraft‐derived observations with <i>in situ</i> research aircraft measurements

Mirza, Andrew K.; Ballard, Su; Dance, Sarah L.; Maisey, Paul; Rooney, G. G.; Stone, E.C.

doi:10.1002/qj.2864

Cited by 19 publications

(41 citation statements)

References 38 publications

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“…Options do exist to potentially improve this situation, including Mesosphere-Stratosphere-Troposphere (MST) radar data [43] or high resolution data from routine radiosonde releases, but are not currently exploited. The availability of high-resolution, high-coverage in-flight measurements of atmospheric motion from "Mode-Selective' (Mode-S) civil aircraft broadcasts is also on the horizon [67]. There is a more notable gap in the verification of near-surface wind impacts of lee waves.…”

Section: Discussionmentioning

confidence: 99%

Mountain Waves in High Resolution Forecast Models: Automated Diagnostics of Wave Severity and Impact on Surface Winds

Sheridan

Brown

2017

Atmosphere

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An automated method producing a diagnostic of the severity of lee waves and their impacts on surface winds as represented in output from a high resolution linear numerical model (3D velocities over mountains (3DVOM)) covering several areas of the U.K. is discussed. Lee waves involving turbulent rotor activity or downslope windstorms represent a hazard to aviation and ground transport, and summary information of this kind is highly valuable as an efficient 'heads-up' for forecasters, for automated products or to feed into impact models. Automated diagnosis of lee wave surface effects presents a particular challenge due to the complexity of turbulent zones in the lee of irregular terrain. The method proposed quantifies modelled wind perturbations relative to those that would occur in the absence of lee waves for a given background wind, and diagnoses using it are found to be quite consistent between cases and for different ranges of U.K. hills. A recent upgrade of the operational U.K. limited area model, the U.K. Variable Resolution Model (UKV) used for general forecasting at the Met Office means that it now resolves lee waves, and its performance is here demonstrated using comparisons with aircraft-and surface-based observations and the linear model. In the future, automated diagnostics may be adapted to use its output to routinely produce contiguous mesoscale maps of lee wave activity and surface impacts over the whole U.K.

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Section: Discussionmentioning

confidence: 99%

Mountain Waves in High Resolution Forecast Models: Automated Diagnostics of Wave Severity and Impact on Surface Winds

Sheridan

Brown

2017

Atmosphere

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“…The Mach temperature T MACH is derived from Mode‐S EHS reports of Mach number M and airspeed V A (de Haan, ; Mirza et al, ), such that:

T_{MACH} = \frac{T_{0}}{A_{0}^{2}} {(\frac{V_{normalA}}{M})}^{2}

where the speed of sound A 0 = 340.294 m/s and the assumed surface temperature T 0 = 288.15 K are reference values defined at mean sea level pressure under international standard atmosphere conditions (ICAO, ).…”

Section: Aggregation Of Mode‐s Ehs Reportsmentioning

confidence: 99%

“…To use as many of the Mode‐S EHS reported data as possible they are aggregated to form a mean T MACH observation. This “aggregated observation” (Mirza et al, ; Mirza, , Ch. 3) is the arithmetic mean of all the Mach temperatures, derived using Equation 1, for all Mode‐S EHS reports received within a defined time period and in a specified horizontal layer.…”

Section: Aggregation Of Mode‐s Ehs Reportsmentioning

confidence: 99%

“…Figure b(i) shows the effect of each low‐pass filter on the computed standard deviation of the T MACH . For comparison also shown are the expected standard deviations for the T MACH using the Mach temperature error equation formulated by Mirza et al (, eq. 16), assuming the following for the Mach number and airspeed: full precision error, precision due to quantization error (Mirza et al, , figs and 11) and precision due to double the quantization error.…”

Section: Temporal Smoothing Using Low‐pass Filtersmentioning

confidence: 99%

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Towards operational use of aircraft‐derived observations: a case study at London Heathrow airport

Mirza

Ballard

Dance

et al. 2019

Meteorological Applications

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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.

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“…For the purposes of this study, only two instruments have been used: a Rosemount non‐deiced temperature sensor to provide an accurate measurement of the temperature, and a nose‐mounted five‐port pressure sensor for the wind observations. These are both considered to provide good measurements of the relevant atmospheric quantities (Mirza et al ). These data are time stamped using the on‐board GPS clock time.…”

Section: Data Sourcesmentioning

confidence: 99%

A comparison of Mode‐S Enhanced Surveillance observations with other in situ aircraft observations

Stone

2018

Quart J Royal Meteoro Soc

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Observations from aircraft are an important element of the global observing network. A promising new observation source, deriving wind and temperature measurements from air traffic management data, has previously been reported on by a small number of groups. This article further investigates the error characteristics by comparing a year's worth of in situ observed winds and temperatures from a commercial British Airways Boeing 747 (B747) with the derived Mode‐Selective (Mode‐S) Enhanced Surveillance (EHS) observations from the Met Office network of Mode‐S receivers. It is shown that, whilst the winds and high‐altitude temperatures are of good quality, they show error profiles with altitude that are different for ascent and descent. The data show that the situation of the aircraft is critical to understand the biases; this is dependent on the aircraft, operator and airport, making corrections infeasible. Further understanding is gained by an intercomparison flight with the UK Facility for Airborne Atmospheric Measurement (FAAM) BAe 146 aircraft. This showed a good comparison with Mode‐S EHS winds, with a RMS difference between the FAAM data and B747 Mode‐S EHS data of 1.5 and 0.9 m/s for the u and v components respectively. Comparing the FAAM data and B747 flight data recorder (FDR) provided RMS differences of 0.6 and 1.4 m/s respectively. This suggests that the data quality from Mode‐S EHS is similar to that which can be achieved from a commercial aircraft. The temperature RMS differences were found to be 1.6 and 0.5 K when the FAAM data were compared to the Mode‐S EHS data and FDR data from the B747 respectively, suggesting that the temperature Mode‐S EHS data are of an inferior quality.

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

Cited by 19 publications

References 38 publications

Mountain Waves in High Resolution Forecast Models: Automated Diagnostics of Wave Severity and Impact on Surface Winds

Mountain Waves in High Resolution Forecast Models: Automated Diagnostics of Wave Severity and Impact on Surface Winds

Towards operational use of aircraft‐derived observations: a case study at London Heathrow airport

A comparison of Mode‐S Enhanced Surveillance observations with other in situ aircraft observations

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