Aircraft Meteorological Data Relay Research Articles

Variational Correction of Aircraft Temperature Bias in the NCEP’s GSI Analysis System

Abstract Various studies have noted that aircraft temperature data have a generally warm bias relative to radiosonde data around 200 hPa. In this study, variational aircraft temperature bias correction is incorporated in the Gridpoint Statistical Interpolation analysis system at the National Centers for Environmental Prediction. Several bias models, some of which include information about aircraft ascent/descent rate, are investigated. The results show that the aircraft temperature bias correction cools down the atmosphere analysis around 200 hPa, and improves the analysis and forecast fits to the radiosonde data. Overall, the quadratic aircraft ascent/descent rate bias model performs better than other bias models tested here, followed closely by the aircraft ascent/descent rate bias model. Two other issues, undocumented in previous studies, are also discussed in this paper. One is the bias correction of aircraft report (AIREP) data. Unlike Aircraft Meteorological Data Relay (AMDAR) data, where unique corrections are applied for each aircraft, bias correction is applied indiscriminately (without regard to tail numbers) to all AIREP data. The second issue is the problem of too many aircraft not reporting time in seconds, or too infrequently, to be able to determine accurate vertical displacement rates. In addition to the finite-difference method employed to estimate aircraft ascent/descent rate, a tensioned-splines method is tested to obtain more continuously smooth aircraft ascent/descent rates and mitigate the missing time information.

4D trajectory estimation based on nominal flight profile extraction and airway meteorological forecast revision

To address the problem of high accuracy strategic four-dimensional (4D) trajectory estimation for air traffic control automation systems, we proposed a method for extracting the nominal flight profile and revising airway meteorological forecasts. First, for extracting flight intention from historical Aircraft Meteorological Data Relay (AMDAR) data, a Dynamic Space Warping (DSW) algorithm is applied to measure the distance between two flight altitude profiles, where the nominal flight altitude profile is regarded as that with the DSW distance nearest to others. To eliminate the influence of meteorological factors, ground speed calculation is combined with the CAS/TAS conversion model to extract the nominal flight airspeed profile. By considering the meteorological forecast error from GRIdded Binary (GRIB) data, Cressman interpolation is employed to revise the original forecasts from GRIB data combined with observations from new AMDAR data relayed by another reference aircraft, which can be used for strategic 4D trajectory estimation for object aircraft. We present a case study to demonstrate that the nominal flight profile obtained reflects flight intention and it eliminates the influence of meteorological factors, while the airway meteorological model provides more accurate meteorological forecasts for strategic 4D trajectory estimation.

Introducing an Approach for Extracting Temperature from Aircraft GNSS and Pressure Altitude Reports in ADS-B Messages

AbstractRecently work has been conducted in using routine air traffic management (ATM) data from aircraft to derive meteorological observations (de Haan; de Haan and Stoffelen). The paper at hand introduces and provides an initial analysis for a method of finding layer temperatures from aircraft broadcast messages. The method is analyzed using error analysis and is shown capable of producing mean layer temperatures with below ±1-K error with a layer thickness of 2000 m. Observed aircraft data have been compared to the expected errors from the analysis and have shown to be consistent to within 0.01 K. An initial comparison using four Aircraft Meteorological Data Relay (AMDAR) flights is also provided. The new layer temperature, existing Mode-S enhanced surveillance (EHS)-derived temperature, and an average Mode-S EHS-derived temperature are all compared to the AMDAR temperatures. The averaged Mode-S EHS-derived layer temperature is shown to have the lowest spread (mean standard deviation K), followed by the layer temperature introduced by this paper (mean standard deviation K), and then the unaveraged Mode-S EHS-derived temperature (mean standard deviation K). The layer temperature method has the advantage that no requested data are required from the aircraft, as all of the required parameters are part of the routine broadcast messages, making the method ideal in areas with a limited air traffic management infrastructure where the existing methods would not work.

Aeronautical diagnostics for Clear-Air Turbulence forecast at Meteofrance in the context of DELICAT European project

A study on Clear-Air Turbulence (abbreviated by CAT) forecast in a Numerical Weather Model is presented in this paper. The main objective of this study is to evaluate ARPEGE Meteofrance-NWP model's ability to reproduce CAT, by calculating various CAT indices at the regional scale (over Europe) in this model. The list of indices used here is inspired from that proposed by R. Sharman & Wolff (2006). Calculated indices are then compared with AMDARs (Aircraft Meteorological DAta Relay) turbulence measurements during winter, early in 2010.This work was performed within DELICAT european project (*DEmonstration of LIdar based Clear-Air Turbulence detection), in the Seventh Research Framework program of the European Union [FP7], in Meteofrance national weather agency.

Reconstruction of Aircraft Trajectories from AMDAR Weather Reports

Abstract It is well known that aircraft-based meteorological measurements exhibit systematic errors depending on various flight dynamic parameters. It is also widely assumed that operational Aircraft Meteorological Data Relay (AMDAR) weather reports by commercial aircraft are affected in a similar way. However, so far, it is not possible to study such systematic errors, because datasets that contain both AMDAR reports and flight dynamic variables are not available. To overcome this deficiency, a method was developed to reconstruct the flight trajectories of aircraft using just the aircraft type and operational AMDAR reports. Because AMDAR reports do not contain information on the flight plan, origin, destination, or the motion vector of the airplane, it is not possible to employ a trajectory solver as used by air traffic control. Instead, the method uses groups of trajectory templates that are fitted to AMDAR data taken during approach or departure. This algorithm allows estimation of the heading, true airspeed, aircraft mass, roll state, pitch angle, and angle of attack of the reporting aircraft for each AMDAR report. For verification, the algorithm was applied to navigation data gathered from two Airbus-manufactured aircraft, of which one is in commercial service and one is used for aviation research. From a total of 48 profiles, a very good agreement of reconstructed and measured values was found.

High-resolution wind and temperature observations from aircraft tracked by Mode-S air traffic control radar

[1] Wind, temperature, and humidity observations from radiosonde and aircraft are the main sources of upper air information for meteorology. For mesoscale meteorology, the horizontal coverage of radiosondes is too sparse. Aircraft observations through Aircraft Meteorological Data Relay (AMDAR) sample an atmospheric profile in the vicinity of airports. However, not all aircraft are equipped with AMDAR or have the system activated. Observations inferred from an enhanced tracking and ranging (TAR) air traffic control radar can fill this gap. These radars follows all aircraft in the airspace visible to the radar for air traffic management. The TAR radar at Schiphol airport in Netherlands has a range of 270 km. This Mode-S radar contacts each aircraft every 4 s on which the transponder in the aircraft responds with a message that contains information on flight level, direction, and speed. Combined with the ground track of an aircraft, meteorological information on temperature and wind can be inferred from this information. Because all aircraft are required to respond to the TAR radar, the data volume is extremely large, being around 1.5 million observations per day. Note that there are no extra costs for this data link. The quality of these observations is assessed by comparison to numerical weather prediction (NWP) model information, AMDAR observations, and radiosonde observations. A preprocessing step is applied to enhance the quality of wind and temperature observations, albeit with a reduced time frequency of one observation of horizontal wind vector and temperature per aircraft per minute. Nevertheless, the number of observations per day is still very large. In this paper it is shown that temperature observations from Mode-S, even after corrections, are not very good; an RMS which is twice as large as AMDAR is observed when compared to NWP. In contrast to the temperature observations, the quality found for wind after correction and calibration is good; it is comparable to AMDAR, slightly worse than radiosonde but certainly very valuable for mesoscale NWP.

Comparison of Boundary-Layer Profiles and Layer Detection by AMDAR and WTR/RASS at Frankfurt Airport

At Frankfurt airport (EDDF) vertical soundings of the lower atmosphere from two independent sources are available. One of them is a wind and temperature profiler (wind temperature radar and radio acoustic sounding system, WTR/RASS) located at the western end of the main pair of runways. The second source is aircraft meteorological data relay (AMDAR), i.e. measurements operationally collected by approaching and departing aircraft. Together, both offer a rare opportunity to compare the performance of these widely used systems. We use 1 year of continuous data to calculate systematic temperature and wind vector differences between both measurement systems. The differences show a clear season-dependent structure in conjunction with typical inversion heights. Possible causes for this behaviour are discussed. Second, we compare the ability of both systems to identify inversion and wind-shear layers above the airport. AMDAR-detected layers are typically higher than wind profiler detections. The layer base is usually detected with more agreement than the top. The mutual probability of detection of inversions is found to be mostly between 40 and 60%.

Aircraft type‐specific errors in AMDAR weather reports from commercial aircraft

AbstractAMDAR (Aircraft Meteorological DAta Relay) automated weather reports from commercial aircraft provide an increasing amount of input data for numerical weather prediction models. Previous studies have investigated the quality of AMDAR data. Few of these studies, however, have revealed indications of systematic errors dependent upon the aircraft type. Since different airlines use different algorithms to generate AMDAR reports, it has remained unclear whether a dependency on the aircraft type is caused by physical properties of the aircraft or by different data processing algorithms. In the present study, a special AMDAR dataset was used to investigate the physical type‐dependent errors of AMDAR reports. This dataset consists of AMDAR measurements by Lufthansa aircraft performing over 300 landings overall at Frankfurt Rhein/Main (EDDF/FRA) on 22 days in 2004. All of this data has been processed by the same software, implying that influences from different processing algorithms should not be expected. From the comparison of single descents to hourly averaged vertical profiles, it is shown that temperature measurements by different aircraft types can have systematic differences of up to 1 K. In contrast, random temperature errors of most types are estimated to be less than 0.3 K. It is demonstrated that systematic deviations in AMDAR wind measurements can be regarded as an error vector, which is fixed to the aircraft reference system. The largest systematic deviations in wind measurements from different aircraft types (more than 0.5 m s−1) were found to exist in the longitudinal direction (i.e. parallel to the flight direction). Copyright © 2008 Royal Meteorological Society

Use and Impact of Automated Aircraft Data in a Global 4DVAR Data Assimilation System

Abstract The use of automated aircraft data [Aircraft Meteorological Data Relay (AMDAR) and Aircraft Communication Addressing and Reporting System (ACARS)] has recently been extended in ECMWF's operational 4DVAR data assimilation system. Herein, a modified data selection procedure is reported on that allows the use of more aircraft profiling data during the aircraft's ascending and descending phase, and more of the most frequent reports at cruise level. It is shown that the accuracy of analyzed jet streams is improved through these changes, as verified against independent (non–real time) aircraft data that had not been used in the experiments. The modifications are shown to have a clear positive impact on the short- and medium-range forecast performance. The revised aircraft usage was implemented operationally in January 2002. The impact in 4DVAR of profiles from American and European automated aircraft in ascending and descending phase has been tested in a data denial impact study, for January and July 2001. This particular impact study was run partly on the request of the WMO/Commission for Basic Systems (CBS) Expert Team on data requirements and the redesign of the global observing system. Their interest is in testing whether a modern data assimilation system (such as 4DVAR) obtains substantial benefit from the aircraft profiles, which sample very irregularly in space and time, given that America and Europe are relatively well covered by radiosondes and wind profilers. The results show a substantial positive impact of the profiling aircraft data on analysis and forecast accuracy. The short-range forecast performance is improved over North America, the North Atlantic, and Europe. In the medium range a clear positive impact is found in the North Atlantic, the European, and Arctic areas in the winter period, and beyond day 6 in the summer period. These results are statistically significant and support the ongoing WMO initiative for further expansion of the AMDAR/ACARS coverage. The results also illustrate the effectiveness of 4DVAR with respect to observations that are irregularly distributed in space and time.