J.J. Biebuma,  J.N. Dike and  John A. Oyeniyi
Department of Electrical/Electronic Engineering, University of Port Harcourt, Nigeria



Air Traffic Control (ATC) radar propagation can encounter significant refractive effects due to presence of atmospheric ducts and superrefractive layers. Non-standard atmospheric conditions can cause ATC aircraft detection problems like target “ghosts” and radar holes”. Countries like Nigeria who has been concerned with elimination of frequent atmospheric-induced air accidents will be prompted to develop radar coverage model for the prediction of anomalous propagation conditions that may be prevalent in airport environments. Using climatology and archived global meteorological datasets of the Lagos Nigeria Airport, radar coverage prediction is presented as an enhanced air traffic control decision aid tool for the new ATC STAR-2000 radar and weather systems which are being currently implemented in Nigeria. Coupled with electromagnetic and terrain data, global meteorological information of the site were fed into the Advanced Refractive Effects Prediction System (AREPS) computer model developed by the Space and Naval Warfare Systems Center (SPAWAR). Prediction of coverage probability mapping of a target in anomalous propagation conditions gave an enhanced height and range display of radar coverage observations for the new ATC radar STAR-2000 system in Lagos International Airport.

Keywords: - Air Traffic Control, Anomalous Propagation, Radar Coverage, Ghosting,
                                 Radar Holes, Radar Star-2000

From numerous researches on atmospheric refractivity, it is a known fact that the performance of Air Traffic Control (ATC) system significantly depends on accurate input of weather and the properties of the atmosphere through which its radar signal propagates. ATC radar space waves may undergo varying intensity of refraction or bending (by Snell’s law); due to spatial and/or temporal variations of temperature, pressure and water vapour content which impact on the waves speed in the atmosphere. In normal and super-refracted atmospheres, the trajectory of the radar space waves is not straight-lined, as it is in vacuum and in homogeneous medium. [Ford, 1996] observed that when the structure of the atmosphere causes abnormal bending of the energy waves, due to inversions of temperature and humidity with height, as evidenced by super-refraction and atmospheric ducts,  anomalous propagation (AP) is said to take place. Intense bending or ducting of ATC radar radiation by the atmosphere can make it appear to the radar set that a target is at a higher altitude than it really is [Sturrock, 1998].
According to [Patterson, 1982], anomalous propagation of electromagnetic (EM) waves in the atmosphere has been as evidenced by over-the-horizon transmissions for many years resulting in the following air traffic control radar coverage problems:

  • “Ghosting” - which is a condition of under-estimated aircraft position. This is a situation

       where a distant aircraft is perceived as near, aided by the over-the-horizon propagation.

  •  “Radar holes” depicting areas showing no aircraft detection, due to erroneous aircraft

  height evaluation.
Target ghost and radar holes constitute complexity to air traffic controller observations. This study employed the hybrid mode of Advanced Propagation Model (APM) to run the [AREPS, 2000] program for a predictive evaluation of radar coverage of Lagos airport STAR-2000 radar, under simulated anomalous propagation conditions.

Statement of the problem
Aderinto et al (2009) of Nigeria Meteorological Organization observed that between 2004 and 2005, Nigeria witnessed frequent air accidents which led to loss of hundreds of lives. This prompted the Government to urgently carry out the implementation of a new and Total Radar Coverage of Nigeria (TRACON). The project consists of installation of new air traffic control radar STAR-2000 and the supporting Automatic Weather Observation System (AWOS). STAR-2000 has both Primary and Secondary surveillance radar systems incorporated. The goal was to fully equip the Airports in Nigeria so as to prevent future occurrence of waste of human lives and properties through air accidents.
The realization of this goal depends on how accurate the new air traffic radar system can detect targets particularly under inversed atmospheric conditions. These conditions lead to what is identified as anomalous propagation whereby aircraft “ghosting” and “radar holes” can lead to erroneous height and range detection and separation of aircrafts.
The need to identify super-refracted and ducted atmospheric conditions where and when they occur has become of concern to air traffic control. It is for this purpose that the study employs relevant global radiosondes datasets of an airport to build the refractivity profile that is fed into AREPS propagation predictor model to evaluate ATC radar coverage.

Significance of the study
It is significant for air traffic controllers to have reliable air traffic situational awareness that will eliminate possibility of errors in the monitoring and control of aircrafts in air and on ground.. This can be achieved by the availability of an enhanced radar propagation display driven by a radar propagation predictor program that has good intelligence of atmospheric refractivity and terrain observations.
Research question:
Under superrefraction and atmospheric ducts, this study seeks to answer the following research questions for the case of Lagos airport:

  1. What is the surface to surface radar propagation behaviour, judging from the

locations of radar transmitters and targets?

  1. For surface to air and air to air propagation behaviour, what are the observations of possible detectable target ranges?

Estimation of atmospheric refractivity
In normal atmosphere, the speed ‘c’ of  EM wave propagation is slightly less than the speed ‘c0’ in vacuum or homogeneous medium, where  c0 = 3 x 108 meters/sec. Precisely, the speed ‘c’ of EM wave in normal atmosphere is:
        c  =  1/n of c0                                                                                                                    (1)
where “n” (close to unity) is the refractive Index of the atmospheric medium.
Specified by [ITU-R, 1999c], the more usable parameter known as the refractivity (N), is defined as:
                N = 106 * (n-1.0)                                                                                                          (2)
The ‘N’ relationship with atmospheric parameters may be derived from the expression:

Where P = atmospheric pressure expressed in hPa, T = Temperature in degrees K,
e = atmospheric water vapour pressure in hPa, and N = Refractivity (dimensionless)We note from this relationship that higher temperature means less refraction, and more moisture means more refraction.
According to [CCIR, 1990], Equation (3) can be re-presented in the form shown in Equation (4),     where the components are given as functions of the height ‘h’.
If the curvature of the earth is taken into account, a modified atmospheric refractivity (or refractive modulus), ‘M’ has also been defined as:
                                                                                                                   (5)                   Where h is the height above earth surface and re is earth’s radius. Normal tropospheric condition is characterized by temperature decrease with height at the rate varying from 6 and 7 degrees Celsius per kilometer. However, where and when temperature inversion occurs with height, the gradient of refractivity N, or the modified refractivity M is negative as in Figure (1). [COAMPS, 2005]

 Figure 1: Schematic of a modified refractivity (M) profile labeled with key parameters that affect EM propagation.[COAMPS 2005]

Tjelta (1996) stated that radar wave propagation under these conditions is described as “anomalous”, which has earliest manifestations like over-the-horizon transmissions. To air traffic controller, anomalous propagation can be an advantage as well as it can cause erroneous target detection.
Modeling EM propagation for refractivity and terrain effects
Since as far back as the year 1940 when the Parabolic Wave Equation (PE) was first derived by
(Leontovich et al, 1940), this parabolic approximation of the wave equation has grown widely with the advent of digital computers and the researches aimed at finding numerical solutions rather than closed-form expressions of the previous ray and mode theories.. According to (Levy, 1999), several researches went into the improving the PE, leading to the development of two most efficient solutions:

  • The split-Step/Fourier technique first introduced by [Hardin et al, 1973] as solution for underwater acoustics problems.
  • The Finite-difference code for geophysics application by [Claerbout, 1976]

Levy, (1999) stated that “the demand for faster algorithms grew for computing radar coverage diagrams or path loss curves for complicated environments led to the development of Hybrid models which combine ray tracing and parabolic Equation techniques for fast solutions of very large radiowave problems” Hybrid model combining Radio Physical Optics (RPO) with PE was pioneered by [Hitney et al, 1992].

Advanced propagation model
In this study, the Advanced Propagation Model (APM) is the internal model used by [AREPS, 2000] to enhance speed, effectively merging the TPEM of [Barrios, 1994] with a Radio Physical Optics (RPO) model [Patterson et al, 1997]. It was run in the full hybrid mode for this research.
APM’s applicable frequency range is from 2 MHz to 95 GHz. and can handle unlimited number of terrain points at any resolution up to 50km height for both airborne and surface borne emitters.
From [Barrios, 1994], some terminologies and mathematical definitions used by APM and related to this thesis are as follows:

  • Propagation Factor ‘F’.

defined as the ratio of the magnitude of the field strength ‘E’ at a point, including antenna pattern effects but normalized to unity gain antennas to the magnitude of the field strength ‘E0’ that would occur at that point under free space conditions if loss-free isotropic antennas were used for both the transmitter and receiver.

  • Propagation Loss in DB ‘L’ – is determined by taking the difference between the free space loss and ‘F’ (in DB), as in equation (2.13):

The first term in equation (2.13) is the free space loss, where ‘r’ is the range and ’ ’ is the wavelength.

  • APM’s PE Model Formulation:- Using the wide-angle propagator, the basic PE formulation derived by [Barrios, 1994], and used in APM is:

Where x and z represent the Cartesian range and height coordinates respectively, k0 is the free space wave number , is the incremental range step over which the field solution is propagated. is the height-varying refractive profile, and and represent the forward and inverse Fourier Transforms, respectively. From [Barrios, 1994], “the transform variable is equal to ,  being the propagation angle referenced from the horizontal. The quantity is a scalar component of the electric field which in the process of normalizing and conversion from spherical to Cartesian coordinates the field retains the explicit range dependence where we can compute the propagation factor as”:

Method and procedure
The study is a quantitative research based on descriptive survey method with input data collected from archived and available sources of environmental properties, radar characteristics, location geography, terrain and target description. Figure 1 shows the Block diagram of the study design to analyze radar propagation behaviour under anomalous propagation conditions. AREPS program was installed and used as specified by [AREPS, 2009].
Input data fed into the AREPS study model are:

  1. Built-in AREPS upper air climatology database of Lagos Oshodi which is produced by GTE Sylvania, under contract to the USA Department of Defence and approved by World Meteorological Organization (WMO).
  2. STAR-2000 radar data from [Thales, 2009] to generate the electromagnetic (EM) input.
  3. Terrain data from [DTED, 2009].
  4.  Target and radar location data as desired variables.

Meteorological data
Two sources of environmental data were accessed for meteorological radiosondes data:

  •  The built-in AREPS upper-air Climatology of Lagos, Nigeria Station (on global Marsden Square number 036) [AREPS, 2000].  This station is one of the 921 observing radiosondes stations where the contracted GTE Sylvania Company compiled numerous statistics of tropospheric ducts and superrefractive layers.
  • For purposes of comparison, a World Meteorological Organization [WMO, 2007] radiosondes sounding data of Abidjan (WMO number 65578) DIAP sounding station accessible online at the University of Wyoming, College of Engineering, Department of Atmospheric Science radiosonde database site was used as a closest data source [Wyoming, 2009]. Figure 2 shows the weather page of the Department of Atmospheric Science, University of Wyoming which has WMO radiosondes soundings data that was accessed. We note that Nigeria is a member of the WMO, but no operational radiosondes station is at present available in any locations within the country. The Abidjan meteorological dataset is considered close to what may be expected for Nigeria.

Figure 2: Weather page of University of Wyoming radiosondes sounding



STUDY PROJECT - (Lagos Nigeria Airport STAR 2000 Radar System)


  SYSTEM                  ENVIRONMENT             GEOGRAPHY

      PLATFORM         RADAR         TARGET      

                              ATMOSPHERE            WIND          TERRAIN

                                                                                   LAT/LONG   BEARING
                   Standard WMO Data File         DTED DATA FILE



Figure 3: Block Diagram of the Study Project Elements

Study model program set up
The AREPS program set up showing input data files selection is as shown on the figure 3.

Figure 4: AREPS Program set up showing input data files from Lagos Oshodi Observations
Figure 4 is the upper air environment creator file, while Table 1 shows the calculated atmospheric refractivity profile used by AREPS for the evaluation of the radar propagation behaviour.
Table 1: AREPS upper air Lagos Nigeria airport January radiosondes data

Height  (m)    





















Figure 5: Summary of AREPS Environment creator for Lagos airport Upperair climatology

Results and analysis
AREPS program was set up to use full hybrid mode of the advanced Propagation Mode (APM) to evaluate and display the path and behaviour maps of the ATC radar STAR-2000 coverage decision on:

  • Probability of detection in height versus range display.
  • Propagation loss in height versus range display
  • Signal-to-noise in height versus range display.

Figure 5 shows Probability of detection display of a Medium Jet at height (2,556m), range (87.6Km), probability of target detection (92.36%, Propagation loss(144.7dB), and azimuth (0.00deg).

Figure 6: Probability of detection display using areps Upperair Lagos airport
Figure 7 is a four-panel display of probability of a medium jet at:

  • Display Height of 102.4 m, and
  • Display range of 139.9 km.

Figure 7: AREPS four-panel display of probability of a medium jet at specified height and range

Analysis and discussion
We can deduce the following from the display maps:

  1. From the height versus range display of Figure 6, air traffic control can evaluate numerically the probability of detection, the propagation loss and signal-to-noise ratio of the radar system. The air traffic controller can use these radar propagation data in combination with the secondary surveillance beacon communication to adequately locate aircrafts in all atmospheric conditions.
  2. From the four-panel display map of Figure 7, the probability of detection as functions of height and range are seen in details that will enhance air traffic controller awareness of air traffic situation.

Conclusion and recommendations
Contribution: For air traffic controllers, the complexity of clear surveillance and detection of aircrafts in air and on ground is heightened by the challenges of weather and non-standard atmospheric conditions. Although the standard air traffic control radar systems provide in-built weather monitoring display functions, some conditions of anomalous radar propagation will give rise to erroneous height and range detection of aircrafts. This research work has provided a means of collecting global climatology data of an airport of interest to build the atmospheric refractivity profile that is fed into a Internet-ready PC based predictor program that air traffic controller can deploy side-by-side to augment standard radar display observations.
Conclusion: In predicting the coverage performance of air traffic control radar system, this thesis focused on the estimation of terrain and refractive effects on the behaviour and variation of the radar space waves propagating in the troposphere around an airport area. Evaluation of the air traffic control radar probability of aircraft detection, together with other observations such as radar propagation loss and signal-to-noise ratio was derived from environmental, terrain and systems inputs. The Study employed an internet-ready predictor program (AREPS) running an internal Advanced Propagation Model (APM) in full hybrid mode for refractive index and terrain factors. A fast efficient display of radar energy propagation at heights of the order of few kilometers and long ranges is obtained.
Recommendations: The study program is built with an internet-ready system that can be networked to provide enhanced air traffic assessment data to an existing air traffic control radar system, such as Nigeria’s new STAR-2000 radar and the automatic weather observing system such as the new Vaisala MIDAS IV Automatic Weather Observing System (AWOS) being implemented at the four major Airports in Nigeria.
In the course of the study, it was also observed that there are no operational World Meteorological Organization (WMO) radiosondes soundings in Nigeria while other West African locations such as Ivory Coast have archived and dynamic WMO datasets that were used in this research work to correlate the built-in AREPS upper-air climatology database. It is recommended that full benefit will be derived from these study results, if Government and Nigeria Meteorological Organization (NIMET) urgently address this issue.

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