Using on Air UAT/ADS-B Signal to Simulate 3D Aviation Weather Information Guan-Jhih Liou Shau-Shiun Jan Department of Aeronautics and Astronautics National Cheng Kung University Tainan 70101, Taiwan tryitagain777@gmail.com Department of Aeronautics and Astronautics National Cheng Kung University Tainan 70101, Taiwan ssjan@mail.ncku.edu.tw avionics for almost all commercial aircrafts. Both ADS-B and UAT are designed to support surveillance needs. However, the messages carried by ADS-B and UAT might have helpful information by which to construct vertical weather profiles that could be added to the current 2D aviation weather model to form a new 3D aviation weather model. With this additional vertical weather profile, the warning capabilities of the current aviation weather alert system could be improved. Abstract The existing unusual weather alert system utilizes information provided by ground meteorological observation stations and simulates unusual weather conditions using a specific computational fluid dynamics model. However, the ground meteorological observation stations convey only 2D weather information near the ground, and it is very difficult to accurately simulate 3D low-level weather conditions using only 2D ground weather information. The purpose of the paper is thus to analyzing the possibility of simulating a 3D fluid flow field describing weather conditions using both meteorological observation data from ADS-B/UAT onboard aircrafts and ground meteorological observation stations. All the meteorological information collected for the paper is calculated to conduct 3D fluid flow field by the CALMET/CALPUFF Modeling system. Keywords ADS-B; UAT; 3D weather simulation;calmet I. INTRODUCTION Weather and aviation have been indivisible from the very beginning of human aviation history. Despite decades of advances in aviation technology, many aviation accidents and incidents are proven to be closely related to unusual weather phenomena [1]. The unusual weather alert system in current use characterizes on-route weather conditions using nearby meteorological observation stations that use only 2D weather information close to the ground and a specific computational fluid dynamic model to simulate the unusual weather conditions [2]. This insufficient weather information may cause accidents because it is very difficult to accurately simulate 3D low-level weather conditions with only 2D ground weather information. For instance, aircrafts may encounter a low-level wind shear during approach or takeoff without any alert or warning. Consequently, this paper is aimed at finding possible parameters from Automatic Dependent Surveillance Broadcast (ADS-B) and/or Universal Access Transceiver (UAT) information onboard aircrafts which could be regarded as possible vertical meteorological observations, and the possibility of simulating a 3D fluid flow field is analyzed using both meteorological observation from the data onboard aircrafts as well as ground meteorological observation stations. In other words, aircrafts are regarded as another type of meteorological observation stations for the purposes of this paper. An attractive component of this work is the use of the ADS-B and/or UAT, which comprise the existing standard 671 In this paper, actual on air ADS-B/UAT data is collected, and this involves the development of data collection units that include a software-defined radio receiver and data processing software. The Universal Software Radio Peripheral (USRP) receiver, a tunable band-pass filer, and a digital TV antenna are used to form the data collection unit in this work. The ADS-B and UAT Intermediate Frequency (IF) signals are demodulated and decoded according to the official Radio Technical Commission for Aeronautics (RTCA) document, DO-260A, DO-267A and DO-282B. After decoding, the collected data is investigated for the necessary meteorological observations; for instance, the wind speed and wind direction measured by aircrafts could be used to determine the air flow direction around the area. To examine these possibilities, a software application called the CALMET/CALPUFF Modeling System is used to test the weather parameters based on information from the aircrafts and the ground stations. Then, a 3D weather simulation result is performed. This paper presents the implementation of the ADS-B/UAT data collections units, the development of the associated signal processing and data decoding scheme, the fluid flow field simulation and the 3D weather simulation results. II. WEATHER INFORMATION For the purpose of conducting a 3D fluid flow field simulation, it is necessary to collect rich weather information from both ground stations for low-level information and from aircrafts for high-level information. In this paper, the weather information is extracted from the onboard ADS-B signal and UAT signal data, Aircraft Meteorological Data Relay (AMDAR) from the Civil Aeronautics Administration (CAA) in Taiwan, as well as from the ground meteorological station observation data from the Data Bank for Atmospheric Research (DBAR) in Taiwan.
(WMO), and it is used to collect meteorological data worldwide by the onboard meteorological facilities on commercial aircrafts. The flight tracks in the AMDAR data from aircrafts worldwide is shown in Fig. 3. A. Automatic Dependent Surveillance Broadcast (ADS-B) / Universal Access Transceiver (UAT) All ADS-B and UAT observational data can be divided into two parts: a ground station segment and an aircraft segment [3]. From the AMDAR data, the following meteorological parameters can be derived: 1) Ground station segment: As shown in Fig. 1, the control station collects observation data from ground meteorological observation stations in neighboring areas and then broadcasts this information in a UAT data link through the radio station. The weather information may be either textually or graphically -depicted. This information allows the pilot to passively collect and display weather and other operational data. UAT signal Ground meteorological observation station Air temperature (static air temperature) Pressure altitude (barometric pressure) Wind speed and wind direction Turbulence Radio station Control station Fig. 1. Structure of meteorological observation data source 2) Aircraft segment:as shown in Fig. 2, ADS-B and UAT provide surveillance service in areas without radar, and these data links broadcast not only aircraft information derived from the navigation system but also meteorological information measured by the onboard sensors on aircrafts. In order to regard aircrafts as another type of meteorological observation station, position information, wind speed and wind direction information for aircrafts are used to build the flow field simulation model under consideration in this paper. Fig. 3. The flight tracks in the AMDAR data from aircrafts worldwide C. Observation of ground meteorological station data from the Data Bank for Atmospheric Research (DBAR) Because there is no UAT signal near Taiwan, ground meteorological station observation data must be derived from another source. The DBAR provides various meteorological data, such as sea surface conditions and satellite images near Taiwan, for all registered users. The DBAR observation data from all Central Weather Bureau ground meteorological stations in Taiwan are used in this paper to establish the 3D flow filed simulation. All CWB ground meteorological stations in Taiwan are shown in Fig. 4, where red triangles indicate the position of the ground stations. In this research, wind speed and wind direction information from ground meteorological observation stations are used to conduct the 3D fluid flow model simulation. ADS-B/UAT signal * : onboard sensor Fig. 2. Weather information onbroad aircrafts from ADS-B/UAT B. Aircraft Meteorological Data Relay (AMDAR) Aircraft Meteorological Data Relay (AMDAR) is a program initiated by the World Meteorological Organization 672
In the CALMET/CALPUFF Modeling System, a fluid flow field is simulated mainly using the CALMET mode. The following introduces not only the input parameters, which include weather information as mentioned in the previous section and some of Taiwan s topographical information, but also some major CALMET algorithms. 26.5 26 25.5 Latitude (degree) 25 A. The CALMET mode input parameter 24.5 1) The terrain elevation data: In order to establish the 3D fluid flow field simulation, the terrain elevation data is needed to compute the affect of regional terrain on the fluid flow field. 24 23.5 23 22.5 22 21.5 118 118.5 119 119.5 120 120.5 121 Longitude (degree) 121.5 122 122.5 3) The meteorological observation data from ground stations: The observation data from ground stations can provide initial horizontal fluid flow field information. Fig. 4. CWB Ground meteorological stations in Taiwan III. 2) The land use categories data: Different land use categories will have different values of some terrain parameters, such as surface roughness, vegetation leaf area index and the Bowen ratio, which are important when establishing the 3D fluid flow field simulation. FLOW FIELD SIMULATION ESTABLISHMENT 4) The meteorological observation data from aircrafts: The observation data from aircrafts can provide initial vertical fluid flow field information which is necessary when conducting the 3D simulation. With the collected weather information, the next step is naturally to choose a simulation model to conduct the 3D fluid flow field simulation. Taiwan, which is the location of the experimental setup, is a small island. Nevertheless, as shown in Fig. 5, there are many mountains, which cause Taiwan s complex terrain. Because of this complex terrain, it is not adequate or appropriate to use an ordinary fluid flow field simulation model to conduct a 3D fluid flow field simulation in Taiwan. Consequently, the CALMET/CALPUFF Modeling System, which has been adopted by the U.S. Environmental Protection Agency (USEPA) as the preferred model for situations involving complex meteorological conditions and complex terrain [4,5], is selected for the purposes of this research to construct the 3D fluid flow field. B. Some major flow field algorithms used in CALMET mode 1) Inverse-distance method: This interpolation scheme allows meteorological observation data from the ground stations or aircrafts to be heavily weighted in the vicinity of the ground stations or aircrafts. The inverse-distance method can be expressed as follows [6]: m ( u0, v0 ) = 25.5 3000 25 2000 Latitude (degree) 24.5 0 23.5-1000 23-2000 Rk2 1 R2 k, (1) 2) Power law equation: The power law equation is used for the vertical extrapolation of the observation data, which can be expressed as follows [6] : -3000 22.5 k ( uobs, vobs )k where u0 indicates the u component of wind speed at a particular grid point; v0 is the v component of wind speed at a particular grid point; ( uobs, vobs ) k represents the u and v components of wind speed observation data at station k, and Rk is the distance from station k to a particular grid point. 1000 24-4000 22-5000 21.5 119 119.5 120 120.5 121 121.5 Longitude (degree) 122 u z = um ( z zm ), P 122.5 (2) where z is the height (m) of the midpoint of the CALMET grid cell; um indicates the measured u component of the wind speed; zm is the measured height of the wind observation; uz Fig. 5. The terrain of Taiwan 673
means the extrapolated u component of the wind speed at height z, and P is the power law exponent. A value of P of 0.143 is used over land, and 0.286 is used over water [6]. A cell-average terrain elevation of zero is used as a flag for water cells. A similar equation applies to the v component of the wind speed. 3) van Ulden and Holtslag wind extrapolation method: This extrapolation method, which describes how the wind angle rotates with changes in height, is another vertical extrapolation method used in CALMET. The method can be expressed as follows [6]: D ( z ) D ( h ) = d1 1 exp ( d 2 z h ), (3) where D ( z ) indicates the turning angle at layer height center z; D ( h ) is the turning angle at reference height h, and d1 = 1.58 and d 2 = 1.0 [6] are empirical constants. IV. EXPERIMENTAL RESULTS A. Experimental setup In this paper, as shown in Fig. 6, a digital TV antenna is used to receive the ADS-B and UAT signals which are at radio frequency 1090MHz and 978MHz, respectively. The antenna connects to the USRP front-end via the tunable band-pass filter and then to a laptop to record the signals. Even though there are a large number of air routes near Taiwan, as shown in Fig. 7, we still can t receive any UAT signal currently in Taiwan. The red star in the figure indicates the location of the experimental setup. Fig. 7. The location of experimental setup and air routes near Taiwan In order to develop the ADS-B and UAT signal data collection units, which include a software-defined radio receiver and data processing software, the UAT signal data processed in this paper are provided by a partner school, Stanford University. The experimental setup at Stanford University is shown as Fig. 8. USRP 1 @1575 MHz GPS antenna PC #1 Tunable band-pass filter @978MHz and 1090MHz PPS Rb Clock UAT antenna Tunable Filter @ 978 MHz Digital TV antenna USRP 2 @978 MHz 8 Laptop Fig. 8. Experimental setup at Stanford University B. Experimental methodology The data collected via the USRP have to be processed in order to obtain weather information from the ADS-B and UAT signals. The ADS-B signal has to be demodulated for binary data. The work of decoding binary data to get the ADS-B header follows immediately after demodulation. The ADS-B header information is dependent on which data format is broadcasted at that time. The next step is to decode application data where information is measured by the onboard sensors of aircrafts. Finally, CRC check technology is applied to check if there is any error bit information in the received data. The decoding work of ADS-B signal follows [7]. USRP@978MHz and 1090MHz Fig. 6. Experimental setup For the UAT signal, the signal processing procedure is almost as same as that used to process the ADS-B signal, which follows [3, 8]. The biggest difference between ADS-B signal information and that of a UAT signal is that a UAT 674
signal is divided into two parts: ADS-B (aircrafts) segment and ground station segment [3]. Demodulation and decoding of ADS-B segment and ground station segment should be done separately. The signal process procedure of ADS-B signal and UAT signal is shown as Fig. 9. period is 0.96 microseconds. However, the USRP sampling rate can only be a rate by which 100MHz can be divisible, such as 4MHz, 5MHz and 10MHz. The sampling rate used for receiving the UAT signal in this paper is 10MHz, which indicates each the sample period is 0.1 microseconds, so each piece of UAT bit information is divided into either 9 or 10 samples. When doing the demodulation of the UAT signal to obtain the binary data initially, the wrong binary data are always acquired regardless of whether 9 continuously grouped samples or 10 continuously grouped samples are used to calculate the average normalized frequency, for which a bit value is 0 if the average is above 0 and 1 if average is smaller than 0. The normalized frequencies for the UAT signal samples are shown in Fig. 11. In the figure, samples in each interval of the broken line are calculated to obtain the average normalized frequency; if 9.6 samples can be calculated for the average under ideal conditions, then the average for the bit information is obtained. As seen in Fig. 11, samples in the intervals of the broken lines are almost at the top or under the red line, which indicates a value of zero in an interval. In this case, the average of the normalized frequecy can be unmistakably distinguished for bit information. Fig. 9. Signal process procedure of ADS-B and UAT After accessing weather information from the ADS-B signal and UAT signal, the following step is to construct the 3D fluid flow field simulation. However, UAT signal is received by Stanford University while ADS-B signal is received in Taiwan, so 3D fluid flow field simulation can t be constructed in Taiwan with ADS-B and UAT signal. As a result, AMDAR and DBAR data are introduced. The following information or data are all together arranged as the input to CALMET/CALPUFF Modeling system: the observation information of ground meteorological stations in Taiwan from DBAR, the AMDAR data which is weather information of aircrafts measured by onboard sensors, the land use categories data, and the terrain elevation data. Finally, all input data mentioned above are calculated by CALMET/CALPUFF Modeling system and the 3D fluid flow field simulation result can be derived from the modeling system. The experiment structure flow chart is shown as Fig. 10. Weather information of aircrafts Weather information of ground meteorological stations AMDAR DBAR 3 Normalized frequency UAT signal 1 0-1 -2 0 38.4 76.8 115.2 153.6 192 Sample 230.4 268.8 307.2 350 Fig. 11. The normalized frequency of the UAT signal (each broken line interval is 9.6 samples) Topography information Land use data 2 Terrain elevation However, instead of 9.6 samples, either 9 or 10 samples can be used to calculate the average normalized frequency in an actual situation. The normalized frequency of the UAT signal with 9 samples each interval is shown as Fig. 12. As seen in the figure, some samples are bigger than zero while some samples are smaller than zero in same intervals starting from the fifth interval. This situation is caused by the accumulation of the samples calculated for the wrong bit information, and this situation may lead to the failure to distinguish the bit information from the average normalized frequency for every subsequent interval. CALMET/CALPUFF Major algorithm: 1. Inverse-distance method 2. Power law equation 3. van Ulden and Holtslag wind extrapolation method 3D fluid flow field simulation result Fig. 10. Experiment structure flow chart C. Topic of experiment 1) Demodulation issue caused by sampling rate: According to [3], the nominal modulation rate of the UAT signal is 1.041667 megabits per second, which means each bit 675
D. Decoding result of the ADS-B and UAT signal The decoding results for the ADS-B signal are shown in the following figures. Fig. 14 illustrates the instant wind information for aircrafts from an ADS-B signal near Taiwan. The different arrow colors represent wind information for the different aircrafts and the position of the aircraft, which is at the starting point of the arrows. In the figure, the direction of the arrows means the wind direction, while the length of the arrows indicates the wind speed. 3 Normalized frequency UAT signal 2 1 0-1 -2 0 36 72 108 144 180 Sample 216 252 288 25.5 324 350 25 Fig. 12. The normalized frequency of UAT signal (each interval of dash line is 9 samples) Latitude (degree) 24.5 The solution to this situation used in this paper is taking turns between 9 samples and 10 samples to calculate the average normalized frequency. The mechanism of this solution is to count the different number of samples with respect to ideal conditions, which take 9.6 samples for a piece of bit information, while taking 9 samples to obtain bit information in the actual condition. If the different number of samples is bigger than 1 sample, the mechanism takes 10 samples for a piece of bit information this time and the different number of samples minus one, and then takes 9 samples continuously. This mechanism solves the problem introduced by the wrong number of samples taken for a piece of bit information perfectly. 24 23.5 23 22.5 22 21.5 119 120 121 122 Longitude (degree) 123 Fig. 14. Instant wind information of aircrafts from ADS-B signal near Taiwan 2) Overdue information issue regarding RTCA DO-267A: In the process of decoding the UAT signal application data, a decoding fault will always be found if following the RTCA DO-267A [8], which is regonized as the official document. An update for the decoding process for UAT application data was issued by the European Technical Standard Order (ETSO) of the European Aviation Safety Agency [9]. In [9], there are some corrections for [8]. An example of the correction for the header of the application data is shown as Fig. 13. When comparing the upper and lower plot of Fig. 13, it can be seen that the length of the segment data is different. This difference would cause the decoding error. This paper follows the updated document by the ETSO to deal with this error. As shown in Fig. 15, this is wind information for a specific descending aircraft for a period of time. The route of the aircraft follows the direction of the dotted black line. Comparing with Fig. 16, which is the plot of the altitude of the specific aircraft versus time, we can see that the direction and the speed of wind change with decreasing altitude. This situation can provide wind information in many layers for the purpose of establishing the 3D fluid flow field simulation. Latitude (degree) 23.2 23 22.8 22.6 22.4 120.2 120.3 120.4 120.5 Longitude (degree) 120.6 Fig. 15. The wind information for a specific aircraft from an ADS-B signal for a period of time Fig. 13. The example of correction for the application data header [9] 676
4 The terrain elevation and distribution of the input data in the simulation field is as shown, respectively, in Fig. 18 and Fig. 19. In Fig. 19, the black triangles represent the locations of the ground meteorological stations in Taiwan while the red circles indicate the positions of aircrafts. The purple cross sign is Taiwan Taoyuan International Airport. Altitude of the aircraft x 10 4.2 Altitude (ft) 4 121.2E 2820 3.8 121.4E 121.6E 121.8E 122.0E 2800 3.6 25.2N UTM North (km) 2790 3.4 2.925 2.93 2.935 Time (sec) 2.94 2.945 4 x 10 2780 2770 25.0N 2760 2750 Fig. 16. The altitude of a specific aircraft versus time 24.8N 2740 After decoding the UAT signal received by Stanford University, the observation data for the ground meteorological stations can be derived. However, there was no wind information in the UAT signal from aircrafts in the decoding results in this research. Fig. 17 shows the wind information for the ground meteorological stations in California from a UAT signal. The blue line in the figure is the boundary of California, and the triangles and the arrows indicate the positions and the observed wind information for the ground meteorological stations. 2730 24.6N 300 310 320 330 340 350 360 370 380 390 UTM East (km) Fig. 18. Terrain elevation of the simulation field 121.2E 2820 121.4E 121.6E 121.8E 122.0E 25.4N 2810 42 2800 40 2790 UTM North (km) Latitude (degree) 4500 m 3900 m 3700 m 3500 m 3300 m 3100 m 2900 m 2700 m 2500 m 2300 m 2100 m 1900 m 1700 m 1500 m 1300 m 1100 m 900 m 700 m 500 m 300 m 100 m -1 m 25.4N 2810 38 36 25.2N 2780 2770 25.0N 2760 34 2750 24.8N 32-126 -124-122 -120-118 Longitude (degree) -116 2740-114 2730 Fig. 17. Wind information for the ground meteorological stations in California from a UAT signal 24.6N 300 Because there are different places receiving the ADS-B and UAT signals, respectively, the information from the ADS-B and UAT signals can t be combined to establish the 3D fluid flow field simulation in Taiwan. 310 320 330 340 350 360 370 380 390 UTM East (km) Fig. 19. The distribution of the input data in the simulation field After processing all the input data, the simulation results calculated by the CALMET/CALPUFF Modeling System are as shown in Fig. 20 and Fig. 21. In Fig. 20, the simulation wind field result in the horizontal direction at the 2200 meter height layer is presented. The colors of the arrows mean the wind speed according to the color bar shown beside the figure. These colors make it easier to determine where the strong wind is. The simulation wind information results in the vertical direction at the layer same with the Fig. 20 are as shown in Fig. 21. In the figure, the wind information is presented as a contour E. Simulation results To verify the possibility of using ADS-B and UAT signals to construct the 3D fluid flow field simulation in Taiwan, ADS-B and UAT signals were replaced by AMDAR data and DBAR data, both of which can provide the same parameters as those of ADS-B and UAT signals. 677
plot. The colors of the contours indicate the vertical wind speed as shown in the color bar on the right side. A positive wind speed value means upward airflow while a negative wind speed value indicates downward airflow. As shown in the red circle in Fig. 21, there is an upward airflow and a downward airflow near Taoyuan International Airport. This makes landing an aircraft at the airport very dangerous. First, the aircraft encounters an upward wind, which may cause the aircraft to move out of its original track, so the pilot may change the pitch angle to fix on the track for landing. However, there suddenly comes a downdraft, which will cause a rapid decrease in the aircraft altitude. This situation is very dangerous, especially when the aircraft is near the ground. UTM North (km) 2820 2810 2800 2790 2780 2770 2760 2750 2740 2730 121.2E 121.4E 121.6E 121.8E 122.0E 300 310 320 330 340 350 360 370 380 390 UTM East (km) 25.4N 25.2N 25.0N 24.8N 24.6N 40 m/s 38 m/s 36 m/s 34 m/s 32 m/s 30 m/s 28 m/s 26 m/s 24 m/s 22 m/s 20 m/s 18 m/s 16 m/s 14 m/s 12 m/s 10 m/s 8 m/s 6 m/s 4 m/s 2 m/s Fig. 20. The simulation wind field result in horizontal direction at the layer of 2200 meters height UTM North (km) 2820 2810 2800 2790 2780 2770 2760 2750 2740 2730 121.2E 121.4E 121.6E 121.8E 122.0E 300 310 320 330 340 350 360 370 380 390 UTM East (km) 25.4N 25.2N 25.0N 24.8N 24.6N Fig. 21. The simulation wind field results in a vertical direction at the 2200 meter height layer 11 m/s 10 m/s 9 m/s 8 m/s 7 m/s 6 m/s 5 m/s 4 m/s 3 m/s 2 m/s 1 m/s 0 m/s -1 m/s -2 m/s -3 m/s -4 m/s -5 m/s -6 m/s -7 m/s -8 m/s -9 m/s -10 m/s -11 m/s -12 m/s Therefore, this is very important to develop accurate 3D weather information for the safety of aviation. If we can detect unusual weather conditions accurately, we can reduce the possibility of weather-related aviation accidents. V. CONSCLUSIONS AND FUTURE WORK In this paper, the development of a ground ADS-B and UAT signal data collection unit that includes signal processing, a data decoding scheme, and information computation is presented. From ADS-B and UAT signals, an investigation of necessary meteorological observation is conducted after decoding the signal. In order to construct the weather information database, we collected weather information not only directly from ADS-B and UAT signals but also from the recorded CAA and DBAR data in Taiwan. Signal processing issues, including demodulation issues, caused by the sampling rate and an overdue information issue are also discussed. The input parameters and some major CALMET/CALPUFF Modeling system algorithms used to model a 3D flow field are also introduced in this paper. The 3D fluid flow field simulation result is constructed by combining weather information from both the ground stations and aircrafts with the data from both AMDAR and DBAR, which provide the same parameters as those of the ADS-B and UAT signal weather parameters. Future work will focus on introducing weather information from ADS-B and UAT signals to the modeling system for the purpose of constructing a 3D fluid flow field simulation in an area where a UAT signal can be received. Furthermore, we can use more meteorological parameters, such as temperature and pressure, to construct a 3D weather simulation in order to include more weather information details. In addition, verification of the CALMET/CALPUFF Modeling System simulation result output can be made by a comparison with the true data as another topic for future work. ACKNOWLEDGMENTS The work presented in this paper is supported by Taiwan Ministry of Science and Technology under project grant: NSC- 102-2221-E-006-079-MY3. The student international travel is supported by Ministry of Education, Taiwan, R.O.C. the Aim for the Top University Project to the National Cheng Kung University (IC103047) and Taiwan Ministry of Science and Technology (MOST-103-2922-I-006-050). The weather data in this paper is supported by Taiwan Civil Aeronautics Administration and Data Bank for Atmospheric Research of Taiwan Typhoon and Flood Research Institute. The authors gratefully acknowledge the supports. The authors would also like to thank Dr. Sherman Lo and Dr. Yu-Hsuan Chen of the Stanford GPS Research Laboratory for their assistance on UAT data collection as well as their thoughtful comments. REFERENCES [1] N. Taneja, Weather related fatal general aviation accidents:can spatial disorientation taining be an effective intervention strategy?, Indian Journal of Aerospace Medicine (IJASM), vol. 46, no. 2, pp 59-64, 2002. [2] J. McCarthy, A Vision of Aviation Weather System to Support Air Traffic Management in the Twenty-First Century, Large Scale Computation and Information Processing in Air Traffic Control, 1993, pp 25-45. 678
[3] UAT MOPS (Minimum Operational Performance Standards for Universal Access Transceiver and Automatic Dependent Surveillence Broadcast ), RTCA/DO-282B. [4] Official CALPUFF Web Site, http://www.src.com/calpuff/calpuff1.htm [5] USEPA, http://www.epa.gov [6] J.S. Scire, F.R. Robe, M.E. Fernau, and R.J. Yamartino, A User s Guide for the CALPUFF Meteorological Model (Version 5), Earth Tech, Concord, MA, 2000. [7] ADS-B MOPS (Minimum Operational Performance Standards for 1090MHz extended squitter Automatic Dependent Surveillence Broadcast and Traffic Information Service Broadcast ), RTCA/DO- 260A, volume 1 and 2. [8] FIS-B MASPS (Minimum Aviation System Performance Standards for Flight Information Service Broadcast data link, Revision A ), RTCA/DO-267A.. [9] FIS-B ETSO (European Technical Standard Order for Aircraft Flight Information Services-Broadcast Data Link Systems and Equipment), EASA/ETSO-C157a. [10] Y.D. Yang, Investigation of CALMET/CALPUFF Modeling System Simulation of Wind Field and Puff Transmission Characteristics in Southern Taiwan. M.S. thesis, Fooyin University, July 2010. 679
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