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Fig. 4. Simulated tracks in backward- and forward-in-time simulation

Figure 4 Simulated tracks in backward- and forward-in-time simulation

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To determine the new step size h by using automatic step size control the following equation has to be solved [6]: The following table shows the chosen methods, the MATLAB function calls (if available) and additional information about the internal formulas (Butcher tableau, error calculation).
To determine the new step size h by using automatic step size control the following equation has to be solved [6]: The following table shows the chosen methods, the MATLAB function calls (if available) and additional information about the internal formulas (Butcher tableau, error calculation).
Fig. 1. Schematic diagram of two-way PTMs (a) Objects released from the true source Sp reach four receptors Ro, Ri, R2, R3. (b) From the found site R3 start a backward-in-time PTM to find source candidates So, S;, Sz, S3 (c) These several source candidates start a forward-in-time PTM. The gray marked ellipses show the distribution of the released particles from every source candidate. [2] In this work possible source candidates will be analysed using a histogram of the particle positions generated by a backward-in-time PTM starting from the found site. The selection of the true source takes place through the analyses of he average position of the particles and the corresponding histogram, because a normal distribution of the particle clusters after the simulation is not determined. Another difference to [2] i is the omission of a random-walk process (see section V.C for details).
Fig. 1. Schematic diagram of two-way PTMs (a) Objects released from the true source Sp reach four receptors Ro, Ri, R2, R3. (b) From the found site R3 start a backward-in-time PTM to find source candidates So, S;, Sz, S3 (c) These several source candidates start a forward-in-time PTM. The gray marked ellipses show the distribution of the released particles from every source candidate. [2] In this work possible source candidates will be analysed using a histogram of the particle positions generated by a backward-in-time PTM starting from the found site. The selection of the true source takes place through the analyses of he average position of the particles and the corresponding histogram, because a normal distribution of the particle clusters after the simulation is not determined. Another difference to [2] i is the omission of a random-walk process (see section V.C for details).
Fig. 2. Steps to interpolate an ocean current component v[i] at position x[Z], depth z; and time ¢; from the netCDF fields A comparison between the available interpolation methods shows that the cubic spline interpolation recreates missing data of the used ocean models most precisely while also taking the most cpu time [8]. The implementation of the Akima interpolation [9] can make allowance for an abrupt change of ocean current conditions in case of tides or different depth streams. For time interpolation, the linear interpolation method was found to be a good compromise between precision and cpu time for data extraction.
Fig. 2. Steps to interpolate an ocean current component v[i] at position x[Z], depth z; and time ¢; from the netCDF fields A comparison between the available interpolation methods shows that the cubic spline interpolation recreates missing data of the used ocean models most precisely while also taking the most cpu time [8]. The implementation of the Akima interpolation [9] can make allowance for an abrupt change of ocean current conditions in case of tides or different depth streams. For time interpolation, the linear interpolation method was found to be a good compromise between precision and cpu time for data extraction.
Fig. 3. Software concept of the flow simulation system To analyze and to provide a graphical illustration of the particle tracks and sites, MATLAB will be used. The conversion of data of the XML files to MATLAB data types occurs by using the xml_io_tools library [15] and [16].
Fig. 3. Software concept of the flow simulation system To analyze and to provide a graphical illustration of the particle tracks and sites, MATLAB will be used. The conversion of data of the XML files to MATLAB data types occurs by using the xml_io_tools library [15] and [16].
TABLE II. SIMULATION PARAMETERS
TABLE II. SIMULATION PARAMETERS
TABLE III. RESULTS OF THE DIFFERENT ODE SOLVERS
TABLE III. RESULTS OF THE DIFFERENT ODE SOLVERS
We use a modified version of the Two-Way PTM, which is described in section II.C. The used version works without a random walk process. The result of the random behavior of the several particle tracks to create possible crash sites are a randomly chosen start site on the red line in Fig. 5 (Marker1: 55.661850°E, 20.910733°S; = Marker2: —55.684904°E, 20.933654°S) and a random chosen found time in a range from 2015-07-25 00:00:00 - 2015-07-28 00:00:00 UTC. The information about the found site and time is from [19] and [20]. We use four ocean current models to simulate the particle tracks over seventeen month in the Indian Ocean. These are, GLBa0.08 (HYCOM GLBa008) and GLBu0.08 HYCOM GLBu008) of HYCOM [3] and the 2 hourly CMEMS 2HOURLY) and 24 hourly (CMEMS 24HOURLY) global-analysis-forecast-phys-001-002 model of _ the Copernicus Marine environment monitoring service (CMEMS) [4]. For all models only the surface current respectively the first depth layer was used. This means for HYCOM z=0 m and for CMEMS z=0.4940254 m. The area of interest is 54°E- 7°E 42°S-10°N for both systems in a time range from March 2014 to August 2015. Fig. 5. Definition of the start region near the beach in St Andre, on the north- eastern coast of La Réunion
We use a modified version of the Two-Way PTM, which is described in section II.C. The used version works without a random walk process. The result of the random behavior of the several particle tracks to create possible crash sites are a randomly chosen start site on the red line in Fig. 5 (Marker1: 55.661850°E, 20.910733°S; = Marker2: —55.684904°E, 20.933654°S) and a random chosen found time in a range from 2015-07-25 00:00:00 - 2015-07-28 00:00:00 UTC. The information about the found site and time is from [19] and [20]. We use four ocean current models to simulate the particle tracks over seventeen month in the Indian Ocean. These are, GLBa0.08 (HYCOM GLBa008) and GLBu0.08 HYCOM GLBu008) of HYCOM [3] and the 2 hourly CMEMS 2HOURLY) and 24 hourly (CMEMS 24HOURLY) global-analysis-forecast-phys-001-002 model of _ the Copernicus Marine environment monitoring service (CMEMS) [4]. For all models only the surface current respectively the first depth layer was used. This means for HYCOM z=0 m and for CMEMS z=0.4940254 m. The area of interest is 54°E- 7°E 42°S-10°N for both systems in a time range from March 2014 to August 2015. Fig. 5. Definition of the start region near the beach in St Andre, on the north- eastern coast of La Réunion
Fig. 7. Possible crash sites using backward-in-time PTM with CMEMS 24HOURLY (First Step of the Two-Way PTM) Fig. 8. Histogram of possible crash sites. Select possible crash site candidates on the basis of numbers of particles in the several sectors
Fig. 7. Possible crash sites using backward-in-time PTM with CMEMS 24HOURLY (First Step of the Two-Way PTM) Fig. 8. Histogram of possible crash sites. Select possible crash site candidates on the basis of numbers of particles in the several sectors
Fig. 6. Possible crash sites using all four ocean current models
Fig. 6. Possible crash sites using all four ocean current models
As an additional possible crash site the hot-spot which was defined in [21] was used. 250 particles for each of the four crash sites were created by a random variation within a radius of 3 km and a random variation of the crash time of 2 hours from an assumed crash time at 2014-03-08 00:19:00 UTC. Fig. 9 shows the result of the possible found sites for the several crash site candidates using a forward-in-time PTM which is the second step of the two-way PTM. The average position of all particles is marked with a black x in a colored circle. In this simulation Source 2 was favored as a possible crash site, because the average position of all particles is the closest to La Réunion. This result has to be verified because only particles which are inside of the area of 1 interest were used for the analyses. Several particles flew more westwards and went outside of the area of interest. In such cases the interpolation algorithms work with a rough approximation of the ocean current field. Fig. 10 shows the histogram of the particles starting from Source 2. Three possible crash sites were chosen by means of the histogram in Fig. 8. The sites were calculated by using all particle positions in the chosen sectors. The calculated average positions are shown in TABLE IV.
As an additional possible crash site the hot-spot which was defined in [21] was used. 250 particles for each of the four crash sites were created by a random variation within a radius of 3 km and a random variation of the crash time of 2 hours from an assumed crash time at 2014-03-08 00:19:00 UTC. Fig. 9 shows the result of the possible found sites for the several crash site candidates using a forward-in-time PTM which is the second step of the two-way PTM. The average position of all particles is marked with a black x in a colored circle. In this simulation Source 2 was favored as a possible crash site, because the average position of all particles is the closest to La Réunion. This result has to be verified because only particles which are inside of the area of 1 interest were used for the analyses. Several particles flew more westwards and went outside of the area of interest. In such cases the interpolation algorithms work with a rough approximation of the ocean current field. Fig. 10 shows the histogram of the particles starting from Source 2. Three possible crash sites were chosen by means of the histogram in Fig. 8. The sites were calculated by using all particle positions in the chosen sectors. The calculated average positions are shown in TABLE IV.
The mechanism of the two-way PTM as described above will now be repeated for the CMEMS 2HOURLY model. The results also show a favored crash site of Source 2 (see TABLE V) which is near the first Source 2 position using CMEMS 24HOURLY. A verification of the result is also necessary, because several particles went outside of the area of interest. Fig. 9. Possible found sites using forward-in-time PTM with CMEMS 24HOURLY (Second Step of the Two-Way PTM)
The mechanism of the two-way PTM as described above will now be repeated for the CMEMS 2HOURLY model. The results also show a favored crash site of Source 2 (see TABLE V) which is near the first Source 2 position using CMEMS 24HOURLY. A verification of the result is also necessary, because several particles went outside of the area of interest. Fig. 9. Possible found sites using forward-in-time PTM with CMEMS 24HOURLY (Second Step of the Two-Way PTM)
Fig. 12. Histogram of possible crash sites. Select possible crash site candidates on the basis of numbers of particles in the several sectors Fig. 11. Possible crash sites using backward-in-time PTM with CMEMS 2HOURLY (First Step of the Two-Way PTM)
Fig. 12. Histogram of possible crash sites. Select possible crash site candidates on the basis of numbers of particles in the several sectors Fig. 11. Possible crash sites using backward-in-time PTM with CMEMS 2HOURLY (First Step of the Two-Way PTM)
Fig. 10. Histogram of possible found sites for Source 2
Fig. 10. Histogram of possible found sites for Source 2
TABLE V. RESULTS OF THE TWO-WAY PTM USING CMEMS 2HOURLY Fig. 14. Histogram of possible found sites for Source 2
TABLE V. RESULTS OF THE TWO-WAY PTM USING CMEMS 2HOURLY Fig. 14. Histogram of possible found sites for Source 2
Fig. 13. Possible found sites using forward-in-time PTM with CMEMS 2HOURLY (Second Step of the Two-Way PTM)
Fig. 13. Possible found sites using forward-in-time PTM with CMEMS 2HOURLY (Second Step of the Two-Way PTM)
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Lagrangian approachInterpolation Methodsflight MH370
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