On 7 March 2014 (UTC), Malaysia Airlines flight 370 vanished without a trace. The aircraft is believed to have crashed in the southern Indian Ocean, but despite extensive search operations the location of the wreckage is still unknown. The first tangible evidence of the accident was discovered almost 17 months after the disappearance. On 29 July 2015, a small piece of the right wing of the aircraft was found washed up on the island of Réunion, approximately 4000 km from the assumed crash site. Since then a number of other parts have been found in Mozambique, South Africa and on Rodrigues Island.
This paper presents a numerical simulation using high-resolution oceanographic and meteorological data to predict the movement of floating debris from the accident. Multiple model realisations are used with different starting locations and wind drag parameters. The model realisations are combined into a superensemble, adjusting the model weights to best represent the discovered debris. The superensemble is then used to predict the distribution of marine debris at various moments in time. This approach can be easily generalised to other drift simulations where observations are available to constrain unknown input parameters.
The distribution at the time of the accident shows that the discovered debris
most likely originated from the wide search area between 28 and
35
Modern sea situational awareness technologies make use of both real-time
information and advanced, long-term reconstructions of the ocean state. Among
many other applications, the ocean reconstructions allow the study of
transport and dispersal scenarios for objects and pollutants at sea. Such
studies are key to preparing better emergency response management plans and
performing post-crisis assessments. The use of numerical simulations for
search-and-rescue modelling dates back to the early 1970s, when the
US Coast Guard introduced its Computer-Assisted Search Planning
system
The use of superensembles, a weighted combination of multiple models, was
first introduced in meteorology by
Malaysia Airlines flight 370 (MH370) was a scheduled passenger flight from
Kuala Lumpur, Malaysia, to Beijing, China. The Boeing 777-200 ER aircraft
carrying 239 passengers and crew disappeared less than an hour after
take-off. Air traffic control (ATC) lost voice and radar contact with the
flight at 17:22 UTC on 7 March 2014, while the aircraft was over the Gulf of
Thailand. Initially it was assumed that the flight had crashed, but analysis
of military radar data showed that the plane had deviated from its planned
flight path and returned towards Malaysia. The aircraft continued flying in a
westward direction and eventually exited the radar coverage at 18:22 UTC
While most on-board communication equipment was inoperable, minimal
communication between the aircraft's satellite terminal and the satellite
network continued until 00:19 UTC. Analysis of this
communication a a part of an engine cowling, discovered in South Africa in March 2016, but
later found to be photographed already on 23 December 2015 part of the horizontal stabiliser, found on a sandbank off the coast of
Mozambique on 27 February 2015 a cabin interior panel, discovered on Rodrigues Island, Mauritius, on
30 March 2016
The following sections will present a numerical simulation that uses all
available information about the crash site and the discoveries of washed-up
debris to predict the distribution of floating debris from the accident. The
paper is organised as follows: in Sect.
Estimated flight path of flight MH370 based on military radar and satellite data analysis. Also indicated are the search areas and the locations where washed-up debris was discovered.
Drift trajectories are modelled using an ensemble of particles drifting on the ocean surface. Due to differences in their initial positions and due to random motion these particles will slowly diverge over time. Each trajectory represents a possible path of the object being studied.
In this model the displacement d
The particles in the model are advected by integrating Eq. (
To account for the fact that the location of the wreckage and the wind drag coefficient are not accurately known, the results presented here use multiple realisations of the model, with varying initial conditions and parameters. The different realisations are then combined into a superensemble.
The superensemble probability
In order to obtain meaningful results from the superensemble, the
coefficients
The superensemble is initialised with 30 model realisations, each containing
5000 particles. The realisations contain all possible combinations of
six different wind drag coefficients and five starting locations. The wind drag
coefficients range from 0 to 2.5 % in increments of 0.5 %, spanning
roughly the range from no wind drag at all to a drag similar to that of a
small boat
The oceanographic and meteorological data for the simulation are provided by
the European Copernicus Marine Environment Monitoring Service Dataset global-analysis-forecast-phys-001-002. Dataset
wind-glo-wind-l4-nrt-observations-012-004.
Figure
The distribution at the time of the discovery in Réunion is shown in
Fig.
Floating aircraft debris probability density in
Floating aircraft debris probability density between March and October 2014. The superensemble weights are calculated based on all five debris observations. The locations of the observations are indicated by the circular red markers.
Floating aircraft debris probability density between January 2015 and May 2016. The superensemble weights are calculated based on all five debris observations. The locations of the observations are indicated by the circular red markers.
In February 2016 (see Fig.
The new debris discoveries can be included into the simulation by
recalculating the model weights
One density distribution that is of particular interest is the distribution
at the time of the accident, shown in Fig.
Figure
In January 2015 (Fig.
While debris may wash up on shore in various locations, it is important to note that there are many other factors that determine whether the debris will be found, recognised and reported to the proper authorities. In sparsely populated areas it may take a long time before debris is discovered, while in developing countries a discovered piece of debris may be discarded because people are not aware of the missing aircraft. It is therefore not unlikely that debris has washed up in other locations but has remained unreported.
The availability of high-resolution oceanographic and meteorological data is key to adequately responding to accidents and emergencies at sea. The case of MH370 especially underlines the importance of data that provide global coverage. With the operational range of modern airliners in excess of 10 000 km, accidents may happen in remote areas that are deprived of most means of communication. In such cases the meteo-oceanographic data might be the only source of information on the circumstances of the accident and the conditions afterwards.
The results presented in this paper show that the aircraft debris discovered
so far in Réunion, Mozambique, South Africa and Rodrigues Island most
likely originated from the wide search area between 28 and
35
Based on the particle distribution, the most probable locations to discover further washed-up debris from flight MH370 are Tanzania, Mozambique, Madagascar and surrounding islands such as Réunion, Mauritius and the Comoros. The time evolution shows that from 2016 onwards the floating debris that remains at sea is spread out, and its distribution changes only slowly. The probability of new debris washing up in the future is therefore quite low.
The results obtained for MH370 show that the superensemble method described in this paper is well-suited for drift simulations where additional observations are available to refine the result. By adjusting the weight of the superensemble members it is straightforward to incorporate information such as the discovered debris into the simulation. Moreover, as this weighting procedure is computationally inexpensive, the method could also be used to provide real-time operational forecasts during an emergency at sea.
This work was performed in the framework of the TESSA Project (Sviluppo di TEcnologie per la Situational Sea Awareness) supported by PON (Ricerca & Competitività 2007–2013), cofunded by EU (European Regional Development Fund), MIUR (Ministero Italiano dell'Università e della Ricerca) and MSE (Ministero dello Sviluppo Economico). This study has been conducted using EU Copernicus Marine Service Information. Edited by: R. Archetti Reviewed by: two anonymous referees