Objectives of the service
The objective of the service is to provide competent organizations with a support during a natural disaster event, ensuring management of the crisis in real time.
We provide a web platform with different vulnerability data in real time such as Flood Prone Area developed during the project (digital terrain models) and the development of a world-wide real-time rainfall (via sev-eral algorithms that correlate infrared satellite images and rain-fall rates).
The final objective is to make an important contribution in order to im-prove citizen resilience, economic activities, and the preservation of the environment thanks to a better anticipation of risks by the industrial sec-tor supported by the insurance world/companies. Solutions thus devel-oped can also be useful in the estimation and management of drought risks and climate change impact on people and Society.
Users and their needs
The customer segments targeted in the pilot demonstration are: insurance companies and Government or State agencies in charge of civil protection for regions (e.g. the Indian Ocean) with limited weather ground data, companies and communities.
In order to establish comprehensive risk reduction/prevention programs and real-time crisis management, these users require an improved and continual supply of worldwide rainfall estimation data to carry out vulnerability analysis.
User’s needs – PREDICT Services
The different users involve in the project are 2 insurances companies, Meteorology Direction of Reunion, Mayotte, and 15 countries of cyclone committee of Indian Ocean
Service/ system concept
The final objective is to make an important contribution in order to im-prove citizen resilience, economic activities, and the preservation of the environment thanks to a better anticipation of risks by the industrial sec-tor supported by the insurance world/companies. Solutions thus devel-oped can also be useful in the estimation and management of drought risks and climate change impact on people and Society.
In parallel, AIRBUS satellites, notably TerraSAR-X and TanDEM-X, scan the target zone in the Indian Ocean in order to generate a 12m Digital Sur-face Model (DSM) aka WorldDEM. This DSM will then be sent to the CEREMA facilities in Aix-en-Provence, as input for its well established Exzeco model. The model will then define the flood prone areas on a large scale and the results will then be sent on to the servers of PREDICT Ser-vices in order to be validated and integrated into the web-platform along with an internal engineering tool.
With this information, PREDICT Services would be able to assist users to build up and improve their contingency plans as well as providing a real-time decision-making support to cope with natural hazards. Thus improving their resilience in response to climatic events and saving potentially exposed people, goods and infrastructures
System and service architecture – COSPARIN project
The real time support implemented by PREDICT Services means that in the case of a potentially damaging event, a team of engineers will watch over the Indian Ocean in order to warn the different end users and provide support for decision making
In order to best meet all the needs of its users, PREDICT Services has developed a multichannel communication system to ensure a continuous information service.
The messages are transmitted through a multichannel communication tool adapted to crisis management and include warning messages (e-mails and SMS) and continuous on-line data on its website.
To be efficient in crisis management, the information provided will be simple, graduated, customized and anticipated. The messages sent by PREDICT Services will give the site managers and safety teams time to anticipate and evaluate the risks that may concern the area permitting them to be able to apply preventive measures regarding their staff, production, network logistics and infrastructure.
The recommended crisis management status information associated with the messages (level of advised mobilization) will be based on PREDICT Services risk expertise. It will be the result of continuous mon-itoring by PREDICT Services and will never be a result of automated warnings.
PREDICT warning levels of risk management
The platform made available to end users will be operational with the in-tegration of different data from the project as well as the risk levels in real time. A model of the platform is presented below
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Recommendations adapted to the event. Warning level is as-sessed throughout the event (the current level being the one identified and colored on the scale).
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A regional risk message about the current and expected risks is updated daily by PREDICT Services engineers.
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Meteorological and hydrological forecast data access, referring to detailed maps and data on institutional websites (when available).
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Estimate COSPARIN Rainfall accumulation / Flood Prone area.
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Info-risks pictograms (small symbols to represent a risk, its nature and the actions to be carried out fed by the PREDICT Services team). By clicking on the pictogram, a bubble pops-up and gives information about the hazard dynamics.
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Elaboration and visualization of safety plans
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A function for extracting the database to inform the managers about all the messages sent to their site entered in the database and assisted by PREDICT Services.
Space Added Value
For modelling Flood Prone Areas, we will use data coming from the Air-bus Defense & Space, which is a digital terrain model (DTM) called WorldDem.
WorldDem data is exceptionally accurate and has the best quality of any other global satellite elevation model currently available on the market, with a vertical accuracy of 5 m (relative) / 10 m (absolute) in a 12 m x 12 m grid.
This data can easily be integrated into G.I.S. tools as well as into WEB platforms in order to create flood prone zone modelling. The models are based on the topography obtained from satellite imagery. (CEREMA modelling).
Example of a Flood Prone area elaborated by Exzeco Model
Risk is determined by estimating rainfall calculated via several algorithms that correlate infrared satellite images (GOES, MSG, FY, HIMAWARI, METOP), microwave satellite data, cloud temperatures and rainfall rates
Furthermore, digital model data (e.g.: rainfall, relative humidity, convective equilibrium level, temperature and wind component) would be extracted from the French global model ARPEGE. All data will be integrated into a specific algorithm developed with the aim to estimate rainfall taking into account the discrimination of stratiform and convective clouds, rainfall estimation, evaporation correction, spatial variability estimation, and adjustments according to the orography.
Current Status
The operational pilot phase has been completed with positive feedback from both users and internal users for the improvement of the detection of extreme events. This data opens up great perspectives in the improvement of risk knowledge and resilience worldwide. As part of the awareness and follow-up of the seventh session of the Platform for Disaster Risk Reduction held in Bali in May 2022 by the UN, also related to the call of the UN and WMO for the "Early Warning for All Project" re-called at COP 27, the COSPARIN project responds perfectly to international expectations (observation and knowledge improvement). Its results and pilots in developing countries will demonstrate its ability to meet the expectations expressed.
