Method and system for automated location-dependent detection of storm risks and exposure-based param

专利摘要:
The invention includes a parametric risk transfer system and method for a parametric risk transfer system (1) based on automated location-dependent probabilistic tropical storm risk and storm impact prediction wherein weather meteorological parameters (401) of weather events are measured at a plurality of delocalized distributed meter stations (40-43) central system (2), and wherein the measured weather measurement parameters include at least measurement parameters (401) of the wind speed and / or highest wind speed in a predefined time frame. A spatially high resolution grid (212) comprising grid cells (2121, 2122, 2123, 2124) is generated over a geographic area of interest with a detection unit (21), the area comprising ground exposed risk units (70-74), wherein a plurality of delocalized distributed weather run stations (40-43) are selected and linked to the grid (212), and wherein each cell (2121, 2122, 2123, 2124) of the grid (212) has a defined distance to each of the delocalized distributed weather run stations (40-43 ) having. A wind field profile is dynamically generated by the system (1), wherein by triggering an indexed wind field parameter (2151, 2152, 2153, 2154) of the wind field profile with a trigger module (221) exceeding a predefined trigger index (2211) an output enable signal (2221) is generated by a signal generator (2221). 222) based on the triggered overflow of the indexed value (2151, 2152, 2153, 2154) and sent to a linked activation device (30, ..., 35), the operation of the activation device (30, ..., 35) being sent from Output enable signal (2221) is controlled.
公开号:CH712882A2
申请号:CH01156/16
申请日:2016-09-07
公开日:2018-03-15
发明作者:Scott Kaplan Alexander；Elizabeth Linkin Megan
申请人:Swiss reinsurance co ltd；
IPC主号:

专利说明:

description
FIELD OF THE INVENTION The present invention relates to systems for automated location-dependent detection of storm risks and for exposure-based parametric risk transfer and corresponding prediction systems for automated location-dependent probabilistic storm forecasting and wind field prediction or for wind field and exposure-induced risk transfer, wind field conditions being measured or recorded and location-dependent probability values ( Exposure). Furthermore, the invention relates to prediction and / or exposure-based risk transfer systems comprising an automated location-dependent detection of storm effects and also relates to prediction and / or parametric exposure-based risk transfer systems on the basis of topologically linked loss distributions and masses of these distributions such as average annual loss (AAL). and Probable Maximum Loess (PML) due to storms. In particular, the invention relates to an adaptive, automated system and method for automated location-dependent probabilistic storm prediction, wind field prediction and prediction of the actual loss distribution (exposure) due to tropical storms such as tropical cyclones, hurricanes and typhoons. Finally, the invention relates to the systems according to the invention based on automated location-dependent detection of storm risks and exposures, linked to appropriate parametric insurance coverage, triggered and / or signaled automated parametric risk transfer systems that can be operated. In particular, the invention relates to a system and a method in which a country-specific storm zone table is used to derive high-resolution data on vulnerability factors for generalized insurance risk factors.
Background of the Invention A corresponding automated storm assessment system and, in particular, an automated, independent parametric risk transfer system based on automatically recognized or measured storm risk exposures are currently urgently lacking. In many countries with specific topological structural conditions or geographical conditions, the provision of a technically correct storm assessment or measurement of the storm risk exposure is almost technically impossible. A look at the loss history shows that economic losses due to storms, especially tropical storms, are as high or even higher than those caused by earthquakes, floods or other dangers. For most of these other dangers, there are already various evaluation and forecasting systems that technically enable automated risk transfer through appropriately designed, independent risk transfer systems. Large amounts of money, work and time are lost due to storm events. In addition, the trend of increasing risk transfer penetration for storms means that the insurance industry and reinsurance industry as well as the countries and their populations are increasingly affected by storm losses. However, to expand storm assessment and forecasting into detailed predictions and impact measurements, the problem of immense amounts of data must be addressed. This is done through completely new concepts on the part of the hazard events as well as the systems and / or processes.
Storms and in particular tropical storms such as cyclones, hurricanes and typhoons etc. cause serious damage in various parts of the world at regular intervals. Climate change has exacerbated the situation considerably and has led to a wide variety of technical problems, including precise forecasting systems and appropriately signaled intervention systems for physical or monetary intervention in order to at least alleviate the problems on the ground. Intelligent forecasting systems are the most important technical means for dealing with such potential future events in advance and alleviating them. Such systems relate to preventive measures and systems such as the steering and initiation of reinforcement measures, current measures during the occurrence such as control, timing and the steering of intervention means , Signaling and alarm systems and finally the control and steering of measures after such a storm event.
In particular, the examples contained in this document relate to storms and tropical storms, while specific types of tropical storms such as cyclones, hurricanes and typhoons, etc. can be treated in the same way. Hurricanes belong to the strongest category of the meteorological phenomenon, which is known as the “tropical cyclone”. Like all tropical cyclones, hurricanes include pre-existing weather disruption, warm tropical seas, humidity, and relatively light high-altitude winds. If the right conditions last long enough, combined they can cause violent winds, unbelievably high waves, torrential rains and floods associated with this phenomenon. For example, the formation of a tropical cyclone and its growth into a hurricane require: 1) a pre-existing weather disorder; 2) sea temperatures of at least 26 ° C to a depth of about 45 m; and 3) winds that are relatively light through the depth of the atmosphere (low wind shear). Typically, tropical storms and hurricanes weaken when their sources of heat and moisture are cut off (which occurs when they move overland) or when they encounter strong wind shear. A weakening hurricane can become stronger again as soon as it moves to a cheaper region. The aftermath of a land hurricane can also cause significant damage. An average of ten tropical storms develop over the Atlantic, the Caribbean and the Gulf of Mexico each year. Many of these remain at sea. Six of these storms develop into hurricanes every year. In one
CH 712 882 A2 average 3-year period, for example, about five hurricanes hit the US coast and cause 50 to 100 deaths from Texas to Maine. Of these, two are typically larger hurricanes (wind speeds over 110 mph).
However, it is technically difficult, if not impossible, to predict the occurrence of such weather events over a long period of time and also to predict and accurately evaluate their actual local effects on the ground. Even with a detailed wind field map that prior art systems cannot provide on an aggregated, high-resolution grid level for definable areas or circles, the path or movement of an existing storm can also be difficult to predict over a period of hours or days. An example is the storm Isaac (AL092012), which lasted from August 21 to September 1, 2012. Isaac was a tropical storm that developed into a Category 1 hurricane on the Saffir-Simpson hurricane wind scale a few hours before it hit land in southeastern Louisiana. The tropical cyclone caused heavy rainfall and inland flooding in parts of the Caribbean as it moved through the Lesser Antilles, and landed along southwest Haiti and eastern Cuba. Isaac became a large tropical cyclone, causing a major storm surge and inland flooding in southern Mississippi and southeastern Louisiana. Isaac is estimated to be directly responsible for 34 deaths, including 24 in Haiti, 5 in the Dominican Republic and 5 in the United States.
To illustrate the technical problems associated with tropical systems prediction and impact assessment systems, the following synoptic story of Isaac may be helpful. One of the problems associated with capturing and predicting the development of tropical storms such as Isaac is caused by the finite nature and structure of automated electronic generated systems. Isaac developed from a tropical turbulent current that moved away from the African coast on August 16. A broad low pressure area developed along the tropical turbulent flow axis south of Cape Verde on August 17, but did not become a well-defined circulation center until August 20 at 12:00 UTC (Universal Time Coordinated). Deep convection was sufficiently organized near the center of the structural low to be classified as a tropical low at 6:00 UTC on August 21, when it centered about 625 nautical miles east of the Lesser Antilles. The deep became stronger and grew 12 hours later into a tropical storm about 450 nautical miles east of the Lesser Antilles. The path of Isaac is shown in Fig. 1, which is a diagram showing the location of Hurricane Isaac from August 21 to September 1, 2012 from the NOAA's Hydrometeorological Prediction Center (National Oceanie and Atmospheric Administration). Table 1 provides an overview of the positions and strengths being tracked.
Date / time(UTC) width(° N) length(° F) print(Mb) wind speed(Kn) stage 20/1200 15.7 44.8 1010 25 Deep 20/1800 15.6 46.8 1009 30 21/0000 15.2 48.5 1008 30 21/0600 14.9 50.1 1007 30 tropical low 21/1200 15.0 51.6 1006 30 21/1800 15.2 53.1 1005 35 tropical storm 22/0000 15.4 54.8 1004 40 22/0600 15.7 56.6 1003 45 22/1200 15.9 58.6 1004 45 22/1800 16.1 60.4 1004 45 23/0000 15.7 62.0 1004 45 23/0600 15.0 63.4 1004 45 23/1200 15.1 65.0 1003 45 23/1800 15.6 66.4 1003 45 24/0000 15.7 67.8 1002 45 24/0600 15.4 69.1 998 45
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Date / time(UTC) width(° N) length(° F) print(Mb) wind speed(Kn) stage 24/1200 15.7 70.4 995 50 24/1800 16.6 71.2 993 55 25/0000 17.3 71.8 992 55 25/0600 18.3 72.7 991 55 25/1200 19.6 73.9 997 50 25/1800 20.8 75.2 997 50 26/0000 21.8 76.7 997 50 26/0600 22.7 78.3 995 55 26/1200 23.4 80.0 995 55 26/1800 23.7 81.4 992 50 27/0000 24.2 82.6 990 50 27/0600 25.0 83.6 989 50 27/1200 15.7 70.4 995 55 27/1800 16.6 71.2 993 60 28/0000 17.3 71.8 992 60 28/0600 18.3 72.7 991 60 28/1200 19.6 73.9 997 65 hurricane 28/1800 20.8 75.2 997 70 29/0000 21.8 76.7 997 70 29/0300 22.7 78.3 995 70 29/0600 23.4 80.0 995 70 29/1200 23.7 81.4 992 65 29/1800 24.2 82.6 990 60 tropical storm 30/0000 25.0 83.6 989 55 30/0600 25.7 84.7 987 55 30/1200 26.3 85.7 982 45 30/1800 26.8 86.7 979 35 31/0000 27.4 87.6 978 30 tropical low 31/0600 28.0 88.3 975 25 31/1200 28.6 88.8 972 20 31/1800 28.9 89.4 967 20 01/0000 29.0 89.7 965 20 01/0600 29.1 90.0 966 20 01/1200 29.0 89.7 965 70 fading minimum pressure
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Date / time(UTC) width(° N) length(° F) print(Mb) wind speed(Kn) stage 29/030025/0600 18.3 72.7 991 55 Meet on land near Jacmel, Haiti 25/1500 20.1 74.5 997 50 Meet on land near Cajobabo, Guantänamo, Cuba 29/0000 28.9 89.4 967 70 Meet on land at Southwest Pass on the Mississippi Estuary 29/0800 29.2 90.2 966 70 Meet on land near port
Fourchon, Louisiana (Table 1: Hurricane Isaac orbit, August 21 to September 1, 2012) The boundary conditions and influences on the route and the wind fields of storms, in particular tropical storms, are complex and technically difficult to grasp. In the example described above, a strong subtropical high-pressure wedge with a deep layer over the western Atlantic caused Isaac to move rapidly 15 to 20 kn west for the next two days. The center of the tropical storm moves through the Leeward Islands between the islands of Guadeloupe and Dominica between August 22, 18:00 UTC and August 23, 00:00 UTC. The strongest winds occurred far north of the middle and extended over the northern Leeward Islands and the Virgin Islands. Isaac continued essentially westward through the eastern Caribbean until early morning on August 24, and aircraft and satellite data indicated that the cyclone's structure was less organized as the low pressure center re-formed further south and the circulation tipped more , On August 24, however, Isaac rebuilt a strength of 55 kn and turned northwest towards Hispaniola. The structure of the cyclone began to emerge with the formation of a more developed inner core and the first signs of an eye, shortly before Isaac landed on the southern coast of Haiti near the city of Jacmel on August 25 at 6:00 UTC. The center of Isaac quickly crossed the narrow southwest peninsula of Haiti and the cyclone weakened somewhat as the circulation interacted with the mountainous terrain of Hispaniola. Isaac continued north-west across the Gulf of Gonave in the early morning hours of August 25, moving just south of the Windward Passage until he joined along the southeast coast of Cuba near Cajobabo, Guantänamo at 15:00 UTC Maximum wind speeds of 50 kn hit land. The center moved from the northern coast of Cuba to the Atlantic near Rafael Freyre, Holguin, at 20:15 UTC. Isaac grew larger as he crossed Haiti and Cuba, and the tropical-force winds stretched up to 180 nautical miles north of the center across the Turks and Caicos Islands and much of the Bahamas.
Over the Atlantic, Isaac turned to the west-northwest and moved faster on August 26 between a large deep layer over the northwestern Caribbean and a mid-tropospheric wedge over the western Atlantic. Isaac had reached a maximum wind speed of 50 knots, while the center moved parallel to the north coast of Cuba to Florida Street and passed south of the Florida Keys during the day. Tropical storm winds, particularly gusts, hit the Florida Keys and South Florida in rainbows that moved across the area for much of the day. Isaac reached the southeastern Gulf of Mexico in the early morning of August 27 and slowly moved west-northwest and northwest until he reached the southwestern edge of the subtropical front. The wind field remained large and microwave data indicated that deep convection was more organized in a ring around the circulation center. Gradually gaining strength as he moved across the Gulf of Mexico, on August 28, at 12:00 UTC, became a hurricane with a center approximately 75 nautical miles southeast of the Mississippi Estuary. A mid-height locking wedge northwest of the hurricane slowed Isaac considerably as he approached the Louisiana coast. This prolonged the strong winds, dangerous storm surges and heavy rainfall along the northern Gulf Coast. Isaac hit land for the first time on August 29 at 00:00 UTC with a maximum wind speed of 70 kn along the Louisiana coast at Southwest Pass at the mouth of the Mississippi. The center then turned back west across the sea and met for the second time on August 29 at 8:00 UTC west of Port Fourchon, Louisiana. Isaac grew weaker as he moved overland to southeast Louisiana and became a tropical storm at 6:00 p.m. UTC on August 29 when the center was approximately 35 nautical miles west-southwest of New Orleans. A medium high above the southeastern United States on August 30 directed northwest across Louisiana and the cyclone weakened to a tropical low at 00:00 UTC on August 31 shortly after moving to the south of Arkansas. The low turned north and moved to the extreme southwest of Missouri during August 31. The center of the circulation then lost its definition over the west of Missouri in the early morning of September 1 and Isaac dissolved shortly after 06:00 UTC about 55 nautical miles west-southwest of Jefferson City, Missouri. The remains of Isaac moved northeast and east through Missouri and Illinois and created several tornadoes over the Mississippi Valley later in September 1.
CH 712 882 A2 In systems according to the prior art, such tracking can, for example, be subjective satellite-based strength estimates according to the Dvorak technique, for example of Tropical Analysis and Forecast Branch (TAFB) and Satellite Analysis Branch (SAB), and / or objective Dvorak estimates from the Cooperative Institute for Meteorological Satellite Studies / University of Wisconsin-Madison (UW-CIMSS) include. Further data and images as shown in FIG. 2 originate, for example, from NOAA satellites in polar orbit comprising the Advanced Microwave Sounding Unit (AMSU), NASA Tropical Rainfall Measuring Mission (TRMM) and Aqua, Advanced Scatterometer (ASCAT) of the European Space Agency, Naval Research Laboratory WindSat and / or Defense Meteorological Satellite Program (DMSP) satellites, which can be useful, among other things, when replicating an existing orbit such as Isaac's. Additional data comes from aircraft observations and measurements including Stepped Frequency Microwave Radiometer (SFMR) at flight altitude and drop wind probe observations of flights, such as the 53rd Weather Reconnaissance Squadron of the US Air Force Reserve Command and / or flights of the WP-3D aircraft of the NOAA Aircraft Operations Center (AOC). At Isaac, for example, the 53rd Weather Reconnaissance Squadron and the NOAA AOC G-IV's G-IV aircraft flew several corresponding synoptic surveillance flights around Isaac. Information from national weather services, such as WSR-88D Doppler radar data from San Juan, Puerto Rico; Miami, Florida; Key West, Florida; and Slidell, Louisiana, can be used to locate the center and obtain speed data for tracking storms when they are near the coast. For example, Meteo France radar data from Guadeloupe and Martinique, as well as radar data from the Cuban Institute of Meteorology, can also help track the center of a tropical storm. Finally, there is another data source in ship reports of winds in tropical storm strength in connection with the tropical storm and / or selected surface observations from land stations and data buoys.
[0010] Wind and pressures are important parameters for tropical storms. In Isaac, for example, the analyzed swelling from Isaac to a tropical storm is based on a wind measured on August 21 at 18:43 UTC at an altitude of 1500 feet with 44 kn, which suggests a maximum wind speed on the ground of about 35 kn, and on reconciled SFMR estimates of around 35 kn on August 21 between 18:00 and 20:00 UTC. Measurements and estimates of the strength of tropical storms are complex. Typically, there is a large discrepancy between the altitude and ground estimates and, for example, there is a likelihood that an adjustment to the SFMR estimates will fully account for the peak of rain densities, especially if the tropical storm has moved across an island or land. The large wind field of a tropical storm can lead to extensive storm surges. For example, Isaac caused extensive storm surges along the northern coast of the Gulf of Mexico, particularly in the southeast of Louisiana, Mississippi and Alabama. The highest storm surge measured by a NOS tidal level was 11.03 feet above normal tide levels in Shell Beach, Louisiana, at the southern end of Lake Borgne. A 6.69 foot storm surge was measured in Pilottown, Louisiana, near the Mississippi Estuary, and a 6.35 foot storm surge was observed in New Orleans at New Canal Station on the south shore of Lake Pontchartrain. In Mississippi, a storm surge of 8 feet from the NOS level was measured at the Bay Waveland Yacht Club. Further to the east, a 4.63-foot storm surge was measured in Mobile Bay, Alabama, at the Coast Guard station, Mobile Sector. Furthermore, the flood levels of tropical storms, typically expressed above the earth, can occur especially in the immediate vicinity of the coast, on lakeshores or dam systems due to the storm surge.
One of the basic problems in the prior art is that there is no true measure of a storm. Hurricane Katrina provided an impressive example. Hurricane Katrina was the eleventh named storm and fifth hurricane of the 2005 Atlantic hurricane season. It was the highest cost natural disaster and one of the five most fatalities in US history. The storm is currently considered the third largest land-based tropical cyclone in the United States after Labor Day Hurricane 1935 and Hurricane Camille 1969. The problem was that people were preparing to leave their homes to find shelter when Hurricane Katrina approached New Orleans. The worst hit were the weakest citizens of the city: poor and elderly, parents with small children, people without a car and people in areas prone to flooding. Katrina, actually a Category 5 storm, was graded to Category 3 when it hit land. However, the forecasting and rating systems were misinterpreted because the category classification of the hurricane was not the optimal measure for the storm's actual destructive power. Many people, authorities and industrial companies did not take the right measures to prevent the catastrophe. Because the forecasting systems did not correctly grasp the effects of the tropical storm, at least 1,833 people died in the hurricane and subsequent flooding, making it the most casualty hurricane in the United States since Hurricane Okeechobee in 1928. Total property damage was valued at $ 108 billion in 2005, four times the amount from Hurricane Andrew in 1992. Hurricane Ike in 2008 and Hurricane Sandy in 2012 caused more damage than Hurricane Andrew. But both were far less destructive than Katrina. Hurricane Katrina flood deficiencies in New Orleans are believed to be the worst failure of the civil engineering guild in US history, leading to a lawsuit against the U.S. Army Corps of Engineers (USACE), who designed and built the dam system in accordance with the Flood Control Act of 1965.
In prior art systems, particularly in the western hemisphere, hurricanes are classified according to the Saffir-Simpson scale, an empirical measure of storm strength. To trigger a category rating of a storm, the systems measure the highest wind speed of a gust of wind over an entire minute. The
CH 712 882 A2
Wind speed is measured at a height of 10 meters because wind speeds increase with the height at which they are measured and storms typically cause the greatest damage at a height of 10 meters. Depending on how high this top speed is, a storm is assigned to one of five different categories in the Saffir-Simpson rating. Table 2 below shows the Saffir-Simpson rating system.
Saffir-Simpson rating system
Category wind speed
Five> 70 m / s,> 137 knots> 157 mph,> 252 km / h
Four 58-70 m / s, 113-136 knots
130-156 mph, 209-251 km / h
Three 50-58 m / s, 96-112 knots
111-129 mph, 178-208 km / h
Two 43-49 m / s, 83-95 knots
96-110 mph, 154-177 km / h
One 33-42 m / s, 64-82 knots
74-95 mph, 119-153 km / h
Other important classifications
Tropical storm 18-32 m / s, 34-63 knots 111-129 mph, 63-118 km / h
Tropical depth <17 m / s, <33 knots <38 mph, <62 km / h (Table 2: Saffir-Simpson hurricane wind scale) The system of the Saffir-Simpson hurricane scale (SS scale) enables one rough estimate of the possible effects of a tropical storm. As seen above, the SS scale defines the hurricane strength by category. A category 1 storm is the weakest hurricane (winds of 64-82 kn); a Category 5 hurricane is the strongest (winds over 135 kn). Regarding the damage caused, it can be said that typically Category 1 storms with winds of 64-82 kn normally cannot cause real damage to building structures. Damage mainly occurs on unpaved mobile homes, bushes and trees. Flooding on the coast and minor damage to jetties can also occur. Category 2 storms with 83-95 kn winches can usually cause some damage to roofing, doors and windows. There may also be significant damage to vegetation, mobile homes, etc., or flood damage to piers, and small watercraft in unprotected berths may detach from the mooring. Category 3 storms with winds of 96-113 kn can usually cause some structural damage to small houses and commercial buildings, with only minor facade damage. Mobile homes are destroyed. Coastal flooding destroys smaller structures, while larger structures are damaged by floating debris. The area can be flooded far inland. Category 4 storms with winds of 114-135 kn can usually cause extensive facade damage and some complete roof structure damage to small houses. Larger erosions can also occur on beach areas. The area can be flooded far inland. Finally, Category 5 storms with winds of more than 135 kn can normally cause complete roof damage to many residential and industrial buildings. Some complete building damage can occur, with small utility buildings being blown or blown away. Floods cause major damage to the lower floors of all structures near the coast. Extensive evacuation of residential areas may be required.
The problem with such prior art systems is that they capture only one aspect of a storm's strength: the highest speed it can reach. Not only is it difficult to measure the approximate top speed, but different organizations typically come to different conclusions depending on their coverage of the wind data. This number does not reveal anything about the size of the storm nor how the overall wind speeds are distributed. An example can be given by considering the presence of two storms: the first is violent but more limited, while the second is larger; and although it has a low maximum wind speed, these wind speeds are spread over a larger area. The Saffir-Simpson scale would give the first storm a higher rating, although the latter can be more destructive. Based on this rating, the people of
CH 712 882 A2 misguided her assessment by Katrina. The rating system based on the SS scale is therefore too simplistic, since the scale does not take into account the physical size of a storm or the amount of precipitation it produces. In addition, unlike the Richter scale used to measure earthquakes, the Saffir-Simpson scale system is not continuous and is divided into a small number of categories. Proposed substitute classifications include the hurricane strength index, which is based on the dynamic pressure generated by the winds of a storm, and the hurricane hazard index, which is based on surface wind speeds, the radius of maximum wind speeds of the storm and the speed of movement. These scales are both continuous like the Richter scale; but none of these scales have been used effectively so far.
[0015] An object of the present invention is to provide a reliable, localized measure for a storm. Storms are dangerous due to the energy stored in the moving air. In a storm, strong winds hit stationary objects such as trees, buildings or the surface of the sea and transfer some of their kinetic energy. Some structures can safely absorb this energy while others give way. Tropical storms, especially hurricanes, form a vortex. Vortices are a consequence of the non-linear equations that govern the flow of fluids. Under normal circumstances, these vortices stop as soon as their energy disappears from the fluid surrounding them. But hurricanes are self-sustaining and are nourished by evaporating pillars of air rising from the warm sea water. Prior art systems based solely on mathematical simulation typically fail to adequately model the dynamics of hurricanes. The simulations are complex. On a larger scale, they must capture the flow of the atmosphere that is responsible for directing the hurricane. On a smaller scale, they must capture the interactions near the core that give the storm its strength. They typically try to bring in just enough essential simulation to reproduce the storm's behavior, while omitting the details that make it impossible to run the simulation with the available processor power.
In addition, the correct forecast of a storm, that is, the local wind speeds, solves only half of the problem. The other half is predicting how destructive it will be. The strength of the storm affects other objects because each object in motion carries a certain amount of energy, called kinetic energy. The kinetic energy of an object depends on its speed squared and is directly proportional to the mass of the object. To put it simply, it can be assumed that wind energy is related to its true potential for destruction due to the true size of a storm. What differentiates this method from the SS scale is the fact that different sizes of wind fields with the same or different wind speeds have to be recorded accordingly, which is much more than just the peak strength of a storm. Therefore, the predicted parameters must at least take into account how the wind speeds are distributed over the entirety of a storm. Some prior art prediction systems, such as those from the National Oceanie and Atmospheric Administration, attempt to measure the strength of a storm by using the so-called kinetic energy of the storm at a particular location. However, these systems also fail to correctly predict the effects because they do not take into account the local topological background of the structure and are necessarily based on simulations that subsequently face the same technical problems as previously described.
Another aspect is that automated risk transfer systems such as automated insurance systems must develop ways to evaluate and parameterize the risks associated with such weather events and price this knowledge in the calculation of insurance products and the sizes and frequencies of damage to be expected over time. Information on use in this regard is available in the form of historical data about storms that have occurred over the years. Around 80 such storms occur every year worldwide. Data is collected for the storms, including position data for storm path or path, wind speeds, barometric pressures and other factors. Such storms are best documented in the North Atlantic (that is, the part of the Atlantic Ocean north of the equator), where reliable data are available that cover over 100 years of activity. Around 10 storms occur in the area of the North Atlantic each year. Historical data is also available for cyclones that occur in the Northwest Pacific. There are about 26 storms there each year. Appropriate data for these Pacific storms have only been available for approximately the past 50 years. Even less data is available for storms in other areas. Using all available historical data, information related to a few hundred storms is available for engineering and researcher review to develop appropriate systems. Such information is useful in assessing storm damage risks in the areas concerned. Due to the unpredictable nature of storm behavior and the number of factors that influence such behavior, the available data set of historical storms is relatively small from a probabilistic point of view. Since this data set only grows due to a relatively small number of storms per year, there is a problem with performing statistical analyzes on the possibility of a storm occurring at a specific location.
In summary, existing systems according to the prior art are not able to correctly take into account the physical size of a storm, the amount of precipitation that it generates, and the effective effects on the local structures. Furthermore, they cannot provide a true predicted measure of a storm and an impact assessment on an aggregated high resolution grid level for predefined area cells and / or circles that
CH 712 882 A2 can be used accordingly for the relevant alarm systems and means, damage prevention systems, damage protection systems and / or automated risk transfer systems.
Summary of the Invention It is an object of the present invention to provide an automated system and method for providing a true measure of a storm occurring and a correct measure and a correct prediction of the associated storm risk, i.e. the storm risk exposure. Another object of the invention is to provide a prediction system for evaluating and measuring the effects of a storm on the basis of the measured storm risk exposure, that is to say for evaluating the risk associated with the effects of a storm. Finally, another task is to provide an automated risk transfer system that can provide a quick payout of funds to provide external expenditure support based on the measured and determined storm risk exposure that facilities experience in the immediate aftermath of a storm.
[0020] According to the present invention, these objects are met in particular with the features of the independent claims. In addition, further advantageous embodiments can be derived from the dependent claims and the corresponding descriptions.
According to the present invention, the aforementioned goals for a parametric risk transfer system based on automated location-dependent probabilistic tropical storm risk and storm impact prediction are achieved in particular in that the risk transfer system based on automated location-dependent probabilistic tropical storm risk and storm impact forecasting weather measurement results with weather event results a plurality of delocalized distributed measuring stations are recorded and sent to a central unit, the measured weather measurement parameters comprising at least measurement parameters of the wind speed and / or the highest wind speed in a predefined time frame; is generated as a spatially high-resolution grid comprising grid cells over a geographic area of interest with a detection unit, the area comprising at least a part of risk-exposed units on the ground, a plurality of delocalized distributed measuring stations being selected and linked to the grid, and wherein each cell the grid has a defined distance from each of the delocalized distributed measuring stations; are transmitted as measurement parameters of the delocalized, distributed measurement stations linked to the grid via a data transmission network to the acquisition unit, the acquisition unit comprising a memory with a searchable data table in which data records comprising transmitted measurement parameters are stored, which are assigned to the corresponding measurement station with the searchable data table; as wind field parameters indexed with a core generator, generated dynamically for different acquisition time frames in accordance with the spatially high-resolution grid and linked to a wind field profile, the wind field parameters for each grid cell of the wind field profile being determined on the basis of the measurement parameters sent, with the sum of the measurement parameters sent from all measurement stations, weighted by the station weighting factor assigned to the corresponding weather history station and grid cell, and normalized across all grid cells; are generated as grid-cell-specific risk exposure parameters with the system on the basis of the indexed wind field parameters of the wind field profile; an output trigger signal is generated by the signal generator on the basis of the triggered exceeding of the grid cell-specific risk exposure parameter and / or indexed wind field value by triggering a grid-cell-specific risk exposure parameter and / or an indexed wind field parameter with a trigger module, and is sent to at least one linked activation device Operation of the activation device is controlled by the transmitted output activation signal. A wind field from each data set can be generated based on a definable wind field profile and a probability is assigned by an interpolation module to each point in the grid, which provides the probability of a specific wind force occurring at a given geographical location and at a given time. The weather measurement parameter can be measured and / or determined in a low spatial resolution with respect to the grid of a wind field profile. For example, the system can generate an aggregated high-resolution raster level for the geographic area or the geographic area based on the low spatial resolution by the measuring stations. The measuring stations can comprise, for example, land-based weather progression stations, satellite-based or aircraft-based or ship-based measuring devices or stations. Furthermore, the weather measurement parameter can be measured and / or determined, for example, in a high temporal resolution. The linked indexed values of the wind field profile can be derived, for example, on the basis of at least the weather measurement parameter and topological parameters and / or geostructural factors for representing topological and / or geological local formations. The station weighting factor can be generated, for example, at least on the basis of the horizontal distance and / or the height difference from the corresponding grid cell. The indexed values can be derived, for example, by an interpolation module based on a country-specific wind zone table according to the horizontal distance and / or the height difference. Furthermore, a station weighting factor can be assigned to each measuring station with a higher value for each selectable grid cell based on the proximity of the measuring station, the closer a measuring station is to the grid line. The invention offers the advantage, among other things, that the system is based primarily on the fact that there is a direct correlation between the speed of sustained winds during a particular storm and the losses occurring on the ground. The system therefore uses geographically distributed weather stations to mirror Varian9
CH 712 882 A2 effects of hurricanes on the ground and enables fast and precise detection of storm effects. The use of weather stations over a larger geographic area enables a more dynamic approach, taking into account how wind speeds can vary across the entire wind field of a storm. A prior art system cannot provide such a dynamic ground impact sensing structure. The system according to the invention does not have to use data from data sources such as the National Hurricane Center, which only reports the highest wind speed of the storm and cannot provide a detailed wind field. By using weather stations, the system according to the invention can deliver the wind speed on the ground at the location of the assets. By dynamically using the measurements from the distributed weather stations, the system can provide a more local view of the reality on the ground. For example, over 100 stations are spread across the Gulf and the east coast near coastal towns. Furthermore, weather history stations are designed especially for strong hurricane winds with up to 140 mph. You are completely independent in the event of a power or communication failure. State-of-the-art systems, on the other hand, such as the National Weather Service, use a weather measurement, which is mainly located at airports, typically far from the relevant assets and the locations to be measured, and are not hurricane-proof and therefore have a higher failure rate. Furthermore, prior art systems typically use a single weather station, which may not be sufficient to capture the effects that occur over a larger geographic area, such as an entire circle. Since hurricane tracks can approach from almost any direction, the system according to the invention has the advantage that it can measure dynamically at several stations and can send the corresponding data dynamically to the central unit, which improves coverage and reduces the risk base. Each station is assigned a weight based on the proximity of the stations to the location or asset of interest. A station that is closer to the values is given a higher weight in the trigger calculation process. This system structure enables storms to be dynamically tracked on the ground in real time, which was previously not possible with prior art systems. A spatially high resolution is, for example, a resolution with a cell size of less than 1 km2, in particular a cell size of less than 10,000 m2. Another advantage of the invention is that a much more economical use of the memory can be achieved, which also significantly affects the performance of the evaluation. For example, instead of storing storm measurement parameters for each of the measurement events, only a dynamically changed wind field map is saved on a highly detailed level (for example, in a 100 m resolution). The wind field maps are generated from the data from the measuring stations and the vicinity and stored on the cell level. Both pieces of information are combined to generate the local storm impact and / or the local storm impact risk at a specific location for all measurement cycles. In addition, better structuring of programs for storm-exposed locations or portfolios can be achieved. Since the contribution of each individual risk to the expected losses can be quantified, location-dependent lower limits can be determined or even certain locations can be excluded from storm cover. Further significant improvements in storm risk assessment can be achieved: for the first time in parameterization and acquisition measurement technology, a detailed and completely stochastic storm assessment system can be achieved. This can have a positive effect on the market positioning of industry and / or insurance and reinsurance and generate new business opportunities. Another advantage of the invention is the availability of a detailed automated storm assessment and storm impact system that enables higher quality property development data to be requested, for example to identify which risk transfer parameters and policies have optimized storm risk coverage. Finally, there can be an improved capacity allocation and determination: With a new wind field approach, expected losses can be consistently assigned to a specific contract. The invention also offers the advantage that topographical and geographic peculiarities such as local wind and storm corridors through geological structures (mountains, etc.) can be correctly recorded through their effect on the ground. Such correlations with the topological shape on the ground can also be correlated by the system according to the invention by means of a corresponding correlation module, for example comprising one or more adaptable correlation parameters. For example, different correlation modules can be used for wind field cell zones arranged at different topological formations. For example, wind field cell zones along a coast that is lower than a certain distance above sea level can be determined by a specific correlation module. The determination of the wind field cells along a coast can, for example, additionally be improved on the basis of storm events, for example comprehensively a method for sea, sea and land floods by hurricanes (Sea, Lake and Overland Surges from Hurricanes, SLOSH) or other available methods.
In an alternative embodiment, the wind field profile for each grid cell is an indexed value by multiplying the one-minute maximum wind speed at each linked delocalized distributed measuring station by the assigned station weighting factor, integrating the indexed value across all linked delocalized distributed measuring stations and normalizing the values of the indexed values generated across the entire wind field profile. This alternative embodiment offers the advantage, among other things, that the dynamic determination of the wind field and the prediction of the storm impact parameters on a specific grid cell can be achieved with improved accuracy and safety.
In an alternative embodiment, when an indexed value is triggered that exceeds the predefined triggering index value, a payment transfer module is activated, with a parametric monetary payment being activated by the payment transfer module to risk-exposed units in the corresponding triggered grid cell trans10
CH 712 882 A2 is fermented. This alternative embodiment has the advantage, among other things, that the present invention provides a parametric risk transfer and risk coverage system that provides fast transfer and payout of automated pooled resources and / or funds to assist in external spending, the facilities arise in the immediate aftermath of a storm in a specific grid cell. State-of-the-art public systems may face liquidity challenges, as there may be significant delays in payment from the federal government. Access to previously agreed funds can provide resources and provide budgetary clarity and operational stability for risk transfer and coverage systems during difficult times. The present invention uses geographically distributed weather stations to reflect the variation effects of hurricanes on the ground and enables rapid regulation when funds are most needed.
In a further alternative embodiment, the predefined triggering index value and / or the station weighting factors assigned to each grid cell for each measuring station can be dynamically adjusted in order to trigger different events on the basis of different sets of features of measuring parameters. This alternative embodiment offers the advantage, among other things, that the system enables further automation of the monitoring operation, in particular of its operation in relation to the dynamic detection of different types of events and the differentiation of the correlation of measured parameters.
In a further alternative embodiment, the system comprises a pattern of different predefined trigger index values, triggered when the various predefined trigger values occur simultaneously as a pattern of the grid cells, whereby the occurrence of a defined event is detected. The pattern for detecting a defined event can trigger, for example, the occurrence of a storm event and / or an event of a tropical storm and / or hurricanes and / or typhoons and / or cyclones. These alternative embodiments offer the advantage, among other things, that the complex structure of a storm event with regard to its wind field and the dependence of the wind field on topological structures and geological formations on the ground can be recorded and taken into account separately by the system. No prior art prediction system shows a similar technical ability. Furthermore, this alternative embodiment offers the ability to have increased sensitivity for detecting storm events and correctly assessing the risk.
In an alternative embodiment, a linking module comprises at least one adaptable event factor for delivering the spatial and / or temporal correlations for the measurements from different weather history stations. This alternative embodiment offers the advantage, among other things, that the topographical and geographic peculiarities such as local wind and storm corridors can be correctly recorded by geological structures (mountains, etc.) due to their impact on the ground. Such correlations with the topological shape on the ground can also be correlated by the system according to the invention by means of a corresponding correlation module, for example comprising one or more adaptable correlation parameters. For example, different correlation modules can be used for wind field cell zones arranged at different topological formations.
In a further alternative embodiment, the predefined trigger index values can be determined on the basis of historical data sets from corresponding portfolios of risk-exposed units on the ground in the corresponding grid cell, the predefined trigger index values being grid cell-specific and by providing a trigger with a spatially high-resolution grid can be determined on the basis of cell-dependent trigger index values. Furthermore, for example, vulnerability factors dependent on the grid cell can be automatically generated for units of a specific grid cell exposed to risk on the ground from the predefined trigger index values dependent on the grid cell and the historical data set of corresponding portfolios of units exposed to risk on the ground. Finally, generalized insurance risks dependent on grid cells can be generated, for example, on the basis of the vulnerability factors for units exposed to risk on the ground, which triggers the activation of an automated insurance system. This embodiment variant offers the advantage, among other things, that the present invention provides a parametric risk transfer and risk coverage solution that provides a quick payout of pooled resources and / or funds to provide support for external expenditure, the facilities in the immediate aftermath of a storm in a specific grid cell. State-of-the-art public systems may face liquidity challenges, as there may be significant delays in payment from the federal government. Access to previously agreed funds can provide resources and provide budgetary clarity and operational stability for risk transfer and risk coverage systems during challenging times. The present invention uses geographically distributed weather stations to reflect the variation effects of hurricanes on the ground and enables rapid regulation when funds are most needed.
In a further alternative embodiment, different correlation modules allow wind-exposed cell zones along a uniform topological exposed evaluation scale, which are arranged in a comparable inherent topology of the landscape, and a measured wind risk exposure on the basis of historical wind data and / or the dynamically recorded measurement data and / or the dynamically generated wind fields are generated. Furthermore, the grid cells can be generated on the basis of cell zones exposed to wind. For example, the generation can additionally include a method for sea, lake and land floods by hurricanes (Sea, Lake and Overland Surges from Hurricanes, SLOSH) or another suitable method according to the prior art
CH 712 882 A2
Technology can be improved. The invention also offers the advantage that the topographical and geographical peculiarities such as local wind and storm corridors through geological structures (mountains, etc.) can be correctly recorded through their effect on the ground. Such correlations with the topological shape on the ground can also be correlated by the system according to the invention by means of a corresponding correlation module, for example comprising one or more adaptable correlation parameters. For example, different correlation modules can be used for wind field cell zones arranged at different topological formations. For example, wind field cell zones along a coast that is lower than a certain distance above sea level can be determined by a specific correlation module. The determination of the wind field cells along a coast can, for example, additionally be improved on the basis of storm events, for example comprehensively a method for sea, sea and land floods by hurricanes (Sea, Lake and Overland Surges from Hurricanes, SLOSH) or other available methods.
In a further alternative embodiment, the parametric risk transfer system is based on an automated resource pooling system for risk sharing the storm risks of a variable number of risk-exposed units by providing dynamic, independent risk protection for the risk-exposed units by the automated resource pooling system, the resource pooling system being an assembly module for processing of risk-related unit data and providing the probability of risk exposure for one or a plurality of the pooled risk-exposed units on the basis of the risk-related component data, the risk-exposed units with the resource pooling system being received by a plurality of payments designed to receive and store payments from the risk-exposed units Modules are linked to pooling their risks and resources, and taking the event-driven core by triggering the grid-cell-specific risk exposure parameter and / or indexed value of the wind field profile, which exceed a predefined triggering index value, offers risk protection for a specific risk-exposed unit on the basis of received and stored payments from the risk-exposed unit, and wherein if the grid cell-specific risk exposure parameter and / or the indexed value of the wind field profile exceeds the predefined trigger index value, a loss linked to the risk-exposed units of a triggered grid cell is clearly covered by the resource pooling system through a parametric transfer of payments from the resource pooling system and automated risk transfer unit to the risk-exposed unit. The resource pooling system may include, for example, an assembly module for processing risk-related component data and delivering the probability of exposure to one or a plurality of the pooled risk-exposed entities, receiving and presumably storing payments from risk exposure components to pool their risks based on the total risk and / or the probability of risk exposure of the pooled risk-exposed units can be determined dynamically. The number of pooled risk-exposed units can, for example, be dynamically adaptable by the resource pooling system to an area in which the non-covariant risks covered by the resource pooling system only affect a relatively small proportion of the fully pooled risk-exposed units at any given time. Whenever a grid-cell-specific risk exposure parameter and / or an indexed value of the wind field profile exceeding the predefined trigger index value is triggered on the basis of the measurement parameters indicating one of the defined storm events, an entire parametric payment can be assigned with the triggering, with at least a first part of the fully assigned payment is triggered when the trigger index is exceeded. This alternative embodiment has the advantage, among other things, that the present invention provides a fully automated parametric risk transfer and risk coverage system that provides fast transfer and payout of pooled resources and / or funds to assist in external spending, the facilities arise in the immediate aftermath of a storm in a specific grid cell. State-of-the-art public systems may face liquidity challenges, as there may be significant delays in payment from the federal government. Access to previously agreed funds can provide resources and provide budgetary clarity and operational stability for risk transfer and risk coverage systems during challenging times. The present invention uses geographically distributed weather stations to reflect the variation effects of hurricanes on the ground and enables rapid regulation when funds are most needed.
In addition to the system as described above and corresponding methods, the present invention also relates to a computer program product comprising computer program code means for controlling one or more processors of the controller so that the controller executes the proposed method; and more particularly it relates to a computer program product comprising a computer readable medium containing the computer program code means for the processors.
Brief Description of the Drawings The present invention will be explained in more detail by way of example with reference to the drawings.
1 shows a block diagram for the schematic representation of an exemplary system 1 for automated location-dependent probabilistic tropical storm risk and storm impact prediction, wherein a spatially high-resolution raster 222 comprising raster cells 2221, 2222, 2223, 2224 via an interes12
CH 712 882 A2 geographic area is generated with a detection unit 12, the area comprises at least a portion of risk-exposed units 70-74 on the ground, wherein a plurality of delocalized distributed measuring stations 40-43 is selected and linked to the grid 122, and wherein each cell 1221, 1222, 1223, 1224 of the grid 122 has a defined distance from each of the delocalized distributed measuring stations 40-43. The measuring stations 40-43 can comprise, for example, land-based weather history stations, satellite-based or aircraft-based or ship-based measuring devices.
Fig. 2 shows a diagram for the schematic representation of orbit positions of hurricane Isaac from August 21 to September 1, 2012. Fig. 2-8 come from the hydro-meteorological forecasting center of the NOAA (National Oceanie and Atmospheric Administration).
3 shows a satellite image of GOES-13 of Hurricane Isaac approaching the Louisiana coast on August 28, 2012 at 18:15 UTC, reaching its peak strength of 70 kn.
Figure 4 shows an example of observation data of the highest tropical storm force or stronger winds (kn) in the Gulf of Mexico or over the southeastern United States during Hurricane Isaac. All observation heights are less than 20 m.
FIG. 5 shows an example of observation data of the highest tropical storm force or stronger wind gusts (kn) in the Gulf of Mexico or over the southeastern states of the USA during hurricane Isaac. All observation heights are less than 20 m.
FIG. 6 shows an example of estimated flood data (feet above earth) measured by USGSS tower flood pressure sensors, USGS flood marks and NOS tide levels in southeastern Louisiana for Hurricane Isaac.
Figure 7 shows an example of rainfall accumulations from Hurricane Isaac and its remains from August 25th to 3rd.
September 2012, as measured by the National Weather Service Hydrometeorological Forecast Center in College Park, MD.
8 shows examples of orbit predictions for Hurricane Isaak from August 21 to September 1, 2012 through various systems according to the prior art (dashed lines with positions for 0,
12, 24, 36, 48 and 72 h), the reference symbol denoting the results of (a) GFSI, (b) EMXI, (c) TVCA and (d) FSSE. The best path in any case is through the thick solid line with positions indicated at 6-hour intervals.
Detailed Description of the Preferred Embodiments Fig. 1 schematically shows an architecture for a possible implementation of an embodiment of the system 1 for automated location-dependent probabilistic tropical storm wind, storm risk and storm impact prediction on the ground depending on local topological and geographic formations and structures. 1 also shows a corresponding automated parametric risk transfer system 1 on the basis of automated, location-dependent probabilistic tropical storm risk and storm impact prediction.
Weather measurement parameters 401, 402, 403, 404 of weather events 60, 64 are measured, recorded with a plurality of delocalized distributed measuring stations 40-43 and sent to a central system 2. The measuring stations 40-43 can comprise, for example, land-based weather history stations, satellite-based or aircraft-based or ship-based measuring devices. The event-triggered system 1 and / or the central system 2 can comprise at least one processor and linked memory modules. The storm event triggered system 1 can also comprise one or more display units and operating elements such as a keyboard and / or graphic pointing devices such as a computer mouse. The measured weather measurement parameters 401, 402, 403, 404 can include at least measurement parameters 401, 402, 403, 404 of the wind speed and / or the highest wind speed in a predefined time frame. For data transmission, the delocalized, distributed measuring stations 40-43 are bidirectionally connected to the central system 2 through the data transmission network 5 or a corresponding data connection for the data transport.
A spatially high-resolution raster 212 comprising raster cells 2121, 212, 2123, 2124 is generated over a geographic area of interest with a detection unit 21, the area comprising at least a part of risk-exposed units 70-74 on the ground, a plurality of delocalized ones distributed measuring stations 40-43 selected and linked to the grid 122. Each cell 1221, 1222, 1223, 1224 of the grid 122 has a defined distance from each of the delocalized, distributed measuring stations 40-43. Measurement parameters 401, 402, 403, 404 of the delocalized distributed measurement stations 40-43 linked to the grid 212 are transmitted via the data transmission network 5 by a corresponding interface module of the central system 2 or the acquisition unit 21 and the linked and selected, delocalized distributed measurement stations 40-43 the detection unit 21 transmitted. The detection unit 21 comprises a memory 213 with a searchable data table 214, in which data records 2141,
CH 712 882 A2
Measurement parameters 401, 402, 403, 404 which have been sent in a comprehensive manner are stored in 2142, 2143, 2144 and are assigned to the corresponding measurement station 40-43 with the searchable data table 214.
For each selectable grid cell 2121,2122, 2123, 2124 and / or risk-exposed unit 70-74 based on the proximity of the measuring station 40-43, for example, a station weighting factor 410, 411,412, 413 can be assigned to each measuring station 40-43 with a higher value the closer a weather history station 40-43 is to grid cell 2121,
2122, 2123, 2124 and / or risk exposed units 70-74. In FIG. 1, reference numerals 401, 411, 421, 431 denote the station weighting factors 410, 411, 421, 431 assigned to the risk-exposed unit 72 on the basis of the proximity Parameters of the risk-exposed unit 70, ..., 74 and / or measuring station 40-43 are based.
With a core engine 211, indexed wind field parameters 2151, 2152, 2153, 2154 are generated dynamically for different acquisition time frames in accordance with the spatially high-resolution grid 212 and linked to a wind field profile 215, the grid field profile 215 for each grid cell 2121, 2122, 2123, 2124 Wind field parameters 410, 411, 412, 413 are determined on the basis of the transmitted measurement parameters 401, 402, 403, 404, while summing the transmitted measurement parameters 401, 402, 403, 404 of the data records 2141, 214, 2143, 2144 via all or a plurality of the measuring stations 40- 43, weighted by that of the corresponding measuring station 40-43 and grid cell 2121.2122,
2123, 2124 assigned station weighting factor and normalized across all grid cells 2121, 2122, 2123, 2124. For example, with the wind field profile 215 for each grid cell 2121, 2122, 2123, 2124 the indexed value 2151, 2152, 2153, 2154 is obtained by multiplying the one-minute intervals -High wind speed at each linked delocalized distributed measuring station 40-43 with the assigned station weighting factor 410, 411.412, 413, integrating the indexed value 2151.2152, 2153, 2154 over all linked delocalized distributed measuring stations 40-43 and normalizing the values of the indexed values 40- 43 generated over the entire wind field profile 215. Alternatively, the weather measurement parameters 401, 402, 403, 404 can preferably be measured and / or determined in a low spatial resolution with respect to the grid 212 of a wind field profile 215. Such a low spatial resolution of the measurements 401, 402, 403, 404 can be achieved by a corresponding number of linked delocalized distributed measurement stations 40-43 and / or by expanding the weather measurement parameter 401.402, 403, 404 by measurements from other sources by other devices such as the distributed measurement station 40-43 measured parameters, for example aircraft measurement data and / or satellite-based measurement data, can be achieved. Such data sources can include, for example, geographic information to define the trajectories of the corresponding historical storms and intensity data to indicate the strength of the storm. A source of such data can be, for example, the National Hurricane Center (NHC), part of the National Oceanie and Atmospheric Administration (NOAA), which is the source for Figures 2-8. Historical storm data can also be generated by generating the alternative storm lanes and wind development distributions, generating a plurality of alternative pressure developments for the historical and alternative lanes. Creating the alternative storm lanes and alternative pressure developments for the historical and alternative lanes can create a relatively large additional source of storms (historical and alternative).
With respect to the foregoing improvements, minimum pressures based on the sea surface temperature (SST) climatology may also be added to records 2141, 214, 2143, 2144. This means that for each location in the grid, the lowest pressure ever observed associated with the highest SST is entered at this particular location. This value serves as the "floor" for alternative pressure values associated with each location in the grid which (as described in more detail below) can be selected in connection with alternative pressure developments for the development of storm event 60, ..., 64. After the addition of minimum pressures, the pressure climatology and / or the indexed wind field parameters 2151, 2152, 2153, 2154 of the wind field 215 can be smoothed, for example. The goals of the smoothing process include one or more of the following: achieving full coverage of the area of interest; smoothing variations in the distributions of pressures, wind field parameters such as wind speed, and wind and pressure derivatives from one grid to neighboring grids; smoothing variations in distributions of minima, maxima and averages of absolute wind speed, pressures and derivatives; and achieving the same number of "observations" at each grid location. This smoothing process results in a more consistent set of wind field related values for the area of interest for use in a sampling process, which is described in more detail below. In the special embodiment described, the quantities to be smoothed are not divider quantities (such as a parameter of the average wind or a pressure quantity at each location), but rather wind parameter-related distributions for each location. Accordingly, the smoothing process is relatively complex.
On the other hand, the system 1 can, for example, also generate an aggregated high-resolution raster level for the geographic area or the geographic area or the raster cell 2121, 212, 2123, 2124 on the basis of the low spatial resolution by the weather history stations 40-43. The weather measurement parameter 401, 402, 403, 404 can be measured and / or determined, for example, in a high temporal resolution with the assigned distributed measuring station 40-43. This enables dynamic real-time detection and risk prediction or determination by the system 1. The indexed wind field parameters 2151, 215, 2153, 2154 associated with the wind field profile 215 can be based, for example, on at least the weather measurement parameters 401, 402, 403, 404 and topological parameters and / or geostructural factors. The station weighting factors 410, 411, 412, 413 can, for example, at least on the basis of the horizontal distance and / or the height difference in relation to the corresponding one
CH 712 882 A2
Raster cell 2121,2122, 2123, 2124 can be generated. Furthermore, the indexed values 2151, 2152, 2153, 2154 can be derived by an interpolation module based on a country-specific wind zone table according to the horizontal distance and / or the height difference.
Alternatively, the predefined trigger index values 2211 can be determined, for example, on the basis of the historical data set of corresponding portfolios of risk-exposed units 70-74 on the ground in the corresponding grid cell 2121, 212, 122, 123, 2124, the predefined trigger index values 2211 being determined grid-specifically, which results in triggering with a spatially high-resolution raster 212 on the basis of cell-specific trigger index values 2211. The predefined trigger index values 2211 can also be improved, for example, by determining them on the basis of the historical data set of corresponding portfolios of risk-exposed units 70-74 on the ground in the corresponding grid cell 2121, 212, 2123, 2124, the predefined trigger index values 2211 and / or station weighting factors 410,411,412,413 are determined grid-specifically, which results in triggering with a spatially high-resolution grid 212 on the basis of cell-specific trigger index values 2211. Furthermore, for example, vulnerability factors dependent on the grid cell 2121,2122, 2123, 2124 for units 70, ..., 74 of a specific grid cell exposed to risk on the ground can automatically depend on the predefined triggering index values 2211 dependent on the grid cell 2121, 2122, 2123, 2124 and the historical one Data set of corresponding portfolios of risk-exposed units 70, ..., 74 are generated on the ground.
By triggering a grid-cell-specific risk exposure parameter and / or an indexed wind field parameter 2151, 2152, 2153, 2154 of the wind field profile 215 that exceeds a predefined value of the trigger index 2211 with a trigger module 221, an output activation signal 2221 is triggered by the signal generator 222 on the basis of the triggered exceeding generated grid-cell-specific risk exposure parameters and / or indexed wind field value and sent to at least one linked activation device 30, ..., 35. The operation of the activation device 30, ..., 35 is controlled by the transmitted output activation signal 2221. The activation device 30,..., 35 can comprise, for example, automated risk transfer units 30, 31, in particular a coupled first 30 and second 31 risk transfer unit. The additional risk transfer units 30, 31 are activated and additionally activated by the output activation signal 2221 of the signal generator 222. The signal generator 222 can also be coupled to automated alarm systems 33, distributed in the grid cells 2121, 212, 2123, 2124 and dynamically activated on the basis of the development of the grid cell-specific risk exposure parameters and / or the indexed wind field parameters 2151, 215, 2153, 2154 of the wind field profile 215 his. Furthermore, the signal generator 222 can be coupled to an automated message system 34, wherein if the indexed value is exceeded in a raster cell 2151, 215, 2153, 2154, the signal generator 222 generates corresponding alarm messages and dynamically on message receiving devices such as mobile telephones that have the spatially high-resolution raster 212 or the grid cells 2121, 212, 2123, 2124 are linked. Finally, system 1 may be coupled by signal generator 222 to any other automated activatable device such as lock gates, etc., to dynamically operate these devices within grid cells 2121, 2122, 2123, 2124 based on changing conditions in wind field profile 215 or the indexed ones To control wind field parameters 2151, 2152, 2153, 2154. For example, the predefined triggering index value 2211 and / or the station weighting factors 401,411,421,431 assigned to each raster cell 2121,2122, 2123, 2124 for each measuring station 40-43 can be dynamically adaptable in order to trigger different events based on different sets of features of measuring parameters. The system 1 may include a pattern of a plurality of different predefined trigger index values 2211 for triggering when the various predefined trigger index values 2211 occur simultaneously as a pattern of the grid cells 2121, 2122, 2123, 2124, thereby detecting the occurrence of a defined event. The pattern for detecting a defined event can trigger, for example, the occurrence of a storm event and / or an event of a tropical storm and / or hurricanes and / or typhoons and / or cyclones. Furthermore, a wind field profile 215 can be generated from each data set, and a probability is assigned by an interpolation module to each point in the spatially high-resolution raster 212, which indicates the probability and / or the risk of a specific wind force occurring at a predetermined geographic location and at a predetermined Time delivers.
System 1 can dynamically detect correlations. For example, a link module of the system 1 can comprise at least one adaptable event factor for delivering the spatial and / or temporal correlations for the measurements from different measuring stations 40-43. In addition, using different correlation modules, wind-exposed cell zones arranged along a uniform topological exposed evaluation scale are generated in a comparable inherent topology of the landscape, and a wind risk exposure is measured on the basis of historical wind data and / or the dynamically acquired measurement data and / or the dynamically generated wind fields 212. The grid cells 2121, 2122, 2123, 2124 can thus be generated on the basis of the wind-exposed cell zones. For example, the generation may additionally be by a method for sea, lake and overland surges from hurricanes (SLOSH) or any other method for predicting or assessing storm event exposure, storm risks and the behavior of storm events 60 ..... 64 can be improved.
[0042] As an alternative embodiment, when triggering grid-cell-specific risk exposure parameters and / or indexed values 2151, 2152, 2153, 2154 in excess of the predefined trigger index value 2211, a payment transfer module of the activation device 30/31 can be activated, with a parametric monetary number 15
CH 712 882 A2 is activated upon activation from the payment transfer module to the risk-exposed units 70-74 in the corresponding triggered grid cell 2121, 2122, 2123, 2124. For example, a generalized risk transfer or insurance risk dependent on the grid cell 2121, 2122, 2123, 2124 is generated with the system 1 on the basis of the vulnerability factors for units 70,... 74 exposed to the ground, which activates an automated risk transfer system 30, 31 triggers. The parametric risk transfer system 1 can, for example, on an automated risk transfer unit 30 with a resource pooling system 301 to divide the storm risks of a variable number of risk-exposed units 70, ..., 74 by providing dynamic, independent risk protection for the risk-exposed units 70, ..., 74 automated resource pooling system 301. The resource pooling system 301 can be an assembly module for processing risk-related unit data 2141, 214, 2143, 2144 and delivering the probability of risk exposure for one or a plurality of the pooled risk-exposed units 70, ..., 74 based on the risk-related component data 2141, 2142, 2143, 2144. The risk-exposed units 70,..., 74 can be connected to the resource pooling system 30 by a plurality of modules for receiving their payments and receiving payments made by the risk-exposed units 70,..., 74 for pooling their risks and resources. The event-triggered trigger module 221 triggers the grid-cell-specific risk exposure parameters and / or indexed wind field parameters 2151, 2152, 2153, 2154 of the wind field profile 212, which exceed a predefined trigger index 2211, in order to base risk protection for a specific risk-exposed unit 70,..., 74 of received and stored payments from the risk-exposed unit 70, ..., 74 to offer. If the grid-cell-specific risk exposure parameters and / or the indexed wind field parameters 2151, 2152, 2153, 2154 of the wind field profile 215 exceed the predefined trigger index value 2211, a link to the risk-exposed units 70,... 74 of a triggered grid cell 2121, 2122, 2123, 2124 becomes more linked Loss clearly covered by the resource pooling system 301 through a parametric transfer of payments from the resource pooling system 301 and the automated risk transfer unit 30 to the risk-exposed unit 70, ..., 74. Thus, the parametric risk transfer and / or insurance system provides a fast, secure transfer and a quick payout of the pooled resources to provide support for external expenses that arise in the immediate aftermath of a storm event 60, ..., 64. Public institutions in particular are facing liquidity challenges, as there may be significant delays in payments from the federal government. Access to pre-agreed resources can provide budgetary clarity and stability in challenging times. The invention uses geographically distributed weather stations to reflect the variation effects of hurricanes on the ground and enables rapid regulation when resources are most urgently needed. The number of pooled risk-exposed units 70,... 74 can be dynamically adaptable by the automated risk transfer unit 30 with the resource pooling system 301 to an area in which the non-covariant risks covered by the resource pooling system 301 represent only a relatively small proportion of the fully pooled risk-exposed units 70, ..., 74 at a specific point in time. Each time a grid-cell-specific risk exposure parameter and / or an indexed wind field parameter 2151, 2152, 2153, 2154 of the wind field profile 215, which exceeds the predefined trigger index 2211, is triggered, on the basis of the wind measurement parameters 401, 402, 403, 404 and / or one the defined storm events 60, ..., 64 displaying data records 2141, 214, 2143, 2144 are assigned an entire parametric payment with the triggering, at least a first part of the fully assigned payment being transferred when the triggering index 2211 or triggering indices 2211 are exceeded , Alternatively, the parametric risk transfer system 1 can provide a dynamic, independent risk protection structure for a variable number of defined risk exposure components 70, ..., 74. In this alternative, the risk exposure components 70,... 74 are with the risk transfer unit 30 of the weather event-triggered system 1 by transferring the storm events 60.
linked risk exposure from the risk exposure components 70, ..., 74 to the risk transfer unit 30 with appropriate, mutually aligned first risk transfer parameters to the automated resource pooling system 301 for pooling electronic payment parameters. Furthermore, the risk transfer unit 30 has a second risk transfer unit 31 with a second automated resource pooling system 311 via the signal generator 222 of the storm event-triggered system 1 by transferring the risk exposure associated with the occurrence of the defined storm risk events 60,..., 64 from the risk transfer unit 30 to the second risk transfer unit 31 linked with appropriate, mutually aligned second risk transfer parameters and correlated aligned second payment transfer parameters for at least partially transferring resources from the resource pooling system 301 to the second resource pooling system 302. For this alternative, if one of the defined storm risk events 60, ..., 64 occurs, loss parameters for measuring the loss on the risk exposure components 70, ..., 74 are recorded and transmitted to the risk transfer unit 30, in which the loss that occurred , automatically covered by the risk transfer unit 30 and / or the second risk transfer unit 31, activated by the signal generator 222 on the basis of the appropriate, mutually oriented second risk transfer parameters generated output activation signal 2221.
The resource pooling systems 301/311 and the automated risk transfer units 30/31 as well as the other activation devices 33-35 are technical devices comprising electronic means used by service providers in the field of risk transfer or insurance technology for the purpose of risk transfer with regard to the occurrence of measurable storm risk events 60-64 can be used. The invention aims to capture, process and automate corresponding processes of the automated risk transfer or insurance systems 30, 31 by complex technical means in order to optimize the interaction of coupled systems 30, 31 and
CH 712 882 A2 to reduce operational requirements. Another aspect that is addressed is the search for ways to synchronize and adapt such processes in relation to the coupling or switching of resource pooling systems 301, 311, which aim at a proven risk protection for risk-exposed units on the basis of technical means. In contrast to standard practice, resource pooling systems 301, 311 also achieve reproducible, dynamically adaptable processes with the desired technical, repeatable accuracy, since they are based entirely on technical means, a process flow and process control / operation. Furthermore, as mentioned, the risk-exposed components 70,... 74, etc. with the risk transfer unit 30 and the resource pooling system 301 are pooled with the plurality of modules designed to receive and store payments from the risk-exposed components 70, 64 to pool their risks connected to a payment data store. Payments can be saved by transferring and saving component-specific payment parameters. The payment amount can be determined dynamically with the resource pooling system 301 on the basis of the total risk of the total pooled risk exposure components 70, ..., 74. For resource pooling, the system 1 may include a monitoring module that requests a regular payment transfer from the risk exposure components 70, 74, etc. to the resource pooling system 301 by the payment transfer module, with the risk protection for the risk exposure components 70, 71 being interrupted by the monitoring module if the regular one
Transfer by the monitoring module is no longer detectable. In an alternative embodiment, the request for regular payment transfers is automatically interrupted or omitted by the monitoring module if the occurrence of risk event indicators in the data flow path of a risk exposure component 70, 74 is triggered. Similarly, the first risk transfer unit 30 can be connected to the resource pooling system 301 with a second risk transfer unit 31 having a second resource pooling system 311 via a second payment transfer module that is used to receive and store payments from the resource pooling system 301 of the first risk transfer unit 30 or insurance unit 30 for the transfer of each the pooled risk and risk exposure components 70, 74 from the first risk transfer unit 30 to the second risk transfer unit 31. The coupling and switching of the two supplementary, independently operated risk transfer units 30, 31 with the resource pooling systems 301, 311 is achieved by the event-triggered signal processing unit 22 with the signal generator 222 for generating and sending corresponding control signals to the first and second risk transfer units 30, 31 and the resource pooling systems 301, 311, respectively , As shown in FIG. 1, the storm event-triggered system 1 comprises a data storage module for recording the risk-related weather measurement parameters 401, 402, 403, 404, risk-related data of the risk-exposed units 70,... 74 and several functional modules; for example in particular the payment transfer modules, the trigger module 221, the signal generator 222, the core engine 211, the aggregation module or the acquisition unit 21. The functional modules can be at least partially as programmed software modules stored on a computer-readable medium, connected in a fixed or removable manner to the processor / the processors of the storm event triggered system 1 or with linked automated units 30, 31. However, a person skilled in the art understands that the functional modules can also be implemented completely using hardware components, units and / or correspondingly implemented modules. As shown in FIG. 1, the storm event-triggered system 1 and its components, in particular first and second resource pooling systems 301, 311, the acquisition unit 21, the trigger module 221, the weather measuring stations 40, 43 with the interfaces, the aggregation module and the payment transfer modules can be carried out by one
Network 5 such as a telecommunications network or any other data transmission network. Network 91 may include a hardwired or wireless network; For example, the Internet, a GSM network (Global System for Mobile Communication), a UMTS network (Universal Mobile Télécommunications System) and / or a WLAN (Wireless Local Region Network) and / or dedicated point-to-point communication lines. In any case, the technical, electronic, money-related configuration for the present system includes appropriate technical, organizational and procedural measures to prevent, contain and detect threats to the security of the structure, in particular the risk of forgery. The resource pooling systems 301, 311 also include all the necessary technical means for electronic money transfer and connection linking, for example as initiated by one or more linked payment transfer modules via an electronic network. The monetary parameters can be based on any possible electronic transfer means such as e-currency, e-money, electronic payment transactions, electronic currency, digital money, digital payment transactions or cyber currency, etc., which can only be exchanged electronically. The first and second payment data stores 61, 62 provide the means for linking and storing monetary parameters linked to a single one of the pooled risk exposure components 21, 22, 23. The present invention can include the use of the aforementioned networks such as computer networks or telecommunications networks, and / or the Internet and digital stored value systems. Electronic payments (EFT), transfer, digital gold currency and virtual currency are further examples of electronic money modalities. Transfers can also include technologies such as financial cryptography and technologies to enable such transfers. For transactions involving monetary parameters, hard electronic currency is preferably used without the technical means of challenging or reversing charges. The resource pooling system 101, 121 supports, for example, transactions that cannot be reversed. The advantage of this arrangement is that the operating costs of the electronic currency system are significantly reduced by the fact that no payment disputes have to be resolved. This also enables electronic currency transactions to be released immediately, which makes the funds immediately available to systems 10, 12. This means using a hard electronic currency
CH 712 882 A2 is more like a cash transaction. However, it is also conceivable to use a soft electronic currency, for example a currency that enables the reversal of payments, for example by providing a “release time” of 72 hours or the like. The electronic monetary parameter change method applies to all connected systems and modules with respect to the resource pooling systems 101, 121 of the present invention, such as the first and second payment transfer modules. The monetary parameter transfer to the first and second resource pooling systems 101, 121 can be initiated by a payment transfer module or on request from the relevant resource pooling system 101 or 121.
List of reference numerals [0044]
Parametric risk transfer system
Central system
acquisition unit
211 core engine
212 Spatially high-resolution grid
2121,2122, 2123, 2124 grid cells
213 memories
214 Searchable data table
2141,2142, 2143, 2144 data sets with sent measurement parameters
215 wind field profile
2151,2152, 2153, 2154 Indexed wind field parameters
Signal processing unit
221 trip module
2211 Predefined trigger index
222 signal generators
2221 Output activation signal 30, ..., 35 activation device
First automated risk transfer unit
301 First automated resource pooling system
Second automated risk transfer unit
311 Second automated resource pooling system
40-43 weather history stations
401.402, 403, 404 weather measurement parameters 410, 411.412, 413 station weighting factors
Data transmission network
60-64 weather events / storm events
70-74 units exposed to risk

权利要求:
Claims (30)
[1]
claims
1. Parametric risk transfer system based on automated location-dependent probabilistic tropical storm risk and storm impact prediction and determination, whereby weather measurement parameters (401.402, 403.404) of weather events (60-64) are measured, recorded with a plurality of delocalized distributed measuring stations (40-43) and on central system (2) are sent, and wherein the measured weather measurement parameters at least
CH 712 882 A2
Measurement parameters (401, 402, 403, 404) of the wind speed and / or highest wind speed in a predefined time frame, characterized in that a spatially high-resolution grid (212) comprising grid cells (2121, 212, 2123, 2124) over a geographic area of interest with a Detection unit (21) is generated, the area comprising at least a part of risk-exposed units (70-74) on the ground, a plurality of delocalized distributed measuring stations (40-43) being selected and linked to the grid (212), and wherein each cell (2121,2122, 2123, 2124) of the grid (212) has a defined distance to each of the delocalized distributed measuring stations (40-43) that measuring parameters (401, 402, 403, 404) of the delocalized distributed linked to the grid (212) Measuring stations (40-43) are transmitted to the detection unit (21) via a data transmission network (50), the detection unit (21) storing a memory cher (213) with a searchable data table (124), in which data records (2141, 214, 2143, 2144) comprising transmitted measurement parameters of the corresponding measuring station (40-43) are stored assigned by the searchable data table (214), that with a Core engine (211) indexed wind field parameters (2151, 2152, 2153, 215) generated dynamically for different acquisition time frames according to the spatially high-resolution grid (212) and linked to a wind field profile (215), whereby for each grid cell (2121, 2122, 2123, 2124 ) of the wind field profile (215) the wind field parameters (2151, 2152, 2153, 2154) are determined on the basis of the sent measurement parameters (401.402.403.404) or data records (2141.2142, 2143.2144), adding up the sent measurement parameters (401.402, 403, 404) of all measuring stations (40-43), weighted by the station weighting factor (410, 411.41) assigned to the corresponding measuring station (40-43) and grid cell (2121, 212, 2123, 2124) 2, 413), and normalizes across all grid cells (2121, 212, 2123, 2124) that grid cell-specific risk exposure parameters are generated with the system (1) on the basis of the indexed wind field parameters (2151, 215, 2153, 2154) of the wind field profile (215) that by triggering a grid-cell-specific risk exposure parameter exceeding a predefined value of the trigger index (2211) with a trigger module (221), an output activation signal (2221) is generated by the signal generator (222) on the basis of the triggered exceeding of the grid-cell-specific risk exposure parameter and to at least one linked activation device (30, ..., 35) is sent, the operation of the activation device (30 ..... 35) being controlled by the transmitted output activation signal (2221).
[2]
2. Parametric risk transfer system according to claim 1, characterized in that using the wind field profile (215) for each grid cell (2121, 2122, 2123, 2124) the indexed value (2151, 2152, 2153, 2154) by multiplying the one-minute Maximum wind speed at each linked delocalized distributed measuring station (40-43) with the assigned station weighting factor, integrating the indexed value (2151, 2152, 2153, 2154) across all linked delocalized distributed measuring stations (40-43) and normalizing the values of the indexed values (2151 , 2152, 2153, 2154) is generated over the entire wind field profile (215).
[3]
3. Parametric risk transfer system according to one of claims 1 or 2, characterized in that when grid-cell-specific risk exposure parameters and / or indexed throws (2151, 2152, 2153, 2154) are triggered in excess of the predefined trigger index value (2211), a payment transfer module of the activation device ( 30, ..., 35) is activated, whereby a parametric monetary payment is activated when activated from the payment transfer module to the risk-exposed units (70-74) in the corresponding triggered grid cell (2121, 2122, 2123, 2124).
[4]
4. Parametric risk transfer system according to one of claims 1 to 3, characterized in that the predefined triggering index value (2211) and / or the station weighting factors (401, 43) assigned to each grid cell (2121, 212, 2123, 2124) for each measuring station (40-43) 411, 421, 431) are dynamically adaptable in order to trigger different events on the basis of different sets of features of measurement parameters (401, 402, 403, 404).
[5]
5. Parametric risk transfer system (1) according to one of claims 1 to 4, characterized in that the system (1) a pattern of different predefined trigger index values (2211) for triggering when the different predefined trigger index values (2211) occur simultaneously as from the grid cells ( 2121, 2122, 2123, 2124) comprises a specific pattern, whereby the occurrence of a defined event is detected.
[6]
6. Parametric risk transfer system (1) according to claim 5, characterized in that the pattern for detecting a defined event triggers the occurrence of a storm event and / or an event of a tropical storm and / or hurricanes and / or typhoons and / or cyclones.
[7]
7. Parametric risk transfer system according to one of claims 1 to 6, characterized in that a wind field profile (215) is generated from each data set, and a probability is assigned by an interpolation module to each point in the spatially high-resolution grid (212), which is the probability of The occurrence of a specific wind strength at a given geographical location and at a given time.
[8]
8. Parametric risk transfer system according to one of claims 1 to 7, characterized in that a link module at least one adaptable factor for an event (60, ..., 64) for delivering the spatial and / or temporal correlations for the measurements from different measuring stations ( 40-43).
CH 712 882 A2
[9]
9. Parametric risk transfer system according to one of claims 1 to 8, characterized in that the weather measurement parameters (401, 402, 403, 404) are measured and / or determined in a low spatial resolution with respect to the grid (212) of a wind field profile (215) become.
[10]
10. Parametric risk transfer system according to claim 9, characterized in that the system (1) on the basis of the low spatial resolution by the weather history stations (40-43) an aggregated high-resolution raster level for the geographic area or the geographic area and / or the raster cell ( 2121, 2122, 2123, 2124).
[11]
11. Parametric risk transfer system according to one of claims 1 to 10, characterized in that the weather measurement parameters (401, 402, 403, 404) are measured and / or determined in a high temporal resolution by the assigned distributed measurement station (40-43).
[12]
12. Parametric risk transfer system according to one of claims 1 to 11, characterized in that the indexed wind field parameters (2151, 2152, 2153, 2154) linked to the wind field profile (215) on the basis of at least the weather measurement parameter (401.402.403.404) and topological parameters and / or geostructural factors.
[13]
13. Parametric risk transfer system according to one of claims 1 to 12, characterized in that the station weighting factors (410, 411, 412, 413) at least on the horizontal distance and / or the height difference in relation to the corresponding grid cell (2121, 212, 2123, 2124) can be generated.
[14]
14. Parametric risk transfer system according to one of claims 1 to 13, characterized in that the indexed values (2151, 215, 2153, 2154) are derived by an interpolation module based on a country-specific wind zone table in accordance with the horizontal distance and / or the height difference.
[15]
15. Parametric risk transfer system according to one of claims 1 to 14, characterized in that predefined trigger index values (2211) on the basis of the historical data set of corresponding portfolios of risk-exposed units (70-74) in the corresponding grid cell (2121, 212, 2123 , 2124) are determined, the predefined triggering index values (2211) and / or station weighting factors (410, 411, 412, 413) being determined in a grid-specific manner, which enables triggering in a spatially high-resolution grid (212) on the basis of cell-specific triggering index values (2211).
[16]
16. Parametric risk transfer system according to one of claims 1 to 15, characterized in that on the grid cell (2121, 2122, 2123, 2124) dependent vulnerability factors automatically for the units (70, ..., 74) exposed to the ground of a specific grid cell from the predefined trigger index values (2211) dependent on the grid cell (2121, 212, 2123, 2124) and the historical data set of corresponding portfolios of risk-exposed units (70, ..., 74) are generated on the ground.
[17]
17. Parametric risk transfer system according to one of claims 15 and 16, characterized in that generalized risk transfer or insurance risks dependent on the grid cell (2121, 2122, 2123, 2124) due to the vulnerability factors for units exposed to the ground (70, ..., 74) are generated, which triggers the activation of an automated risk transfer system (30, 31).
[18]
18. Parametric risk transfer system according to one of claims 15 to 17, characterized in that by different correlation modules wind exposed cell zones arranged along a uniform topological exposed evaluation scale in a comparable inherent topology of the landscape and measured wind risk exposure on the basis of historical wind data and / or the dynamic detected Measurement data and / or the dynamically generated wind fields (212) are generated.
[19]
19. Parametric risk transfer system according to claim 18, characterized in that the grid cells (2121, 212, 2123, 2124) are generated on the basis of the cell zones exposed to the wind.
[20]
20. Parametric risk transfer system according to claim 19, characterized in that additionally generating by a method for sea, sea and land floods by hurricanes (Sea, Lake and Overland Surges from Hurricanes, SLOSH) or any other method for predicting or evaluating the Storm event exposure that improves storm risks and the behavior of storm events 60, ..., 64.
[21]
21. Parametric risk transfer system (1) according to one of claims 1 to 20, characterized in that the parametric risk transfer system (1) on an automated risk transfer unit (30) with a resource pooling system (301) for risk sharing the storm risks of a variable number of risk-exposed units (70 , ..., 74) by providing dynamic, independent risk protection for the risk-exposed units (70, ..., 74) by the automated resource pooling system (301), the resource pooling system (301) being an assembly module for processing risk-related unit data (2141,2142, 2143,2144) and providing the probability of risk exposure for one or a plurality of the pooled risk-exposed units (70, ..., 74) on the basis of the risk-related component data (2141, 2142, 2143, 2144), the risk-exposed units (70, ..., 74) with the resource pooling system (30) by a plurality of vo n for receiving and storing payments from the risk-exposed units (70, ..., 74) trained receiving modules for pooling their risks and resources, and wherein the event-driven
CH 712 882 A2
Trigger module (221) triggers the grid-cell-specific risk exposure parameter and / or indexed wind field parameters (2151, 215, 2153, 2154) of the wind field profile (212) that exceed a predefined trigger index (2211) in order to provide risk protection for a specific risk-exposed unit (70, .. ., 74) on the basis of received and stored payments from the risk-exposed unit (70, ..., 74), and if the indexed wind field parameters (2151, 2152, 2153, 2154) of the wind field profile (215) den a predefined trigger index value (2211), a loss clearly associated with the risk-exposed units (70, ..., 74) of a triggered grid cell (2121,2122,2123, 2124) by the resource pooling system (301) through a parametric transfer of payments from the resource pooling system ( 301) and automated risk transfer unit (30) to the risk-exposed unit (70, ..., 74) is covered.
[22]
22. Parametric risk transfer system (1) according to claim 21, characterized in that the automated risk transfer unit (30) with the resource pooling system (301) is an assembly module for processing risk-related component data of the risk-exposed units (70, ..., 74) and Providing the likelihood of risk exposure for one or a plurality of the pooled risk exposed entities (70, ..., 74), wherein receiving and presumably storing payments from risk exposure components (70, ..., 74) to pool their risks the basis of the total risk and / or the probability of risk exposure of the pooled risk-exposed units (70, ..., 74) can be determined dynamically.
[23]
23. Parametric risk transfer system (1) according to one of claims 21 to 22, characterized in that the number of pooled risk-exposed units (70, .... 74) is dynamically combined by the automated risk transfer unit (30) with the resource pooling system (301) Area is adaptable in which the non-covariant risks covered by the resource pooling system (301) only affect a relatively small proportion of the fully pooled risk-exposed units (70, ..., 74) at a certain point in time.
[24]
24. Parametric risk transfer system (1) according to one of claims 21 to 23, characterized in that each time an indexed wind field parameter (2151, 215, 2153, 2154) of the wind field profile (215) and / or one that exceeds the predefined trigger index (2211) is triggered linked risk exposure parameters based on the wind measurement parameters (401, 402, 403, 404) and / or the data records (2141, 214, 2143, 2144) indicating one of the defined storm events (60, 64), an entire parametric payment is assigned with the triggering, at least a first portion of the fully allocated payment is triggered when the trigger index (2211) or trigger index (2211) is exceeded.
[25]
25. Parametric risk transfer system (1) according to one of claims 21 to 24, characterized in that the parametric risk transfer system (1) provides a dynamic, independent risk protection structure for a variable number of defined risk exposure components (70, ..., 74), the Risk exposure components (70, ..., 74) with the risk transfer unit (30) of the weather event-triggered system (1) by transferring the risk exposure associated with the occurrence of the defined storm risk events (60, ..., 64) from the risk exposure components (70, .. ., 74) to the risk transfer unit (30) with appropriate, mutually oriented first risk transfer parameters for the automated resource pooling system (301) for pooling electronic payment parameters, the risk transfer unit (30) with a second risk transfer unit (31) with a second automated resource pooling system (311 ) via the signal ore euger (222) of the storm event-triggered system (1) by transferring the risk exposure associated with the occurrence of the defined storm risk events (60, ..., 64) from the risk transfer unit (30) to the second risk transfer unit (31) with appropriate, mutually oriented second risk transfer parameters and correlated aligned second payment transfer parameters for at least partially transferring resources from the resource pooling system (301) to the second resource pooling system (302), and, if one of the defined storm risk events (60, ..., 64) occurs, loss parameters for measuring the loss to the Risk exposure components (70, ..., 74) are detected and sent to the risk transfer unit (30), and the loss that has occurred is automatically activated by the risk transfer unit (30) and / or the second risk transfer unit (31), with that by the signal generator (222 ) based on the appropriate, alternating output activation signal (2221) generated, is automatically covered.
[26]
26. Parametric risk transfer system according to one of claims 1 to 25, characterized in that measuring stations (40-43) comprise land-based weather history stations, satellite-based or aircraft-based or ship-based measuring devices.
[27]
27. Parametric risk transfer system according to one of claims 1 to 26, characterized in that for each selectable grid cell (2121, 212, 2123, 2124), based on the proximity of the measuring station (40-43), a station weighting factor (410, 411, 412, 413) each measuring station (40-43) with a higher value, the closer a weather history station (40-43) to the grid cell (2121, 2122, 2123, 2124) is assigned.
[28]
28. Method for a parametric risk transfer system based on automated location-dependent probabilistic tropical storm risk and storm impact prediction, whereby weather measurement parameters (401, 402, 403, 404) are measured from weather events (60-64), recorded with a plurality of delocalized distributed measuring stations (40-43) and are sent to a central system (2), and the measured weather measurement parameters are at least
CH 712 882 A2
Measurement parameters (401, 402, 403, 404) of the wind speed and / or highest wind speed in a predefined time frame, characterized in that a spatially high-resolution grid (212) comprising grid cells (2121, 212, 122, 123, 124) over a geographic area of interest with a detection unit (21) is generated, the area comprising at least a part of risk-exposed units (70-74) on the ground, a plurality of delocalized distributed measuring stations (40-43) being selected and linked to the grid (212), and each Cell (2121,2122, 2123, 2124) of the grid (212) has a defined distance to each of the delocalized distributed measuring stations (40-43) that has measurement parameters (401, 402, 403, 404) of the delocalized distributed measuring stations linked to the grid (212) (40-43) with a data transmission network (50) are transmitted to the detection unit (21), the detection unit (21) being a memory (213) with a searchable data table (124), in which data records (2141, 2142, 2143, 2144) comprising transmitted measured parameters of the corresponding measuring station (40-43) are stored with the searchable data table (214) that are stored with a core engine (211) indexed wind field parameters (2151, 2152, 2153,2154) are generated dynamically for different acquisition time frames in accordance with the spatially high-resolution grid (212) and linked to a wind field profile (215), whereby for each grid cell (2121, 2122, 2123,2124) of the wind field profile (215), the wind field parameters (2151, 2152, 2153, 2154) are determined on the basis of the sent measurement parameters (401.402.403.404) or data records (2141.2142, 2143.2144), adding up the sent measurement parameters (401.402, 403, 404 ) of all weather history stations (40-43), weighted by the station weighting factor (410.411. assigned to the corresponding measuring station (40-43) and grid cell (2121.2122, 2123, 2124) , 412, 413), and normalized across all grid cells (2121,2122, 2123, 2124) that grid cell-specific risk exposure parameters with the system (1) based on the indexed wind field parameters (2151,2152, 2153,2154) of the wind field profile (215) generated that by triggering a grid-cell-specific risk exposure parameter and / or an indexed wind field parameter (2151, 2152, 2153, 215) of the wind field profile (215) exceeding a predefined value of the trigger index (2211) with an trigger module (221) by an output activation signal (2221) generates the signal generator (222) based on the triggered exceeding of the grid cell-specific risk exposure parameter and / or indexed wind field value (2151, 2152, 2153, 2154) and sends it to at least one linked activation device (30, ..., 35), the operation the activation device (30, ..., 35) is controlled by the transmitted output activation signal (2221).
[29]
29. The method according to claim 28, characterized in that measuring stations (40-43) comprise land-based weather profile stations, satellite-based or aircraft-based or ship-based measuring devices.
[30]
30. The method according to any one of claims 28 or 29, characterized in that for each selectable grid cell (2121,2122, 2123, 2124) based on the proximity of the measuring station (40-43) a station weighting factor (410, 411, 412, 413 ) each measuring station (40-43) with a higher value, the closer a weather history station (40-43) is to the grid cell (2121, 2122, 2123, 2124).
CH 712 882 A2
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同族专利:

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公开号 | 公开日

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CH712882B1|2021-08-16|

引用文献:

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法律状态:

优先权:

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申请号 | 申请日 | 专利标题

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CH01156/16A|CH712882B1|2016-09-07|2016-09-07|Process for automated location-dependent probabilistic storm forecast and wind field forecast and for automated wind field and exposure-induced risk transfer.|CH01156/16A| CH712882B1|2016-09-07|2016-09-07|Process for automated location-dependent probabilistic storm forecast and wind field forecast and for automated wind field and exposure-induced risk transfer.|