Predicting the impact of tropical cyclones
阅读说明：本技术 预测热带气旋的影响 (Predicting the impact of tropical cyclones ) 是由 J·N·梅尔斯 M·莫斯 J·波特 M·鲁特 于 2019-12-09 设计创作，主要内容包括：一种热带气旋分析系统,其存储多个天气状况中的每一个的多个范围,识别预报的热带气旋,识别所述预报的热带气旋的预测路径,识别沿着所述预报的热带气旋的预测路径的每个国家或地区,并且对于沿着所述预报的热带气旋的预测路径的每个国家或地区,识别所述国家或地区中的可归因于所述预报的热带气旋的预报的天气状况,将所述国家或地区中的所述预报的天气状况与所述多个天气状况中的每一个的所述多个范围进行比较,基于所述国家或地区中的所述预报的天气状况与所述多个范围的比较表征所述国家或地区中的所述预报的热带气旋,以及输出所述表征以显示给用户。(A tropical cyclone analysis system that stores a plurality of ranges for each of a plurality of weather conditions, identifies a forecasted tropical cyclone, identifies a predicted path for the forecasted tropical cyclone, identifies each country or region along the forecasted predicted path for the tropical cyclone, and for each country or region along the predicted path of the forecasted tropical cyclone, identifying a forecasted weather condition in the country or region attributable to the forecasted tropical cyclone, comparing the forecasted weather condition in the country or region to the plurality of ranges for each of the plurality of weather conditions, characterizing the forecasted tropical cyclone in the country or region based on the comparison of the forecasted weather condition in the country or region to the plurality of ranges, and outputting the characterization for display to a user.)
1. A method for forecasting an impact of a forecasted tropical cyclone, the method comprising:
storing a plurality of ranges for each of a plurality of weather conditions;
identifying a forecasted tropical cyclone;
identifying a predicted path for the forecasted tropical cyclone;
identifying each country or region along the forecasted predicted path of tropical cyclones; and
for each country or region along the predicted path of tropical cyclone of the forecast:
identifying a forecasted weather condition in the country or region attributable to the forecasted tropical cyclone;
comparing the forecasted weather conditions in the country or region to the plurality of ranges for each of the plurality of weather conditions;
characterizing the forecasted tropical cyclones in the country or region based on a comparison of the forecasted weather conditions in the country or region to the plurality of ranges; and
outputting the representation for display to a user.
2. The method of claim 1, further comprising:
storing a plurality of ranges of predicted effect of the tropical cyclone;
determining one or more demographics of a geographic region in a predicted path of the forecasted tropical cyclone;
predicting an effect of the forecasted tropical cyclone in the country or region based on one or more of the forecasted weather conditions attributable to the forecasted tropical cyclone and the one or more demographics of a geographic area in a predicted path of the forecasted tropical cyclone; and
comparing the predicted effect of the forecasted tropical cyclone in the country or region to the plurality of ranges of predicted effects,
wherein the characterization of the forecasted tropical cyclone in the country or region is further based on a comparison of a predicted effect of the forecasted tropical cyclone in the country or region to the plurality of ranges of predicted effects.
3. The method of claim 2, further comprising:
determining one or more geographical features of a geographical area in a predicted path of the forecasted tropical cyclone,
wherein the predicted effect is further predicted based on the one or more geographic features of a geographic area in a predicted path of the forecasted tropical cyclone.
4. The method of claim 2, further comprising:
determining one or more geological features of a geographic region in a predicted path of the forecasted tropical cyclone,
wherein the predicted effect is further predicted based on the one or more geological features of a geographic region in a predicted path of the forecasted tropical cyclone.
5. The method of claim 2, wherein the predicted effect is a predicted economic impact of the forecasted tropical cyclone.
6. The method of claim 5, wherein the forecasted predicted economic impact of tropical cyclone is estimated by:
identifying economic impact of tropical cyclones in the past;
identifying each economic scale affected by each of the past tropical cyclones;
scaling the economic impact based on the size of the affected economy at each past tropical cyclone;
identifying a weather condition for each of the past tropical cyclones;
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and past weather conditions for each of the past tropical cyclones; and
generating a model estimating the economic impact of the tropical cyclones based on a correlation between the scaled economic impact of each of the past tropical cyclones and past weather conditions of each of the past tropical cyclones.
7. The method of claim 6, further comprising:
identifying demographic characteristics of a geographic area affected by each of the past tropical cyclones; and
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and the demographic of the geographic area affected by each of the past tropical cyclones,
wherein the model estimating the economic impact of tropical cyclones is further generated based on a correlation between the scaled economic impact of each of the past tropical cyclones and demographic characteristics of a geographic area affected by each of the past tropical cyclones.
8. The method of claim 6, further comprising:
identifying a geographic feature of a geographic region of the country or region in a predicted path of the forecasted tropical cyclone;
identifying geographic features of a geographic area affected by each of the past tropical cyclones;
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and the geographic characteristics of the geographic area affected by each of the past tropical cyclones, wherein:
the model estimating the economic impact of tropical cyclones is further generated based on a correlation between the scaled economic impact of each of the past tropical cyclones and a geographic feature of a geographic area affected by each of the past tropical cyclones; and is
The estimated economic impact of the forecasted tropical cyclone in the country or region is further based on geographic features of a geographic region of the country or region in a predicted path of the forecasted tropical cyclone.
9. The method of claim 6, further comprising:
identifying geological features of a geographic region of the country or region in a predicted path of the forecasted tropical cyclone;
identifying geological features of a geographic region affected by each of the past tropical cyclones; and
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and the geological features of the geographic area affected by each of the past tropical cyclones, wherein:
the model estimating the economic impact of tropical cyclones is further generated based on a correlation between the scaled economic impact of each of the past tropical cyclones and geological features of a geographic area affected by each of the past tropical cyclones; and is
The estimated economic impact of the forecasted tropical cyclone in the country or region is further based on geological features of a geographic region of the country or region in a predicted path of the forecasted tropical cyclone.
10. The method of claim 1, wherein the comparison of the forecasted weather conditions to the plurality of ranges and the characterization of the forecasted tropical cyclone based on the comparison are performed by a hardware computer processor without human intervention.
11. A system for forecasting an impact of a forecasted tropical cyclone, the system comprising:
one or more databases storing:
a plurality of ranges for each of a plurality of weather conditions; and
a forecasted weather condition comprising a forecasted tropical cyclone and a predicted path of the forecasted tropical cyclone;
one or more servers that:
identifying each country or region along the forecasted predicted path of tropical cyclones; and
for each country or region along the predicted path of tropical cyclone of the forecast:
identifying a forecasted weather condition in the country or region attributable to the forecasted tropical cyclone;
comparing the forecasted weather conditions in the country or region to the plurality of ranges for each of the plurality of weather conditions;
characterizing the forecasted tropical cyclones in the country or region based on a comparison of the forecasted weather conditions in the country or region to the plurality of ranges; and
outputting the representation for display to a user.
12. The system of claim 11, wherein:
the one or more databases store a plurality of ranges of predicted effects of tropical cyclones; and is
The one or more servers are configured to:
determining one or more demographics of a geographic region in a predicted path of the forecasted tropical cyclone;
predicting an effect of the forecasted tropical cyclone in the country or region based on one or more of the forecasted weather conditions attributable to the forecasted tropical cyclone and the one or more demographics of a geographic area in a predicted path of the forecasted tropical cyclone;
comparing the predicted effect of the forecasted tropical cyclone in the country or region to the plurality of ranges of predicted effects; and
characterizing the forecasted tropical cyclone in the country or region further based on a comparison of the forecasted tropical cyclone's predicted effect and the plurality of ranges of predicted effects in the country or region.
13. The system of claim 12, wherein the one or more servers are further configured to:
determining one or more geographic features of a geographic region in a predicted path of the forecasted tropical cyclone; and
predicting the effect further based on the one or more geographic features of a geographic area in a predicted path of the forecasted tropical cyclone.
14. The system of claim 12, wherein the one or more servers are further configured to:
determining one or more geological features of a geographic region in a predicted path of the forecasted tropical cyclone; and
predicting the effect further based on the one or more geological features of a geographic region in a predicted path of the forecasted tropical cyclone.
15. The system of claim 12, wherein the predicted effect is a predicted economic impact of the forecasted tropical cyclone.
16. The system of claim 15, wherein the one or more servers predict the forecasted tropical cyclone economic impact by:
identifying economic impact of tropical cyclones in the past;
identifying each economic scale affected by each of the past tropical cyclones;
scaling the economic impact based on the size of the affected economy at each past tropical cyclone;
identifying a weather condition for each of the past tropical cyclones;
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and past weather conditions for each of the past tropical cyclones; and
generating a model estimating the economic impact of the tropical cyclones based on a correlation between the scaled economic impact of each of the past tropical cyclones and past weather conditions of each of the past tropical cyclones.
17. The system of claim 16, wherein the one or more servers are further configured to:
identifying demographic characteristics of a geographic area affected by each of the past tropical cyclones;
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and the demographic of the geographic area affected by each of the past tropical cyclones; and
generating the model estimating the economic impact of tropical cyclones further based on correlations between the scaled economic impact of each of the past tropical cyclones and demographic characteristics of geographic areas affected by each of the past tropical cyclones.
18. The system of claim 16, wherein the one or more servers are further configured to:
identifying a geographic feature of a geographic region of the country or region in a predicted path of the forecasted tropical cyclone;
identifying geographic features of a geographic area affected by each of the past tropical cyclones;
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and the geographic features of the geographic area affected by each of the past tropical cyclones;
generating the model estimating the economic impact of tropical cyclones further based on a correlation between the scaled economic impact of each of the past tropical cyclones and a geographic feature of a geographic area affected by each of the past tropical cyclones; and
estimating an economic impact of the forecasted tropical cyclone in the country or region further based on a geographic feature of a geographic region of the country or region in the forecasted predicted path of the tropical cyclone.
19. The system of claim 16, wherein the one or more servers are further configured to:
identifying geological features of a geographic region of the country or region in a predicted path of the forecasted tropical cyclone;
identifying geological features of a geographic region affected by each of the past tropical cyclones;
determining a correlation between the economic impact of the scaling of each of the past tropical cyclones and the geological features of the geographic area affected by each of the past tropical cyclones;
modeling the economic impact of the tropical cyclones is further generated based on a correlation between the scaled economic impact of each of the past tropical cyclones and geological features of a geographic area affected by each of the past tropical cyclones; and
estimating the economic impact of the forecasted tropical cyclone in the country or region is further based on geological features of a geographic region of the country or region in a predicted path of the forecasted tropical cyclone.
20. The system of claim 11, wherein the one or more servers compare the forecasted weather conditions to the plurality of ranges and characterize the forecasted tropical cyclone based on the comparison without human intervention.
Background
Tropical cyclone is a fast rotating storm system characterized by a low pressure center, closed low level atmospheric circulation, strong wind and thunderstorm spiral arrangement that produces heavy rain. Tropical cyclones have different names, depending on their location and intensity, including hurricanes, typhoons, tropical storms, cyclonic storms, tropical subatmospheric pressures, and (for short) cyclones. Tropical cyclones occurring in the eastern and pacific northeast are commonly referred to as "hurricanes". The tropical cyclone that occurs in the northwest pacific is commonly referred to as "typhoon". In the southern pacific or indian ocean, similar storms are commonly referred to as "tropical cyclones" or "strong cyclone storms".
Tropical cyclones are one of the strongest natural disasters known to man. During tropical cyclones, residential, commercial, industrial, and public buildings-as well as critical infrastructure such as traffic, water, energy, and communication systems-may be damaged or destroyed by several effects associated with tropical cyclones. Wind and water are dual hazards associated with tropical cyclones, which can be extremely destructive, fatal and costly when combined with other natural processes.
In addition to affecting individuals, homes and communities, tropical cyclones have a profound effect on the environment, particularly estuaries and coastal habitats. Tropical cyclones can produce strong winds that cause the crowns of forests to fall off completely and cause drastic structural changes in the luxuriant ecosystem of trees. Animals may be killed by tropical cyclones or may be indirectly affected by habitat and food supply changes caused by strong winds, storm tides and heavy rainfall. Endangered species may be severely affected, such as the puerto rico parrots (Amazona vittata), with population numbers that were reduced to half of the original after the 1989 hurricane Hugo crossing. Hurricane Gilbert in 1988 pushed cushmer thrush (Toxostoma guttatum), which was found only in the island of cushmer (Cozumel), mexico, to the edge of extinction.
Various storm components associated with tropical cyclones (e.g., storm tides, billows, and landslides) can move large amounts of soil and eventually remodel coastal landscapes. Hurricanes such as Ivan (2004), Katrina and Rita (2005), and Gustav and Ike (2008) have caused variations of about 100 meters (328 feet) in coastline positions in some areas. Land losses due to Katrina and Rita hurricanes alone are estimated to be approximately 73 square miles.
By changing the environmental conditions of coastal habitats, tropical cyclones induce a range of direct and indirect ecological reactions, ranging from direct to long-term. No two tropical cyclones are the same in terms of environmental effects. Individual characteristics, such as the speed of advance, the size, the intensity and the precipitation of storms, play an important role in the type and time frame of tropical cyclone effects. Depending on many of these factors, even tropical storms can result in loss of life and severe damage to property and infrastructure.
Saffir-Simpson hurricane class (SSHWS) is a tool used by meteorologists to measure tropical cyclone intensity. Like the enhanced Fujita wind level used to measure tornadoes, SSHWS classifies tropical cyclones into several categories based on the sustained wind speed during storms.
To be classified as a hurricane, the one minute maximum sustained wind speed for tropical cyclones must be at least 74mph (33 m/s; 64 kts; 119 km/h). Based on the maximum continuous wind speed, the classification ranges from class 1 to class 5, as follows:
unfortunately, in reviewing historical records, using only current and forecasted wind speeds as an indicator of true damage, destructiveness, and life-threatening potential of tropical cyclones has proven to be inaccurate and imprecise. Thus, wind speed alone is not sufficient in an attempt to communicate to the public, government officers and emergency personnel effects such as may result from storm surge, flooding and waste of soil quality (landslides, etc.). Furthermore, the financial impact of tropical cyclones cannot be explained by wind speed alone. Other variables associated with natural processes in storm and storm predicted paths need to be considered to accurately and precisely elucidate the true, potential damage, destructive and financial impact of tropical cyclones.
Therefore, there is a need for a system and method that more accurately and precisely characterizes the forecasted tropical cyclones than prior art methods that only consider the wind speed of each storm. Furthermore, a process is needed that uses mathematical rules rather than human subjective determinations. By providing a more complete picture of the potential damage and disruption of current or forecasted storms, the disclosed systems and methods provide government officials, emergency personnel, and the public with more information and enable better decisions to be made at critical times before and during tropical cyclones. All of these benefits help to save lives and limit potential damage to property.
Disclosure of Invention
To overcome those and other deficiencies of the prior art, a tropical cyclone analysis system is provided that stores a plurality of ranges for each of a plurality of weather conditions, identifies a forecasted tropical cyclone, identifies a predicted path for the forecasted tropical cyclone, identifies each country or region along the forecasted predicted path for the tropical cyclone, and for each country or region along the forecasted predicted path for the tropical cyclone, identifies a forecasted weather condition in the country or region attributable to the forecasted tropical cyclone, compares the forecasted weather condition in the country or region to the plurality of ranges for each of the plurality of weather conditions, characterizes the forecasted weather condition in the country or region based on the comparison of the forecasted weather condition in the country or region to the plurality of tropical ranges, and outputting the representation for display to a user.
The tropical cyclone analysis system may further store a plurality of ranges of predicted effects of a forecasted tropical cyclone, determine one or more demographics of a geographic area in a predicted path of the forecasted tropical cyclone, predict an effect of the forecasted tropical cyclone in the country or region based on one or more of the forecasted weather conditions attributable to the forecasted tropical cyclone and the one or more demographics of a geographic area in a predicted path of the forecasted tropical cyclone, and compare the forecasted predicted effect of the tropical cyclone in the country or region to the plurality of ranges of predicted effects. In those embodiments, the characterization of the forecasted tropical cyclone in the country or region is further based on a comparison of a predicted effect of the forecasted tropical cyclone in the country or region to the plurality of ranges of predicted effects.
The tropical cyclone analysis system may further determine one or more geographic or geological features of a geographic region in the predicted path of the forecasted tropical cyclone. In those embodiments, the predicted effect of the forecasted tropical cyclone is further predicted based on the one or more geographic or geological features of the geographic region in the forecasted path of the forecasted tropical cyclone.
In some embodiments, the predicted effect of the forecasted tropical cyclone may be a predicted economic impact of the forecasted tropical cyclone. In those embodiments, the predicted economic impact of the forecasted tropical cyclone is estimated by: identifying economic impacts of past tropical cyclones, identifying a scale of each economy affected by each of the past tropical cyclones, scaling the economic impacts based on the scale of the affected economy at each past tropical cyclone, identifying weather conditions of each of the past tropical cyclones, determining a correlation between the scaled economic impacts of each of the past tropical cyclones and the past weather conditions of each of the past tropical cyclones, and generating a model estimating the economic impacts of the tropical cyclones based on the correlation between the scaled economic impacts of each of the past tropical cyclones and the past weather conditions of each of the past tropical cyclones. In those embodiments, the model that estimates the economic impact of tropical cyclones may be further generated based on correlations between the scaled economic impact of each of the past tropical cyclones and demographic or geographic or geological features of the geographic area affected by each of the past tropical cyclones.
In any of the foregoing embodiments, the comparison of the forecasted weather conditions to the plurality of ranges and the characterization of the forecasted tropical cyclones based on the comparison may be performed by a hardware computer processor without human intervention.
All of the foregoing embodiments provide significant technical and public safety benefits when compared to existing methods (Saffir-Simposon hurricane wind class) that rely solely on the forecasted maximum sustained wind speed. By characterizing each tropical cyclone based on a plurality of forecasted weather conditions (and, in some embodiments, characteristics of a geographic region in the forecasted path of the tropical cyclone), the tropical cyclone analysis system can more accurately predict-and more fully convey-threats to life and property caused by the forecasted tropical cyclones.
Drawings
Aspects of the exemplary embodiments may be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the exemplary embodiments, wherein:
fig. 1 is a block diagram illustrating a tropical cyclone analysis system 100 according to an exemplary embodiment of the present invention;
fig. 2 is a diagram illustrating an overview of an architecture of a tropical cyclone analysis system according to an exemplary embodiment of the present invention;
FIG. 3 is a flowchart illustrating a process of modeling past economic impact of past tropical cyclones according to an exemplary embodiment of the present invention;
FIG. 4 is a flowchart illustrating a process for characterizing threats posed by each current or forecasted tropical cyclone in accordance with an exemplary embodiment of the present invention;
FIG. 5 is a diagram of a graphical user interface that outputs a representation of a forecasted tropical cyclone for display to a user, according to an exemplary embodiment of the present invention; and
figure 6 is another diagram of a graphical user interface that outputs a representation of a forecasted tropical cyclone for display to a user, according to an exemplary embodiment of the present invention.
Detailed Description
Reference is now made to the accompanying drawings, which illustrate various views of exemplary embodiments of the invention. In the drawings and description of the drawings herein, certain terminology is used for convenience only and is not to be taken as a limitation on the embodiments of the invention. Moreover, in the drawings and the following description, like numbers refer to like elements throughout.
System architecture
Fig. 1 is a block diagram illustrating a tropical cyclone analysis system 100 according to an exemplary embodiment of the present invention.
As shown in fig. 1, the tropical cyclone analysis system 100 includes one or more databases 110, an analysis engine 180, and a graphical user interface 190. The one or more databases 110 include historical weather data 112, historical weather impact data 114, and forecasted weather conditions 116. Further, the one or more databases 110 may include demographic data 124, geographic data 126, and/or geological data 128. In some embodiments, one or more databases 110 may also include historical demographic data 134. In some embodiments, one or more databases 110 may additionally include historical geographic data 136 and historical geological data 138.
The historical weather data 112 includes information indicating the path and time of the past tropical cyclone. For each of the past tropical cyclones, the historical weather data 112 also includes information indicative of the severity of each of the past tropical cyclones as measured by several separate weather conditions occurring as a result of the past tropical cyclones. For each past tropical cyclone, for example, the historical weather data 112 may include information indicative of wind speed (e.g., maximum sustained wind speed), precipitation (e.g., total accumulated precipitation caused by past tropical cyclones, maximum precipitation per day caused by past tropical cyclones, etc.), storm surge, coastal flooding, Accumulated Cyclone Energy (ACE), surface pressure, high temperature, low temperature, etc. Historical weather data 112 may be received from publicly available sources (e.g., National Oceanic and Atmospheric Administration (NOAA) storm event database), private sources (e.g., AccuWeather corporation, AccuWeather Enterprise Solutions corporation), and so forth.
The historical weather effect data 114 includes information indicating the economic impact of each of the tropical cyclones in the past. The economic impact of each of the past tropical cyclones may include direct damage to property and crops as well as indirect damage attributable to the past tropical cyclones (e.g., power outages, lost sales, shipping delay data, reduced consumer expenditures, reduced access to retail and service locations, increased traffic speeds, etc.). Historical weather impact data 114 may be received from publicly available sources, such as NOAA storm event databases (which aggregate information from county, state, and federal emergency officials), local law enforcement officials, sky warning observers, National Weather Service (NWS) damage surveys, newspaper cutting services, insurance, the general public, etc.), information from industry-specific commercial and non-commercial entities (e.g., insurance claim information), third party sources, and so forth. The analysis engine 180 may also use Economic forecasting models, such as those developed by Solomon M.Hsiang and arc S.Jina, to estimate The Effect of each past tropical cyclone on Long-term Economic Growth (see, e.g., Hsiang et al, The practical efficiency of Environmental Catastrophe on Long-Run Economic Growth: Evidence From 6,700 cycles, NBER Working Paper No.20352, 7 months 2014). The tropical cyclone analysis system 100 can also use customer-specific data (received from customers) to determine customer-specific impact of tropical cyclones in the past.
The demographic data 124 may include, for example, a concentration of land and home industries (residential, commercial, and industrial) per unit area, a family size, an income level, a crowd education level, a crowd age level, and/or a crowd density for a geographic area (e.g., a geographic area in an inferred path of forecasted tropical cyclones). For example, information indicative of those demographic components may be received from one or more third parties (such as the U.S. census, world banks, etc.). As described below, the tropical cyclone analysis system 100 can also model the effect of past tropical cyclones with populations of geographic regions in the path of the past tropical cyclones (and use the model to predict the predicted effect of tropical cyclones). Because the population of the geographic region may have moved over time, the tropical cyclone analysis system 100 may also store historical population data 134 indicating the population of the geographic region in the path of the tropical cyclones in the past. The historical demographic data 134 may similarly be received from a third party (e.g., the U.S. census, world bank, etc.) or may be estimated based on currently available information.
The geographic data 126 may include, for example, information indicative of the geography, terrain grade, and/or terrain orientation of the geographic area (i.e., the geographic area in the past path of tropical cyclones and the geographic area in the forecasted path of the forecasted tropical cyclones). The geographic data 126 may be received from a third party. For example, the geographic data 126 may be determined based on imagery from Synthetic Aperture Radar (SAR), Landsat satellites, and the like. If it is determined that the geography, terrain grade, and/or terrain orientation have meaningfully moved over time, the tropical cyclone analysis system 100 may also store historical geographic data 136 indicating the geography and/or geography of a geographic area in the path of the tropical cyclone in the past. The historical demographic data 136 may similarly be received from third parties, determined based on past imagery, or estimated based on currently available information.
The geological data 128 may include information indicative of site-specific seismic activity, soil stability data, soil type, and nature and exposure of bedrock for a geographic region (i.e., a geographic region in the path of past tropical cyclones and a geographic region in the predicted path of predicted tropical cyclones). The geological data 128 may be received from one or more third parties, such as the United States Geological Survey (USGS), the natural resource conservation bureau (NRCS), the United States Department of Agriculture (USDA), and the like. If it is determined that the geological conditions of the geographic region have meaningfully changed over time, the tropical cyclone analysis system 100 may also store historical geological data 138 indicative of geological features of the geographic region in the path of the tropical cyclones in the past. The historical geological data 138 may similarly be received from third parties, estimated based on currently available information, and the like.
The forecasted weather conditions 116 include information indicative of a predicted location, a predicted time, and a predicted value of the forecasted weather conditions associated with the forecasted tropical cyclone. The forecasted weather conditions 116 can include wind speed (e.g., maximum sustained wind speed), precipitation (e.g., total accumulated precipitation, maximum precipitation per day, etc.), storm surge, coastal flooding, accumulated cyclonic energy, surface pressure, high temperature, low temperature, and the like. Forecasted Weather conditions 116 and events may be received from AccuWeather, AccuWeather Enterprise Solutions, National Weather Service (NWS), National Hurricane Center (NHC), other government agencies (such as the environmental part of Canada, the United kingdom Weather service, the Japan Weather service, etc.), private companies (such as Weather precision Technologies, Inc.), and the like. The forecasted weather conditions 116 can be determined using a numerical weather prediction model (or set of models) of the atmosphere and the ocean to predict weather based on current weather conditions.
Fig. 2 is a diagram illustrating an overview of an architecture 200 of the tropical cyclone analysis system 100 according to an exemplary embodiment of the present invention.
As shown in FIG. 2, the architecture 200 may include one or more servers 210 and one or more storage devices 220 connected to a plurality of remote computer systems 240 (such as one or more personal systems 250 and one or more mobile computer systems 260) via one or more networks 230.
One or more servers 210 may include an internal storage device 212 and a processor 214. The one or more servers 210 may be any suitable computing device, including, for example, web servers hosting websites accessible by the remote computer system 240 and application servers. The one or more storage devices 220 may include internal storage devices 212 and/or external storage devices of the one or more servers 210. The one or more storage devices 220 may also include any non-transitory computer-readable storage medium, such as an external hard disk array or solid state memory. Network 230 may include any combination of the internet, a cellular network, a Wide Area Network (WAN), a Local Area Network (LAN), etc. Communication via the network 230 may be accomplished through wired and/or wireless connections. Remote computer system 240 may be any suitable electronic device configured to send and/or receive data via network 230. The remote computer system 240 may be, for example, a network-connected computing device, such as a personal computer, notebook computer, smartphone, Personal Digital Assistant (PDA), tablet, notebook computer, portable weather detector, Global Positioning Satellite (GPS) receiver, network-connected vehicle, wearable device, and so forth. Personal computer system 250 may include internal storage device 252, processor 254, output device 256, and input device 258. One or more mobile computer systems 260 may include an internal storage device 262, a processor 264, an output device 266, and an input device 268. The internal storage devices 212, 252, and/or 262 may include one or more non-transitory computer-readable storage media, such as a hard disk or solid-state memory, for storing software instructions that, when executed by the processor 214, 254, or 264, perform relevant portions of the features described herein. Processors 214, 254, and/or 264 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and/or the like. Processors 214, 254, and 264 may be implemented as a single semiconductor chip or as more than one chip. Output devices 256 and/or 266 may include a display, speakers, external ports, and the like. The display may be any suitable device configured to output visible light, such as a Liquid Crystal Display (LCD), a light emitting polymer display (LPD), a light emitting diode display (LED), an organic light emitting diode display (OLED), and the like. Input devices 258 and/or 268 may include a keyboard, mouse, trackball, still or video camera, touchpad, and the like. The touchpad may overlap or be integrated with the display to form a touch-sensitive display or touch screen.
Returning to fig. 1, the one or more databases 110 may be any organized set of information, whether stored on a single tangible device or on multiple tangible devices, and may be stored in, for example, one or more storage devices 220. The analysis engine 180 may be implemented by software instructions stored on one or more of the internal storage devices 212, 252, and/or 262 and executed by one or more of the processors 214, 254, or 264. The graphical user interface 190 may be any interface that allows a user to input information for transmission to the tropical cyclone analysis system 100 and/or output information received from the tropical cyclone analysis system 100 to the user. The graphical user interface 190 may be implemented by software instructions stored on one or more of the internal storage devices 212, 252, and/or 262 and executed by one or more of the processors 214, 254, or 264.
Modeling the impact of past tropical cyclones
To characterize the threat posed by current and forecasted tropical cyclones, the system can predict the economic impact of each current and forecasted tropical cyclone. To date, the economic impact of current and forecasted weather events has been estimated by humans (e.g., meteorologists, climatists, economists, etc.) who make subjective determinations. However, these subjective determinations have a number of drawbacks. In addition to the increased time it takes for an individual (or a group of people) to make these subjective determinations, these subjective determinations are inconsistent because they depend on the skill level and the temperament of the individual (or people) making these determinations. To overcome those drawbacks of the prior art, the tropical cyclone analysis system 100 may employ specific mathematical rules to predict the estimated economic impact of each current and forecasted tropical cyclone. The tropical cyclone analysis system 100 can identify those mathematical rules by modeling past economic impacts of past tropical cyclones.
Fig. 3 is a flowchart illustrating a process 300 for modeling past economic impact of past tropical cyclones according to an exemplary embodiment of the present invention. The modeling process 300 may be performed by one or more servers 210 executing the analysis engine 180. As described below, some of the operations included in the modeling process 300 may be optional and included only in some embodiments of the tropical cyclone analysis system 100. Furthermore, as one of ordinary skill in the art will recognize, the operations in the modeling process 300 do not necessarily need to be performed in the order in which they are illustrated in FIG. 3 and described below.
The economic impact of the past tropical cyclone is identified in step 302. As mentioned above, the historical weather effect data 114 stored in the one or more databases 110 includes information indicative of the economic impact of past tropical cyclones. The economic impact of each of the past tropical cyclones may include direct damage to property and crops as well as indirect damage attributable to the past tropical cyclones (e.g., power outages, lost sales, shipping delay data, reduced consumer expenditures, reduced access to retail and service locations, increased traffic speeds, etc.). The Economic impact of each of The past tropical Cyclones may also include The Effect of each past tropical cyclone on Long-term Economic Growth as determined using Economic forecasting models such as The models developed by Solomon M.Hsiang and air S.Jina (see, e.g., Hsiang et al, The practical efficiency of Environmental Catasterphe on Long-Run Economic Growth: Evaference From 6,700 cycles, NBER 203work Paper No. 52, 7 months 2014).
Tropical cyclones are dynamic weather events that have characteristics that change as the storm moves along its path. For example, the standing wind in a tropical cyclone generally increases as the storm accumulates on the ocean and then dissipates as the storm moves over the sea and/or land. If a tropical cyclone lands in two different regions (or countries) in two different periods of time during its lifetime, the same tropical cyclone will cause very different weather conditions (and have very different economic impact) in those locations. Indeed, the economic impact of a tropical cyclone in one country or region may be completely independent of the weather conditions of the tropical cyclone when it lands in another country or region. Thus, the tropical cyclone analysis system 100 may treat tropical cyclones landing in two different regions or countries as two separate storms and may store the weather conditions (and economic impact) and the weather conditions (and economic impact) of the tropical cyclones in the first country or region separately.
The economic impact of the past tropical cyclones (identified in step 302) is scaled in step 304 based on the economic size of the impact at each past tropical cyclone. Such overcord cyclones have occurred in the past at different locations and at different times. The economic impact of those tropical cyclones may have varied significantly in the past based on the economic size of the affected geographic area during the affected time period. Meanwhile, the purpose of the process 300 is to model the economic impact of weather conditions attributed to past tropical cyclones, regardless of when and where those past tropical cyclones land. Thus, the economic impact of past tropical cyclones is scaled based on the size of the affected economy at each past tropical cyclone to control the size of the affected economy and isolate the economic impact of past weather conditions attributable to past tropical cyclones.
The weather conditions of the past tropical cyclone are identified in step 306. As mentioned above, the historical weather data 112 included in the one or more databases 110 includes information indicative of the severity of each past tropical cyclone as measured by several individual weather conditions that occur as a result of the past tropical cyclone. For each past tropical cyclone, for example, the historical weather data 112 may include information indicative of wind speed (e.g., maximum sustained wind speed), precipitation (e.g., total accumulated precipitation caused by past tropical cyclones, maximum precipitation per day caused by past tropical cyclones, etc.), storm surge, coastal flooding, Accumulated Cyclone Energy (ACE), surface pressure, high temperature, low temperature, etc. Historical weather data 112 may be received from publicly available sources (e.g., National Oceanic and Atmospheric Administration (NOAA) storm event database), private sources (e.g., AccuWeather corporation, AccuWeather Enterprise Solutions corporation), and so forth.
Demographics of geographic areas affected by each of the past tropical cyclones may be identified in step 314. Even when scaling based on the economic size of the affected tropical cyclone at each past tropical cyclone, the tropical cyclone analysis system 100 can determine that the economic impact of the past tropical cyclone depends on one or more of the demographics of the affected geographic area. As noted above, the demographic data 124 stored in the one or more databases 110 may include, for example, the concentration of land and home industries (residential, commercial, and industrial) per unit area, family size, income level, crowd education level, crowd age level, and/or crowd density for geographic areas in the path of past tropical cyclones. If available, the tropical cyclone analysis system 100 can instead utilize past demographic characteristics (during each past period of the tropical cyclone) stored as historical population data 134 in one or more databases 110.
Geographic characteristics of the geographic area affected by each past tropical cyclone may be identified in step 316. As mentioned above, the geographic data 126 stored in the one or more databases 110 may include, for example, information indicative of terrain, terrain grade, and/or terrain orientation of geographic areas in the past tropical cyclonic path. If available, the tropical cyclone analysis system 100 can instead utilize past geographic features (during each past period of tropical cyclones) stored as historical geographic data 136 in the one or more databases 110.
Geological features of the geographic region affected by each of the past tropical cyclones may be identified in step 316. As noted above, the geological data 128 stored in the one or more databases 110 may include, for example, site-specific seismic activity indicative of geographic regions in the path of past tropical cyclones, soil stability data, soil types, and the nature and exposure of bedrocks. If available, the tropical cyclone analysis system 100 may instead utilize past geological features (during each past period of tropical cyclones) stored as historical geological data 138 in one or more databases 110.
In step 360, a correlation is determined between the economic impact of the scaling of each of the past tropical cyclones (determined in step 304) and the past weather conditions of each of the past tropical cyclones (determined in step 306), and in some embodiments, the demographic characteristics of the affected geographic area (determined in step 314), the geographic characteristics of the affected geographic area (determined in step 316), and/or the geological characteristics of the affected geographic area (determined in step 318). To determine those correlations, the analysis engine 180 can take the form of artificial intelligence (e.g., a machine learning algorithm) to identify correlations using the above-described data sets as training data without requiring explicit instructions programmed for how to determine those correlations. The analysis engine 180 may employ any statistical modeling technique to identify correlations between the scaled economic impact of each past tropical cyclone and past weather conditions and other independent variables. For example, the analysis engine 180 may use multiple regressors (independent variables) to apply a regression algorithm to the scaled economic impact Y (dependent variable) following the equation:
Y＝β0+β1X1+β2X2+…+βkXkwherein
Wherein XkIs a k number of predictor variables (e.g., weather conditions, demographic characteristics, geographic characteristics, and/or geological characteristics); and is
βkIs based on a predictor variable X by analysis engine 180kA regression coefficient determined by correlation with economic impact Y of scaling of the tropical cyclone in the past.
Alternatively, the tropical cyclone analysis system 100 may group the past tropical cyclones based on the severity of each past tropical cyclone (as determined by the scaling economic impact of each storm) and then identify past weather conditions (and optionally other independent variables) associated with the tropical cyclones in each group. In those embodiments, the past tropical cyclones are sorted by economic impact of scaling in step 330, and a threshold is established in step 332 such that the past tropical cyclones are grouped in step 334.
In embodiments where past tropical cyclones are grouped based on severity, the analysis engine 180 uses statistical modeling in step 360 to identify past weather conditions (and optionally other independent variables) related to past tropical cyclones with a scaled economic impact within the range of each group. In a preferred embodiment, for each argument found to significantly predict the economic impact of tropical cyclones, the analysis engine 180 identifies a series of ranges, where each range is associated with the economic impact of tropical cyclones included in each group.
For example, the following chart groups tropical cyclones based on 2019 dollars in economic impact and arguments (in this example, average rainfall, maximum sustained wind, and storm surge), which the analysis engine 180 can determine predict those tropical cyclones within the group that have economic impact.
Some of the independent variables that can predict economic impact may depend on two or more weather conditions/characteristics. For example, the average rainfall (shown above) may predict the economic impact due to flooding. However, the combination of geological features (e.g., soil type) in the geographic region of the path of the tropical cyclone and the forecasted average rainfall may be more predictive of economic impact due to flooding. The combination of two or more weather conditions/characteristics may also better capture the climate of the event, which may predict the long term economic impact of the forecasted tropical cyclone.
Influence of forecast characterizing forecast tropical cyclone
In addition to the wind velocity magnitudes used by the current methods, the tropical cyclone analysis system 100 utilizes additional components to better characterize the threats posed by each current and forecasted tropical cyclone.
Figure 4 is a flowchart illustrating a process 400 for characterizing threats posed by each current or forecasted tropical cyclone in accordance with an exemplary embodiment of the present invention. The following description includes identifying forecasted weather conditions (and predicted paths) for the forecasted tropical cyclone. However, as one of ordinary skill in the art will recognize, the same characterization process 400 may be performed to characterize the current tropical cyclone using current weather conditions (and current path). The characterization process 400 may be performed by one or more servers 210 executing the analysis engine 180. As described below, some of the operations included in the characterization process 400 are optional and are included only in some embodiments of the tropical cyclone analysis system 100. Further, as one of ordinary skill in the art will recognize, the operations in the characterization process 400 do not necessarily need to be performed in the order in which they are illustrated in fig. 4 and described below.
A forecasted tropical cyclone is identified in step 402. As mentioned above, the analysis engine 180 may identify a forecasted tropical cyclone by analyzing the forecasted weather conditions 116 received by the third party, which forecasted weather conditions 116 may be forecasted based on the current weather conditions using a numerical weather forecast model (or set of models) of the atmosphere and the ocean.
A predicted path for the tropical cyclone is identified in step 402. As one of ordinary skill in the art will recognize, the predicted path may be a cone to represent a probabilistic determination of the predicted likely path of tropical cyclones.
Each country/region along the predicted path is identified in step 406. As mentioned above, tropical cyclones are dynamic weather events that have characteristics that change as a storm moves along its path. If a tropical cyclone lands in two different regions (or countries) in two different periods of time during its lifetime, the same tropical cyclone will cause very different weather conditions (and have very different economic impact) in those locations. Thus, the tropical cyclone analysis system 100 may treat each land parcel and/or island group as a separate country/region (e.g., the eastern caribbean, puerto rico, the seashore and the dominican republic, cuba, bahama, continental america, etc.) and perform the remaining steps of the characterization process 400 separately for each country/region in which a forecasted tropical cyclone is predicted to land. As a result, when the same tropical cyclone logs in two different countries or regions, the tropical cyclone analysis system 100 can characterize it as expected to be in two different categories. For example, when a tropical cyclone lands on bahama, it may be expected to be a category 4 hurricane, and by the time the same storm lands on the continental united states, it may be expected to be a category 2 hurricane.
The forecasted weather conditions 116 are identified in step 408. As mentioned above, the forecasted weather conditions 116 may be received by a third party and may be forecasted based on current weather conditions using a numerical weather prediction model (or set of models) of the atmosphere and the ocean. If the forecasted tropical cyclone is not predicted to land in the country/region, the tropical cyclone analysis system 100 may identify the forecasted weather conditions that are forecasted to occur in the country/region.
In some embodiments, demographic (and/or geographic and/or geological) characteristics of the geographic region in the predicted path of the forecasted tropical cyclone are determined in step 412. As noted above, the demographic data 124 may include, for example, a concentration of land and home industries (residential, commercial, and industrial) per unit area, a family size, an income level, a crowd education level, a crowd age level, and/or a crowd density for a geographic region in a predicted path of tropical cyclones; the geographic data 126 may include, for example, information indicative of the terrain, terrain grade, and/or terrain orientation of a geographic area in a predicted path of a forecasted tropical cyclone; and geological data 128 may include, for example, information indicative of site-specific seismic activity, soil stability data, soil type, and nature and exposure of bedrock for a geographic region in the predicted path of the forecasted tropical cyclone.
In some embodiments, the forecasted tropical cyclone economic impact is estimated in step 414. The predicted economic impact can be estimated by subjectively evaluating the population (and other) characteristics of the country/region and the forecasted weather conditions of the tropical cyclone forecasted. In other embodiments, the predicted economic impact may be determined by the analysis engine 180, for example, using a model developed by the analysis engine 180 using the modeling process 300.
In step 420, the forecasted weather conditions 114 (and, in some embodiments, characteristics of the geographic region in the predicted path) for the forecasted tropical cyclone are compared to thresholds (e.g., some of the thresholds identified by the modeling process 300 described above). For example, the tropical cyclone analysis system 100 may compare the forecasted weather conditions 114 for the tropical cyclone to the following thresholds.
Class I
Average rainfall
Maximum sustained maximum wind speed
Storm surge
Economic impact
<1
<3 inch
<74 miles per hour
<3 feet
Not significantly affecting
1
3-8 inches
74-95 miles per hour
3-6 feet
<100 billion dollars
2
8-15 inches
96-110 miles per hour
6-10 feet
100-
3
15-22 inches
111-129 miles per hour
10-15 feet
400- "990 billion dollars
4
22-30 inches
130-156 miles per hour
15-20 feet
1000- "1990 billion dollars
5
>30 inches
>156 miles per hour
>20 feet
$ 2000+ billion
As mentioned above, with reference to the modeling process 300, some of the thresholds are used to characterize a forecasted tropical cyclone based on a predicted effect that depends on two or more weather conditions/characteristics. For example, instead of average rainfall (as shown above), the analysis engine 180 may estimate the predicted flood, e.g., based on a combination of geological features (e.g., soil type) in the geographic region of the path of the tropical cyclone and the forecasted average rainfall, and compare the predicted flood to a flood threshold. In another example, the analysis engine 180 may estimate people and/or property affected by floods, for example, based on a combination of population data 124 (e.g., crowd density), geological features (e.g., soil type), and forecasted average rainfall in a geographic region of a predicted path of tropical cyclones. Further, the analysis engine 180 may compare the predicted climate of the event (as determined by the two or more characteristics) to a climate threshold.
In some embodiments, the analysis engine 180 may classify the forecasted tropical cyclone in step 430 by selecting a highest category indicated by any of the characteristics of the geographic area in the forecasted path of the forecasted tropical cyclone and/or the forecasted weather conditions. For example, tropical cyclones may be classified as category 2 by Saffir-Simpson hurricane rating (because the maximum sustained wind speed is predicted to be 96-110 miles per hour), but the analysis engine 180 may characterize the same forecasted tropical cyclone as a category 3 storm, for example, if the average rainfall is predicted to be between 15 inches and 22 inches, or if a storm surge is predicted to be between 10 feet and 15 feet, or if the economic impact is predicted to be between $ 400 billion and $ 990 billion.
In other embodiments, the analysis engine 180 may classify the forecasted tropical cyclone in step 442 by selecting a category based on a single forecasted weather condition (e.g., a maximum sustained wind speed as used in the Saffir-Simpson hurricane class), determine whether a magnitude of one or more additional components (e.g., characteristics of a geographic area in the forecasted path of the additional forecasted tropical cyclone and/or the forecasted weather condition) is within some predetermined range, and increase or decrease the characterization by a predetermined amount associated with the additional component. For example, a forecasted tropical cyclone may initially be characterized by selecting a category based on maximum sustained wind speed as follows:
maximum wind velocity
Class I
<74 miles per hour
<1
74-95 miles per hour
1
96-110 miles per hour
2
111-129 miles per hour
3
130-156 miles per hour
4
>156 miles per hour
5
In those embodiments, one additional component may be the current and/or forecasted maximum rainfall per day, and the analysis engine 180 may employ the following ranges and adjustments associated with those ranges.
The most rainfall (in inches, daily)
Adjustment of
<5 inch/24 hours
-0.5
>5<10 inch/24 hours
-0.25
>10<15 inches/24 hours
0
>15<20 inch/24 hour
+0.5
>20 inch/24 hour
+1.0
Additionally or alternatively, the additional component may be current and/or forecasted days with a rainfall of 10+ inches per day, and the analysis engine 180 may employ the following ranges and adjustments associated with those ranges.
Rainfall (10+ inch/day)
Adjustment of
<2 days
-0.5
2-3 days
-0.25
For 3-4 days
0
4-5 days
+0.5
>5 days
+1.0
Additionally or alternatively, the additional component may be a current or forecasted Accumulated Cyclone Energy (ACE) of tropical cyclones, and the analysis engine 180 may employ the following ranges and adjustments associated with those ranges.
Accumulating cyclonic energy
Adjustment of
<40
-0.5
>40<50
-0.25
>50<60
0
>60<70
+0.5
>70
+1.0
Additionally or alternatively, the additional component may be a current or forecasted surface pressure of the tropical cyclone, and the disclosed system may employ the following ranges and adjustments associated with those ranges.
Surface pressure
Adjustment of
>1,000mb
-0.5
>975<1,000mb
-0.25
>950<975mb
0
>925<950mb
+0.5
>925
+1.0
Additionally or alternatively, the additional component may be a current or forecasted storm surge of tropical cyclones, and the analysis engine 180 may employ the following ranges and adjustments associated with those ranges.
Storm surge
Adjustment of
<5 feet
-0.5
>5<10 feet
-0.25
>10<15 feet
0
>15<25 feet
+0.5
>25 feet
+1.0
Additionally or alternatively, the additional component may be coastal flooding of a geographic area in the forecast path of tropical cyclones, and the analysis engine 180 may employ the following ranges and adjustments associated with those ranges.
Coastal flood
Adjustment of
<1 mile
-0.5
>1<3 mile
-0.25
>3<5 mile
0
>5<10 miles of
+0.5
>10 miles of
+1.0
Additionally or alternatively, the additional component may be a Melton terrain index for a geographic area in a forecast path of tropical cyclones, and the analysis engine 180 may employ the following ranges and adjustments associated with those ranges.
Melton topographic index
Adjustment of
<25
-0.5
>25<35
-0.25
>35<50
0
>50<75
+0.5
>75
+1.0
Additionally or alternatively, the additional component may be a soil liquefaction index of a geographic region in a forecast path of the tropical cyclone.
Additionally or alternatively, the additional components may include a manufacturing index and/or an infrastructure density for a geographic region in a forecast path for tropical cyclones.
Additionally or alternatively, the additional component may be a crowd density of a geographic area in a forecast path of tropical cyclones, and the analysis engine 180 may employ the following ranges and adjustments associated with those ranges.
Population density
Adjustment of
<7,500 people per square mile
-0.5
>7,500<8,500 people per square mile
-0.25
>8,500<10,000 people per square mile
0
>10,000<12,500 people per square mile
+0.5
>12,500 people per square mile
+1.0
In an alternative embodiment, the tropical cyclone analysis system 100 may store predetermined coefficients associated with each range of each weather condition (or characteristic), compare each forecasted weather condition of the forecasted tropical cyclone (and, optionally, the characteristic of the geographic region in the forecasted path of the tropical cyclone) to a threshold for each range to identify the relevant coefficients, and multiply the initial characterization of the forecasted tropical cyclone by the relevant coefficients.
Finally, in an alternative embodiment, the tropical cyclone analysis system 100 may store a predetermined coefficient associated with each weather condition (or characteristic) indicative of a weight of each weather condition (or characteristic) and characterize each forecasted tropical cyclone by multiplying each forecasted weather condition (and, optionally, a characteristic of a geographic area in the forecasted path of the tropical cyclone) of the forecasted tropical cyclone by the coefficient associated with the weather condition (or characteristic) indicative of the weight of the weather condition (or characteristic).
The tropical cyclone analysis system 100 can also adjust any adjusted characterization using a decimal or fractional number by rounding (e.g., up, rounding to the nearest integer) or using other rules (e.g., any tropical cyclone whose adjusted characterization is equal to or greater than 5 is category 5) such that the characterization output by the tropical cyclone analysis system 100 uses the same scale (category 1 to category 5) as the Saffir-Simpson hurricane wind scale familiar to consumers.
The analysis engine 180 outputs a representation of the predicted tropical cyclone, e.g., via a graphical user interface 190, via one or more networks 230, etc. Fig. 5 and 6 are graphs of a representation of a forecasted tropical cyclone output for display to a user.
All of the foregoing embodiments provide significant technical and public safety benefits when compared to existing methods (Saffir-Simpson hurricane levels) that rely solely on the forecasted maximum sustained wind speed. By characterizing each tropical cyclone based on a plurality of forecasted weather conditions 116 (and, in some embodiments, characteristics of a geographic region in the forecasted path of the tropical cyclone), the tropical cyclone analysis system 100 can more accurately predict-and more fully convey-threats to life and property caused by the forecasted tropical cyclones.
While preferred embodiments have been set forth above, those of ordinary skill in the art having reviewed this disclosure will readily appreciate that other embodiments can be implemented within the scope of the invention. The disclosure of particular technology is also illustrative and not limiting. Accordingly, the invention should be construed as limited only by the claims.