APPARATUS AND METHOD FOR PROVIDING ENVIRONMENTAL PREDICTIVE INDICATORS TO EMERGENCY RESPONSE MANAGERS

• US 20120330549A1
• Filed: 05/30/2012
• Published: 12/27/2012
• Est. Priority Date: 11/13/2008
• Status: Active Grant

• Current Assignee: Saint Louis University
• Original Assignee: Saint Louis University
• Inventors: Dannevik, William, Pasken, Robert W.
• Granted Patent: US 8,494,774 B2
• Time in Patent Office: Days
• US Class Current: 702/3
• CPC Class Codes: G01W 1/00 Meteorology
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09
• CPC Class Codes: G01W 2001/006 Main server receiving weather information from several sub-stations

Abstract

A method of predicting weather-exacerbated threats, said method comprising inputting localized weather measurement data into a weather threat prediction system; predicting future localized weather conditions based on said localized weather measurement data combined with modeling from National Weather Service Data; inputting natural environment and infrastructure data into said weather threat prediction system; correlating said infrastructure data with said predicted future localized weather conditions; and determining a threat level index over a region, a threat level indicating an area having a certain probabilistic likelihood of being harmed by said future weather conditions.

Claims

1. A computer system for predicting weather-related threats, comprising:

a computer system having a memory and one or more processors;

one or more processor executable programs stored in the memory and executed by the one or more processors, where the one or more programs include,

instructions for data ingesting and combining at a computer larger scale numerical weather forecast data with high spatial and high temporal resolution data from a local mesonet thereby creating real time a forecast model having higher spatial and temporal correlation factors between observed and predicted weather conditions;

quality control processing the data from the local mesonet by determining if data is within predetermined criteria including one or more of, sensor performance, expected data content, expected data range, temporal data continuity, spatial data consistency and data adjustments for infrastructure; and

instructions for deriving meteorological fields using current and predicted meteorological fields including one or more of divergence, vorticity, moisture advection, wind velocity shear, wind velocity deformation, and gradient wind strength, thereby maximizing temporal and spatial resolution.

2. The system as recited in claim 1, where the weather forecast data is from a local mesonet having a plurality of monitoring stations placed for achieving high spatial resolution with respect to varying spacing of infrastructure of concern sufficient for assigning a Threat Index value based on type of asset, weather threat for asset;

and local conditions and further sufficient for presenting a user interface the Threat Index value in graphical form corresponding to the infrastructure of concern.

3. The system as recited in claim 1, where the one or more programs further include:

instructions for tuning the forecast model to fit local conditions as measured real-time by the local mesonet by selecting parameterization schemes based on the local conditions as measured real-time by the local mesonet and based on the differences between predicted conditions and local conditions as measured.

4. The system as recited in claim 3, where the one or more programs further include:

instructions for presenting a User Interface adapted to allow input modification data to modify results of the forecast model based on historical data, recent trends and conditions known by the operator.

5. The system as recited in claim 4, where the one or more programs further include:

instructions for initializing lateral boundary conditions based on the larger scale numerical weather forecast data and lower boundary conditions based on climatological geographic data for a domain of the forecast model.

6. A computer system for predicting weather-related threats, comprising:

a computer system having a memory and one or more processors;

one or more processor executable programs stored in the memory and executed by the one or more processors, where the one or more programs include,

instructions for initializing lateral boundary conditions based on large scale numerical weather forecast data and lower boundary conditions based on climatological geographic data including one or more of seasonal variations in vegetation and soil thereby implementing an earth and atmosphere model domain;

instructions for transforming into a uniform data grid sensor data real time from a local mesonet having a plurality of monitoring stations communicably linked over a wide area network and geographically and strategically placed based on infrastructure data and natural environmental and man-made regional characteristic data and based on achieving high spatial resolution with respect to the varying spacing of infrastructure of concern within a given region; and

instructions for combining the earth and atmosphere model domain boundary conditions and the sensor data collected from the local mesonet thereby implementing a forecasting model.

7. The system as recited in claim 6, where the weather forecast data is from a local mesonet having a plurality of monitoring stations placed for achieving high spatial resolution with respect to varying spacing of infrastructure of concern sufficient for assigning a Threat Index value based on type of asset, weather threat for asset;

and local conditions and further sufficient for presenting a user interface the Threat Index value in graphical form corresponding to the infrastructure of concern.

8. The system as recited in claim 7, where the one or more programs further include:

instructions for monitoring data from the local mesonet and large scale numerical weather forecast data for out of tolerance conditions by quality control processing the data from the local mesonet by determining if data is within predetermined criteria including one or more of, sensor performance, expected data content, expected data range, temporal data continuity, spatial data consistency and data adjustments for infrastructure.

9. The system as recited in claim 8, where the one or more programs further include:

instructions for combining regional topographical, climatological and infrastructure data to create a forecast model.

10. The system as recited in claim 9, where the one or more programs further include:

instructions for deriving meteorological fields using current and predicted meteorological fields including one or more of divergence, vorticity, moisture advection, wind velocity shear, wind velocity deformation, and gradient wind strength, thereby maximizing temporal and spatial resolution.

11. A computer implemented method for providing environmentally predictive indicators and related threats, comprising the steps of:

assembling at the computer system meteorological sensor data from a plurality of monitoring stations communicably linked over a wide area network and placed geographically based on infrastructure data and natural environmental and man-made regional characteristic data, time averaging the meteorological sensor data over a one minute period, and transmitting the aggregate meteorological sensor data in a data package to a central server having program instructions where when said program instructions are executed are adapted to perform the steps of,

initializing lateral boundary conditions based on large scale numerical weather forecast data and lower boundary conditions based on climatological geographic data for an earth and atmosphere model domain, and

combining model domain boundary conditions and sensor data transformed into a uniform data grid for implementing a forecasting model.

12. The method as recited in claim 11, where the data package if formatted for generating weather prediction data based on the forecasting model and outputting the weather prediction data to a user interface.

13. The method as recited in claim 12, where the meteorological sensor data is from a local mesonet having a plurality of monitoring stations placed for achieving high spatial resolution with respect to varying spacing of infrastructure of concern sufficient for assigning a Threat Index value based on type of asset, weather threat for asset;

and local conditions and further sufficient for presenting a user interface the Threat Index value in graphical form corresponding to the infrastructure of concern.

14. The method as recited in claim 13, where the User Interface has an input device adapted to allow the operator to input modification data and further comprising the step of modifying results based on historical data, recent trends and conditions known by the operator.

15. The method as recited in claim 14, wherein climatological geographic data further includes land topology.

16. The method as recited in claim 15, wherein infrastructure data further includes population density data, where said threat level index value indicates the probability that winds will spread a harmful agent toward a location with a high population density.

17. The method as recited in claim 16, wherein infrastructure data includes flammable substance location data, and where said Threat Index Value indicates the probability that winds will spread a fire toward a location which may contain the flammable substance.

18. The method as recited in claim 17, wherein each of the plurality of monitoring stations are spaced apart less than 10 km, one with respect to the other.

19. The method as recited in claim 18, further comprising the step of monitoring with a Quality Control module of the computing system data from Local Mesonet stations and National Weather Service and detecting out of tolerance conditions by quality control processing the data from the local mesonet by determining if data is within predetermined criteria including one or more of, sensor performance, expected data content, expected data range, temporal data continuity, spatial data consistency and data adjustments for infrastructure.

20. The method as recited in claim 19, where one or more of each of the plurality of monitoring stations measure one or more of weather conditions including temperature, humidity, atmospheric pressure, wind speed and precipitation rate.

21. A computer implemented method, comprising the steps of:

combining at a computer larger scale numerical weather forecast data with high spatial and high temporal resolution data from a local mesonet thereby creating a forecast model having higher spatial and temporal correlation factors between observed and predicted weather conditions;

quality control processing the data from the local mesonet by determining if data is within predetermined criteria including one or more of, sensor performance, expected data content, expected data range, temporal data continuity, spatial data consistency and data adjustments for infrastructure; and

deriving meteorological fields using current and predicted kinematic and dynamic meteorological fields including one or more of divergence, vorticity, moisture advection, wind velocity shear, wind velocity deformation, and gradient wind strength,

thereby maximizing temporal and spatial resolution.

22. The computer implemented method as recited in claim 21, further comprising the step of:

combining regional topographical, climatological and infrastructure data to create the forecast model.

23. The computer implemented method as recited in claim 22, further comprising the steps of:

tuning the forecast model to fit local conditions as measured real-time by the local mesonet by selecting parameterization schemes based on the local conditions as measured real-time by the local mesonet and the differences between predicted conditions and local conditions as measured.

24. The computer implemented method as recited in claim 23, further comprising the steps of:

initializing lateral boundary conditions based on the larger scale numerical weather forecast data and lower boundary conditions based on climatological geographic data for a domain of the forecast model.

25. A computer implemented method, comprising the steps of:

polling a central server at a uniform time interval for meteorological sensor data;

receiving at the computer system the meteorological sensor data from a plurality of monitoring stations communicably linked over a wide area network and placed geographically based on infrastructure data and natural environmental and man-made regional characteristic data transmitted to the central server and said computer system having program instructions where when said program instructions are executed perform the steps of,

initializing real time lateral boundary conditions based on large scale numerical weather forecast data and lower boundary conditions based on climatological geographic data for an earth and atmosphere model domain, and incorporating data from the plurality of monitoring stations to improve real time lateral and lower boundary conditions of a forecasting model and

combining model domain boundary conditions and sensor data transformed into a uniform data grid for implementing the forecasting model.

26. The method as recited in claim 25, where when said program instructions are executed they further perform the steps of generating weather prediction data based on the forecasting model and outputting the weather prediction data to a user interface.

27. The method as recited in claim 26, where when said program instructions are executed they further perform the steps of assigning a Threat Index value based on type of asset, weather threat for asset;

and local conditions and further presenting the Threat Index value in graphical form to the user interface.

28. The method as recited in claim 27, wherein climatological geographic data further includes land topology.

29. A computer implemented method, comprising the steps of:

initializing at a computer lateral boundary conditions based on large scale numerical weather forecast data and lower boundary conditions based on climatological geographic data thereby implementing an earth and atmosphere model domain;

transforming into a uniform data grid sensor data from a local mesonet having a plurality of monitoring stations communicably linked over a wide area network and placed geographically based on infrastructure data and natural environmental and man-made regional characteristic data; and

combining the earth and atmosphere model domain boundary conditions and the sensor data collected from the local mesonet thereby implementing a forecasting model, and

combining static infrastructure data with the climatological geographic data and the meteorological sensor data to determine a threat index.

30. The computer implemented method as recited in claim 29, further comprising the step of:

assigning a Threat Index value based on a type of asset, weather threat for the asset; and

local conditions and further presenting the Threat Index value in graphical form to a user interface.

31. A non-transitory computer readable storage medium for use in conjunction with a computer system associated with a local mesonet, the computer readable storage medium storing one or more programs for execution by the computer system, the one or more programs comprising:

instructions for data ingesting and combining at a computer larger scale numerical weather forecast data with high spatial and high temporal resolution data from a local mesonet thereby creating real time a forecast model having higher spatial and temporal correlation factors between observed and predicted weather conditions;

quality control processing the data from the local mesonet by determining if data is within predetermined criteria including one or more of, sensor performance, expected data content, expected data range, temporal data continuity, spatial data consistency and data adjustments for infrastructure; and

instructions for deriving meteorological fields using current and predicted meteorological fields including one or more of divergence, vorticity, moisture advection, wind velocity shear, wind velocity deformation, and gradient wind strength, thereby maximizing temporal and spatial resolution.

32. The non-transitory computer readable storage medium as recited in claim 31, where the one or more programs further comprise:

instructions for combining regional topographical, climatological and infrastructure data to create a forecast model.

33. The non-transitory computer readable storage medium as recited in claim 32, where the one or more programs further comprise:

instruction for tuning the forecast model to fit local conditions as measured real-time by the local mesonet by selecting parameterization schemes based on the local conditions as measured real-time by the local mesonet and the differences between predicted conditions and local conditions as measured.

34. A non-transitory computer readable storage medium for use in conjunction with a computer system associated with a local mesonet, the computer readable storage medium storing one or more programs for execution by the computer system, the one or more programs comprising:

instructions for initializing lateral boundary conditions based on large scale numerical weather forecast data and lower boundary conditions based on climatological geographic data including one or more of seasonal variations in vegetation and soil thereby implementing an earth and atmosphere model domain;

instructions for transforming into a uniform data grid sensor data real time from a local mesonet having a plurality of monitoring stations communicably linked over a wide area network and geographically and strategically placed based on infrastructure data and natural environmental and man-made regional characteristic data and based on achieving high spatial resolution with respect to the varying spacing of infrastructure of concern within a given region; and

instructions for combining the earth and atmosphere model domain boundary conditions and the sensor data collected from the local mesonet thereby implementing a forecasting model.

35. The non-transitory computer readable storage medium as recited in claim 34, where the one or more programs further comprise:

instructions for assigning a Threat Index value based on a type of asset, weather threat for the asset; and

local conditions and further presenting the Threat Index value in graphical form to the user interface.

36. A system for predicting weather-related threats, comprising:

a network meteorological weather monitoring stations having a plurality of monitoring stations communicably linked over a wide area network to a central server where each of said plurality of locally spaced apart monitoring stations operable to transmit sensor data to said central server and where each of said plurality of monitoring stations are geographically place based on a type of local weather threat parameter, a type of local asset parameter, and a local condition parameter thereby forming a local Mesonet;

said central server having a memory and one or more processors, where one or more processor having executable programs stored in the memory and executed by the one or more processors, where the one or more programs include

instructions for transmitting sensor data and other weather data to a computing system communicably linked to said central server over said wide area network and having an initialization module adapted for initializing the boundary conditions for a model domain.

37. The system as recited in claim 36, where the type of local asset parameter is a parameter selected from a group of parameters representative of a utility power line, a high tension tower and a school.

38. The system as recited in claim 37, where the type of local condition parameter that is representative of local climatology.

39. A system for predicting weather-related threats, comprising:

a computing system communicably linked to a central server over a wide area network where said computing system having a memory and one or more processors, where one or more processor having executable programs stored in the memory and executed by the one or more processors, where the one or more programs include instructions for receiving local mesonet data gathered at the central server from a plurality of monitoring stations that are geographically place based on a type of local weather threat parameter, a type of local asset parameter, and a local condition parameter thereby forming a local Mesonet, and said computing system having an initialization module adapted for initializing the boundary conditions for a EAS model domain, where said initialization module has a pre-processing sub-module including a Geo-Grid function operable to extract data from a geographic database to define the model domain lower boundary condition, an UnGrib function operable to extract data imported from national weather service to define lateral boundary conditions, and a Metgrid function operable to transform data from the UnGrib and the Geo-Grid for the EAS model domain.

40. The system as recited in claim 39, where said computing system further comprises an ingestion module adapted for ingesting initial EAS model domain boundary conditions and combining with Local Mesonet to get Weather Research Forecasting model by importing Local Mesonet Data from the Local Mesonet and transformed by an ObsGrid sampling function into uniform data grid and storing into MetGrid;

and

said computing system further comprising a parameterization scheme module adapted for selecting and tuning a parameterization scheme based at least partially on Local Mesonet Data.

said computing system further comprising a Prediction module adapted for generating weather prediction data based on the Weather Research Forecasting model and outputting the weather prediction data to a user interface adapted for operator viewing.

41. A non-transitory computer readable storage medium for use in conjunction with a computer system associated with a local mesonet, the computer readable storage medium storing one or more programs for execution by the computer system, the one or more programs comprising:

polling a central server at a uniform time interval for meteorological sensor data;

receiving at a computer system the meteorological sensor data from a plurality of monitoring stations communicably linked over a wide area network and placed geographically based on infrastructure data and natural environmental and man-made regional characteristic data transmitted to the central server and said computer system having program instructions where when said program instructions are executed perform the steps of,

initializing real time lateral boundary conditions based on large scale numerical weather forecast data and lower boundary conditions based on climatological geographic data for an earth and atmosphere model domain, and incorporating data from the plurality of monitoring stations to improve real time lateral and lower boundary conditions of a forecasting model and

combining model domain boundary conditions and sensor data transformed into a uniform data grid for implementing the forecasting model.