Apparatus and method for providing environmental predictive indicators to emergency response managers

• US 9,274,250 B2
• Filed: 05/30/2012
• Issued: 03/01/2016
• Est. Priority Date: 11/13/2008
• Status: Active Grant

• Current Assignee: Saint Louis University
• Original Assignee: Saint Louis University
• Inventors: Pasken, Robert W., Dannevik, William
• Primary Examiner(s): Huynh, Phuong
• Application Number:
  US13/484,252
• Publication Number:
  US 20120303278A1
• Time in Patent Office: 1,371 Days
• Field of Search: 702/3
• US Class Current: 1/1
• 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

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; and

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

instructions for combining at a computer larger scale numerical weather forecast data with high spatial and high temporal resolution data from a local Doppler radar system thereby initializing and creating boundary conditions for a forecast model having higher spatial and temporal correlation factors between observed and predicted weather conditions;

instructions for deriving three dimensional meteorological fields using current observed and predicted meteorological fields including three dimensional wind fields, wind perturbations and temperature perturbations; and

instructions for providing an inversion technique to compute realistic perturbation pressures and perturbation temperature fields, and error magnitudes in the recovered fields, which can be used to predict wind field changes and wind loading over a time duration.

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

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

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

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

instructions for tuning the forecast model to fit local conditions as measured real-time by a 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 processor executable programs further include:

instructions for presenting a user interface adapted to allow an operator to 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 processor executable 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; and

one or more processor executable programs stored in the memory and executed by the one or more processors, where the one or more processor executable 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 thereby implementing an earth and atmosphere model domain;

instructions for combining at a computer larger scale numerical weather forecast data with high spatial and high temporal resolution data from a local Doppler radar system thereby initializing and creating boundary conditions for a forecast model having higher spatial and temporal correlation factors between observed and predicted weather conditions;

instructions for performing downward integration from a top boundary thereby deriving three dimensional meteorological fields using current observed and predicted meteorological fields including three dimensional wind fields, wind perturbations and temperature perturbations; and

instructions for providing an inversion technique to compute realistic perturbation pressures and perturbation temperature fields, and error magnitudes in recovered fields, which can be used to predict wind field changes and wind loading over a time duration.

7. The system as recited in claim 6, where the one or more processor executable programs further include:

instructions for assigning a Threat Index value based on a type of an asset, a weather threat for the asset and local conditions, and further presenting the Threat Index value in graphical form to a user interface.

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

instructions for monitoring data from a local mesonet and large scale numerical weather forecast data for out of tolerance conditions.

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

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

10. The system as recited in claim 9, where the one or more processor executable 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 a computer system meteorological sensor data from a plurality of Doppler radar 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 transmitting the meteorological Doppler radar data to a central server having program instructions where when said program instructions are executed are adapted to perform the steps of;

combining at a computer larger scale numerical weather forecast data with high spatial and high temporal resolution data from the plurality of Doppler radar monitoring stations thereby initializing and creating boundary conditions for a forecast model having higher spatial and temporal correlation factors between observed and predicted weather conditions;

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 the meteorological Doppler radar data transformed into a uniform data grid for implementing a forecasting model.

12. The method as recited in claim 11, where when said program instructions are executed they are further adapted to perform the steps of 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 when said program instructions are executed they are further adapted to perform the steps of assigning a Threat Index value based on a type of an asset, a weather threat for the asset;

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

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 the climatological geographic data further includes land topology.

16. The method as recited in claim 15, wherein the infrastructure data further includes population density data, where said Threat 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 the 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 Doppler radar monitoring stations are spaced apart less than 200 miles, 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.

20. The method as recited in claim 19, where one or more of each of the plurality of the Doppler radar 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:

deriving unknown model parameters from observations distributed in space and time;

inserting Doppler radar observations into a numerical model and determining the state of the numerical model;

inserting a wind radial velocity, adjusting two other wind components with respect to a continuity equation and some mean quantities;

retrieving pressure and temperature perturbations;

refining quality control, editing and analysis techniques thereby providing a more accurate three dimensional wind field recovered from the observed radial velocities and radar reflectivities;

combining regional topographical, climatological and infrastructure data to create a forecast model; and

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

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

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

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

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

inserting a wind radial velocity from the meteorological sensor data, adjusting two other wind components with respect to a continuity equation and some mean quantities;

retrieving pressure and temperature perturbations; and

refining quality control, editing and analysis techniques thereby providing a more accurate three dimensional wind field recovered from observed radial velocities and radar reflectivities.

24. The method as recited in claim 23, where when said program instructions are executed they further perform the steps of generating weather prediction data based on a forecasting model and outputting the weather prediction data to a user interface.

25. The method as recited in claim 24, where when said program instructions are executed they further perform the steps of assigning a Threat Index value based on a type of an asset, a weather threat for the asset;

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

26. The method as recited in claim 25, wherein the climatological geographic data further includes land topology.

27. 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 Doppler radar 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;

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

inserting a wind radial velocity from the sensor data, and adjusting two other wind components with respect to the continuity equation and some mean quantities;

retrieving pressure and temperature perturbations; and

refining quality control, editing and analysis techniques thereby providing a more accurate three dimensional wind field recovered from observed radial velocities and radar reflectivities.

28. The computer implemented method as recited in claim 27, further comprising the step of:

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

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