WEATHER DETECTION USING SATELLITE COMMUNICATION SIGNALS

• US 20100052919A1
• Filed: 08/19/2009
• Published: 03/04/2010
• Est. Priority Date: 08/26/2008
• Status: Active Grant

• Current Assignee: ViaSat Incorporated
• Original Assignee: ViaSat Incorporated
• Inventors: Mills, Raymond L.
• Granted Patent: US 8,730,086 B2
• Time in Patent Office: Days
• US Class Current: 340/602
• CPC Class Codes: G01S 13/955 mounted on satellite
• CPC Class Codes: G01W 1/04 giving only separate indications of the variables measured
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09
• CPC Class Codes: H04B 7/18513 Transmission in a satellite or space-based system
• CPC Class Codes: H04W 52/245 taking into account received signal strength

Abstract

Disclosed is a satellite weather detection system that uses atmospheric precipitation density data. Subscribers detect the signal strength and signal-to-noise ratio of signals that are transmitted from the satellite. Upstream transmit power data is also collected, which is needed to achieve a given SNR at a gateway. The values of the downstream signal strength and signal-to-noise ratio data as well as the upstream transmit power data are normalized and compared with current atmospheric data. The data can be color coded and graphically displayed to show weather patterns. Location and velocity of high precipitation density cells can be tracked to predict movement of storms.

Claims

1. A method of detecting atmospheric precipitation density using signal-to-noise ratio data relating to the signal-to-noise ratio of subscriber received signals transmitted from a satellite to subscribers at subscriber locations comprising:

receiving said subscriber received signals at said subscriber locations;

generating said signal-to-noise ratio data relating to said signal-to-noise ratio of said subscriber received signals;

transmitting said signal-to-noise ratio data from said subscribers to a central office;

normalizing said signal-to-noise ratio data by averaging said signal-to-noise ratio data over a plurality of time periods to produce averaged signal-to-noise ratio data;

comparing said signal-to-noise ratio data for a current time period with said averaged signal-to-noise ratio data to generate differential signal-to-noise ratio data that is representative of atmospheric precipitation density.

2. The method of claim 1 wherein said process of normalizing said signal-to-noise ratio data comprises:

averaging said signal-to-noise ratio data for all of the subscribers in each cell for said plurality of time periods to generate an average signal-to-noise ratio value for each cell for each of said plurality of time periods;

averaging said average signal-to-noise ratio values for an extended period.

3. The method of claim 2 wherein said process of normalizing said data further comprises:

averaging said average signal-to-noise ratio value for each cell with other average signal-to-noise ratio values for adjacent cells.

4. The method of claim 3 further comprising:

graphically displaying said differential signal-to-noise ratio data to produce a graphical representation of said atmospheric precipitation density.

5. The method of claim 1 further comprising the process of:

identifying locations of high precipitation density cells during each of said plurality of time periods that have a predetermined precipitation density;

calculating velocity and direction of movement of a storm based upon said locations of said high precipitation density cells over said plurality of time periods to predict movement and velocity of said storm for time periods that are subsequent to said plurality of time periods.

6. The method of claim 5 wherein said process of identifying locations of high precipitation density cells further comprises:

identifying a closest cell of said high precipitation density cells for said storm during each time period of said plurality of time periods.

7. A method of detecting atmospheric precipitation density using signal strength data relating to the signal strength of subscriber received signals transmitted from a satellite to subscribers at subscriber locations comprising:

receiving said subscriber received signals at said subscriber locations;

generating said signal strength data relating to said signal strength of said subscriber received signals;

transmitting said signal strength data from said subscribers to a central office;

normalizing said signal strength data by averaging said signal strength data over a plurality of time periods to produce averaged signal strength data;

comparing said signal strength data for a current time period with said averaged signal strength data to generate differential signal strength ratio data that is representative of atmospheric precipitation density.

8. The method of claim 7 wherein said process of normalizing said signal strength data comprises:

averaging said signal strength data for all of the subscribers in each cell for said plurality of time periods to generate an average signal strength value for each cell for each of said plurality of time periods;

averaging said average signal strength values for an extended period.

9. The method of claim 8 wherein said process of normalizing said signal strength data further comprises:

averaging said average signal strength value for each cell with other average signal strength values for adjacent cells.

10. The method of claim 7 further comprising:

graphically displaying said differential signal strength data to produce a graphical representation of said atmospheric precipitation density.

11. The method of claim 7 further comprising the process of:

identifying locations of high precipitation density cells during each of said plurality of time periods that have a predetermined precipitation density;

calculating velocity and direction of movement of a storm based upon said locations of said high precipitation density cells to predict movement and velocity of said storm for time periods that are subsequent to said plurality of time periods.

12. The method of claim 11 wherein said process of identifying locations of high precipitation density cells further comprises:

identifying a closest cell of said high precipitation density cells for said storm during each time period of said plurality of time periods.

13. A system for detecting atmospheric precipitation density using signal-to-noise ratio data relating to the signal-to-noise ratio of downstream signals transmitted from a satellite to subscribers at known subscriber locations comprising:

a subscriber transceiver that receives said downstream signal and transmits an upstream signal;

a subscriber modem that receives said downstream signals from said transceiver, determines said signal-to-noise ratio data of said downstream signal and encodes said upstream signal with said signal-to-noise ratio data;

a control system that receives said upstream signal, extracts said signal-to-noise ratio data from said upstream signal and has a processor that normalizes said signal-to-noise ration data by averaging said signal-to-noise ratio data over a plurality of time periods to produce averaged signal-to-noise ration data, compares said signal-to-noise ration data for a current time period with said averaged signal-to-noise ration data to generate differential signal-to-noise ratio data that is representative of atmospheric precipitation density.

14. The system of claim 13 further comprising:

a display that graphically displays said differential signal-to-noise ration data to show said atmospheric precipitation density.

15. The system of claim 14 wherein said graphical display further comprises:

a display that graphically displays said differential signal-to-noise ratio data in color.

16. The system of claim 13 wherein said control system has a processor that:

identifies locations of high precipitation density cells during each of said plurality of time periods that have at least a predetermined precipitation density;

calculates velocity and direction of movement of a storm based upon said locations of said high precipitation density cells over said plurality of time periods to predict movement and velocity of said storm for time periods that are subsequent to said plurality of time periods.

17. The system of claim 16 wherein said processor further performs the function of:

identifying a closest cell of said high precipitation density cells for said storm during each time period of said plurality of time periods.

18. The system of claim 17 wherein said processor performs functions of normalizing said signal strength data that further comprise:

averaging said signal strength data for all of the subscribers in each cell for said plurality of time periods to generate an average signal strength value for each cell for each of said plurality of time periods;

averaging said average signal strength values for said plurality of time periods.

19. The system of claim 18 wherein said processor performs functions of normalizing said signal strength data that further comprises:

averaging said average signal strength value for each cell with other average signal strength values for adjacent cells.

26. The system of claim 19 wherein said processor performs functions of normalizing said signal strength data that further comprises:

averaging said average signal strength value for each cell with other average signal strength values for adjacent cells.

20. A system for detecting atmospheric precipitation density using signal strength data relating to the signal strength of downstream signals transmitted from a satellite to subscribers at known subscriber locations comprising:

a subscriber transceiver that receives said downstream signal and transmits an upstream signal;

a subscriber modem that receives said downstream signals from said transceiver, determines said signal strength data of said downstream signal and encodes said upstream signal with said signal strength data;

a control system that receives said upstream signal, extracts said signal strength data from said upstream signal and has a processor that normalizes said signal strength data by averaging said signal strength data over a plurality of time periods to produce averaged signal strength data, compares said signal strength data for a current time period with said averaged signal strength data to generate differential signal strength data that is representative of atmospheric precipitation density.

21. The system of claim 20 further comprising:

a display that graphically displays said differential signal strength data to show said atmospheric precipitation density.

22. The system of claim 21 wherein said graphical display further comprises:

a display that graphically displays said differential signal strength data in color.

23. The system of claim 22 wherein said control system has a processor that:

identifies locations of high precipitation density cells during each of said plurality of time periods that have at least a predetermined precipitation density;

calculates velocity and direction of movement of a storm based upon said locations of said high precipitation density cells over said plurality of time periods to predict movement and velocity of said storm for time periods that are subsequent to said plurality of time periods.

24. The system of claim 23 wherein said processor further performs the function of:

identifying a closest cell of said high precipitation density cells for said storm during each time period of said plurality of time periods.

25. The system of claim 24 wherein said processor performs functions of normalizing said signal strength data that further compromise:

averaging said signal strength data for all of the subscribers in each cell for said plurality of time periods to generate an average signal strength value for each cell for each of said plurality of time periods;

averaging said average signal strength values for said plurality of time periods.