SYSTEMS AND METHODS FOR GAUSSIAN DECOMPOSITION OF WEATHER RADAR DATA FOR COMMUNICATION

• US 20100245167A1
• Filed: 03/25/2010
• Published: 09/30/2010
• Est. Priority Date: 03/25/2009
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Zeng, Dongsong, Kirk, James Christopher, Bunch, Brian Paul
• Granted Patent: US 8,144,048 B2
• Time in Patent Office: Days
• US Class Current: 342/26.B
• CPC Class Codes: G01S 13/953 mounted on aircraft
• CPC Class Codes: G01S 7/003 Transmission of data between radar, sonar or lidar systems and remote stations
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Exemplary methods and systems provide for processing weather data received from a weather radar system. An exemplary embodiment receives radar reflectivity data, decomposes the received radar reflectivity data into multiple adaptive Gaussian component functions, selects at least one parameter from the decomposed Gaussian component functions, generates the compressed weather radar display data based on the at least one selected parameter, and communicates the weather radar display data from the craft.

Claims

1. A method for transmitting compressed weather radar display data from a craft, the method comprising:

receiving radar reflectivity data;

decomposing the received radar reflectivity data into multiple adaptive Gaussian component functions;

selecting at least one parameter from the decomposed Gaussian component functions;

generating the compressed weather radar display data based on the at least one selected parameter; and

communicating the compressed weather radar display data from the craft.

2. The method of claim 1, wherein the compressed weather radar display data includes the decomposed Gaussian component functions.

3. The method of claim 2, further comprising:

receiving the compressed weather radar display data from the craft at least one of a ground station and another aircraft;

generating weather radar display data from the received compressed weather radar display data based upon the decomposed Gaussian component functions.

4. The method of claim 1, wherein decomposing the received radar reflectivity data into multiple adaptive Gaussian component functions is based upon a three-dimensional Gaussian base function defined by:

5. The method of claim 1, wherein the received radar reflectivity data is three-dimensional radar reflectivity data, and further comprising:

decomposing the received three-dimensional radar reflectivity data into multiple adaptive, three-dimensional Gaussian component functions.

6. The method of claim 5, wherein the received three-dimensional radar reflectivity data is near-term three-dimensional radar reflectivity data and far-term three-dimensional radar reflectivity data beyond 40 nautical miles (nm), and further comprising:

determining weather data from the near-term three-dimensional radar reflectivity data and the far-term three-dimensional radar reflectivity data;

decomposing the weather data into the multiple adaptive, three-dimensional Gaussian component weather information functions;

determining turbulence data from the near-term three-dimensional radar reflectivity data;

decomposing the turbulence data into multiple adaptive, three-dimensional Gaussian component weather information functions; and

communicating the decomposed turbulence data.

7. The method of claim 6, wherein the received three-dimensional radar reflectivity data is near-term three-dimensional radar reflectivity data and far-term three-dimensional radar reflectivity data beyond 40 nautical miles (nm), and further comprising:

training a neural network based on the received near-term three-dimensional radar reflectivity data and far-term three-dimensional radar reflectivity data;

predicting turbulence data based on the received near-term three-dimensional radar reflectivity data at ranges greater than 40 nm using the neural network; and

communicating the predicted turbulence data.

8. The method of claim 7, further comprising:

decomposing the predicted turbulence data into the multiple adaptive, three-dimensional Gaussian component functions.

9. The method of claim 5, further comprising:

selecting a plurality of parameters, wherein the plurality of parameters comprise a center position, an amplitude, and a dimensional standard deviation.

10. A system operable to transmit compressed weather radar display data from a craft, the system comprising:

a weather radar system configured to generate radar reflectivity data from the craft out to ranges greater than 40 nautical miles (nm);

a processing device in signal communication with the weather radar system, wherein the processing device is configured to receive the radar reflectivity data from the weather radar system, to decompose the received radar reflectivity data into multiple adaptive Gaussian component functions, to select at least one parameter from the decomposed multiple adaptive Gaussian component functions, to generate weather radar display data based on the at least one selected parameter; and

a transceiver in signal communication with the processing device, wherein the transceiver is configured to receive the compressed weather radar display data from the processing device and to communicate the compressed weather radar display data from the craft.

11. The method of claim 10, wherein the compressed weather radar display data includes the multiple adaptive Gaussian component functions.

12. The system of claim 10, further comprising:

a neural network,

wherein the processing device is further configured to train the neural network to predict far-term turbulence information based on near-term radar reflectivity data used to determine near-term turbulence, and based on far-term radar reflectivity data, and wherein the predicted turbulence data is communicated from the craft.

13. The system of claim 12,

wherein the processing device is further configured to:

decompose radar reflectivity data at near-term distances into multiple adaptive, three-dimensional Gaussian component functions corresponding to weather information and turbulence information; and

decompose the turbulence data at far-term distances into multiple adaptive, three-dimensional Gaussian component functions corresponding to weather information, and

wherein the neural network predicts the turbulence information at the far-term distances based upon the determined far-term multiple adaptive, three-dimensional Gaussian component functions corresponding to the weather information.

14. The system of claim 13, wherein the near-term distances are less than 40 nm and the far-term distances are greater than 40 nm.

15. The system of claim 10, wherein the at least one selected parameter comprises one or more of a center position, an amplitude, and a dimensional standard deviation.

16. A system for transmitting compressed weather radar display data from a craft, the system comprising:

a means for receiving radar reflectivity data;

a means for decomposing the received radar reflectivity data into multiple adaptive Gaussian component functions, for selecting at least one parameter from the decomposed Gaussian component functions, and for generating the compressed weather radar display data based on the at least one selected parameter; and

a means for communicating the compressed weather radar display data from the craft.

17. The system of claim 16, wherein the received radar reflectivity data is three-dimensional radar reflectivity data, and wherein the means for decomposing is configured to decompose the received three-dimensional radar reflectivity data into multiple adaptive, three-dimensional Gaussian component functions.

18. The system of claim 17, further comprising:

a neural network configured to select one or more first parameters from the multiple adaptive, three-dimensional Gaussian component functions;

select one or more second parameters from the multiple adaptive, three-dimensional Gaussian component functions;

apply the one or more first parameters to one of an input or output side of the neural network; and

apply the one or more second parameters to a side of the neural network opposite the side with the applied first parameters.

19. The system of claim 18, wherein the parameters comprise one or more of a center position, an amplitude, and a dimensional standard deviation.