Severe weather detection, classification and localization using dual-polarization radar

• US 10,324,180 B1
• Filed: 09/18/2013
• Issued: 06/18/2019
• Est. Priority Date: 09/18/2013
• Status: Active Grant

• Current Assignee: Baron Services Incorporated
• Original Assignee: Baron Services Incorporated
• Inventors: Lawton, Michael, Purves, Nathan, Purves, David
• Primary Examiner(s): Barker, Matthew M
• Application Number:
  US14/030,459
• Time in Patent Office: 2,099 Days
• CPC Class Codes: G01S 13/581 using transmission of interrupted pulse modulated waves and based upon the Doppler effect resulting from movement of targets
• CPC Class Codes: G01S 13/95 for meteorological use
• CPC Class Codes: G01S 13/951 ground based
• CPC Class Codes: G01S 13/958 Theoretical aspects
• CPC Class Codes: G01S 7/024 using polarisation effects in waveguides H01P1/165; for aerials H01Q, e.g. H01Q15/22, H01Q15/24, H01Q19/195
• CPC Class Codes: G01S 7/025 involving the transmission of linearly polarised waves
• 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

The present disclosure provides a system that predicts the occurrence and location of a severe weather event including a non-transitory tangible media containing software or firmware encoded thereon for operation by one or more processors that receive a plurality of weather variables, at least one of said weather variables being from radar data from a dual-polarization radar, where the processor (i) generates at least one derived radar variable based on the weather variables, (ii) identifies a geographical region of interest, (iii) validates the presence of the region of interest, (iv) determines whether there is a vertical column of regions of interest, wherein the presence of the vertical column of regions of interest is indicative of the vertical size of the severe weather event and (viii) validates the presence of the vertical columns of regions of interest.

Claims

1. A system that predicts the occurrence and location of a severe weather event comprising:

a. one or more processors;

b. a non-transitory tangible media containing software or firmware encoded thereon for operation by the one or more processors that receive a plurality of weather variables, at least one of said weather variables being from radar data from a dual-polarization radar;

where the one or more processors are configured (i) to generate at least one derived radar variable based on the weather variables, (ii) to identify at least one geographic region of interest by determining if the at least one derived radar variables is indicative of a potential severe weather event in the geographical area, (iii) to validate the presence of the geographic region of interest by validating the presence of at least one of a tornado debris signature, a hail core, a region of high shear or a tornado vertex therein, (iv) to determine whether there is a vertical column of regions of interest within the geographic of interest, wherein the presence of the vertical column of regions of interest is indicative of the vertical size of the severe weather event, (v) to validate the presence of the vertical columns of regions of interest by scoring each column by applying a test to a set of features of the vertical columns of interest, and (vi) to display a graphical representation of the location of the severe weather event.

2. The system of claim 1 wherein the weather variables comprise reflectivity (Z), Doppler velocity (V), spectrum width (W), differential reflectivity (ZDR), differential phase (Φ

DP) and cross-correlation coefficient (ρ

hv).

3. The system of claim 1 wherein the weather variables include at least two of the following reflectivity (Z), Doppler velocity (V), spectrum width (W), differential reflectivity (ZDR), differential phase (Φ

DP) or cross-correlation coefficient (ρ

hv).

4. The system of claim 1 wherein the derived weather variable is selected from the group consisting of specific differential phase (K_DP) and shear rate (S).

5. The system of claim 1 wherein the derived weather variable comprises specific differential phase (KDP) and shear rate (S).

6. The system of claim 1 further comprising the processor being configured to receive auxiliary data measurements.

7. The system of claim 6 wherein the auxiliary data measurements are selected from the group consisting of Convective Available Potential Energy (“

CAPE”

) and model-derived Atmospheric Freezing Level.

8. The system of claim 7 wherein the processor uses at least one of the auxiliary data measurement to generate the derived radar variable.

9. The system of claim 1 wherein the severe weather event is a tornado.

10. The system of claim 1 wherein the processor identifies a region of interest by calculating the probability that the region of interest is part of a tornado debris signature, said calculation comprising comparing at least one derived radar variable for the region of interest to at least one derived radar variable of a plurality of additional regions of interest.

11. The system of claim 1 wherein the processor validates the presence of the region of interest by comparing the value of at least one derived radar variable for a first region of interest with the value of at least one derived radar variable for second region of interest, wherein the second region of interest is larger than and includes the first region of interest.

12. The system of claim 11 wherein the at least one derived radar variable is specific differential phase (KDP).

13. A computer implemented method of determining the existence of a severe weather event comprising the steps of:

a. collecting radar data from a dual-polarization radar, said radar data comprising a weather variable;

b. processing the radar data to determine at least one derived radar variable;

c. identifying a geographic region of interest by determining if the at least one derived radar variable is indicative of the existence of a severe weather event in the geographic region of interest;

d. validating the presence of at least one of a tornado debris signature, a hail core, a region of high shear or a tornado vertex within the geographic region of interest;

e. identifying vertical columns of regions of interest within the geographic region of interest;

f. validating the presence of the vertical columns of regions of interest by scoring each column by applying a test to a set of features of the vertical columns; and

g. displaying a graphical representation of the location of the severe weather event.

14. The method of claim 13 wherein the weather variables comprise at least one of the following:

reflectivity (Z), Doppler velocity (V), spectrum width (W), differential reflectivity (ZDR), differential phase (Φ

DP) and cross-correlation coefficient (ρ

hv).

15. The method of claim 13 wherein the derived weather variable is selected from the group consisting of specific differential phase (K_DP) and shear rate (S).

16. The method of claim 13 wherein the derived weather variable comprises specific differential phase (KDP) and shear rate (S).

17. The method of claim 13 further comprising identifying a first region of interest by calculating the probability that the first region of interest is part of a tornado debris signature, said calculation comprising comparing at least one derived radar variable for the first region of interest to at least one derived radar variable of a plurality of additional regions of interest near or adjacent to the first region of interest.

18. The method of claim 13 further comprising validating the presence of the region of interest by comparing the value of at least one derived radar variable for a first region of interest with the value of at least one derived radar variable for second region of interest, wherein the second region of interest is larger than and includes the first region of interest.