APPARATUS AND METHOD FOR LAGRANGIAN PRECIPITATION SENSING

• US 20160313470A1
• Filed: 12/10/2014
• Published: 10/27/2016
• Est. Priority Date: 12/12/2013
• Status: Abandoned Application

• Current Assignee: King Abdullah University of Science and Technology
• Original Assignee: King Abdullah University of Science and Technology
• Inventors: Abdelkader, Mohamed, Claudel, Christian, Shaqura, Mohammad, Hoteit, Ibrahim
• Application Number:
  US15/104,233
• Publication Number:
  US 20160313470A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64U 2101/35 for science, e.g. meteorology
• CPC Class Codes: G01W 1/08 Adaptations of balloons, missiles, or aircraft for meteorological purposes; Radiosondes transmission of measurements G01D; transmitting circuits H04B; balloons in general B64B; aeroplanes in general B64D; missiles in general F42B15/00
• CPC Class Codes: G01W 1/14 Rainfall or precipitation gauges measuring volume in general G01F

Abstract

A weather measurement system can include an aerial vehicle. An array of accelerometers can be disposed across the surface of the aerial vehicle. The accelerometers can be configured to measure raindrops impacting the surface. The measurements can include number per unit time and intensity. The system can include a processing unit configured to receive measurement data from the array and to process the measurement data into preprocessed data.

Claims

1. A weather measurement system, comprising:

an aerial vehicle having a surface;

an array of accelerometers disposed across the surface, wherein the accelerometers are configured to measure raindrops impacting the surface; and

a processing unit configured to receive measurement data from the array and to process the measurement data into preprocessed data.

2. The system of claim 1, wherein the aerial vehicle comprises an internal measurement unit in communication with an embedded computer.

3. The system of claim 2, wherein the embedded computer is configured to fuse preprocessed data with information from the internal measurement unit and to dynamically estimate a precipitation level.

4. The system of claim 3, further comprising a server in communication with the aerial vehicle, wherein the server is configured to receive weather data and to receive dynamically estimated precipitation level from the aerial vehicle, and wherein the server is further configured to estimate a three-dimensional rain map.

5. The system of claim 1, further comprising a server in communication with the aerial vehicle, wherein the server is configured to receive the preprocessed data and perform an ensemble Kalman filtration of the preprocessed data to estimate a three-dimensional rain map.

6. A method for dynamically estimating a three-dimensional rain map, comprising the steps of:

counting numbers and intensities of raindrops with an array of accelerometers, wherein the raindrops are impacting a surface of an aerial vehicle, wherein the array is disposed across the surface, and wherein measuring the counting produces measurement data;

preprocessing the measurement data with a processing unit in order to generate preprocessed data.

7. The method of claim 6, further comprising:

performing a data fusion algorithm utilizing the preprocessed data and information from an internal measurement unit to generation fused data; and

dynamically estimating a precipitation level based on the fused data.

8. The method of claim 7, wherein a server is in communication with the aerial vehicle, and wherein the server receives weather data and the dynamically estimated precipitation level from the aerial vehicle, and the server therewith estimates a three-dimensional rain map.

9. The method of claim 7, wherein dynamically estimating a precipitation level is further based on extrinsic weather data to the aerial vehicle, wherein the extrinsic weather data is obtained from a source other than the aerial vehicle.

10. The method of claim 9, further comprising utilizing an ensemble Kalman filter in generating a three-dimensional rain map based on the dynamically estimated precipitation level, the weather data, and the extrinsic weather data.

11. The method of claim 7, wherein the aerial vehicle is one of a plurality of substantially similar aerial vehicles, and wherein dynamically estimating the precipitation level is further based on measurements from the plurality.