Rainfall, snowfall forecast apparatus and method

• US 5,406,481 A
• Filed: 06/28/1994
• Issued: 04/11/1995
• Est. Priority Date: 06/30/1993
• Status: Expired due to Term

• Current Assignee: Nippon Telegraph and Telephone Corporation
• Original Assignee: Nippon Telegraph and Telephone Corporation
• Inventors: Shinozawa, Kazuhiko, Uchiyama, Tadasu, Sonehara, Noboru
• Primary Examiner(s): McElheny, Jr., Donald E.
• Application Number:
  US08/266,541
• Time in Patent Office: 287 Days
• Field of Search: 364/420, 340/601, 395/22, 395/931
• US Class Current: 702/3
• CPC Class Codes: G01S 13/95 for meteorological use
• CPC Class Codes: G01S 15/885 Meteorological systems
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
• CPC Class Codes: Y10S 706/931 Weather

Abstract

An apparatus is presented for providing a short time range forecast with relative high accuracy from weather radar images of cloud reflection data by incorporating physical properties of cloud in the forecasting method. The method consists of defining a plurality of lattice points on a radar image, and multiplying the reflection data from a group of neighboring lattice points obtained at a specific past point in time with selected coefficients. The products of multiplication are summed, and transformed into image data by specific function based on the properties relating to cloud. Squared errors of the difference between the computational reflection data and the observed reflection data are iterated to a value below a predetermined threshold value to select the coefficients, and these coefficients are used to provide forecasting of reflection data at a specific future point in time.

Claims

1. An apparatus for forecasting a quantity of rainfall or snowfall in a specified region from reflection intensity images corresponding to cloud distribution conditions obtained from weather radar images, said apparatus comprising:

(a) input means for inputting reflection intensities from a plurality of lattice points defined on said reflection intensity images;

(b) memory means for memorizing the reflection intensities inputted by said input means;

(c) calculation means for multiplying the reflection intensities from a group of neighboring lattice points memorized in said memory means at a specific past time with a separate coefficient for each lattice point, summing the products of multiplication, transforming the values of the product of summation using a specific function based on cloud related properties to generate computational present data, and iterating until a specific predetermined threshold value is obtained so that the squared error of a difference between said computational present data and present data being observed presently becomes smaller than said threshold value;

(d) forecast means for predicting future reflection intensities for each lattice point of said group of neighboring lattice points at a future time, by summing products of the present reflection intensities and said coefficients generated by said calculation means to generate a product summation, and transforming said product summation using said specific function;

(e) decision means for determining the quantity of rainfall or snowfall in said specified region at said future time based on the reflection intensities predicted by said forecast means.

2. An apparatus as claimed in claim 1, wherein said apparatus performs parallel computations by having a plurality of calculation sections, in said calculation means, to correspond with each of said lattice points;

assigning means for assigning a calculation section to each of said lattice points; and

parallel calculation means for performing parallel computations by using said calculation sections assigned by said assigning means.

3. An apparatus as claimed in claim 1, wherein said apparatus performs parallel computations by having a plurality of calculation sections, in said forecast means, to correspond with each of said lattice points;

assigning means for assigning a calculation section to each of said lattice points; and

parallel calculation means for performing parallel computations by using said calculation sections assigned by said assigning means.

4. An apparatus as claimed in claim 1, wherein said specific function is a non-linear function cloud related properties.

5. An apparatus as claimed in claim 1, wherein said specific function is a linear function approximating properties of cloud.

6. An apparatus as claimed in claim 1, wherein said apparatus is provided with means for calculating parameters expressing harmonization of said coefficients based on cloud related properties of dispersion, translation, sink and source.

7. A method for forecasting the quantity of rainfall or snowfall in a specified region from reflection intensity images corresponding to cloud distribution conditions obtained from weather radar images, said method comprising the steps of:

(a) inputting and memorizing reflection intensities from a plurality of lattice points defined on said reflection intensity images;

(b) multiplying reflection intensities memorized in memory means for each of a group of lattice points at a specific past time with a separate coefficient for each lattice point;

(c) summing the products of multiplication;

(d) transforming the values of the product of summation using a specific function based on cloud related properties to generate computational present data;

(e) iterating until a specific predetermined threshold value is obtained so that the squared error of a difference between the computational present data and the observed present data becomes smaller than said threshold value;

(f) predicting future reflection intensities for each lattice point of said group of lattice points at a future time, by summing products of the present reflection intensities and said coefficients generated by said calculation means to generate a product summation, and transforming said product summation using said specific function; and

(g) determining the quantity of rainfall or snowfall in said specified region at said future time based on the reflection intensities predicted by said forecast means.

8. A method as claimed in claim 7, wherein said specific function is a non-linear function based on cloud related properties.

9. A method as claimed in claim 7, wherein said specific function is a linear function approximating properties of cloud.

10. A method as claimed in claim 7, wherein said method includes a step of calculating parameters expressing harmonization of said coefficients based on cloud related properties of dispersion, translation, sink and source.