WO2013125527A1 - 気象予測装置及び気象予測方法 - Google Patents

気象予測装置及び気象予測方法 Download PDF

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Publication number
WO2013125527A1
WO2013125527A1 PCT/JP2013/054012 JP2013054012W WO2013125527A1 WO 2013125527 A1 WO2013125527 A1 WO 2013125527A1 JP 2013054012 W JP2013054012 W JP 2013054012W WO 2013125527 A1 WO2013125527 A1 WO 2013125527A1
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WIPO (PCT)
Prior art keywords
observation
value
grid
observation value
advection model
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Ceased
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PCT/JP2013/054012
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English (en)
French (fr)
Japanese (ja)
Inventor
文彦 水谷
隆一 武藤
篤志 榊原
大輔 物江
愛実 小林
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Toshiba Corp
Chuden Cti Co Ltd
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Toshiba Corp
Chuden Cti Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Toshiba Corp, Chuden Cti Co Ltd filed Critical Toshiba Corp
Priority to IN5828DEN2014 priority Critical patent/IN2014DN05828A/en
Priority to JP2014500716A priority patent/JP6034361B2/ja
Priority to BR112014018277-9A priority patent/BR112014018277B1/pt
Priority to HK15102001.6A priority patent/HK1201587B/xx
Priority to CN201380004713.9A priority patent/CN104040378B/zh
Priority to EP13752228.0A priority patent/EP2818899B1/en
Publication of WO2013125527A1 publication Critical patent/WO2013125527A1/ja
Priority to US14/323,276 priority patent/US9645283B2/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Definitions

  • Embodiments of the present invention relate to a weather prediction device and a weather prediction method.
  • weather forecasting is performed by calculating atmospheric flows using observation data obtained by weather radar, etc., or GPV (Grid Point Value) data provided by the Japan Meteorological Agency.
  • GPV Grid Point Value
  • An object of the present embodiment is to provide a weather prediction device and a weather prediction method that can improve the accuracy of short-term weather prediction.
  • the weather prediction device is a device that divides the prediction target region into a grid shape and performs weather prediction for each grid, receiving means for receiving the observation value for each grid at a first time interval, and Advection model calculation means for calculating a predicted value for each grid at a second time interval shorter than the first time interval by using a first observation value received by the reception means as an initial value, and the reception When a second observation value after the first observation value is received by the means, the advection model is based on a difference between the second observation value and the predicted value corresponding to the observation time of the second observation value. And correcting means for correcting.
  • the weather prediction method is a method of performing weather prediction for each grid by dividing the prediction target region into a grid by a computer, and receives observation values for each grid at a first time interval.
  • FIG. 1 is a block diagram illustrating a configuration of a weather prediction apparatus according to the present embodiment.
  • FIG. 2 is a flowchart showing the weather prediction calculation process.
  • FIG. 3 is a diagram schematically illustrating the advection calculation process.
  • FIG. 4 is a diagram illustrating an example of the advection calculation result.
  • FIG. 1 is a block diagram showing a configuration of a weather prediction apparatus according to the present embodiment.
  • the weather prediction apparatus 100 includes a communication interface 11, a communication processing unit 12, an observation data storage unit 13, an advection model calculation unit 14, and an advection model correction unit 15.
  • the weather prediction device 100 is connected to the network NT through the communication interface 11, and performs communication between the weather data server DS0 and the radar site data servers DS1 and DS2 on the network NT.
  • the communication processing unit 12 receives weather observation information observed by the weather radar from the data servers DS1 and DS2 at the radar site via the network NT.
  • the radar site data servers DS1 and DS2 receive precipitation information and wind information (radar observation information) for each grid obtained by dividing the prediction target area in a grid pattern, for example, every 3 minutes (first time interval).
  • radar observation information has the time resolution and spatial resolution of radar, and in the case of a large parabolic antenna type mechanical scanning antenna used in a national observation network, it is about 1 km mesh every 3 to 5 minutes. In the case of an array weather radar, it can be received at a coverage of 20 to 60 km at a mesh of about 100 to 250 m every 10 to 30 seconds.
  • the communication processing unit 12 receives wide area wind prediction information (GPV data wind information) distributed from the weather data server DS0.
  • the meteorological observation data received by the communication processing unit 12 is stored in the observation data storage unit 13.
  • the advection model calculation unit 14 uses the observation value (first observation value) stored in the observation data storage unit 13 as an initial value, and uses the advection model, for example, every 10 seconds (second time interval) to detect the spatial resolution of the radar. Calculates and outputs the predicted value for each grid equivalent to.
  • the advection model correction unit 15 calculates the advection model corresponding to the second observation value and the observation time of the second observation value.
  • the advection model is corrected based on the difference from the predicted value by the unit 14.
  • FIG. 2 is a flowchart showing the weather prediction calculation process.
  • FIG. 3 schematically illustrates the advection calculation process.
  • the weather prediction device 100 receives radar observation information and wind information of GPV data by the communication processing unit 12 every 3 minutes, for example, and stores them in the observation data storage unit 13.
  • the advection model calculation unit 14 sets a physical quantity (water substance distribution, wind direction / wind speed distribution) obtained from the observation result one hour before (three minutes before) as an initial value (step S1).
  • the advection model calculation unit 14 performs prediction calculation of the water substance for each lattice using the advection model (step S2), and repeats the prediction calculation by the advection model in step S2, for example, every 10 seconds until the latest observation time is passed. Perform (step S3).
  • a three-dimensional CUL (Cubic Lagrange) method is used for the advection model, and the wind direction and the wind speed are constant in the time direction. Note that the present invention can also be applied when the wind direction and wind speed change in the time direction.
  • the wind direction / velocity field obtained using the VVP (Volume Velocity Processing) method or Gal-Chen method which is a three-dimensional wind analysis method. It can be used for advection direction and speed.
  • the advection model correction unit 15 determines the difference between the observation result of the water substance at the observation time and the prediction result of the water substance of the advection model corresponding to the observation time. (Error amount) is obtained at each grid point (step S4). And the advection model correction
  • Figure 4 shows an example of the prediction results using the advection model.
  • the water substance is generated at a position where the value of (observation result ⁇ prediction result of the advection model) is positive, and the water substance disappears at a position where the value is negative. I think. Assuming that the generation and disappearance of water substances will continue to occur at the same position for the next ten or more minutes, the amount of generation and disappearance of water substances per unit time is used as the correction value.
  • the corrected advection model is obtained by adding the correction term obtained in step S5 to the advection model. Specifically, it can be expressed by the following formula.
  • the advection model calculation unit 14 performs prediction calculation using the corrected advection model until the prediction target time (for example, 30 minutes ahead), with the observation result at the latest observation time of the water substance as an initial value. (Steps S6, 7).
  • the advection model calculation part 14 outputs the prediction result of step S6 as a predicted value of a water substance.
  • the above embodiment is based on the advection model and performs prediction while correcting the advection model based on the difference between the observed value and the predicted value based on the advection model.
  • this configuration it is possible to improve the accuracy of short-term weather prediction and accurately predict cumulonimbus clouds that develop rapidly.
  • precipitation prediction has been described as an example, but in addition to this, it is also possible to predict the distribution of pollen, yellow sand, dust, pollutants in the atmosphere, and the like.
  • flow velocity of the sea or river instead of the wind speed, it is possible to predict the distribution of plankton or oil that has flowed over the sea, or the distribution of chemical substances discharged into the river.

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  • Engineering & Computer Science (AREA)
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PCT/JP2013/054012 2012-02-20 2013-02-19 気象予測装置及び気象予測方法 Ceased WO2013125527A1 (ja)

Priority Applications (7)

Application Number Priority Date Filing Date Title
IN5828DEN2014 IN2014DN05828A (enExample) 2012-02-20 2013-02-19
JP2014500716A JP6034361B2 (ja) 2012-02-20 2013-02-19 気象予測装置及び気象予測方法
BR112014018277-9A BR112014018277B1 (pt) 2012-02-20 2013-02-19 Aparelho de previsão do tempo e método de previsão do tempo
HK15102001.6A HK1201587B (en) 2012-02-20 2013-02-19 Meteorological forecasting device and meteorological forecasting method
CN201380004713.9A CN104040378B (zh) 2012-02-20 2013-02-19 气象预测装置以及气象预测方法
EP13752228.0A EP2818899B1 (en) 2012-02-20 2013-02-19 Meteorological forecasting device and meteorological forecasting method
US14/323,276 US9645283B2 (en) 2012-02-20 2014-07-03 Weather prediction apparatus and weather prediction method

Applications Claiming Priority (2)

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JP2012-034367 2012-02-20
JP2012034367 2012-02-20

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US14/323,276 Continuation US9645283B2 (en) 2012-02-20 2014-07-03 Weather prediction apparatus and weather prediction method

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WO2013125527A1 true WO2013125527A1 (ja) 2013-08-29

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EP (1) EP2818899B1 (enExample)
JP (1) JP6034361B2 (enExample)
CN (1) CN104040378B (enExample)
BR (1) BR112014018277B1 (enExample)
IN (1) IN2014DN05828A (enExample)
WO (1) WO2013125527A1 (enExample)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018205214A (ja) * 2017-06-07 2018-12-27 大成建設株式会社 雨量予測装置
WO2025243418A1 (ja) * 2024-05-22 2025-11-27 Ｎｔｔ株式会社 制御装置、及び、制御方法

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* Cited by examiner, † Cited by third party
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CN106526710A (zh) * 2016-10-19 2017-03-22 陈文飞 一种雾霾预测方法及装置
US11268973B2 (en) * 2018-03-16 2022-03-08 Texas Tech University System Space-to-time conversion technique using remotely sensed velocity fields
CN108256696B (zh) * 2018-03-16 2021-10-26 电子科技大学 一种结合状态预测和粒子群优化的组网雷达天线配置方法
US11009625B2 (en) 2019-03-27 2021-05-18 The Climate Corporation Generating and conveying comprehensive weather insights at fields for optimal agricultural decision making
CN110196215A (zh) * 2019-06-24 2019-09-03 四川长虹电器股份有限公司 花粉粉层浓度和种类实时监测系统及方法
CN111458769B (zh) * 2020-05-26 2021-05-28 南京大学 用于输电线路环境气象数据预测的方法及系统

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JP2000187082A (ja) * 1998-12-21 2000-07-04 Fuji Electric Co Ltd 降雨予測補正方法、その装置、及び記録媒体
JP3727762B2 (ja) 1997-09-30 2005-12-14 富士電機ホールディングス株式会社 コンピュータによる降雨予測方法及び降雨予測プログラムを記録したコンピュータ読み取り可能な記録媒体
JP2005351866A (ja) * 2004-06-14 2005-12-22 Kansai Electric Power Co Inc:The 降雨量予測方法及び降雨量予測プログラム
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US6801856B2 (en) * 2001-10-19 2004-10-05 Mitsubishi Heavy Industries, Ltd. Atmosphere condition prediction method
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CN100541169C (zh) * 2003-06-13 2009-09-16 三菱重工业株式会社 扩散物质的扩散状况预测方法及扩散状况预测系统
JP4882469B2 (ja) * 2006-04-13 2012-02-22 富士通株式会社 気象予測プログラム、気象予測装置および気象予測方法
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JP3727762B2 (ja) 1997-09-30 2005-12-14 富士電機ホールディングス株式会社 コンピュータによる降雨予測方法及び降雨予測プログラムを記録したコンピュータ読み取り可能な記録媒体
JP2000187082A (ja) * 1998-12-21 2000-07-04 Fuji Electric Co Ltd 降雨予測補正方法、その装置、及び記録媒体
JP2005351866A (ja) * 2004-06-14 2005-12-22 Kansai Electric Power Co Inc:The 降雨量予測方法及び降雨量予測プログラム
JP2007187478A (ja) * 2006-01-11 2007-07-26 Toshiba Corp 気象予測システム

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018205214A (ja) * 2017-06-07 2018-12-27 大成建設株式会社 雨量予測装置
WO2025243418A1 (ja) * 2024-05-22 2025-11-27 Ｎｔｔ株式会社 制御装置、及び、制御方法

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Publication number Publication date
BR112014018277B1 (pt) 2021-11-16
US9645283B2 (en) 2017-05-09
JPWO2013125527A1 (ja) 2015-07-30
HK1201587A1 (en) 2015-09-04
EP2818899A4 (en) 2015-10-07
EP2818899A1 (en) 2014-12-31
EP2818899B1 (en) 2017-09-20
CN104040378A (zh) 2014-09-10
IN2014DN05828A (enExample) 2015-05-15
BR112014018277A2 (pt) 2018-05-22
US20140316704A1 (en) 2014-10-23
CN104040378B (zh) 2018-01-16
JP6034361B2 (ja) 2016-11-30

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