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 PDFInfo
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- 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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- 238000013277 forecasting method Methods 0.000 title 1
- 238000012937 correction Methods 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000000034 method Methods 0.000 claims description 22
- 238000012821 model calculation Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 abstract description 12
- 239000000126 substance Substances 0.000 description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 8
- 238000013500 data storage Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008034 disappearance Effects 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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"
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- 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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Abstract
Description
Claims (8)
- 予測対象領域を格子状に分割して格子毎に気象予測を行う装置であって、
第1時間間隔で前記格子毎の観測値を受信する受信手段と、
前記受信手段により受信された第1観測値を初期値とし、移流モデルを用いて前記第1時間間隔より短い第2時間間隔で前記格子毎の予測値を演算する移流モデル演算手段と、
前記受信手段により前記第1観測値以後の第2観測値が受信された場合に、前記第2観測値と、前記第2観測値の観測時刻に対応する前記予測値との差に基づいて前記移流モデルを補正する補正手段と
を具備することを特徴とする気象予測装置。 - 前記格子は、気象レーダの空間分解能と同等であることを特徴とする請求項1に記載の気象予測装置。
- 前記移流モデルは、3次元CUL(Cubic Lagrange)法を用いることを特徴とする請求項1記載の気象予測装置。
- 前記移流モデルは、VVP(Volume Velocity Processing)法やGal-Chen法を用いることを特徴とする請求項1記載の気象予測装置。
- コンピュータによって予測対象領域を格子状に分割して格子毎に気象予測を行う方法であって、
第1時間間隔で前記格子毎の観測値を受信する受信ステップと、
前記受信ステップで受信された第1観測値を初期値とし、移流モデルを用いて前記第1時間間隔より短い第2時間間隔で前記格子毎の予測値を演算する移流モデル演算ステップと、
前記受信ステップで前記第1観測値以後の第2観測値が受信された場合に、前記第2観測値と、前記第2観測値の観測時刻に対応する前記予測値との差に基づいて前記移流モデルを補正する補正ステップと
を有することを特徴とする気象予測方法。 - 前記格子は、気象レーダの空間分解能と同等であることを特徴とする請求項5に記載の気象予測方法。
- 前記移流モデルは、3次元CUL(Cubic Lagrange)法を用いることを特徴とする請求項5記載の気象予測方法。
- 前記移流モデルは、VVP(Volume Velocity Processing)法やGal-Chen法を用いることを特徴とする請求項5記載の気象予測方法。
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EP13752228.0A EP2818899B1 (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 |
JP2014500716A JP6034361B2 (ja) | 2012-02-20 | 2013-02-19 | 気象予測装置及び気象予測方法 |
IN5828DEN2014 IN2014DN05828A (ja) | 2012-02-20 | 2013-02-19 | |
BR112014018277-9A BR112014018277B1 (pt) | 2012-02-20 | 2013-02-19 | Aparelho de previsão do tempo e método de previsão do tempo |
US14/323,276 US9645283B2 (en) | 2012-02-20 | 2014-07-03 | Weather prediction apparatus and weather prediction method |
HK15102001.6A HK1201587A1 (en) | 2012-02-20 | 2015-02-27 | Meteorological forecasting device and meteorological forecasting method |
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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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CN104040378B (zh) | 2018-01-16 |
BR112014018277A2 (pt) | 2018-05-22 |
EP2818899A1 (en) | 2014-12-31 |
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JP6034361B2 (ja) | 2016-11-30 |
US9645283B2 (en) | 2017-05-09 |
BR112014018277B1 (pt) | 2021-11-16 |
CN104040378A (zh) | 2014-09-10 |
IN2014DN05828A (ja) | 2015-05-15 |
JPWO2013125527A1 (ja) | 2015-07-30 |
EP2818899A4 (en) | 2015-10-07 |
EP2818899B1 (en) | 2017-09-20 |
HK1201587A1 (en) | 2015-09-04 |
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