WO2021077729A1 - Procédé de prédiction de la foudre - Google Patents

Procédé de prédiction de la foudre Download PDF

Info

Publication number
WO2021077729A1
WO2021077729A1 PCT/CN2020/090434 CN2020090434W WO2021077729A1 WO 2021077729 A1 WO2021077729 A1 WO 2021077729A1 CN 2020090434 W CN2020090434 W CN 2020090434W WO 2021077729 A1 WO2021077729 A1 WO 2021077729A1
Authority
WO
WIPO (PCT)
Prior art keywords
lightning
order
forecast
meteorological
meteorological parameters
Prior art date
Application number
PCT/CN2020/090434
Other languages
English (en)
Chinese (zh)
Inventor
方玉河
李健
王钊
陈玥
吴大伟
陶汉涛
许远根
陈扬
张磊
林卿
姜志博
高攀
李旺
Original Assignee
国网电力科学研究院武汉南瑞有限责任公司
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 国网电力科学研究院武汉南瑞有限责任公司 filed Critical 国网电力科学研究院武汉南瑞有限责任公司
Priority to AU2020372283A priority Critical patent/AU2020372283A1/en
Publication of WO2021077729A1 publication Critical patent/WO2021077729A1/fr

Links

Classifications

    • 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"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning

Definitions

  • the invention relates to the technical field of disaster prevention and reduction, in particular to a lightning prediction method.
  • Thunder and lightning are often accompanied by lightning and thunder. It is also called lightning. It is a very spectacular and extremely destructive natural phenomenon. The location of thunder and lightning is mostly in the cumulonimbus with intense convection process, or between the electrified thundercloud and the ground protrusion. The occurrence and development of the lightning process is the result of the combined effect of many natural and physical conditions such as atmospheric motion and the earth's magnetic field. As a strong discharge phenomenon, the current value during the occurrence of lightning can reach tens of thousands of amperes. Moreover, the instantaneous voltage of lightning is also very high, reaching several million volts. Therefore, the power of a mid-to-low intensity thunderstorm can reach about 10 million watts, which is equivalent to the output power of a small nuclear power plant.
  • Lightning warning is an indispensable part of the country's disastrous weather forecast, improving its accuracy and forecasting service level, which is closely related to the development of the whole society and the safety of various industries and people's lives.
  • Common lightning forecasting and early warning methods are mainly radar data extrapolation, direct forecasting with numerical models, empirical forecasts based on meteorological elements, and short-term forecasts based on atmospheric electric field instruments.
  • direct forecasting with numerical models has high accuracy, but requires The computing power is very large and the cost is very high; the calculation amount required for the extrapolation of radar data and the empirical forecast method based on meteorological elements is far smaller than the numerical model, but the accuracy rate is low; methods such as short-term forecast based on atmospheric electric field instrument The forecast result is more accurate, but the forecast time effect is very small.
  • the existing lightning early warning methods have disadvantages such as low accuracy, too large required computing resources, and too small forecasting timeliness. How to reduce the trial use of computing power, save costs, improve forecast timeliness, and achieve better accuracy are currently problems that need to be resolved.
  • the purpose of the present invention is to provide a lightning prediction method, which has a small amount of calculation, low cost and high forecast accuracy.
  • a lightning prediction method includes the following steps:
  • S2 Calculate the high-order meteorological parameters related to lightning based on the high-order meteorological parameters of the area to be predicted;
  • S4 Based on the random forest algorithm, calculate the correlation degree of each high-order meteorological parameter with thunder and lightning, and select the high-order meteorological parameter with a high degree of correlation with thunder and lightning;
  • S5 Use XGBoost algorithm to establish a forecast model based on forecast timeliness, forecast times, and high-order meteorological parameters that are highly correlated with lightning;
  • the basic meteorological parameters include temperature, humidity, dew point, vorticity, air pressure, convective precipitation, non-convective precipitation, convective effective potential energy, and radar reflectivity at different altitudes in the area to be predicted.
  • the high-level meteorological parameters include A index, K index, Sabouraud index, and strong weather threat index.
  • step S3 gridding the lightning positioning observation data refers to using a grid method to convert the lightning positioning observation data into a grid with the same longitude, latitude, and resolution as the basic meteorological parameters. Grid data.
  • step S4 based on the random forest algorithm, the specific method for calculating the correlation degree of each high-order meteorological parameter with lightning is: taking each high-order meteorological parameter as the feature vector and using the gridded lightning The positioning observation data is used as the target vector to establish a random forest model, and then the outer bag function is used as an evaluation index to calculate the importance of each feature vector, and determine the degree of correlation between high-order meteorological parameters and lightning according to the importance of each feature vector.
  • step S5 based on the forecast timeliness, forecast times, and high-order meteorological parameters that are highly correlated with lightning, the XGBoost algorithm is used to establish a forecast model as follows: For each high-order meteorological parameter, use high-order meteorological parameters The historical data of the parameters is the feature vector, the grid-processed lightning location historical observation data is the target vector, the linear regression function is the objective parameter, and the hyperopt algorithm is used to perform Bayesian adjustment of the hyperparameters in the XGBoost algorithm to construct The forecast model of high-order meteorological parameters and lightning data at different forecast times is a multi-time forecast model.
  • using a forecast model to predict the spatial distribution and occurrence probability of lightning includes:
  • the present invention has the following beneficial effects:
  • the lightning forecasting method disclosed by the present invention significantly reduces the calculation amount and greatly reduces the calculation cost; moreover, it uses random forest algorithm and XGBoost algorithm to establish a forecast model.
  • this method has the advantages of small calculation amount and low cost; moreover, compared with the traditional linear model-based meteorological statistical model, it introduces It has more nonlinearity and higher complexity, so the accuracy is higher and its forecast time is equal to the input global model forecast time, up to more than ten days.
  • the invention discloses a lightning prediction method, which includes the following steps:
  • S2 Calculate the high-order meteorological parameters related to lightning based on the high-order meteorological parameters of the area to be predicted;
  • S4 Based on the random forest algorithm, calculate the degree of correlation between high-order meteorological parameters and lightning, and select high-order meteorological parameters with high degree of correlation with lightning. This is because when the random forest algorithm is used to judge the importance of high-order meteorological parameters, There is no need to consider whether the high-level meteorological parameters are linearly separable, and there is no need to normalize or standardize features;
  • S5 Use the XGBoost algorithm to establish a forecast model based on the forecast timeliness, forecast times, and high-order meteorological parameters that are highly correlated with lightning.
  • the XGBoost algorithm is one of the boosting algorithms, and the idea of the Boosting algorithm is to integrate many weak classifiers to form a strong classifier. Moreover, since XGBoost is a boosted tree model, it integrates many tree models in At the same time, a strong classifier is formed.
  • the objective function of lightning forecast is a linear regression function. For each forecast time and each time period, Bayesian optimization method is used to determine the maximum depth, tree Optimize the coefficients such as the number, learning rate, sampling number, and the minimum sample proportion of the end node, and then every period of time, the new observation data obtained is put into the training sample, and the training is retrained to obtain a new forecast model. Therefore, in the present invention, the forecasting effect of the forecasting model can be continuously improved.
  • the basic meteorological parameters include temperature, humidity, dew point, vorticity, air pressure, convective precipitation, non-convective precipitation, convective effective potential energy, and radar reflectivity at different altitudes in the area to be predicted, specifically , Obtain the 72-hour, 3-hour-by-three-hour forecast of the temperature, humidity, dew point, vorticity and other variables of each pressure layer from the EC global forecast model, and obtain the ground convective precipitation, non-convective precipitation, convective effective potential energy and other variables; Obtain radar reflectivity and so on in the forecast mode.
  • the high-level meteorological parameters include A index, K index, Sabouraud index and strong weather threat index, among which:
  • A T850-T500-(T850-Td850)-(T700-Td700)-(T500-Td500);
  • the strong weather threat index is defined as:
  • SWEA 12*Td850+20*(TT-49)+4*WF850+2*WF500+125*(sin(WD500-WD850)+0.2), where: TT is the total index value, if the sub-item of the formula is less than 0, does not count this sub-item, that is, the value is 0, WF is in "m/s" as the unit, the rightmost sub-item must satisfy WD850 at 130° ⁇ 250°, WD500 at 210° ⁇ 310°, WD500 is greater than WD850, Calculate when both WF850 and WF500 are greater than 7.5m/s, otherwise it is 0.
  • T temperature
  • Td potential temperature
  • WF wind speed
  • WD wind direction
  • the value of the suffix stands for the pressure layer where the variable is located.
  • step S3 gridding the lightning location observation data refers to using the grid method to convert the lightning location observation data into grid data with the same longitude, latitude and resolution as the basic meteorological parameters. This is Because the lightning positioning observation data is station data, the gridding method can be used to convert the lightning positioning observation data into grid data with the same longitude, the same latitude, and the same resolution as the basic meteorological parameters.
  • step S4 based on the random forest algorithm, the specific method for calculating the correlation degree of each high-order meteorological parameter with lightning is: taking each high-order meteorological parameter as the feature vector and using the gridded lightning The positioning observation data is used as the target vector to establish a random forest model, and then the outer bag function is used as an evaluation index to calculate the importance of each feature vector, and determine the degree of correlation between high-order meteorological parameters and lightning according to the importance of each feature vector.
  • step S5 based on the forecast timeliness, forecast times, and high-order meteorological parameters that are highly correlated with lightning, the XGBoost algorithm is used to establish a forecast model as follows: For each high-order meteorological parameter, use high-order meteorological parameters The historical data of the parameters is the feature vector, the grid-processed lightning location historical observation data is the target vector, the linear regression function is the objective parameter, and the hyperopt algorithm is used to perform Bayesian adjustment of the hyperparameters in the XGBoost algorithm to construct The forecast model of high-order meteorological parameters and lightning data at different forecast times is the multi-time forecast model, which is specifically: (1) For each high-order meteorological parameter, the grid-processed lightning positioning observation data Historical data is the target vector, linear regression function is the objective parameter, and the hyperopt algorithm is used to perform Bayesian adjustment of the hyperparameters in the XGBoost algorithm such as the number of iterations, the number of trees, and the depth of the tree; (2) In each forecast At the time
  • the XGBoost algorithm is used to establish the forecast model because the XGBoost algorithm has the following advantages: (1) The XGBoost algorithm supports linear classifiers, which is equivalent to the introduction of L1 and L2 regularization terms in logistic regression (classification problem) And linear regression (regression problem); (2) The XGBoost algorithm does a second-order Taylor expansion of the cost function, and introduces the first-order derivative and the second-order derivative, so that we can clearly understand what the whole goal is, and step by step Deduced how to learn the tree; (3) When the sample has missing values, XGBoost can automatically learn the splitting direction; (4) XG Boost draws on the approach of RF and supports column sampling, which can not only prevent overfitting, but also Reduce the amount of calculation; (5) The cost function of the XGBoost algorithm introduces a regularization term to control the complexity of the model.
  • the regularization term includes the number of all leaf nodes, and the square sum of the L2 modulus of the score output by each leaf node. From the perspective of Bayesian variance, the regular term reduces the variance of the model and prevents the model from overfitting; (6) XGBoost allocates the learning rate to the leaf nodes after each iteration, reduces the weight of each tree, and reduces each tree. The influence of the tree provides a better learning space for the following; (7) XGBoost tool supports parallelism, but it is not the granularity of the tree, but the granularity of the feature. The most time-consuming step of the decision tree is to sort the value of the feature. XGBoost is Before iteration, pre-sort and save it as a block structure.
  • the structure is reused, which reduces the calculation of the model; the block structure also provides the possibility of parallelism for the model.
  • the gain of each feature can be performed in multiple threads;
  • Parallel approximate histogram algorithm when the tree node is split, the gain of each node needs to be calculated If the amount of data is large, sort the features of all nodes to obtain the optimal segmentation point. This greedy method is extremely time-consuming.
  • the approximate histogram algorithm is introduced to generate efficient segmentation points, that is, split A certain value after subtracting a certain value before splitting to obtain a gain.
  • a threshold is introduced. When the gain is greater than the threshold, the split is performed.
  • XGBoost is the most commonly used and one of the most effective models for machine learning modeling of structured data.
  • a forecast model to predict the spatial distribution and occurrence probability of lightning includes:

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Economics (AREA)
  • Artificial Intelligence (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Tourism & Hospitality (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Marketing (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne un procédé de prédiction de la foudre, consistant à : obtenir des paramètres météorologiques de base d'une zone à prédire ; calculer des paramètres météorologiques d'ordre supérieur associés à la foudre sur la base des paramètres météorologiques d'ordre supérieur de ladite zone ; obtenir des données d'observation de positionnement de la foudre de ladite zone, et effectuer un traitement de grille sur les données d'observation de positionnement de la foudre ; sur la base d'un algorithme de forêt aléatoire, calculer le degré de corrélation entre chaque paramètre météorologique d'ordre supérieur et la foudre, et sélectionner le paramètre météorologique d'ordre supérieur ayant le degré de corrélation le plus élevé avec la foudre ; établir un modèle de prévision en utilisant un algorithme XGBoost basé sur la rapidité d'exécution de prévision et le temps de prévision ; et sur la base des paramètres météorologiques d'ordre supérieur de ladite zone, prédire la distribution spatiale et la probabilité de survenue de la foudre en utilisant le modèle de prédiction.
PCT/CN2020/090434 2019-10-23 2020-05-15 Procédé de prédiction de la foudre WO2021077729A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2020372283A AU2020372283A1 (en) 2019-10-23 2020-05-15 Lightning prediction method

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201911011363.4A CN110796299A (zh) 2019-10-23 2019-10-23 一种雷电预测方法
CN201911011363.4 2019-10-23

Publications (1)

Publication Number Publication Date
WO2021077729A1 true WO2021077729A1 (fr) 2021-04-29

Family

ID=69440985

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/090434 WO2021077729A1 (fr) 2019-10-23 2020-05-15 Procédé de prédiction de la foudre

Country Status (3)

Country Link
CN (1) CN110796299A (fr)
AU (1) AU2020372283A1 (fr)
WO (1) WO2021077729A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113191568A (zh) * 2021-05-21 2021-07-30 上海市气象灾害防御技术中心(上海市防雷中心) 基于气象的城市运行管理大数据分析预测方法和系统
CN114252706A (zh) * 2021-12-15 2022-03-29 华中科技大学 一种雷电预警方法和系统
CN114442198A (zh) * 2022-01-21 2022-05-06 广西壮族自治区气象科学研究所 一种基于加权算法的森林火险气象等级预报方法
CN115273440A (zh) * 2022-07-23 2022-11-01 河南泽阳实业有限公司 一种基于大数据智能分析算法的预警装置
CN116341391A (zh) * 2023-05-24 2023-06-27 华东交通大学 基于STPM-XGBoost模型的降水预测方法

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110796299A (zh) * 2019-10-23 2020-02-14 国网电力科学研究院武汉南瑞有限责任公司 一种雷电预测方法
CN111695593A (zh) * 2020-04-29 2020-09-22 平安科技（深圳）有限公司 基于XGBoost的数据分类方法、装置、计算机设备及存储介质
CN111694047B (zh) * 2020-05-09 2021-03-23 吉林大学 基于多通道奇异谱的钻孔应变网络拓扑结构异常检测方法
CN111913236A (zh) * 2020-07-13 2020-11-10 上海眼控科技股份有限公司 气象数据处理方法、装置、计算机设备和存储介质
CN111897030A (zh) * 2020-07-17 2020-11-06 国网电力科学研究院有限公司 一种雷暴预警系统及方法
CN111915846B (zh) * 2020-08-11 2021-08-03 安徽亿纵电子科技有限公司 一种基于云计算的智能云防雷运维系统
CN112731564B (zh) * 2020-12-26 2023-04-07 安徽省公共气象服务中心 一种基于多普勒天气雷达数据的雷电智能预报方法
CN112764129B (zh) * 2021-01-22 2022-08-26 易天气(北京)科技有限公司 一种雷暴短临预报方法、系统及终端
CN113239946B (zh) * 2021-02-02 2023-10-27 广东工业大学 一种输电线路载流量的校核方法
CN113204903B (zh) * 2021-04-29 2022-04-29 国网电力科学研究院武汉南瑞有限责任公司 一种预测雷电的方法
CN113283653B (zh) * 2021-05-27 2024-03-26 大连海事大学 一种基于机器学习和ais数据的船舶轨迹预测方法
CN114518612A (zh) * 2022-02-14 2022-05-20 广东省气象公共安全技术支持中心 雷暴风险预警方法、系统及电子设备

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150161150A1 (en) * 2013-12-10 2015-06-11 Weather Decision Technologies, Inc. Four dimensional weather data storage and access
CN104950186A (zh) * 2014-03-31 2015-09-30 国际商业机器公司 雷电预测的方法和装置
CN108052734A (zh) * 2017-12-12 2018-05-18 中国电力科学研究院有限公司 一种基于气象参数对雷电流幅值进行预测的方法及系统
CN108427041A (zh) * 2018-03-14 2018-08-21 南京中科九章信息技术有限公司 雷电预警方法、系统、电子设备和存储介质
CN110796299A (zh) * 2019-10-23 2020-02-14 国网电力科学研究院武汉南瑞有限责任公司 一种雷电预测方法

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105068149B (zh) * 2015-07-24 2017-04-12 国家电网公司 一种基于多信息综合的输变电设备雷电监测和预报方法
CN110334732A (zh) * 2019-05-20 2019-10-15 北京思路创新科技有限公司 一种基于机器学习的空气质量预报方法和装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150161150A1 (en) * 2013-12-10 2015-06-11 Weather Decision Technologies, Inc. Four dimensional weather data storage and access
CN104950186A (zh) * 2014-03-31 2015-09-30 国际商业机器公司 雷电预测的方法和装置
CN108052734A (zh) * 2017-12-12 2018-05-18 中国电力科学研究院有限公司 一种基于气象参数对雷电流幅值进行预测的方法及系统
CN108427041A (zh) * 2018-03-14 2018-08-21 南京中科九章信息技术有限公司 雷电预警方法、系统、电子设备和存储介质
CN110796299A (zh) * 2019-10-23 2020-02-14 国网电力科学研究院武汉南瑞有限责任公司 一种雷电预测方法

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113191568A (zh) * 2021-05-21 2021-07-30 上海市气象灾害防御技术中心(上海市防雷中心) 基于气象的城市运行管理大数据分析预测方法和系统
CN113191568B (zh) * 2021-05-21 2024-02-02 上海市气象灾害防御技术中心(上海市防雷中心) 基于气象的城市运行管理大数据分析预测方法和系统
CN114252706A (zh) * 2021-12-15 2022-03-29 华中科技大学 一种雷电预警方法和系统
CN114442198A (zh) * 2022-01-21 2022-05-06 广西壮族自治区气象科学研究所 一种基于加权算法的森林火险气象等级预报方法
CN114442198B (zh) * 2022-01-21 2024-03-15 广西壮族自治区气象科学研究所 一种基于加权算法的森林火险气象等级预报方法
CN115273440A (zh) * 2022-07-23 2022-11-01 河南泽阳实业有限公司 一种基于大数据智能分析算法的预警装置
CN116341391A (zh) * 2023-05-24 2023-06-27 华东交通大学 基于STPM-XGBoost模型的降水预测方法
CN116341391B (zh) * 2023-05-24 2023-08-04 华东交通大学 基于STPM-XGBoost模型的降水预测方法

Also Published As

Publication number Publication date
AU2020372283A1 (en) 2021-11-25
CN110796299A (zh) 2020-02-14

Similar Documents

Publication Publication Date Title
WO2021077729A1 (fr) Procédé de prédiction de la foudre
Mokhtar et al. Estimation of SPEI meteorological drought using machine learning algorithms
Anandhi et al. Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine
Huang et al. An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records
Saxena et al. A review study of weather forecasting using artificial neural network approach
Wei RBF neural networks combined with principal component analysis applied to quantitative precipitation forecast for a reservoir watershed during typhoon periods
De Luca et al. Extreme rainfall in the Mediterranean
Deng et al. Visibility Forecast for Airport Operations by LSTM Neural Network.
Li et al. Multivariable time series prediction for the icing process on overhead power transmission line
Omeje et al. Performance of hybrid neuro-fuzzy model for solar radiation simulation at Abuja, Nigeria: a correlation based input selection technique
Novitasari et al. Weather parameters forecasting as variables for rainfall prediction using adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR)
Hussain et al. Wavelet coherence of monsoon and large‐scale climate variabilities with precipitation in Pakistan
Baki et al. Parameter calibration to improve the prediction of tropical cyclones over the Bay of Bengal using machine learning–based multiobjective optimization
Bao et al. Application of lightning spatio-temporal localization method based on deep LSTM and interpolation
Xu et al. A stochastic and non‐linear representation of model uncertainty in a convective‐scale ensemble prediction system
Elkharrim et al. Using statistical downscaling of GCM simulations to assess climate change impacts on drought conditions in the northwest of Morocco
Lu et al. Lightning strike location identification based on 3D weather radar data
Aggarwal et al. A comprehensive review of numerical weather prediction models
Kober et al. Examination of a stochastic and deterministic convection parameterization in the COSMO model
Zhang et al. A novel combinational forecasting model of dust storms based on rare classes classification algorithm
Wang et al. Using machine learning to analyze the changes in extreme precipitation in southern China
Baudhanwala et al. Machine learning approaches for improving precipitation forecasting in the Ambica River basin of Navsari District, Gujarat
Alves et al. Lightning Warning Prediction with Multi-source Data
アンナススワルディ et al. Neuro-fuzzy approaches for modeling the wet season tropical rainfall
de Almeida et al. Artificial neural network for data assimilation by WRF model in Rio de Janeiro, Brazil

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20880070

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2020372283

Country of ref document: AU

Date of ref document: 20200515

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20880070

Country of ref document: EP

Kind code of ref document: A1