This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.展开更多
We used a weather research and forecasting model to simulate a torrential rainstorm that occurred in Xinjiang,China during June 16–17,2016.The model successfully simulated the rainfall area,precipitation intensity,an...We used a weather research and forecasting model to simulate a torrential rainstorm that occurred in Xinjiang,China during June 16–17,2016.The model successfully simulated the rainfall area,precipitation intensity,and changes in precipitation.We identified a clear wave signal using the two-dimensional fast Fourier transform method;the waves propagated westwards,with wavelengths of 45–20 km,periods of 50–120 min,and phase velocities mainly concentrated in the-25 m/s to-10 m/s range.The results of wavelet cross-spectral analysis further confirmed that the waves were gravity waves,peaking at 11:00 UTC,June 17,2016.The gravity wave signal was identified along 79.17–79.93°E,81.35–81.45°E and 81.5–81.83°E.The gravity waves detected along 81.5–81.83°E corresponded well with precipitation that accumulated in 1 h,indicating that gravity waves could be considered a rainstorm precursor in future precipitation forecasts.展开更多
为研究雷电活动中动力因子与地闪的相关性,对京津冀地区发生的一次雷暴天气过程进行WRF(weather research and forecasting)模式模拟,计算获得雷电过程中动力特征量,与地闪实况资料对比分析得出动力因子与地闪的相关性。模拟结果表明:...为研究雷电活动中动力因子与地闪的相关性,对京津冀地区发生的一次雷暴天气过程进行WRF(weather research and forecasting)模式模拟,计算获得雷电过程中动力特征量,与地闪实况资料对比分析得出动力因子与地闪的相关性。模拟结果表明:最大上升速度与地闪频数变化趋势一致,极值后20分钟会出现地闪频数的极值。风垂直切变的大值与地闪频数呈正相关,闪电中心位置随时间的演变与风切变最大值位置随时间的演变一致。风垂直切变的大值与正地闪分布趋势有一定的相关关系。WRF模式动力输出场对地闪发生的位置和时间有很好的提示作用,可用于对雷电天气的预报与研究。展开更多
为了分析天气研究及预报WRF(weather research and forecasting)模式对我国天气现象的适用性,利用WRF模式对2003年淮河汛期的3次梅雨锋暴雨过程进行了一组数值模拟试验,对3次天气过程模拟试验采用相同的参数设置,模拟区域根据实况降水...为了分析天气研究及预报WRF(weather research and forecasting)模式对我国天气现象的适用性,利用WRF模式对2003年淮河汛期的3次梅雨锋暴雨过程进行了一组数值模拟试验,对3次天气过程模拟试验采用相同的参数设置,模拟区域根据实况降水落区作了相应设置。模拟结果分析表明,WRF模式能有效模拟我国梅雨锋暴雨的环流背景和天气形势,较好地反映影响暴雨的中尺度系统的发生和发展等特征,还可以较好地模拟出雨带的范围、位置和走向,对降水中心的模拟基本可用。展开更多
该文提出一种机载双极化气象雷达多种降水粒子回波仿真方法。该方法基于T-Matrix方法以及天气预报模式(Weather Research and Forecasting,WRF),首先利用WRF建模仿真气象场景;其次考虑降水粒子为球形条件下,结合T-Matrix方法和微物理特...该文提出一种机载双极化气象雷达多种降水粒子回波仿真方法。该方法基于T-Matrix方法以及天气预报模式(Weather Research and Forecasting,WRF),首先利用WRF建模仿真气象场景;其次考虑降水粒子为球形条件下,结合T-Matrix方法和微物理特性计算6种降水粒子反射率因子;最后应用雷达气象方程获得6种类型降水粒子回波信号,实现机载极化气象雷达降水粒子回波信号仿真。仿真结果表明,该方法的仿真结果可准确反映气象特征,与实测数据的对比分析进一步证实了所提方法的有效性、可靠性。展开更多
为了研究海表面温度(sea surface temperature,SST)对低空大气波导数值模拟的影响,针对南海海域基于天气研究与预报(weather research and forecasting,WRF)模式开展了不同SST对低空大气波导数值模拟的影响研究.结果表明:精确的SST对低...为了研究海表面温度(sea surface temperature,SST)对低空大气波导数值模拟的影响,针对南海海域基于天气研究与预报(weather research and forecasting,WRF)模式开展了不同SST对低空大气波导数值模拟的影响研究.结果表明:精确的SST对低空大气波导数值模拟影响最大,其次是更新周期;美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的最优插值SST给出的大气波导模拟结果最好,正确率为68.2%,且波导底高平均误差和标准差最小,这是由于其模拟的相对湿度和气温变化较为准确,其次为气候预报再分析系统(climate forecast system reanalysis,CFSR)给出的SST方案较好;此外不同嵌套网格方式对大气波导数值模拟也有影响,在最优方案中子网格模拟的大气波导正确率和发生概率分别提高了11.8%和10.4%,虚报率降低了2.4%.该研究可为南海低空大气波导的精确预报提供技术支撑.展开更多
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
基金Project supported by China Special Fund for Meteorological Research in the Public Interest(Grant No.GYHY201406002)the National Natural Science Foundation of China(Grant Nos.41575065 and 41405049)+1 种基金the National Natural Science Foundation International Cooperation Project,China(Grant No.41661144024)Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA17010100)
文摘We used a weather research and forecasting model to simulate a torrential rainstorm that occurred in Xinjiang,China during June 16–17,2016.The model successfully simulated the rainfall area,precipitation intensity,and changes in precipitation.We identified a clear wave signal using the two-dimensional fast Fourier transform method;the waves propagated westwards,with wavelengths of 45–20 km,periods of 50–120 min,and phase velocities mainly concentrated in the-25 m/s to-10 m/s range.The results of wavelet cross-spectral analysis further confirmed that the waves were gravity waves,peaking at 11:00 UTC,June 17,2016.The gravity wave signal was identified along 79.17–79.93°E,81.35–81.45°E and 81.5–81.83°E.The gravity waves detected along 81.5–81.83°E corresponded well with precipitation that accumulated in 1 h,indicating that gravity waves could be considered a rainstorm precursor in future precipitation forecasts.
文摘为研究雷电活动中动力因子与地闪的相关性,对京津冀地区发生的一次雷暴天气过程进行WRF(weather research and forecasting)模式模拟,计算获得雷电过程中动力特征量,与地闪实况资料对比分析得出动力因子与地闪的相关性。模拟结果表明:最大上升速度与地闪频数变化趋势一致,极值后20分钟会出现地闪频数的极值。风垂直切变的大值与地闪频数呈正相关,闪电中心位置随时间的演变与风切变最大值位置随时间的演变一致。风垂直切变的大值与正地闪分布趋势有一定的相关关系。WRF模式动力输出场对地闪发生的位置和时间有很好的提示作用,可用于对雷电天气的预报与研究。
文摘为了分析天气研究及预报WRF(weather research and forecasting)模式对我国天气现象的适用性,利用WRF模式对2003年淮河汛期的3次梅雨锋暴雨过程进行了一组数值模拟试验,对3次天气过程模拟试验采用相同的参数设置,模拟区域根据实况降水落区作了相应设置。模拟结果分析表明,WRF模式能有效模拟我国梅雨锋暴雨的环流背景和天气形势,较好地反映影响暴雨的中尺度系统的发生和发展等特征,还可以较好地模拟出雨带的范围、位置和走向,对降水中心的模拟基本可用。
文摘该文提出一种机载双极化气象雷达多种降水粒子回波仿真方法。该方法基于T-Matrix方法以及天气预报模式(Weather Research and Forecasting,WRF),首先利用WRF建模仿真气象场景;其次考虑降水粒子为球形条件下,结合T-Matrix方法和微物理特性计算6种降水粒子反射率因子;最后应用雷达气象方程获得6种类型降水粒子回波信号,实现机载极化气象雷达降水粒子回波信号仿真。仿真结果表明,该方法的仿真结果可准确反映气象特征,与实测数据的对比分析进一步证实了所提方法的有效性、可靠性。
基金the National Key Project of China(No.GJXM92579)the Aero⁃nautic Science Foundation of China(No.2018ZA53014)the Shenyang Key Laboratory of Aircraft Icing and Ice Protection.
文摘为了研究海表面温度(sea surface temperature,SST)对低空大气波导数值模拟的影响,针对南海海域基于天气研究与预报(weather research and forecasting,WRF)模式开展了不同SST对低空大气波导数值模拟的影响研究.结果表明:精确的SST对低空大气波导数值模拟影响最大,其次是更新周期;美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration,NOAA)提供的最优插值SST给出的大气波导模拟结果最好,正确率为68.2%,且波导底高平均误差和标准差最小,这是由于其模拟的相对湿度和气温变化较为准确,其次为气候预报再分析系统(climate forecast system reanalysis,CFSR)给出的SST方案较好;此外不同嵌套网格方式对大气波导数值模拟也有影响,在最优方案中子网格模拟的大气波导正确率和发生概率分别提高了11.8%和10.4%,虚报率降低了2.4%.该研究可为南海低空大气波导的精确预报提供技术支撑.
