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
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.
