The Vertical Total Electron Content(VTEC)of the ionosphere is a crucial parameter for describing the distribution and dynamic changes within the ionosphere.The study utilizes Dual Hybrid Attentional UNet(DHA-UNet)mode...The Vertical Total Electron Content(VTEC)of the ionosphere is a crucial parameter for describing the distribution and dynamic changes within the ionosphere.The study utilizes Dual Hybrid Attentional UNet(DHA-UNet)model to achieve higher forecasting performance for global VTEC predictions under the condition of data acquisition delays.Initially,this study uses the first Hybrid Attentional UNet(HA-UNet)model to predict the intermediate missing data.The missing data are caused by delays in data processing,making the Global Ionosphere Map(GIM)for the current day unavailable.Subsequently,the predicted results from the first HA-UNet model are concatenated with the input data to serve as the input data for the second HA-UNet model,yielding the final prediction results.The performance of DHA-UNet model is then evaluated under varying solar and geomagnetic activity conditions.Evaluation results demonstrate that the DHA-UNet model exhibits higher forecasting accuracy and stability compared to commonly used temporal and spatiotemporal forecasting models.Compared to CODG VTEC,the DHA-UNet model achieves Mean Absolute Error(MAE)values of 2.60 TECU,3.07 TECU,3.78 TECU,and 6.45TECU during quiet,weak,moderate,and strong geomagnetic storm periods,respectively,in years of high solar activity.In years of low solar activity,the model achieves MAE values of 1.00 TECU,1.15 TECU,and 1.54 TECU during quiet,weak,and moderate geomagnetic storm periods,respectively.Even during strong geomagnetic storms,55%of the residuals from the DHA-UNet model fall within the-5.0 TECU to 5.0 TECU range,surpassing other commonly used models.Compared to the C1PG forecasting product,the DHA-UNet model shows particularly notable improvements in accuracy during the spring and winter seasons,as well as in mid-to high-latitude regions.展开更多
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.展开更多
基金funded by the National Key R&D Program of China(No.2022YFB3904402)the National Natural Science Foundation of China(Nos.42474037 and U2233217)。
文摘The Vertical Total Electron Content(VTEC)of the ionosphere is a crucial parameter for describing the distribution and dynamic changes within the ionosphere.The study utilizes Dual Hybrid Attentional UNet(DHA-UNet)model to achieve higher forecasting performance for global VTEC predictions under the condition of data acquisition delays.Initially,this study uses the first Hybrid Attentional UNet(HA-UNet)model to predict the intermediate missing data.The missing data are caused by delays in data processing,making the Global Ionosphere Map(GIM)for the current day unavailable.Subsequently,the predicted results from the first HA-UNet model are concatenated with the input data to serve as the input data for the second HA-UNet model,yielding the final prediction results.The performance of DHA-UNet model is then evaluated under varying solar and geomagnetic activity conditions.Evaluation results demonstrate that the DHA-UNet model exhibits higher forecasting accuracy and stability compared to commonly used temporal and spatiotemporal forecasting models.Compared to CODG VTEC,the DHA-UNet model achieves Mean Absolute Error(MAE)values of 2.60 TECU,3.07 TECU,3.78 TECU,and 6.45TECU during quiet,weak,moderate,and strong geomagnetic storm periods,respectively,in years of high solar activity.In years of low solar activity,the model achieves MAE values of 1.00 TECU,1.15 TECU,and 1.54 TECU during quiet,weak,and moderate geomagnetic storm periods,respectively.Even during strong geomagnetic storms,55%of the residuals from the DHA-UNet model fall within the-5.0 TECU to 5.0 TECU range,surpassing other commonly used models.Compared to the C1PG forecasting product,the DHA-UNet model shows particularly notable improvements in accuracy during the spring and winter seasons,as well as in mid-to high-latitude regions.
基金supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
