Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation...Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation.However,long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales.In this study,we used a daily SSM dataset at a 0.05°×0.05°spatial resolution over the Qilian Mountains,China and proposed a hybrid Convolutional Long Short-Term Memory(ConvLSTM)-Nudging model,which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting.We trained and evaluated the SSM predictive performance of four models(Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),ConvLSTM,and ConvLSTM with Squeeze-and-Excitation(SE)attention mechanism(ConvLSTM-SE))in both short-term and long-term scenarios.The results showed that all the models perform well under short-term predictions,but the accuracy decrease substantially in long-term predictions.Therefore,we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases.Comprehensive evaluations demonstrate that Nudging significantly improves all the models,with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario.Relative to those of the best-performing ConvLSTM model for long-term forecasts,when observation noiseδ=0.00 and observation fraction obs=50.0%,the coefficient of determination(R2)of ConvLSTM-Nudging increases by approximately 82.1%,while its mean absolute error(MAE)and root mean squared error(RMSE)decrease by approximately 84.8%and 77.3%,respectively;the average Pearson correlation coefficient(r)improves by approximately 23.6%,and Bias is reduced by 98.1%.These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions,they are prone to error accumulation and systematic drift in long-term autoregressive predictions.Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases,thereby achieving robust long-term SSM forecasting.展开更多
利用WRF(Weather Research Forecast)模式及其自带的Nudging同化系统,结合通过质量控制的三峡地区2 588个自动站的2014年1月观测资料,进行同化自动站观测试验,建立了三峡地区3 km高分辨率气温场,并与加入NCEP稀疏观测站点的稀疏场试验...利用WRF(Weather Research Forecast)模式及其自带的Nudging同化系统,结合通过质量控制的三峡地区2 588个自动站的2014年1月观测资料,进行同化自动站观测试验,建立了三峡地区3 km高分辨率气温场,并与加入NCEP稀疏观测站点的稀疏场试验和未同化试验在月平均温度场和逐时温度变化两个方面进行了综合对比分析。结果表明:与未同化试验相比,同化自动站观测后,大部分地区平均气温场偏差减小至±0.5℃以内;平原、丘陵、山区气温逐时绝对偏差均减小至1℃以内,逐时气温的相关系数超过0.9,偏差范围减小1.14℃以上,均方根误差减幅达0.55℃以上;同化自动站观测后,泰勒图中平原和丘陵的相对标准差接近于1,山区减小至1.11。同化自动站观测试验的结果优于同化稀疏场试验,较好地建立了三峡地区2014年1月气温场,为该地区建立高分辨率温度场提供了有效参考。展开更多
If you don’t use it,you lose it.School breaks,during which students do not regularly participate in instruction,can therefore have negative consequences on learning.This is especially true for mathematics learning si...If you don’t use it,you lose it.School breaks,during which students do not regularly participate in instruction,can therefore have negative consequences on learning.This is especially true for mathematics learning since skills build progressively on earlier materials.How can we bridge these gaps in formal instruction?The Keeping in School Shape(KisSS)program is a mobile,engaging,innovative,and cost-effective way of using technology to help students who have time off between related math courses stay fresh on prerequisite knowledge and skills.Founded on learning theory and designed on a model of behavioral change,the KiSS program embodies retrieval practice and nudges by sending students a daily multiple-choice review problem via text messaging over school break.After rating their confidence in solving the daily problem students receive feedback and a solution.This study explores measures of participation,accuracy,and confidence in an implementation of the KiSS program over winter break between two sequential introductory engineering courses at a large state university in the Southwest United States.Results indicate that careful attention should be paid to the construction of the first few days of the program,and that encouragement,problem difficulty may improve participation.展开更多
基金funded by the National Natural Science Foundation of China(42461053)the Department of Education of Gansu Province:Higher Education Innovation Fund Project(2023B-064)+1 种基金the Youth Doctoral Fund Project(2024QB-014)the Natural Science Foundation of Gansu Province(25JRRA012).
文摘Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation.However,long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales.In this study,we used a daily SSM dataset at a 0.05°×0.05°spatial resolution over the Qilian Mountains,China and proposed a hybrid Convolutional Long Short-Term Memory(ConvLSTM)-Nudging model,which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting.We trained and evaluated the SSM predictive performance of four models(Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),ConvLSTM,and ConvLSTM with Squeeze-and-Excitation(SE)attention mechanism(ConvLSTM-SE))in both short-term and long-term scenarios.The results showed that all the models perform well under short-term predictions,but the accuracy decrease substantially in long-term predictions.Therefore,we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases.Comprehensive evaluations demonstrate that Nudging significantly improves all the models,with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario.Relative to those of the best-performing ConvLSTM model for long-term forecasts,when observation noiseδ=0.00 and observation fraction obs=50.0%,the coefficient of determination(R2)of ConvLSTM-Nudging increases by approximately 82.1%,while its mean absolute error(MAE)and root mean squared error(RMSE)decrease by approximately 84.8%and 77.3%,respectively;the average Pearson correlation coefficient(r)improves by approximately 23.6%,and Bias is reduced by 98.1%.These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions,they are prone to error accumulation and systematic drift in long-term autoregressive predictions.Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases,thereby achieving robust long-term SSM forecasting.
文摘利用WRF(Weather Research Forecast)模式及其自带的Nudging同化系统,结合通过质量控制的三峡地区2 588个自动站的2014年1月观测资料,进行同化自动站观测试验,建立了三峡地区3 km高分辨率气温场,并与加入NCEP稀疏观测站点的稀疏场试验和未同化试验在月平均温度场和逐时温度变化两个方面进行了综合对比分析。结果表明:与未同化试验相比,同化自动站观测后,大部分地区平均气温场偏差减小至±0.5℃以内;平原、丘陵、山区气温逐时绝对偏差均减小至1℃以内,逐时气温的相关系数超过0.9,偏差范围减小1.14℃以上,均方根误差减幅达0.55℃以上;同化自动站观测后,泰勒图中平原和丘陵的相对标准差接近于1,山区减小至1.11。同化自动站观测试验的结果优于同化稀疏场试验,较好地建立了三峡地区2014年1月气温场,为该地区建立高分辨率温度场提供了有效参考。
文摘If you don’t use it,you lose it.School breaks,during which students do not regularly participate in instruction,can therefore have negative consequences on learning.This is especially true for mathematics learning since skills build progressively on earlier materials.How can we bridge these gaps in formal instruction?The Keeping in School Shape(KisSS)program is a mobile,engaging,innovative,and cost-effective way of using technology to help students who have time off between related math courses stay fresh on prerequisite knowledge and skills.Founded on learning theory and designed on a model of behavioral change,the KiSS program embodies retrieval practice and nudges by sending students a daily multiple-choice review problem via text messaging over school break.After rating their confidence in solving the daily problem students receive feedback and a solution.This study explores measures of participation,accuracy,and confidence in an implementation of the KiSS program over winter break between two sequential introductory engineering courses at a large state university in the Southwest United States.Results indicate that careful attention should be paid to the construction of the first few days of the program,and that encouragement,problem difficulty may improve participation.
