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
Tides represent a crucial dynamic process in the ocean and play a vital role in both marine and atmospheric studies;thus,accurate simulation of tidal processes is of the utmost importance in tidal circulation models.B...Tides represent a crucial dynamic process in the ocean and play a vital role in both marine and atmospheric studies;thus,accurate simulation of tidal processes is of the utmost importance in tidal circulation models.Based on the sequential data assimilation method and the concept of the Kalman gain matrix,this paper proposes a new nudging method with spatially dependent coefficients for tidal assimilation.The spatial-dependent nudging method not only retains the advantages of the traditional nudging method but also facilitates the direct determination of a more reasonable spatial distribution of nudging coefficients.Utilizing the M_(2)tidal constituent(the main lunar semidiurnal tide)as an illustration,we conducted assimilation experiments of sea-level data to the barotropic circulation and tide model to assess the global harmonic constants of the M_(2)constituent.The results demonstrate that the spatial-dependent nudging method successfully mitigates deviations of tidal phase lag.Following assimilation using the new method,the deviations of the M_(2)tidal amplitude and phase lag can be reduced by 47%and 18%compared to the traditional nudging method,respectively,while the respective values for the non-assimilated case are as much as 9%and 11%.We also applied the S-nudging method to realistic tidal simulations and noted a significantly enhanced effectiveness relative to traditional methods,making it highly valuable for modeling oceanic tidal circulations.展开更多
In this study,a latent heat nudging lightning data assimilation(LDA)method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager(LMI)onboard the Feng-Yun-4A(FY-4A)satellite...In this study,a latent heat nudging lightning data assimilation(LDA)method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager(LMI)onboard the Feng-Yun-4A(FY-4A)satellite based on the Weather Research and Forecasting(WRF)model.In this LDA method,the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature.The positive temperature perturbations in the mixed-phase region are then assimilated by a nudging method to adjust the latent heat within the convective system.Meanwhile,the water vapor mixing ratio is adapted to the temperature perturbations accordingly to constrain the relative humidity to remain unchanged.This method considers the physical nature of the convective system,in contrast with other LDA methods that establish an empirical or statistical relationship between the lightning flash rates and model variables.The impact of this LDA method on short-term(≤6 h)forecasts was evaluated using two severe convective events in eastern China:a multi-region heavy rainfall event and a thunderstorm high-wind event.The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period,leading to a more reasonable storm environment.In the forecast fields,the simulations with LDA produced more realistic convective structures,resulting in an improvement in forecasts of precipitation and high winds.展开更多
This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can...This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.展开更多
This study investigated the simulations of three months of seasonal tropical cyclone （TC） activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment （CTL）, the TC freq...This study investigated the simulations of three months of seasonal tropical cyclone （TC） activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment （CTL）, the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.展开更多
With the Weather Research and Forecasting model （WRFV3.2.1）, the application of specmun nudging tech- niques in numerical simulation of the genesis and development of typhoon Longwang （2005） is evaluated in this w...With the Weather Research and Forecasting model （WRFV3.2.1）, the application of specmun nudging tech- niques in numerical simulation of the genesis and development of typhoon Longwang （2005） is evaluated in this work via four numerical experiments with different nudging techniques. It is found that, due to the ability to capture the large-scale fields and to keep the meso-to small-scale features derived from the model dynamics, the experiment with spectrum nudging technique can simulate the formation, intensification and motion of Longwang properly. The improve- ment on the numerical simulation of Longwang induced by the spectrum nudging depends on the nudging coefficients. A weak spectrum nudging does not make significant improvement on the simulation of Longwang. Although the experi- ment with four-dimensional data assimilation, i.e., FDDA, also derives the genesis and movement of Longwang appro- priately, it fails to simulate the intensifying process of Longwang properly. The reason is that, as the large-scale features derived from the model are nudged to the observational data, the meso- to small-processes produced by the model dy- namics important to the intensification of typhoon are nearly smoothed by FDDA.展开更多
The performance of spectral nudging in an investigation of the 2010 East Asia summer monsoon was assessed using the Weather Research and Forecasting (WRF) model, forced by 1-degree NCEP Global Final Analysis (FNL). Tw...The performance of spectral nudging in an investigation of the 2010 East Asia summer monsoon was assessed using the Weather Research and Forecasting (WRF) model, forced by 1-degree NCEP Global Final Analysis (FNL). Two pairs of experiments were made, spectral nudging (SP) and non-spectral nudging (NOSP), with five members in each group. The members were distinguished by different initial times, and the analysis was based on the ensemble mean of the two simulation pairs. The SP was able to constrain error growth in large-scale circulation in upper-level, during simulation, and generate realistic regional scale patterns. The main focus was the model ability to simulate precipitation. The Tropical Rainfall Measuring Mission (TRMM) 3B42 product was used for precipitation verification. Mean precipitation magnitude was generally overestimated by WRF. Nevertheless, SP simulations suppressed overestimation relative to the NOSP experiments. Compared to TRMM, SP also improved model simulation of precipitation in spatial and temporal distributions, with the ability to reproduce movement of rainbands. However, extreme precipitation events were suppressed in the SP simulations.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.
基金supported by the National Natural Science Foundation of China(Grant No.42276011 and Grant No.U23A2032).
文摘Tides represent a crucial dynamic process in the ocean and play a vital role in both marine and atmospheric studies;thus,accurate simulation of tidal processes is of the utmost importance in tidal circulation models.Based on the sequential data assimilation method and the concept of the Kalman gain matrix,this paper proposes a new nudging method with spatially dependent coefficients for tidal assimilation.The spatial-dependent nudging method not only retains the advantages of the traditional nudging method but also facilitates the direct determination of a more reasonable spatial distribution of nudging coefficients.Utilizing the M_(2)tidal constituent(the main lunar semidiurnal tide)as an illustration,we conducted assimilation experiments of sea-level data to the barotropic circulation and tide model to assess the global harmonic constants of the M_(2)constituent.The results demonstrate that the spatial-dependent nudging method successfully mitigates deviations of tidal phase lag.Following assimilation using the new method,the deviations of the M_(2)tidal amplitude and phase lag can be reduced by 47%and 18%compared to the traditional nudging method,respectively,while the respective values for the non-assimilated case are as much as 9%and 11%.We also applied the S-nudging method to realistic tidal simulations and noted a significantly enhanced effectiveness relative to traditional methods,making it highly valuable for modeling oceanic tidal circulations.
基金supported by the National Key Research and Development Program of China(2017YFC1501902)the Natural Science Foundation of Shanghai Science and Technology Committee(21ZR1457700).
文摘In this study,a latent heat nudging lightning data assimilation(LDA)method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager(LMI)onboard the Feng-Yun-4A(FY-4A)satellite based on the Weather Research and Forecasting(WRF)model.In this LDA method,the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature.The positive temperature perturbations in the mixed-phase region are then assimilated by a nudging method to adjust the latent heat within the convective system.Meanwhile,the water vapor mixing ratio is adapted to the temperature perturbations accordingly to constrain the relative humidity to remain unchanged.This method considers the physical nature of the convective system,in contrast with other LDA methods that establish an empirical or statistical relationship between the lightning flash rates and model variables.The impact of this LDA method on short-term(≤6 h)forecasts was evaluated using two severe convective events in eastern China:a multi-region heavy rainfall event and a thunderstorm high-wind event.The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period,leading to a more reasonable storm environment.In the forecast fields,the simulations with LDA produced more realistic convective structures,resulting in an improvement in forecasts of precipitation and high winds.
文摘This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.
基金funded by the Korea Meteorological Administration Research and Development Program under grant KMIPA 2015–2083
文摘This study investigated the simulations of three months of seasonal tropical cyclone （TC） activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment （CTL）, the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.
基金Nature Science Foundation of China(41475046,41130964)State Key Program of China(2012CB417201)
文摘With the Weather Research and Forecasting model （WRFV3.2.1）, the application of specmun nudging tech- niques in numerical simulation of the genesis and development of typhoon Longwang （2005） is evaluated in this work via four numerical experiments with different nudging techniques. It is found that, due to the ability to capture the large-scale fields and to keep the meso-to small-scale features derived from the model dynamics, the experiment with spectrum nudging technique can simulate the formation, intensification and motion of Longwang properly. The improve- ment on the numerical simulation of Longwang induced by the spectrum nudging depends on the nudging coefficients. A weak spectrum nudging does not make significant improvement on the simulation of Longwang. Although the experi- ment with four-dimensional data assimilation, i.e., FDDA, also derives the genesis and movement of Longwang appro- priately, it fails to simulate the intensifying process of Longwang properly. The reason is that, as the large-scale features derived from the model are nudged to the observational data, the meso- to small-processes produced by the model dy- namics important to the intensification of typhoon are nearly smoothed by FDDA.
基金Supported by the Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-YW-Q11-02)
文摘The performance of spectral nudging in an investigation of the 2010 East Asia summer monsoon was assessed using the Weather Research and Forecasting (WRF) model, forced by 1-degree NCEP Global Final Analysis (FNL). Two pairs of experiments were made, spectral nudging (SP) and non-spectral nudging (NOSP), with five members in each group. The members were distinguished by different initial times, and the analysis was based on the ensemble mean of the two simulation pairs. The SP was able to constrain error growth in large-scale circulation in upper-level, during simulation, and generate realistic regional scale patterns. The main focus was the model ability to simulate precipitation. The Tropical Rainfall Measuring Mission (TRMM) 3B42 product was used for precipitation verification. Mean precipitation magnitude was generally overestimated by WRF. Nevertheless, SP simulations suppressed overestimation relative to the NOSP experiments. Compared to TRMM, SP also improved model simulation of precipitation in spatial and temporal distributions, with the ability to reproduce movement of rainbands. However, extreme precipitation events were suppressed in the SP simulations.
