Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the trai...Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.展开更多
Building codes have widely considered the shear wave velocity to make a reliable subsoil seismic classification, based on the knowledge of the mechanical properties of material deposits down to bedrock. This approach ...Building codes have widely considered the shear wave velocity to make a reliable subsoil seismic classification, based on the knowledge of the mechanical properties of material deposits down to bedrock. This approach has limitations because geophysical data are often very expensive to obtain. Recently, other alternatives have been proposed based on measurements of background noise and estimation of the H/V amplification curve. However, the use of this technique needs a regulatory framework before it can become a realistic site classification procedure. This paper proposes a new formulation for characterizing design sites in accordance with the Algerian seismic building code (RPA99/ver.2003), through transfer functions, by following a stochastic approach combined to a statistical study. For each soil type, the deterministic calculation of the average transfer function is performed over a wide sample of 1-D soil profiles, where the average shear wave (S-W) velocity, V<sub>s</sub>, in soil layers is simulated using random field theory. Average transfer functions are also used to calculate average site factors and normalized acceleration response spectra to highlight the amplification potential of each site type, since frequency content of the transfer function is significantly similar to that of the H/V amplification curve. Comparison is done with the RPA99/ver.2003 and Eurocode8 (EC8) design response spectra, respectively. In the absence of geophysical data, the proposed classification approach together with micro-tremor measures can be used toward a better soil classification.展开更多
One of the most important issues in inertial confinement fusion (ICF) is to study the uniformity of the radiation field around the implosion pellet containing fuel.To this end,a numerical method linking Monte Carlo wi...One of the most important issues in inertial confinement fusion (ICF) is to study the uniformity of the radiation field around the implosion pellet containing fuel.To this end,a numerical method linking Monte Carlo with iteration method is presented for calculating the radiation transfer problems in a cavity.The detail of the calculation scheme is described and some numerical examples are also given.展开更多
基金Supported by the National Natural Science Foundation of China (42175144)。
文摘Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.
文摘Building codes have widely considered the shear wave velocity to make a reliable subsoil seismic classification, based on the knowledge of the mechanical properties of material deposits down to bedrock. This approach has limitations because geophysical data are often very expensive to obtain. Recently, other alternatives have been proposed based on measurements of background noise and estimation of the H/V amplification curve. However, the use of this technique needs a regulatory framework before it can become a realistic site classification procedure. This paper proposes a new formulation for characterizing design sites in accordance with the Algerian seismic building code (RPA99/ver.2003), through transfer functions, by following a stochastic approach combined to a statistical study. For each soil type, the deterministic calculation of the average transfer function is performed over a wide sample of 1-D soil profiles, where the average shear wave (S-W) velocity, V<sub>s</sub>, in soil layers is simulated using random field theory. Average transfer functions are also used to calculate average site factors and normalized acceleration response spectra to highlight the amplification potential of each site type, since frequency content of the transfer function is significantly similar to that of the H/V amplification curve. Comparison is done with the RPA99/ver.2003 and Eurocode8 (EC8) design response spectra, respectively. In the absence of geophysical data, the proposed classification approach together with micro-tremor measures can be used toward a better soil classification.
基金Project supported in part by the National Natural Science Foundation of China and the National High-Tech ICF Committee in China.
文摘One of the most important issues in inertial confinement fusion (ICF) is to study the uniformity of the radiation field around the implosion pellet containing fuel.To this end,a numerical method linking Monte Carlo with iteration method is presented for calculating the radiation transfer problems in a cavity.The detail of the calculation scheme is described and some numerical examples are also given.
