To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 ...To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.展开更多
Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where on...Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only one radar altimeter.In this paper,taking account of the data from multiple radar altimeters available,we introduced a multi-task learning method,called trace-norm regularized multi-task learning(TNR-MTL),for SSB estimation.Corresponding to each individual task,TNR-MLT involves only three parameters.Hence,it is easy to implement.More importantly,the convergence of TNR-MLT is theoretically guaranteed.Compared with the commonly used STL models,TNR-MTL can effectively utilize the shared information between data from multiple altimeters.During the training of TNR-MTL,we used the JASON-2 and JASON-3 cycle data to solve two correlated SSB estimation tasks.Then the optimal model was selected to estimate SSB on the JASON-2 and the HY-270-71 cycle intersection data.For the JSAON-2 cycle intersection data,the corrected variance(M)has been reduced by 0.60 cm^2 compared to the geophysical data records(GDR);while for the HY-2 cycle intersection data,M has been reduced by 1.30 cm^2 compared to GDR.Therefore,TNR-MTL is proved to be effective for the SSB estimation tasks.展开更多
We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2）. Driven by pre-industrial forcing, the in...We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2）. Driven by pre-industrial forcing, the integration of FGOALS-s2 was found to have remained stable, with no obvious climate drift over 600 model years. The linear trends of sea SST and sea surface salinity （SSS） were -0.04℃ （100 yr）-1 and 0.01 psu （100 yr）-1, respectively. The simulations of oceanic temperatures, wind-driven circulation and thermohaline circulation in FGOALS-s2 were found to be comparable with observations, and have been substantially improved over previous FGOALS-s versions （1.0 and 1.1）. However, significant SST biases （exceeding 3℃） were found around strong western boundary currents, in the East China Sea, the Sea of Japan and the Barents Sea. Along the eastern coasts in the Pacific and Atlantic Ocean, a warm bias （〉3℃） was mainly due to overestimation of net surface shortwave radiation and weak oceanic upwelling. The difference of SST biases in the North Atlantic and Pacific was partly due to the errors of meridional heat transport. For SSS, biases exceeding 1.5 psu were located in the Arctic Ocean and around the Gulf Stream. In the tropics, freshwater biases dominated and were mainly caused by the excess of precipitation. Regarding the vertical dimension, the maximal biases of temperature and salinity were located north of 65°N at depths of greater than 600 m, and their values exceeded 4℃ and 2 psu, respectively.展开更多
The total dose effect of an AD678 with a BiMOS process is studied.We investigate the performance degradation of the device in different bias states and at several dose rates.The results show that an AD678 can endure 3...The total dose effect of an AD678 with a BiMOS process is studied.We investigate the performance degradation of the device in different bias states and at several dose rates.The results show that an AD678 can endure 3 krad（Si） at low dose rate and 5 krad（Si） at a high dose rate for static bias.The sensitive parameters to the bias states also differ distinctly.We find that the degradation is more serious on static bias.The underlying mechanisms are discussed in detail.展开更多
基金The National Key R&D Program of China under contract No.2016YFC1401004the National Natural Science Foundation of China under contract Nos 41406207,41176157 and 41406197
文摘To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.
基金This work was supported by the Major Project for New Generation of AI(No.2018AAA0100400)the National Natural Science Foundation of China(No.41706010)+1 种基金the Joint Fund of the Equipments Pre-Research and Ministry of Education of China(No.6141A020337)and the Fundamental Research Funds for the Central Universities of China.
文摘Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only one radar altimeter.In this paper,taking account of the data from multiple radar altimeters available,we introduced a multi-task learning method,called trace-norm regularized multi-task learning(TNR-MTL),for SSB estimation.Corresponding to each individual task,TNR-MLT involves only three parameters.Hence,it is easy to implement.More importantly,the convergence of TNR-MLT is theoretically guaranteed.Compared with the commonly used STL models,TNR-MTL can effectively utilize the shared information between data from multiple altimeters.During the training of TNR-MTL,we used the JASON-2 and JASON-3 cycle data to solve two correlated SSB estimation tasks.Then the optimal model was selected to estimate SSB on the JASON-2 and the HY-270-71 cycle intersection data.For the JSAON-2 cycle intersection data,the corrected variance(M)has been reduced by 0.60 cm^2 compared to the geophysical data records(GDR);while for the HY-2 cycle intersection data,M has been reduced by 1.30 cm^2 compared to GDR.Therefore,TNR-MTL is proved to be effective for the SSB estimation tasks.
基金supported by the National Key Program for Developing Basic Sciences(GrantNos. 2010CB950502)the "Strategic Priority Research Program-Climate Change:Carbon Budget and Related Issues" of the Chinese Academy of Sciences (Grant No.XDA05110302)+1 种基金the National Natural Science Foundation of China(Grant Nos. 40906012 and 41023002)National High Technology Research and Development Program of China(Grant No. 2010AA012303)
文摘We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2）. Driven by pre-industrial forcing, the integration of FGOALS-s2 was found to have remained stable, with no obvious climate drift over 600 model years. The linear trends of sea SST and sea surface salinity （SSS） were -0.04℃ （100 yr）-1 and 0.01 psu （100 yr）-1, respectively. The simulations of oceanic temperatures, wind-driven circulation and thermohaline circulation in FGOALS-s2 were found to be comparable with observations, and have been substantially improved over previous FGOALS-s versions （1.0 and 1.1）. However, significant SST biases （exceeding 3℃） were found around strong western boundary currents, in the East China Sea, the Sea of Japan and the Barents Sea. Along the eastern coasts in the Pacific and Atlantic Ocean, a warm bias （〉3℃） was mainly due to overestimation of net surface shortwave radiation and weak oceanic upwelling. The difference of SST biases in the North Atlantic and Pacific was partly due to the errors of meridional heat transport. For SSS, biases exceeding 1.5 psu were located in the Arctic Ocean and around the Gulf Stream. In the tropics, freshwater biases dominated and were mainly caused by the excess of precipitation. Regarding the vertical dimension, the maximal biases of temperature and salinity were located north of 65°N at depths of greater than 600 m, and their values exceeded 4℃ and 2 psu, respectively.
文摘The total dose effect of an AD678 with a BiMOS process is studied.We investigate the performance degradation of the device in different bias states and at several dose rates.The results show that an AD678 can endure 3 krad（Si） at low dose rate and 5 krad（Si） at a high dose rate for static bias.The sensitive parameters to the bias states also differ distinctly.We find that the degradation is more serious on static bias.The underlying mechanisms are discussed in detail.
