Long-Term Sea Level Variability in the Yellow Sea and East China Sea

Using the satellite altimeter maps of sea level anomaly (MSLA) and tidal gauge data, this chapter gives an investigation of the long-term sea level variability (SLV) and sea level rise (SLR) rate in the Yellow Sea (YS) and East China Sea (ECS). Correlation analysis shows that the satellite altimeter is effective and capable of revealing the coastal SLV. To investigate the regional correlation of SLV in the YS and ECS, tidal gauge station data are used as references. Based on the monthly maps of correlation coefficient (CC) of SLV at tidal stations with the gridded MSLA data, we find that the existence of Kuroshio decreases the correlation between the coastal and Pacific sea levels. The empirical mode decomposition (EMD) method is applied to derive the SLR trend on each MSLA grid point in the YS and ECS. According to the two-dimensional geographical distribution of the SLR rate, one can see that the sea level on the eastern side of the Kuroshio mainstream rises faster than that on the western side. Both the YS and ECS SLR rates averaged over 1993–2010 are slower than the globally averaged SLR rate. This implies that although the SLV in the two seas is affected by global climate change, it could be mostly influenced by local effects

On the Quality of HY-2A Scatterometer Wind

Presentación para el International Ocean Vector Winds Science Team (2015 IOVWST) Meeting, 19-21 May 2015, Portland, Oregon.-- 39 pagesPeer Reviewe

Controlling Reversible Expansion of Li2O2 Formation and Decomposition by Modifying Electrolyte in Li-O2 Batteries

锂空电池分别使用空气中的氧气和金属锂作为正负极活性材料，具有极高的能量密度。但是，这一体系尚不能实现商业化的应用，其中一些关键问题未能解决。由于其正极活性材料是气体，使得电化学反应涉及气-液-固三相界面，电极过程十分复杂。与其它二次电池相比，空气电极需要考虑结构因素和催化因素。不仅要改善氧气电化学反应的动力学迟缓问题，还要考虑放电产物的驻留空间问题。董全峰教授课题组在前期开展了基于空气电极固相表面电催化研究，并结合电极结构方面的问题，构筑了有利于氧气发生反应的仿生开放式结构电极。 该研究工作主要由化学化工学院2015级iChEM直博生林晓东（第一作者）在董全峰教授、郑明森副教授和龚磊副教授的共同指导下完成，理论计算由袁汝明助理教授（共同第一作者）完成，曹勇、丁晓兵、蔡森荣、韩博闻等学生参与了部分工作。周志有教授和洪宇浩博士生在电化学微分质谱方面给予大力的帮助与支持。【Abstract】The aprotic lithium-oxygen (Li-O2) battery has attracted worldwide attention because of its ultrahigh theoretical energy density. However, its practical application is critically hindered by cathode passivation, large polarization, and severe parasitic reactions. Here, we demonstrate an originally designed Ru(Ⅱ) polypyridyl complex (RuPC) though which the reversible expansion of Li2O2 formation and decomposition can be achieved in Li-O2 batteries. Experimental and theoretical results revealed that the RuPC can not only expand the formation of Li2O2 in electrolyte but also suppress the reactivity of LiO2 intermediate during discharge, thus alleviating the cathode passivation and parasitic reactions significantly. In addition, an initial delithiation pathway can be achieved when charging in turn; thus, the Li2O2 products can be decomposed reversibly with a low overpotential. Consequently, the RuPC-catalyzed Li-O2 batteries exhibited a high discharge capacity, a low charge overpotential, and an ultralong cycle life. This work provides an alternative way of designing the soluble organic catalysts for metal-O2 batteries.This work was supported by the National 973 Program (2015CB251102), the Key Project of National Natural Science Foundation of China (21673196, 21621091, 21703186, 21773192),and the Fundamental Research Funds for the Central Universities (20720150042,20720150043). The authors thank Prof. Eric Meggers at Philipps-Univeristaet Marburg for his discussion about the synthesis of RuPC complex; Prof. Gang Fu at Xiamen University for his instructive discussions in DFT calculations; Lajia Yu and Dandan Tao at Xiamen University for their assistance in EPR experiments and UV-Vis spectroscopy experiments, respectively; and Yu Gu and Tao Wang at Xiamen University for their discussions in XPS results and CV data,respectively. 该工作得到科技部重大基础研究计划（项目批准号：2015CB251102）、国家自然科学基金（项目批准号：21673196、21621091、21703186、21773192）和中央高校基本科研业务费专项资金（项目批准号：20720150042、20720150043）的资助。 此外，感谢傅钢教授在理论计算方面的讨论和建议，Eric Meggers教授在配合物合成上的讨论，泉州师范学院吴启辉教授和化学化工学院谷宇博士生在X射线光电子能谱方面的帮助，于腊佳老师在电子顺磁共振实验上的帮助，陶丹丹博士生在紫外可见光谱测试上的帮助以及王韬博士生在循环伏安方面的讨论

Past, Present and Future Marine Microwave Satellite Missions in China

Over the past 60 years, China has made fruitful achievements in the field of ocean microwave remote sensing satellite technology. A long-term plan has now been formulated for the development of Chinese ocean satellites, as well as the construction of a constellation of ocean dynamic environmental and ocean surveillance satellites. These will gradually form China’s ocean monitoring network from space, thereby playing important roles in future ocean resource and environmental monitoring, marine disaster prevention and reduction, and global climate change. In this review manuscript, the developmental history of ocean microwave satellites and the development status of oceanic microwave remote sensing satellites in China are reviewed. In addition, China’s achievements in the field of oceanic microwave remote sensing satellite technology are summarized, and the future development of China’s ocean microwave remote sensing satellite program is analysed

High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ0 by the HW-GMF and the measured σ0 by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions

High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ0 by the HW-GMF and the measured σ0 by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions

A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter

Analysis of Global Sea Level Change Based on Multi-Source Data

Global sea level rise is both a major indicator and consequence of global warming. At present, global warming is causing sea level rise in two main ways: one is the thermal expansion of sea water, and the other is the injection of large amounts of fresh water into the ocean after glaciers and ice sheets melt. In this paper, satellite altimeter data are used to study the total changes of global sea level from 2002 to 2020. Different from most previous studies, this study proposes a calculation method of sea level anomaly using only the along track altimetry data, which is similar to considering the satellite points as tide gauges, in order to avoid the error caused by interpolation in the map data. In addition, GRACE satellite data are used to calculate the changes of global sea level caused by water increase; temperature and salinity data are used to calculate the changes from ocean thermal expansion. Next, using satellite altimetry data, the calculation results show that the global sea level rise rate in the period of 2002–2020 is 3.3 mm/a. During this period, the sea level change caused by the increase of sea water calculated with GRACE satellite data is 2.07 mm/a, and that caused by the thermal expansion of seawater is 0.62 mm/a. The sea level rise caused by the increase of water volume accounts for 62.7% of the total sea level rise

Analysis of Global Sea Level Change Based on Multi-Source Data

Global sea level rise is both a major indicator and consequence of global warming. At present, global warming is causing sea level rise in two main ways: one is the thermal expansion of sea water, and the other is the injection of large amounts of fresh water into the ocean after glaciers and ice sheets melt. In this paper, satellite altimeter data are used to study the total changes of global sea level from 2002 to 2020. Different from most previous studies, this study proposes a calculation method of sea level anomaly using only the along track altimetry data, which is similar to considering the satellite points as tide gauges, in order to avoid the error caused by interpolation in the map data. In addition, GRACE satellite data are used to calculate the changes of global sea level caused by water increase; temperature and salinity data are used to calculate the changes from ocean thermal expansion. Next, using satellite altimetry data, the calculation results show that the global sea level rise rate in the period of 2002–2020 is 3.3 mm/a. During this period, the sea level change caused by the increase of sea water calculated with GRACE satellite data is 2.07 mm/a, and that caused by the thermal expansion of seawater is 0.62 mm/a. The sea level rise caused by the increase of water volume accounts for 62.7% of the total sea level rise