The Taiwanese government has established a complete tide gauge network along the coastline for accurate sea-level monitoring. In this study, we analyze several factors impacting the determination of absolute or geocentric sea-level trends—including ocean tides, inverted barometer effect, datum shift, and vertical land motion—using tide gauge records near Taiwan, from 1993–2015. The results show that datum shifts and vertical land motion have a significant impact on sea-level trends with a respective average contribution of 7.3 and 8.0 mm/yr, whereas ocean tides and inverted barometer effects have a relatively minor impact, representing 9% and 14% of the observed trend, respectively. These results indicate that datum shifts and vertical land motion effects have to be removed in the tide gauge records for accurate sea-level estimates. Meanwhile, the estimated land motions show that the southwestern plain has larger subsidence rates, for example, the Boziliao, Dongshi, and Wengang tide gauge stations exhibit a rate of 24–31 mm/yr as a result of groundwater pumping. We find that the absolute sea-level trends around Taiwan derived from tide gauges or satellite altimetry agree well with each other, and are estimated to be 2.2 mm/yr for 1993–2015, which is significantly slower than the global average sea-level rise trend of 3.2 mm/yr from satellite altimeters. Finally, a recent hiatus in sea-level rise in this region exhibits good agreement with the interannual and decadal variabilities associated with the El Niño-Southern Oscillation and Pacific Decadal Oscillation. Full article

The monitoring and prognosis of cell degradation in lithium-ion (Li-ion) batteries are essential for assuring the reliability and safety of electric and hybrid vehicles. This paper aims to develop a reliable and accurate model for online, simultaneous state-of-charge (SOC) and state-of-health (SOH) estimations of Li-ion batteries. Through the analysis of battery cycle-life test data, the instantaneous discharging voltage (V) and its unit time voltage drop, V′, are proposed as the model parameters for the SOC equation. The SOH equation is found to have a linear relationship with 1/V′ times the modification factor, which is a function of SOC. Four batteries are tested in the laboratory, and the data are regressed for the model coefficients. The results show that the model built upon the data from one single cell is able to estimate the SOC and SOH of the three other cells within a 5% error bound. The derived model is also proven to be robust. A random sampling test to simulate the online real-time SOC and SOH estimation proves that this model is accurate and can be potentially used in an electric vehicle battery management system (BMS). Full article

Spherical harmonics (SH) and mascon solutions are the two most common types of solutions for Gravity Recovery and Climate Experiment (GRACE) mass flux observations. However, SH signals are degraded by measurement and leakage errors. Mascon solutions (the Jet Propulsion Laboratory (JPL) release, herein) exhibit weakened signals at submascon resolutions. Both solutions require a scale factor examined by the CLM4.0 model to obtain the actual water storage signal. The Slepian localization method can avoid the SH leakage errors when applied to the basin scale. In this study, we estimate SH errors and scale factors for African hydrological regimes. Then, terrestrial water storage (TWS) in Africa is determined based on Slepian localization and compared with JPL-mascon and SH solutions. The three TWS estimates show good agreement for the TWS of large-sized and humid regimes but present discrepancies for the TWS of medium and small-sized regimes. Slepian localization is an effective method for deriving the TWS of arid zones. The TWS behavior in African regimes and its spatiotemporal variations are then examined. The negative TWS trends in the lower Nile and Sahara at −1.08 and −6.92 Gt/year, respectively, are higher than those previously reported. Full article

The Qinghai-Tibetan plateau (QTP), also known as the Third Pole and the World Water Tower, is the largest and highest plateau with distinct and competing surface and subsurface processes. It is covered by a large layer of discontinuous and sporadic alpine permafrost which has degraded 10% during the past few decades. The average active layer thickness (ALT) increase rate is approximately 7.5 cm·yr⁻¹ from 1995 to 2007, based on soil temperature measurements from 10 borehole sites along Qinghai-Tibetan Highway, and approximately 6.3 cm·yr⁻¹, 2006–2010, using soil temperature profiles for 27 monitoring sites along Qinghai-Tibetan railway. In this study, we estimated the ALT and its AL thickening rate in the northern QTP near the railway using ALOS PALSAR L-band small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) data observed land subsidence and the corresponding ALT modeling. The InSAR estimated ALT and AL thickening rate were validated with ground-based observations from the borehole site WD4 within our study region, indicating excellent agreement. We concluded that we have generated high spatial resolution (30 m) and spatially-varying ALT and AL thickening rates, 2007–2009, over approximately an area of 150 km² of permafrost-covered region in the northern QTP. Full article

Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space to that in radiance space, thus requiring a lot of computational resources especially for hyperspectral instruments with thousands of channels. On the other hand, assimilating the retrieved physical parameters is computationally more efficient as they are already in thermodynamic states, which can be compared with NWP model outputs through the objective analysis scheme. A microwave (MW) sounder and an infrared (IR) sounder have their respective observational limitation due to the characteristics of adopted spectra. The MW sounder observes at much larger field-of-view (FOV) compared to an IR sounder. On the other hand, MW has the capability to reveal the atmospheric sounding when the clouds are presented, but IR observations are highly sensitive to clouds, The advanced IR sounder is able to reduce uncertainties in the retrieved atmospheric temperature and moisture profiles due to its higher spectral-resolution than the MW sounder which has much broader spectra bands. This study tries to quantify the optimal use of soundings retrieved from the microwave sounder AMSU and infrared sounder AIRS onboard the AQUA satellite in the regional Weather and Research Forecasting (WRF) model through three-dimensional variational (3D-var) data assimilation scheme. Four experiments are conducted by assimilating soundings from: (1) clear AIRS single field-of-view (SFOV); (2) retrieved from using clear AMSU and AIRS observations at AMSU field-of-view (SUP); (3) all SFOV soundings within AMSU FOVs must be clear; and (4) SUP soundings which must have all clear SFOV soundings within the AMSU FOV. A baseline experiment assimilating only conventional data is generated for comparison. Various atmospheric state variables at different pressure levels are used to assess the impact from assimilating these different data by comparing them with European Centre for Medium Range Weather Forecast (ECMWF) reanalysis data. Results indicate assimilation of SUP soundings improve the mid and upper troposphere, whereas assimilation of SFOV soundings has positive impact on the lower troposphere. Two additional assimilation experiments are carried out to determine the combination of SUP and SFOV soundings that will provide the best performance throughout the troposphere. The results indicate that optimal combination is to assimilate clear-sky matched IR retrievals with non-matched MW soundings. Full article

The Tibetan Plateau (TP) has been observed by satellite optical remote sensing, altimetry, and gravimetry for a variety of geophysical parameters, including water storage change. However, each of these sensors has its respective limitation in the parameters observed, accuracy and spatial-temporal resolution. Here, we utilized an integrated approach to combine remote sensing imagery, digital elevation model, and satellite radar and laser altimetry data, to quantify freshwater storage change in a twin lake system named Chibuzhang Co and Dorsoidong Co in the central TP, and compared that with independent observations including mass changes from the Gravity Recovery and Climate Experiment (GRACE) data. Our results show that this twin lake, located within the Tanggula glacier system, remained almost steady during 1973–2000. However, Dorsoidong Co has experienced a significant lake level rise since 2000, especially during 2000–2005, that resulted in the plausible connection between the two lakes. The contemporary increasing lake level signal at a rate of 0.89 ± 0.05 cm·yr⁻¹, in a 2° by 2° grid equivalent water height since 2002, is higher than the GRACE observed trend at 0.41 ± 0.17 cm·yr⁻¹ during the same time span. Finally, a down-turning trend or inter-annual variability shown in the GRACE signal is observed after 2012, while the lake level is still rising at a consistent rate. Full article

Water level (WL) and water volume (WV) of surface-water bodies are among the most crucial variables used in water-resources assessment and management. They fluctuate as a result of climatic forcing, and they are considered as indicators of climatic impacts on water resources. Quantifying riverine WL and WV, however, usually requires the availability of timely and continuous in situ data, which could be a challenge for rivers in remote regions, including the Mekong River basin. As one of the most developed rivers in the world, with more than 20 dams built or under construction, Mekong River is in need of a monitoring system that could facilitate basin-scale management of water resources facing future climate change. This study used spaceborne sensors to investigate two dams in the upper Mekong River, Xiaowan and Jinghong Dams within China, to examine river flow dynamics after these dams became operational. We integrated multi-mission satellite radar altimetry (RA, Envisat and Jason-2) and Landsat-5/-7/-8 Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+)/Operational Land Imager (OLI) optical remote sensing (RS) imageries to construct composite WL time series with enhanced spatial resolutions and substantially extended WL data records. An empirical relationship between WL variation and water extent was first established for each dam, and then the combined long-term WL time series from Landsat images are reconstructed for the dams. The R² between altimetry WL and Landsat water area measurements is >0.95. Next, the Tropical Rainfall Measuring Mission (TRMM) data were used to diagnose and determine water variation caused by the precipitation anomaly within the basin. Finally, the impact of hydrologic dynamics caused by the impoundment of the dams is assessed. The discrepancy between satellite-derived WL and available in situ gauge data, in term of root-mean-square error (RMSE) is at 2–5 m level. The estimated WV variations derived from combined RA/RS imageries and digital elevation model (DEM) are consistent with results from in situ data with a difference at about 3%. We concluded that the river level downstream is affected by a combined operation of these two dams after 2009, which has decreased WL by 0.20 m·year⁻¹ in wet seasons and increased WL by 0.35 m·year⁻¹ in dry seasons. Full article

Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery’s degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (V_dis) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using V_dis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients. Full article