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The surface morphology of lake ice remarkably changes under the combined influence of thermal and mechanical forces. However, research on the surface morphology of lake ice and its interaction with climate is scarce. A large-scale linear structure has repeatedly appeared on satellite images of Chagan Lake in recent years. The Geostationary Ocean Color Imager (GOCI), with a 1 h revisit, and Landsat 8 Operational Land Imager (OLI), with a spatial resolution of 30 m, provide the possibility for the study of hourly changes in the large-scale linear structure. We merged the Landsat and GOCI images, using an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and extracted the lengths and angles of the linear structure. We monitored the hourly changes in the surface morphology during the cold season from 2018 to 2019. The average length of the linear structure in the completely frozen period was 21 141.57 ± 68.36 m. The average azimuth angle was 335.48 ± 0.23∘, nearly perpendicular to the domain wind in winter. Through two field investigations during the two recent cold seasons, we verified the linear structure as being ice fractures and ridges. The evolution of surface morphology is closely associated with air temperature, wind, and shoreline geometry.

Based on MOD09GA/MYD09GA surface reflectance data, a new MODIS snow-cover-extent (SCE) product from 2000 to 2020 over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets – Terra-MODIS and Aqua-MODIS SCE datasets and a final daily cloud-gap-filled (CGF) SCE dataset. The first two datasets are generated mainly through optimizing snow-cover discriminating rules over land-cover types, and the latter dataset is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. The validation against 362 China Meteorological Administration (CMA) stations shows that during snow seasons the overall accuracy (OA) values of the three datasets are larger than 93 %, all of the omission error (OE) values are constrained within 9 %, and all of the commission error (CE) values are constrained within 10 %. Bias values of 0.98, 1.02, and 1.03 demonstrate on a whole that there is no significant overestimation nor a significant underestimation. Based on the same ground reference data, we found that the new product accuracies are obviously higher than standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For example, compared with the CE of 23.78 % that the MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 % versus 89.54 % of MOD10A1F product and 84.36 % of MYD10A1F product. Besides, as expected, snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates that the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China; thereby, it can better serve for hydrological, climatic, environmental, and other related studies there.

The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45∘ N, the existing SWE products are associated with a limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of SWE data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing SWE products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare SWE products on a global scale. We evaluated the accuracy of the RRM SWE product using hemispheric-scale snow course (HSSC) observational data and Russian snow survey data. The mean absolute error (MAE), RMSE, R, and R2 between the RRM SWE products and observed SWEs are 0.21, 25.37 mm, 0.89, and 0.79, respectively. The accuracy of the RRM SWE dataset is improved by 28 %, 22 %, 37 %, 11 %, and 11 % compared with the original AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System (GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely heavily on an independent SWE product; it takes full advantage of each SWE dataset, and it takes into consideration the altitude factor. The MAE ranges from 0.16 for areas within m elevation to 0.29 within the 800–900 m elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71 mm for areas within m elevation to 31.14 mm within the >1000 m elevation range. The RMSE is best in the Finland region and worst in the Canadian region. This method has good stability, is extremely suitable for the production of snow datasets with large spatial scales, and can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate SWE data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from “A Big Earth Data Platform for Three Poles” (https://doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).

A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

The regional role and trends of freshwater ice are critical factors for aquatic ecosystems, climate variability, and human activities. The ice regime has been scarcely investigated in the Songhua River Basin of northeast China. Using daily ice records of 156 hydrological stations across the region, we examined the spatial variability in the river ice phenology and river ice thickness from 2010 to 2015 and explored the role of snow depth and air temperature on the ice thickness. The river ice phenology showed a latitudinal distribution and a changing direction from southeast to northwest. We identified two spatial clusters based on Moran's I spatial autocorrelation, and results showed that the completely frozen duration with high values clustered in the Xiao Hinggan Range and that with low values clustered in the Changbai Mountains at the 95 % confidence level. The maximum ice thickness over 125 cm was distributed along the ridge of the Da Hinggan Range and Changbai Mountains, and the maximum ice thickness occurred most often in February and March. In three subbasins of the Songhua River Basin, we developed six Bayesian regression models to predict ice thickness from air temperature and snow depth. The goodness of the fit (R2) for these regression models ranged from 0.80 to 0.95, and the root mean square errors ranged from 0.08 to 0.18 m. Results showed significant and positive correlations between snow cover and ice thickness when freshwater was completely frozen. Ice thickness was influenced by the cumulative air temperature of freezing through the heat loss of ice formation and decay instead of just air temperature.

The East Asian winter monsoon (EAWM) is greatly influenced by many factors that can be classified as anthropogenic forcing and natural forcing. Here we explore the contribution of anthropogenic influence to the change in the EAWM over the past decades. Under all forcings observed during 1960–2013 (All-Hist run), the atmospheric general circulation model is able to reproduce the climatology and variability of the EAWM-related surface air temperature and 500 hPa geopotential height and shows a statistically significant decreasing EAWM intensity with a trend coefficient of ∼-0.04 yr−1, which is close to the observed trend. By contrast, the simulation, which is driven by the same forcing as the All-Hist run but with the anthropogenic contribution to them removed, shows no decreasing trend in the EAWM intensity. By comparing the simulations under two different forcing scenarios, we further reveal that the responses of the EAWM to the anthropogenic forcing include a rise of 0.6∘ in surface air temperature over East Asia as well as weakening of the East Asian trough, which may result from the poleward expansion and intensification of the East Asian jet forced by the change in temperature gradient in the troposphere. Additionally, compared with the simulation without anthropogenic forcing, the frequency of strong (weak) EAWM occurrence is reduced (increased) by 45 % (from 0 to 10/7). These results indicate that the weakening of the EAWM during 1960–2013 may be mainly attributed to the anthropogenic influence.

The abundance or lack of rainfall affects peoples’ life and activities. As a major component of the global hydrological cycle (Chokngamwong & Chiu, 2007), accurate representations at various spatial and temporal scales are crucial for a lot of decision making processes. Climate models show a warmer and wetter climate due to increases of Greenhouse Gases (GHG). However, the models’ resolutions are often too coarse to be directly applicable to local scales that are useful for mitigation purposes. Hence disaggregation (downscaling) procedures are needed to transfer the coarse scale products to higher spatial and temporal resolutions. The aim of this paper is to examine the changes in the statistical parameters of rainfall at various spatial and temporal resolutions. The TRMM Multi-satellite Precipitation Analysis (TMPA) at 0.25 degree, 3 hourly grid rainfall data for a summer is aggregated to 0.5,1.0, 2.0 and 2.5 degree and at 6, 12, 24 hourly, pentad (five days) and monthly resolutions. The probability distributions (PDF) and cumulative distribution functions(CDF) of rain amount at these resolutions are computed and modeled as a mixed distribution. Parameters of the PDFs are compared using the Kolmogrov-Smironov (KS) test, both for the mixed and the marginal distribution. These distributions are shown to be distinct. The marginal distributions are fitted with Lognormal and Gamma distributions and it is found that the Gamma distributions fit much better than the Lognormal.

The adverse weather may significantly impact urban traffic speed and travel time. Understanding the influence of the rainstorm to urban traffic speed is of great importance for traffic management under stormy weather. This study aims to investigate the impact of rainfall intensity on traffic speed in the Shenzhen (China) during the period 1 July 2015–31 August 2016. The analysis was carried out for five 1-h periods on weekdays during the morning periods (6:00 AM–11:00 AM). Taxi-enabled GPS tracking data obtained from Shenzhen city are used in the analysis. There are several findings in this study. Firstly, nearly half of the roads are significantly affected by the rainstorm. Secondly, the proportion of positive correlated roads is about 35 %, but there still are some roads with uncorrelated traffic speed variation rates (SVR) and rainfall intensities. Thirdly, the impact of the rainstorm on traffic speed is not homogeneous but with obvious spatial difference. This research provides useful information that can be used in traffic management on a city-wide scale under stormy weather.

Snow cover on the Qinghai–Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

To solve the problem of relative camera pose estimation, a method using optimization with respect to the manifold is proposed. Firstly from maximum-a-posteriori (MAP) model to nonlinear least squares (NLS) model, the general state estimation model using optimization is derived. Then the camera pose estimation model is applied to the general state estimation model, while the parameterization of rigid body transformation is represented by Lie group/algebra. The jacobian of point-pose model with respect to Lie group/algebra is derived in detail and thus the optimization model of rigid body transformation is established. Experimental results show that compared with the original algorithms, the approaches with optimization can obtain higher accuracy both in rotation and translation, while avoiding the singularity of Euler angle parameterization of rotation. Thus the proposed method can estimate relative camera pose with high accuracy and robustness.

Stroke is a serious worldwide medical condition that causes neurological function disability. Diffusional kurtosis imaging, which measures the non-Gaussianity of water diffusion, has been demonstrated to be a sensitive biomarker in many neuro-pathologies. This study explores the relationship between neural function recovery and transformation of the ischemic lesion and/or corticospinal tract during the sub-acute phase after stroke by using diffusional kurtosis imaging. We performed a prospective study of function recovery and K metrics of 43 patients with sub-acute ischemic stroke in the middle cerebral artery territory. The effect of rehabilitation treatment was evaluated using both the Fugl-Meyer motor function score and modified Barthel index score at post-treatment compared with admission, and patients were allocated to two groups: good and poor rehabilitation effect (GRE and PRE). Metrics of diffusional kurtosis imaging within ischemic lesion and along the corticospinal tract were acquired, respectively. All three relative axial diffusional kurtoses (rKas) along the corticospinal tract in the GRE group ( n = 21) were significantly larger than those of the PRE group ( n = 22), including rKa in the posterior limb of internal capsule, rKa in the cerebral peduncle, and rKa in the basal part of the pons ( p = 0.014, 0.005, and 0.021, respectively). This multi-parametric magnetic resonance imaging study showed that diffusional kurtosis imaging has the potential to complement existing stroke imaging techniques and revealed its own advantages in elucidating the possible biophysical mechanism of functional restoration underlying ischemic stroke.

By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.

We report the frequency dependence of the ferromagnetic resonance linewidth of the free layer in magnetic tunnel junctions with all perpendicular–to–the–plane magnetized layers. While the magnetic–field–swept linewidth nominally shows a linear growth with frequency in agreement with Gilbert damping, an additional frequency–dependent linewidth broadening occurs that shows a strong asymmetry between the absorption spectra for increasing and decreasing external magnetic field. Inhomogeneous magnetic fields produced during reversal of the reference and pinned layer complex is demonstrated to be at the origin of the symmetry breaking and the linewidth enhancement. Consequentially, this linewidth enhancement provides indirect information on the magnetic coercivity of the reference and pinned layers. These results have important implications for the characterization of perpendicular magnetized magnetic random access memory bit cells.

Contemporary scientists have vested interest in trying to understand the climatology of the North Atlantic Basin since this region is considered as the genesis for hurricane formation that eventually get shipped to the tropical Atlantic region and the Caribbean. The effects of atmospheric water cycle and the climate of West Africa and the Atlantic basin are hugely impacted by the radiative forcing of Saharan dust. The focus area in this paper would be to improve the dust detection schemes by employing the use of multi sensor measurements in the thermal emissive wavelengths using legacy sensors such as Terra (T) and Aqua (A) MODerate-resolution Imaging Spectroradiometer (MODIS), fusing with Ozone Monitoring Instrument (OMI). Previous work by Hao and Qu (2007) had considered a limited number of thermal infrared channels which led to a correlation coefficient R2 value of 0.765 between the Aerosol Optical Thickness (AOT) at 550 nm and the modeled dust index. In this work, we extend the thermal infrared based dust detection by employing additional channels: the 8.55 μm which has shown high sensitivity to the Saharan dust, along with water vapor channel of 7.1 μm and cloud top channel of 13.1 μm. Also, the dust pixels were clearly identified using the OMI based aerosol types. The dust pixels were cleanly segregated from the other aerosol types such as sulfates, biomass, and other carbonaceous aerosols. These improvements led to a much higher correlation coefficient R2 value of 0.85 between the modified dust index and the AOT in comparison to the previous work. The key limitations from the current AOT products based on MODIS and were put to test by validating the improved dust detection algorithm. Two improvements were noted. First, the dust measurement radiometry using MODIS is significantly improved by at least an order of 2. Second the spatial measurements are enhanced by a factor of at least 10.

Although the electron density profiles (EDPs) from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurement have been validated by ionosonde data at a number of locations during the solar minimum period, the performance of COSMIC measurements at different latitudes has not been well evaluated, particularly during the solar maximum period. In this paper the COSMIC ionospheric peak parameters (peak electron density of the F region – NmF2; peak height of the F region – hmF2) are validated by the ionosonde data from an observation chain in China during the solar maximum period of 2011–2013. The validations show that the COSMIC measurement generally agrees well with the ionosonde observation. The error in NmF2 from COSMIC and ionosonde measurements varies with latitude. At midlatitude stations, the differences between COSMIC NmF2s and those of ionosondes are very slight. However, COSMIC NmF2 overestimates (underestimates) that of the ionosonde at the north (south) of the equatorial ionization anomaly (EIA) crest. The relative errors of hmF2s are much lower than those of NmF2s at all stations, which indicates the EDP retrieval algorithm of the COSMIC measurement has a better performance in determining the ionospheric peak height. The root mean square errors (RMSEs) of NmF2s (hmF2s) are higher (lower) during the daytime than during the nighttime at all stations. Correlation analysis shows that the correlations for both NmF2s and hmF2s are comparably good (correlation coefficients > 0.9) at midlatitude stations, while correlations of NmF2 (correlation coefficients > 0.9) are higher than those of hmF2 (correlation coefficients > 0.8) at low-latitude stations.

The spatial and temporal variations of snowcover distribution, and snowmelt runoff are considered as sensitive indicators for climatic change. The purpose of this paper is to analyze and forecast the responses of snowmelt runoff to climate change in an inland river basin. The upper basin of Heihe River in Northwestern China was chose as the study area, and the observation data from the meteorological and hydrological stations were utilized to analyze the status and regularity of the climatic change over the past 50 years. Snow cover area was obtained by an optimized technology using Moderate Resolution Imaging Spectroradiometer data with Normalized Difference Snow Index adjustment and topographic correction. A concept of potential snowmelt was suggested to illustrate the response of spatial snowmelt to climate change. The results show that the annual SCA proportion and the potential snowmelt keep an increasing trend since 2000. There is a negative relationship between annual air temperature and SCA proportion from 2000 to 2008. Snowmelt Runoff Model was chose to simulate snowmelt runoff and scenario forecast the change trend of snowmelt runoff in this region. The results show that climatic warming was apparent in the upper basin of Heihe River over the past 50 a. Annual average air temperature of three different weather stations located in the basin has increased 2.1 °C, 2.6 °C and 2.9 °C respectively from 1956 to present. The snowmelt runoff has increased obviously from 1970 to present. With different warming climate scenarios, the results by using SRM simulating showed that the first occurred time of snowmelt runoff shift ahead and discharge become larger as responses of snowmelt runoff to air temperature increasing, and the influence of temperature rising on average discharge of the whole snow season is not obvious.

Abstract Boundary dynamics are crucial for the transport of energy, mass, and momentum in geospace. The recently discovered plasmapause surface wave (PSW) plays a key role in the inner magnetosphere dynamics. However, a comprehensive investigation of spatial variations of the PSW remains absent. In this study, we elucidate the spatial characteristics of a PSW through observations from multiple spacecrafts in the magnetosphere. Following the initiation of the PSW, quasi‐periodic injections of energetic ions, rather than electrons, are suggested to serve as energy source of the PSW. Based on the distinct wave and particle signatures, we categorize the PSW into four regions: seed region, growth region, stabilization region and decay region, spanning from nightside to afternoon plasmapause. These findings advance our understanding of universal boundary dynamics and contribute to a deeper comprehension of the pivotal roles of surface waves in the energy couplings within the magnetosphere‐plasmasphere‐ionosphere system.

ABSTRACT Hepatitis E virus (HEV) infection is an important global public health issue. HEV infections are recognized as a zoonotic disease. Swine are believed to be the main reservoir of HEV. Recently, yaks, cows, and yellow cattle have been reported as new reservoirs of HEV. However, whether other species of cattle and buffaloes are sensitive to HEV infection is unknown. To investigate the prevalence of HEV infection in buffaloes, enzyme-linked immunosorbent assay (ELISA) and reverse transcription-nested polymerase chain reaction (RT-nPCR) were performed. Only one buffalo was positive to anti-HEV IgM antibody (1/106, 0.94%), and none were positive for anti-HEV IgG antibody. To our surprise, five serum (5/106, 4.72%) and three milk samples (3/40, 7.50%) from buffaloes were positive to HEV RNA. All strains of HEV isolated from buffaloes belong to genotype 4. Results indicate that buffaloes may be a new reservoir of HEV.

Abstract Ionospheric outflow has been shown to be a dominant ion source of Earth's magnetosphere. However, most studies in the literature are about ionospheric outflow injected into the nightside magnetosphere. We still know little about ionospheric outflow injected into the dayside magnetosphere and its further energization after it enters the magnetosphere. Here, with data from Magnetospheric Multiscale mission, we report direct observations of the modulation of dayside ionospheric outflow ions by ultralow frequency (ULF) waves. The observations indicate that the modulation is mass dependent, which demonstrates the possibility of using ULF waves as a mass spectrometer to identify ion species. Moreover, the measurement suggests that polarization drift may play a role in O+ modulation, which may lead to a true acceleration and even nonadiabatic behavior of O+. This interaction scenario can work throughout the whole magnetosphere and impact upon the plasma environment and dynamics.