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Silicone foam (SF) is a porous silicone rubber with a lower density, higher elasticity, and good thermal stability. In this work, we selected aluminum spheres and carbon fiber (CF) as thermally conductive fillers to prepare hybrid SF. After optimization, we found that Al and CF hybrid SF (Al-CF-SF) has a higher thermal conductivity (1.37 W·m–1·K–1) than the single-filler filled SF (CF-SF, 1.2 W·m–1·K–1 or Al-SF, 0.52 W·m–1·K–1) under the same filling amount of 60 wt%. The finite element simulation was used further to explore the thermal conductive mechanism of the hybrid SF. Meanwhile, the compressive and tensile modulus of the material (CF-SF) was increased to 10.8 and 3.3 MPa compared with pure SF, respectively, and the mechanical properties were improved. In addition, infrared thermography further demonstrated that Al-CF-SF has a faster heat transfer rate under relaxation and applied pressure.

NASA's Operation IceBridge mission flew over the Ross Sea, Antarctica (20 and 27 November 2013) and collected data with Airborne Topographic Mapper (ATM) and Digital Mapping System (DMS). Using the DMS and reflectivity of ATM L1B, leads are detected to define local sea level height. The total freeboard is then obtained and converted to ice thickness. The estimated mean sea-ice thickness values are found to be in the 0.48–0.99 m range. Along the N-S track, sea ice was thinner southward rather than northward of the fluxgate, resulting in two peaks of modal thickness: 0.35 m (south) and 0.7 m (north). This supports that new ice produced in coastal polynyas is transported northward by katabatic winds off the ice-shelf. The lowest (2%) elevation method used for freeboard retrieval for ICESat is also tested for ATM data. It is found that the lowest elevation method tends to overestimate freeboard, but mean values are less affected than mode values. Using mean thickness values of ICESat and ATM along the ‘fluxgate’, separating the shelf from the deep ocean, the exported ice volume at this ‘fluxgate’ is found to be higher during the ICESat years (2003–2008) than during the IceBridge year (2013).

Kubernetes (K8s) defines standardized APIs for container-based cluster orchestration such that it becomes possible for application managers to deploy their applications in a portable and interopable manner. However, a practical problem arises when the same application must be replicated in a distributed fashion across different edge, fog and cloud sites; namely, there will not exist a single K8s vendor that is able to provision and manage K8s clusters across all these sites. Hence, the problem of feature incompatibility between different K8s vendors arises. A large number of documented features in the open-source distribution of K8s are optional features that are turned off by default but can be activated by setting specific combinations of parameters and plug-in components in configuration manifests for the K8s control plane and worker node agents. However, none of these configuration manifests are standardized, giving K8s vendors the freedom to hide the manifests behind a single, more restricted, and proprietary customization interface. Therefore, some optional K8s features cannot be activated consistently across K8s vendors and applications that require these features cannot be run on those vendors. In this paper, we present a unified, vendor-agnostic feature management approach for consistently configuring optional K8s features across a federation of clusters hosted by different Kubernetes vendors. We describe vendor-agnostic reconfiguration tactics that are already applied in industry and that cover a wide range of optional K8s features. Based on these tactics, we design and implement an autonomic controller for declarative feature compatibility management across a cluster federation. We found that the features configured through our vendor-agnostic approach have no impact on application performance when compared with a cluster where the features are configured using the configuration manifests of the open-source K8s distribution. Moreover, the maximum time to complete reconfiguration of a single feature is within 100 seconds, which is 6 times faster than using proprietary customization interfaces of mainstream K8s vendors such as Google Kubernetes Engine. However, there is a non-negligible disruption to running applications when performing the reconfiguration to an existing cluster; this disruption impact does not appear using the proprietary customization methods of the K8s vendors due to the use of rolling upgrade of cluster nodes. Therefore, our approach is best applied in the following three use cases: (i) when starting up new K8s clusters, (ii) when optional K8s features of existing clusters must be activated as quickly as possibly and temporary disruption to running applications can be tolerated or (iii) when proprietary customization interfaces do not allow to activate the desired optional feature.

Objective: Hepatitis B virus (HBV) has a worldwide distribution and remains a leading public health problem in China. Method: Automated chemiluminescence microparticle immunoassay was used to test all five markers of HBV serology in serum samples among 696,048 patients, pregnant women, and normal subjects in Beijing from 2008 to 2018. Results: The overall prevalence of subjects categorized as previous/ occult HBV infection, inactive HBsAg carrier, active HBV infection, HBsAg, HBV susceptible, and immune via vaccination was 29.4%, 4.8%, 1.4%, 6.4%, 33.9% and 30.3%, respectively; men had a significantly higher prevalence of HBV infection than women. The prevalence of HBsAg was around 0.5% in subjects ≤ 10 years of age, increased dramatically to 3.7% in subjects between 11 and 20 years of age, reached the highest level of 7.9% in subjects between 41 and 50 years of age, and finally decreased to 2.8% in subjects ≥ 81 years of age. During the 10 years from 2008 to 2018, the prevalence of HBsAg was stabilized at about 6.0%, and indicators of HBV susceptibility, previous/ occult HBV infection, and immunity via vaccination were not further improved, despite the constant implementation of HBV vaccination since 1992. All four age groups (21 − 30y, 31 − 40y, 41 − 50y and 51 − 60y) of the normal adult population were found to have a significantly lower prevalence of HBsAg and HBV susceptibility but significantly higher prevalence of immunity via vaccination compared with corresponding age groups of the sub-total population. Conclusions: Although high coverage has been established among infants and young children, their vaccination alone could not reduce HBV infection in the adult Chinese population quickly. Adult populations with more vaccinated individuals are found to have fewer individuals with HBsAg. Vaccination in adults or at least in high-risk adults is an urgent need to decrease horizontal HBV transmission in China. Keywords: Hepatitis B virus (HBV), Hepatitis B surface antigen (HBsAg), Anti-HBs, Catch-up vaccination, Chemiluminescence microparticle immunoassay (CMIA)

The ocean and atmosphere exert stresses on sea ice that create elongated cracks and leads which dominate the vertical exchange of energy, especially in cold seasons, despite covering only a small fraction of the surface. Motivated by the need of a spatiotemporal analysis of sea ice lead distribution, a practical workflow was developed to classify the high spatial resolution aerial images DMS (Digital Mapping System) along the Laxon Line in the NASA IceBridge Mission. Four sea ice types (thick ice, thin ice, open water, and shadow) were identified, and relevant sea ice lead parameters were derived for the period of 2012–2018. The spatiotemporal variations of lead fraction along the Laxon Line were verified by ATM (Airborne Topographic Mapper) surface height data and correlated with coarse spatial resolution sea ice motion, air temperature, and wind data through multiple regression models. We found that the freeboard data derived from sea ice leads were compatible with other products. The temperature and ice motion vorticity were the leading factors of the formation of sea ice leads, followed by wind vorticity and kinetic moments of ice motion.

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

Continuous monitoring of stream water quality is needed as it has significant impacts on human and ecological health and well-being. Estimating water quality between sampling dates requires model simulation based on the available geospatial and water quality data for a given watershed. Models such as the Soil and Water Assessment Tool (SWAT) can be used to estimate the missing water quality data. In this study, SWAT was used to estimate water quality at a monitoring station near the outlet of the San Antonio River. Precipitation data from both rain gauges and weather radar were used to force the SWAT simulations. Virtual rain gauges which were based on weather radar data were created in the approximate centres of the 163 sub-watersheds of the San Antonio River Basin for SWAT simulations. This method was first tested in a smaller watershed in the middle of the Guadalupe River Basin resulting in increased model efficiency in simulating surface run-off. The method was then applied to the San Antonio River watershed and yielded good simulations for surface run-off (R2 = 0.7), nitrate (R2 = 0.6) and phosphate (R2 = 0.5) at the watershed outlet (Goliad, TX – USGS (United States Geological Survey) gauge) as compared to observed data. The study showed that the proper use of weather radar precipitation in SWAT model simulations improves the estimation of missing water quality data.

The measurement of sea ice elevation above sea level or the “freeboard” depends upon an accurate retrieval of the local sea level. The local sea level has been previously retrieved from altimetry data alone by the lowest elevation method, where the percentage of the lowest elevations over a particular segment length scale was used. Here, we provide an evaluation of the scale dependence on these local sea level retrievals using data from NASA Operation IceBridge (OIB) which took place in the Ross Sea in 2013. This is a unique dataset of laser altimeter measurements over five tracks from the Airborne Topographic Mapper (ATM), with coincidently high-spatial resolution images from the Digital Mapping System (DMS), that allows for an independent sea level validation. The local sea level is first calculated by using the mean elevation of ATM L1B data over leads identified by using the corresponding DMS imagery. The resulting local sea level reference is then used as ground truth to validate the local sea levels retrieved from ATM L2 by using nine different percentages of the lowest elevation (0.1%, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 3.5%, and 4%) at seven different segment length scales (1, 5, 10, 15, 20, 25, and 50 km) for each of the five ATM tracks. The closeness to the 1:1 line, R2, and root mean square error (RMSE) is used to quantify the accuracy of the retrievals. It is found that all linear least square fits are statistically significant (p < 0.05) using an F test at every scale for all tested data. In general, the sea level retrievals are farther away from the 1:1 line when the segment length scale increases from 1 or 5 to 50 km. We find that the retrieval accuracy is affected more by the segment length scale than the percentage scale. Based on our results, most retrievals underestimate the local sea level; the longer the segment length (from 1 to 50 km) used, especially at small percentage scales, the larger the error tends to be. The best local sea level based on a higher R2 and smaller RMSE for all the tracks combined is retrieved by using 0.1–2% of the lowest elevations at the 1–5 km segment lengths.

Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

As part of the Polynyas and Ice Production in the Ross Sea (PIPERS) project, the IcePod system onboard the LC-130 aircraft based at McMurdo Station was flown over the Ross Sea, Antarctica in November 2016 and 2017, with the purpose of repeating the same lines that NASA’s Operation IceBridge (OIB) aircraft flew over in 2013. We resampled the lidar data into 70 m pixels (similar to the footprint size of OIB L2 and ICESat data) and took the mean of the lowest 2% elevation values of 25 km (50 km) length along a flight track as the local sea level of the central 25 km (50 km). Most of the IcePod data were over the same flight lines taken by OIB in 2013, so the total freeboard changes from 2013 to 2016 and 2017 were examined. Combining with the ICESat (2003–2008), we obtained a better picture of total freeboard and its interannual variability in the Ross Sea. The pattern of the sea ice distribution supports that new ice produced in coastal polynyas was transported northward by katabatic winds off the ice shelf. Compared to ICESat years, sea ice near the coast was thicker, while sea ice offshore was thinner in the more recent OIB/IcePod years. The results also showed that, in general, sea ice was thicker in 2017 compared to 2013 or 2016—0.02–0.55 m thicker in total freeboard.

High sea ice production (SIP) generates high-salinity water, thus, influencing the global thermohaline circulation. Estimation from passive microwave data and heat flux models have indicated that the Ross Ice Shelf polynya (RISP) may be the highest SIP region in the Southern Oceans. However, the coarse spatial resolution of passive microwave data limited the accuracy of these estimates. The Sentinel-1 Synthetic Aperture Radar dataset with high spatial and temporal resolution provides an unprecedented opportunity to more accurately distinguish both polynya area/extent and occurrence. In this study, the SIPs of RISP and McMurdo Sound polynya (MSP) from 1 March–30 November 2017 and 2018 are calculated based on Sentinel-1 SAR data (for area/extent) and AMSR2 data (for ice thickness). The results show that the wind-driven polynyas in these two years occurred from the middle of March to the middle of November, and the occurrence frequency in 2017 was 90, less than 114 in 2018. However, the annual mean cumulative SIP area and volume in 2017 were similar to (or slightly larger than) those in 2018. The average annual cumulative polynya area and ice volume of these two years were 1,040,213 km2 and 184 km3 for the RSIP, and 90,505 km2 and 16 km3 for the MSP, respectively. This annual cumulative SIP (volume) is only 1/3–2/3 of those obtained using the previous methods, implying that ice production in the Ross Sea might have been significantly overestimated in the past and deserves further investigations.

BackgroundPrimary cytomegalovirus (CMV) infection is prevalent worldwide and usually results in latency in immunocompetent populations. Reactivation of latent CMV can cause life-threatening complications in immunocompromised hosts.MethodsWe used the CMV Brite assay to test CMV antigenemia (pp65) in whole blood samples from 22,192 patients with or without autoimmune diseases in Beijing during 2008-2018.ResultsThe overall prevalence of CMV antigenemia was 19.5% (9.7%, males; 26.0%, females). The prevalence of CMV antigenemia was 35.1%, 58.6% and 11.4% in whole patients with autoimmune diseases, in patients with systemic lupus erythematosus (SLE) and in patients with non-SLE autoimmune diseases, respectively. All patients with non-autoimmune diseases, patients with HIV/AIDS or transplantation were found to have 5.0%, 27% or 14.8%, respectively. Patients≤20 years with SLE had a significantly higher prevalence of CMV antigenemia than did all SLE patients, on average. Patients>51 years with non-SLE autoimmune diseases had a significantly higher prevalence than did all patients with non-SLE autoimmune diseases, on average. The prevalence of CMV antigenemia in patients admitted to intensive-care units (ICUs) were 9.2%, which was significantly higher than that among all patients with non-autoimmune diseases. Patients with SLE had 23.8% of negative conversion of CMV antigenemia, significantly lower than the percentage of patients with non-SLE autoimmune (64.3%) and non-autoimmune (61.0%) diseases. The mean number of days to negative conversion of CMV antigenemia in patients with SLE was 35.3±35.8 days, which was significantly longer than that in patients with non-SLE autoimmune diseases (15.4±11.9 days) and non-autoimmune diseases (13.6±7.7 days).ConclusionsCMV antigenemia is found more likely in women than in men, more prevalently in patients with SLE than those with HIV/AIDS or transplant recipients, more frequently in patients admitted to ICUs. Patients with SLE had prolonged CMV antigenemia. The role of CMV appears important in SLE.

Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.

In arid regions, C3 vegetation is assumed to be more sensitive to precipitation and CO2 fertilization than C4 vegetation. In this study, normalized difference vegetation index (NDVI) is used to examine vegetation growth in the arid Lower Heihe River Basin, northwestern China, for the past three decades. The results indicate that maximum NDVI (MNDVI) of the area increases over the years and is significantly correlated with precipitation (R = 0.47 and p < 0.01), not temperature (R = −0.04). The upper limit of C3 vegetation cover of the area shows a yearly rising trend of 0.6% or an overall increase of 9% over the period of 25 years, primarily due to the CO2 fertilization effect (CO2 rising 14%) over the same period. C3 dominant areas can be potentially distinguished by both MNDVI asynchronous seasonality and a significant relation between MNDVI and cumulative precipitation. This study provides a potential tool of identifying C3 vegetation from C4 vegetation and confirms the CO2 fertilization effect in this arid region.

Snow is an important water resource and greatly influences water availability in the downstream areas. In this study, snow cover variations of the Upper Heihe River Basin (UHRB) during hydrological years (HY) 2003–2013 (September through August) is examined using the flexible multiday-combined MODIS snow cover products. Spatial distribution and pattern of snow cover from year to year for the basin is found to be relatively stable, with maximum snow cover area (SCA) and snow cover days occurring in HY2004, HY2008 and HY2012. A method, based on correlation coefficients between SCA and climate factors (mainly air temperature and precipitation), is presented to identify the threshold altitude that determines contributions of climate factors to SCA. A threshold altitude of 3650 ± 150 m is found for the UHRB, where below this altitude, both air temperature (Tair) and precipitation are negative factors on SCA, except in the winter season when both are positive factors. Above the threshold altitude, precipitation acts as a positive factor except in summer, while Tair is a negative factor except in autumn. Overall, Tair is the primary controlling factor on SCA below the threshold altitude, while precipitation is the primary controlling factor on SCA above the threshold altitude.

BACKGROUND:Chlamydia pneumoniae (C. pneumoniae) is an obligate intracellular bacterium and a human pathogen that causes respiratory infectious diseases. More than 50% of the adult population worldwide was once infected with C. pneumoniae, but investigations into this topic are insufficient in mainland China. METHODS:Anti-C. pneumoniae IgG and IgM antibodies were detected using micro-immunofluorescence test in serum samples of patients visiting Peking Union Medical College Hospital between 2008 and 2017 for routine medical purposes, and the aim of this retrospective study was to analyze the test results. RESULTS:Among 12,050 serum specimens tested for anti-C. pneumoniae IgG and IgM antibodies, the overall prevalence of anti-C. pneumoniae IgG antibodies was 86.6%, 87.2% for men and 86.0% for women. Adult men (>20 years) were found to have a significantly higher prevalence of anti-C. pneumoniae IgG than women (χ2 = 30.32, P = 0.000). 3 to 5 years old patients were observed to have the lowest prevalence of anti-C. pneumoniae IgG, 42.8%, then increased with age, reaching the highest level of 98.6% in patients over 70 years of age. In the 10,434 specimens with C. pneumoniae IgG antibodies, the total geometric mean titer (GMT) for C. pneumoniae IgG was 45.71. Although GMTs were found to be significantly higher among all men than among all women (t = 5.916, P = 0.000), sex difference actually began in patients over 40 years of age and increased in the elderly. In the total 12,050 specimens, 1.2% had anti-C. pneumoniae IgM, 3.3% had anti-C. pneumoniae IgG with titers equal to or greater than 1:512; 0.39% had ≥4-fold increasing titers of antibodies in acute and convalescent phase paired samples, and 4.4% were finally confirmed to have acute antibodies against C. pneumoniae. 6 to 10 years old patients were found to have the highest rate of both IgM antibodies (3.9%) and acute antibodies (6.2%) against C. pneumoniae. Acute antibodies against C. pneumoniae were found to be more frequent in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD, 14.0%, χ2 = 20.43, P = 0.000), patients with pneumonia (7.8%, χ2 = 51.87, P = 0.000) and patients with acute respiratory tract infection (12.3%, χ2 = 60.91, P = 0.000) than among all patients (4.4%). Both anti-C. pneumoniae IgG and IgM antibodies should be tested for acute antibodies against C. pneumoniae as testing for either alone will underestimate by a maximum of two-thirds the incidence of acute antibodies against C. pneumoniae. CONCLUSIONS:More than 86% of Chinese patients on an average were once infected with C. pneumoniae. Adult men had both a higher prevalence and higher levels of antibodies than women. 6 to10 year old patients were found to have the most frequent acute infection of C. pneumoniae. C. pneumoniae is associated with AECOPD, pneumonia and acute respiratory tract infection. Anti-C. pneumoniae IgG and IgM should be tested simultaneously to avoid underestimation of acute antibodies against C. pneumoniae.

Using new, daily cloud-free snow-cover products, this study examines snow cover dynamics and their response to climate change. The results demonstrate that the daily cloud-free snow-cover products not only posses the advantages of the AMSR-E (unaffected by weather conditions) and MODIS (relatively higher resolution) products, but are also characterized by high snow and overall classification accuracies (~85% and ~98%, respectively), substantially greater than those of the existing daily snow-cover products for all sky conditions and very similar to, or even slightly greater than, those of the daily MODIS products for clear-sky conditions. Using the snow-cover products, we analyzed the snow cover dynamics over the Tibetan Plateau and determined that the maximum number of snow-covered days (SCD) in a year followed a decreasing tendency from 2003 to 2010, with a decrease in snow-covered area (SCA) equivalent to 55.3% of the total Tibetan Plateau area. There is also a slightly increasing tendency in the maximum snow cover area (SCA), and a slightly decreasing tendency in the persistent snow cover area (i.e., pixels of SCD > 350 days) was observed for the 8-year period, which was characterized by increases in temperature (0.09 °C/year) and in precipitation (0.26 mm/year). This suggests that, on the Tibetan Plateau, changes in temperature and precipitation exert a considerable influence on the regional SCD and SCA, as well as the distribution of persistent snow cover.

Arctic sea-ice extent (in summer) has been shrinking since the 1970s. However, we have little knowledge of the detailed spatial variability of this shrinking. In this study, we examine the (latitudinal) ice extent along each degree of longitude, using the monthly Arctic ice index data sets (1979–2012) from the National Snow and Ice Data Center. Statistical analysis suggests that: (1) for summer months (July–October), there was a 34-year declining trend in sea-ice extent at most regions, except for the Canadian Arctic Archipelago, Greenland and Svalbard, with retreat rates of 0.0562–0.0898 latitude degree/year (or 6.26–10.00 km/year, at a significance level of 0.05); (2) for sea ice not geographically muted by the continental coastline in winter months (January–April), there was a declining trend of 0.0216–0.0559 latitude degree/year (2.40–6.22 km/year, at a significance level of 0.05). Regionally, the most evident sea-ice decline occurred in the Chukchi Sea from August to October, Baffin Bay and Greenland Sea from January to May, Barents Sea in most months, Kara Sea from July to August and Laptev Sea and eastern Siberian Sea in August and September. Trend analysis also indicates that: (1) the decline in summer ice extent became significant (at a 0.05 significance level) since 1999 and (2) winter ice extent showed a clear changing point (decline) around 2000, becoming statistically significant around 2005. The Pacific–Siberian sector of the Arctic accounted for most of the summer sea-ice decline, while the winter recovery of sea ice in the Atlantic sector tended to decrease.

The applicability of hyperspectral remote sensing models for forage nitrogen (N) retrieval during different growth periods is limited. This study aims to develop a multivariate model feasible for estimating the forage N for the growth periods (June to November) in an alpine grassland ecosystem. The random forest (RF) algorithm is employed to determine the optimum combinations of 38 spectral variables capable of capturing dynamic variations in forage N. The results show that (1) throughout the growth period, the red-edge first shifts toward longer wavelengths and then shifts toward shorter wavelengths, the amplitude (AMP) and absorption depth (AD) gradually decrease, and the absorption position (AP) changes slightly; (2) the importance of spectral variables for forage N estimation differs during the different growth periods; (3) the multivariate model achieves better results for the first four periods (June to October) than for the last period (when the grass is completely senesced) (V-R2: 0.58−0.68 versus 0.23); and (4) for the whole growth period (June to November), the prediction accuracy of the general N estimation model validated by the unknown growth period is lower than that validated by the unknown location (V-R2 is 0.28 and 0.55 for the validation strategies of Leave-Time-Out and Leave-Location-Out, respectively). This study demonstrates that the changes in the spectral features of the red wavelength (red-edge position, AMP and AD) are well coupled with the forage N content. Moreover, the development of a multivariate RF model for estimating alpine grasslands N content during different growth periods is promising for the improvement of both the stability and accuracy of the model.

Global horizontal irradiance (i.e., shortwave downward solar radiation received by a horizontal surface on the ground) is an important geophysical variable for climate and energy research. Since solar radiation is attenuated by clouds, its variability is intimately associated with the variability of cloud properties. The spatial distribution of clouds and the daily, monthly, seasonal, and annual solar energy potential (i.e., the solar energy available to be converted into electricity) derived from satellite estimates of global horizontal irradiance are explored over the state of Texas, USA and surrounding regions, including northern Mexico and the western Gulf of Mexico. The maximum (minimum) monthly solar energy potential in the study area is 151–247 kWhm−2 (43–145 kWhm−2) in July (December). The maximum (minimum) seasonal solar energy potential is 457–706 kWhm−2 (167–481 kWhm−2) in summer (winter). The available annual solar energy in 2015 was 1295–2324 kWhm−2. The solar energy potential is significantly higher over the Gulf of Mexico than over land despite the ocean waters having typically more cloudy skies. Cirrus is the dominant cloud type over the Gulf which attenuates less solar irradiance compared to other cloud types. As expected from our previous work, there is good agreement between satellite and ground estimates of solar energy potential in San Antonio, Texas, and we assume this agreement applies to the surrounding larger region discussed in this paper. The study underscores the relevance of geostationary satellites for cloud/solar energy mapping and provides useful estimates on solar energy in Texas and surrounding regions that could potentially be harnessed and incorporated into the electrical grid.

Global water cycle dynamics involve the exchange of water and energy matter among the atmosphere, hydrosphere, geosphere, cryosphere, and biosphere [...]

Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number > Digital elevation > Slope > River density > Flash Flood preventions > Topographic Wetness Index > annual maximum 24 h precipitation > annual maximum 3 h precipitation.

Snow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas.

Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites.

Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine meadow grasslands. In this study, field measurements of grassland cover in Gannan Prefecture, from 2014 to 2016, were acquired using unmanned aerial vehicle (UAV) technology. Single-factor parametric and multi-factor parametric/non-parametric cover inversion models were then constructed based on 14 factors related to grassland cover, and the dynamic variation of the annual maximum cover was analyzed. The results show that (1) nine out of 14 factors (longitude, latitude, elevation, the concentrations of clay and sand in the surface and bottom soils, temperature, precipitation, enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) exert a significant effect on grassland cover in the study area. The logarithmic model based on EVI presents the best performance, with an R2 and RMSE of 0.52 and 16.96%, respectively. Single-factor grassland cover inversion models account for only 1–49% of the variation in cover during the growth season. (2) The optimum grassland cover inversion model is the artificial neural network (BP-ANN), with an R2 and RMSE of 0.72 and 13.38%, and SDs of 0.062% and 1.615%, respectively. Both the accuracy and the stability of the BP-ANN model are higher than those of the single-factor parametric models and multi-factor parametric/non-parametric models. (3) The annual maximum cover in Gannan Prefecture presents an increasing trend over 60.60% of the entire study area, while 36.54% is presently stable and 2.86% exhibits a decreasing trend.

Estimates of solar irradiance at the earth’s surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its accuracy against ground observations is needed. In this study, satellite estimates of surface solar irradiance from Geostationary Operational Environmental Satellite (GOES) are compared with ground observations at two sites, namely the main campus of the University of Texas at San Antonio (UTSA) and the Alamo Solar Farm of San Antonio (ASF). The comparisons are done mostly on an hourly timescale, under different cloud conditions classified by cloud types and cloud layers, and at different solar zenith angle intervals. It is found that satellite estimates and ground observations of surface solar irradiance are significantly correlated (p < 0.05) under all sky conditions (r: 0.80 and 0.87 on an hourly timescale and 0.94 and 0.91 on a daily timescale, respectively for the UTSA and ASF sites); on the hourly timescale, the correlations are 0.77 and 0.86 under clear-sky conditions, and 0.74 and 0.84 under cloudy conditions, respectively for the UTSA and ASF sites, and mostly >0.60 under different cloud types and layers for both sites. The correlations under cloudy-sky conditions are mostly stronger than those under clear-sky conditions at different solar zenith angles. The correlation coefficients are mostly the smallest with solar zenith angle in the range of 75–90° under all sky, clear-sky and cloudy-sky conditions. At the ASF site, the overall bias of GOES surface solar irradiance is small (+1.77 Wm−2) under all sky while relatively larger under clear-sky (−22.29 Wm−2) and cloudy-sky (+40.31 Wm−2) conditions. The overall good agreement of the satellite estimates with the ground observations underscores the usefulness of the GOES surface solar irradiance estimates for solar energy studies in the San Antonio area.

Snow albedo feedback is one of the most crucial feedback processes that control equilibrium climate sensitivity, which is a central parameter for better prediction of future climate change. However, persistent large discrepancies and uncertainties are found in snow albedo feedback estimations. Remotely sensed snow cover products, atmospheric reanalysis data and radiative kernel data are used in this study to quantify snow albedo radiative forcing and its feedback on both hemispheric and global scales during 2003–2016. The strongest snow albedo radiative forcing is located north of 30°N, apart from Antarctica. In general, it has large monthly variation and peaks in spring. Snow albedo feedback is estimated to be 0.18 ± 0.08 W∙m−2∙°C−1 and 0.04 ± 0.02 W∙m−2∙°C−1 on hemispheric and global scales, respectively. Compared to previous studies, this paper focuses specifically on quantifying snow albedo feedback and demonstrates three improvements: (1) used high spatial and temporal resolution satellite-based snow cover data to determine the areas of snow albedo radiative forcing and feedback; (2) provided detailed information for model parameterization by using the results from (1), together with accurate description of snow cover change and constrained snow albedo and snow-free albedo data; and (3) effectively reduced the uncertainty of snow albedo feedback and increased its confidence level through the block bootstrap test. Our results of snow albedo feedback agreed well with other partially observation-based studies and indicate that the 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) models might have overestimated the snow albedo feedback, largely due to the overestimation of surface albedo change between snow-covered and snow-free surface in these models.

Based on a widely used satellite precipitation product (TRMM Multi-satellite Precipitation Analysis 3B43), we analyzed the spatiotemporal variability of precipitation over the Pacific Ocean for 1998–2014 at seasonal and interannual timescales, separately, using the conventional empirical orthogonal function (EOF) and investigated the seasonal patterns associated with El Niño–Southern Oscillation (ENSO) cycles using season-reliant empirical orthogonal function (SEOF) analysis. Lagged correlation analysis was also applied to derive the lead/lag correlations of the first two SEOF modes for precipitation with Pacific Decadal Oscillation (PDO) and two types of El Niño, i.e., central Pacific (CP) El Niño and eastern Pacific (EP) El Niño. We found that: (1) The first two seasonal EOF modes for precipitation represent the annual cycle of precipitation variations for the Pacific Ocean and the first interannual EOF mode shows the spatiotemporal variability associated with ENSO; (2) The first SEOF mode for precipitation is simultaneously associated with the development of El Niño and most likely coincides with CP El Niño. The second SEOF mode lagged behind ENSO by one year and is associated with post-El Niño years. PDO modulates precipitation variability significantly only when ENSO occurs by strengthening and prolonging the impacts of ENSO; (3) Seasonally evolving patterns of the first two SEOF modes represent the consecutive precipitation patterns associated with the entire development of EP El Niño and the following recovery year. The most significant variation occurs over the tropical Pacific, especially in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ); (4) Dry conditions in the western basin of the warm pool and wet conditions along the ITCZ and SPCZ bands during the mature phase of El Niño are associated with warm sea surface temperatures in the central tropical Pacific, and a subtropical anticyclone dominating over the northwestern Pacific. These findings may be useful for prediction of seasonal precipitation anomalies over the Pacific Ocean during El Niño years and recovery years.

This study represents advancements in the state-of-the-art of the solar energy industry by leveraging LiDAR-based building characterization for city-wide, distributed solar photovoltaics, solar maps, highlighting the distribution of solar energy across the city of San Antonio. A methodology is implemented to systematically derive the tilt and azimuth angles of each rooftop and to quantify solar direct, diffuse, and global horizontal irradiance for hundreds of buildings in a LiDAR tile scale, by using already established methodologies that are typically only applied to a single location or building rooftop. The methodology enables the formulation of typical meteorological data, measured or forecasted time series of irradiances over distributed assets. A new concept on the subject of distributed solar plant (DSP) design is also introduced, by using the building rooftop tilt and azimuth angles, to strategically optimize the use and adoption of solar incentives according to the grid age and its vulnerabilities to solar variability in the neighborhoods. The method presented here shows that on an hourly basis DSP design could provide a 5% and 9% of net load capacity support per hour in the afternoon and morning times, respectively. Our results show that standard building rooftop tilt angles in the south Texas region has significant impact on the total amount of the energy over the course of a day, though its impact on the shapes of the daily energy profile is relatively insignificant when compared to the azimuth angle. Building surfaces’ azimuth angle is the most important factor to determine the shape of daily energy profile and its peak location within a day. The methodology developed in this study can be employed to study the potential solar energy in other regions and to match the design of distributed solar plants to the capacity needs on specified distribution grids.