A spiral cloud belt matching (SCBeM) technique is proposed for automatically locating the tropical cyclone (TC) center position on the basis of multi-band geo-satellite images. The technique comprises four steps: fusion of multi-band geo-satellite images, extraction of TC cloud systems, construction of a spiral cloud belt template (CBT), and template matching to locate the TC center. In testing of the proposed SCBeM technique on 97 TCs over the western North Pacific during 2012–2015, the median error (ME) was 50 km. An independent test of another 29 TCs in 2016 resulted in a ME of 54 km. The SCBeM performs better for TCs with intensity above “typhoon” level than it does for weaker systems, and is not suitable for use on high-latitude or landfall TCs if their cloud band formations have been destroyed by westerlies or by terrain. The proposed SCBeM technique provides an additional solution for automatically and objectively locating the TC center and has the potential to be applied conveniently in an operational setting. Intercomparisons between the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) and SCBeM methods using events from 2014 to 2016 reveal that ARCHER has better location accuracy. However, when IR imagery alone is used, the ME of SCBeM is 54 km, and in the case of low latitudes and low vertical wind shear the ME is 45–47 km, which approaches that of ARCHER (49 km). Thus, the SCBeM method is simple, has good time resolution, performs well and is a better choice for those TC operational agencies in the case that the microwave images, ASCAT, or other observations are unavailable.
The evolution of Typhoon Mujigae (2015) during the landfall period is determined using potential vorticity (PV) based on a high-resolution numerical simulation. Diabatic heating from deep moist convections in the eyewall produces a hollow PV tower extending from the lower troposphere to the middle levels. Since the potential temperature and wind fields could be highly asymmetric during landfall, the fields are divided into symmetric and asymmetric components. Thus, PV is split into three parts: symmetric PV, first-order asymmetric PV, and quadratic-order asymmetric PV. By calculating the azimuth mean, the first-order term disappears. The symmetric PV is at least one order of magnitude larger than the azimuthal mean quadratic-order term, nearly accounting for the mean cyclone. Furthermore, the symmetric PV tendency equation is derived in cylindrical coordinates. The budget terms include the symmetric heating term, flux divergence of symmetric PV advection due to symmetric flow, flux divergence of partial first-order PV advection due to asymmetric flow, and the conversion term between the symmetric PV and quadratic-order asymmetric term. The diagnostic results indicate that the symmetric heating term is responsible for the hollow PV tower generation and maintenance. The symmetric flux divergence largely offsets the symmetric heating contribution, resulting in a horizontal narrow ring and vertical extension structure. The conversion term contribution is comparable to the mean term contributions, while the contribution of the partial first-order PV asymmetric flux divergence is apparently smaller. The conversion term implicitly contains the combined effects of processes that result in asymmetric structures. This term tends to counteract the contribution of symmetric terms before landfall and favor horizontal PV mixing after landfall.
Xinjiang in China is one of the areas worst affected by coal fires. Coal fires cannot only waste a large amount of natural resources and cause serious economic losses, but they also cause huge damage to the atmosphere, the soil, the surrounding geology, and the environment. Therefore, there is an urgent need to effectively explore remote sensing based detection of coal fires for timely understanding of their latest development trend. In this study, in order to investigate the distribution of coal fires in an accurate and reliable manner, we exploited both Landsat-8 optical data and Sentinel-1A synthetic aperture radar (SAR) images, using the generalized single-channel algorithm and the InSAR time-series analysis approach, respectively, for coal fire detection in the southern part of the Fukang region of Xinjiang, China. The generalized single-channel algorithm was used for land surface temperature information extraction. Meanwhile, the time-series InSAR analysis technology was employed for estimating the surface micro deformation information, which was then used for building a band-pass filter. The suspected coal fire locations could then be established by a band-pass filtering operation on the obtained surface temperature map. Finally, the locations of the suspected coal fires were validated by the use of field survey data. The results indicate that the integration of thermal infrared remote sensing and radar interferometry technologies is an efficient investigation approach for coal fire detection in a large-scale region, which would provide the necessary spatial information support for the survey and control of coal fires.
As a basic property of cloud, accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons. Millimeter-wave cloud radar is an important means of identifying cloud type. Here, we develop a fuzzy logic algorithm that depends on radar range-height-indicator (RHI) data and takes into account the fundamental physical features of different cloud types. The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar. The input parameters of the algorithm include average reflectivity factor intensity, ellipse long axis orientation, cloud base height, cloud thickness, presence/absence of precipitation, ratio of horizontal extent to vertical extent, maximum echo intensity, and standard variance of intensities. The identified cloud types are stratus (St), stratocumulus (Sc), cumulus (Cu), cumulonimbus (Cb), nimbostratus (Ns), altostratus (As), altocumulus (Ac) and high cloud. The cloud types identified using the algorithm are in good agreement with those identified by a human observer. As a case study, the algorithm was applied to typhoon Khanun (1720), which made landfall in south-eastern China in October 2017. Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.
With a Multi-Regional Input-Output model, this study quantifies global final energy demands’ grey water footprint (GWF) based on the latest available data. In 2009, 9.10 km3 of freshwater was required to dilute the pollutants generated along the life-cycle supply chain of global energy final demands to concentrations permitted by relevant environmental regulations. On a national level, final energy demands in China, USA, India, Japan, and Brazil required the largest GWF of 1.45, 1.19, 0.79, 0.51, and 0.45 km3 respectively, while European countries have the highest energy demands GWF per capita. From the producer perspective, the largest GWF was generated in BRIC countries, i.e., Russia (1.54 km3), China (1.35 km3), India (0.92 km3) and Brazil (0.56 km3) to support global final energy demands. Because of global trading activities, a country or region’s final energy demands also give rise to water pollutants beyond its territorial boundaries. Cyprus, Greece, Luxembourg, and Malta almost entirely rely on foreign water resources to dilute water pollutants generated to meet their final energy demands. Energy demands in BRIC countries have the least dependency on external water resources. On a global average, 56.9% of GWF for energy demands was generated beyond national boundaries. Energy demands in the global north are inducing water pollutions in the global south.
Analyzing the spatial patterns of net primary productivity (NPP) and its driving forces in transnational areas provides a solid basis for understanding regional ecological processes and ecosystem services. However, the spatial patterns of NPP and its driving forces have been poorly understood on multiple scales in transnational areas. In this study, the spatial patterns of NPP in the transnational area of the Tumen River (TATR) in 2016 were simulated using the Carnegie Ames Stanford Approach (CASA) model, and its driving forces were analyzed using a stepwise multiple linear regression model. We found that the total amount of NPP in the TATR in 2016 was approximately 14.53 TgC. The amount of NPP on the Chinese side (6.23 TgC) was larger than those on the other two sides, accounting for 42.88% of the total volume of the entire region. Among different land-use and land-cover (LULC) types, the amount of NPP of the broadleaf forest was the largest (11.22 TgC), while the amount of NPP of the bare land was the smallest. The NPP per unit area was about 603.21 gC/(m2·yr) across the entire region, while the NPP per unit area on the Chinese side was the largest, followed by the Russian side and the DPRK’s side. The spatial patterns of NPP were influenced by climate, topography, soil texture, and human activities. In addition, the driving forces of the spatial patterns of NPP in the TATR had an obvious scaling effect, which was mainly caused by the spatial heterogeneity of climate, topography, soil texture, and human activities. We suggest that effective land management policies with cooperation among China, the DPRK, and Russia are needed to maintain NPP and improve environmental sustainability in the TATR.
A WRF (Weather Research and Forecasting Model) / CALMET (California Meteorological Model) coupled system is used to investigate the impact of physical representations in CALMET on simulations of the near-surface wind field of Super Typhoon Meranti (2016). The coupled system is configured with a horizontal grid spacing of 3 km in WRF and 500 m in CALMET, respectively. The model performance of the coupled WRF/CALMET system is evaluated by comparing the results of simulations with observational data from 981 automatic surface stations in Fujian Province. The root mean square error (RMSE) of the wind speed at 10 m in all CALMET simulations is significantly less than the WRF simulation by 20%–30%, suggesting that the coupled WRF/CALMET system is capable of representing more realistic simulated wind speed than the mesoscale model only. The impacts of three physical representations including blocking effects, kinematic effects of terrain and slope flows in CALMET are examined in a specified local region called Shishe Mountain. The results show that before the typhoon landfall in Xiamen, a net downslope flow that is tangent to the terrain is generated in the west of Shishe Mountain due to blocking effects with magnitude exceeding 10m/s. However, the blocking effects seem to take no effect in the strong wind area after typhoon landfall. Whether being affected by the typhoon strong wind or not, the slope flows move downslope at night and upslope in the daytime due to the diurnal variability of the local heat flux with magnitude smaller than 3m/s. The kinematic effects of terrain, which are speculated to play a significant role in the typhoon strong wind area, can only be applied to atmospheric flows in stable conditions when the wind field is quasi-nondivergent.
Global change affected by multiple factors, the consequences of which continue to be far-reaching, has the characteristics of large spatial scale and long-time scale. The demand for Earth observation technology has been increasing for large-scale simultaneous observations and stable global observation over the long-term. A Moon-based observation platform, which uses sensors on the nearside lunar surface, is considered a reasonable solution. However, owing to a lack of appropriate processing methods for optical sensor data, global change study using this platform is not sufficient. This paper proposes two optical sensor imaging processing methods for the Moon-based platform: area imaging processing method (AIPM) and global imaging processing method (GIPM), primarily considering global change characteristics, optical sensor performance, and motion law of the Moon-based platform. First, the study proposes a simulation theory which includes the construction of a Moon–Sun elevation angle model and a global image mosaicking method. Then, coverage images of both image processing methods are simulated, and their features are quantitatively analyzed. Finally, potential applications are discussed. Results show that AIPM, whose coverage is mainly affected by lunar revolution, is approximately between 0% and 50% with a period of 29.5 days, which can help the study of large-scale instant change phenomena. GIPM, whose coverage is affected by Earth revolution, is conducive to the study of long term global-scale phenomena because of its sustained stable observation from 67°N–67°S on the Earth. AIPM and GIPM have great advantages in Earth observation of tripolar regions. The existence of top of the atmosphere (TOA) albedo balance line is verified from the GIPM perspective. These two imaging methods play a significant role in linking observations acquired from the Moon-based platform to Earth large-scale geoscience phenomena, and thus lay a foundation for using this platform to capture global environmental changes and new discoveries.
The Finite Volume Community Ocean Model (FVCOM) was adapted to the Northern South China Sea (NSCS) to investigate the seasonality of coastal circulation, as well as along-shelf and cross-shelf transport. In fall and winter, southwestward current dominates the NSCS shelf, while the current’s direction shifts to northeast in summer. The circulation pattern in spring is more complicated: both southwestward and northeastward currents are detected on the NSCS shelf. The mean shelf circulation pattern in winter does not show the permanent counter-wind South China Sea Warm Current (SCSWC) along the 100–200 m isobaths. Meanwhile, the model results indicate a northeastward current flowing along 50–100 m isobaths in spring. Southwestward along-shelf transport varies from 0.30–1.93 Sv in fall and winter, and it redirects to northeast in summer ranging from 0.44–1.09 Sv. Onshore transport is mainly through the shelf break segment southeast of the Pearl River Estuary.