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Integration of perennial grass species into the current food production systems, especially in the agropastoral regions worldwide, may produce multiple benefits including, among others, a more stable productivity and a smaller eco-environmental footprint. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. In all 5 runway corridors this is achieved with a proportionately smaller decrease-even with no changes in 2 cases-in the hit rate, highlighting the efficiency of the GRR function. The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion.
#Black lagoon season 1 subtitles english zip series#
The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes.
#Black lagoon season 1 subtitles english zip pdf#
To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.The species of bacteria detected depended on land use, (A) well, (B) wadi, (C) dry lake, and (D) desert steppe. The species of bacteria saltated with dust were identified at multiple points. The measurement results at each location are shown in the particle size (3 μm, 5 μm). Dust particles are more likely to soar near the well due to overgrazing, and it can be seen that dry lake beds, wadi, and desert steppe are easy to suspend in the wind because the size of the sand particles is considerably small.
The figure shows the pattern of dust particle saltation at study sites according to the wind speed. The particle data are analyzed by kernel density using dust particle saltation experiments. The Gobi Desert of inland Asia is one of the major sources of Asian dust storm.