Detection of ionospheric scintillation effects using LMD–DFA

The performance and measurement accuracy of global navigation satellite system (GNSS) receivers is greatly affected by ionospheric scintillations. Rapid amplitude and phase variations in the received GPS signal, known as ionospheric scintillation, affects the tracking of signals by GNSS receivers. Hence, there is a need to investigate the monitoring of various activities of the ionosphere and to develop a novel approach for mitigation of ionospheric scintillation effects. A method based on Local Mean Decomposition (LMD)–Detrended Fluctuation Analysis (DFA) has been proposed. The GNSS data recorded at Koneru Lakshmaiah (K L) University, Guntur, India were considered for analysis. The carrier to noise ratio (C/N₀) of GNSS satellite vehicles were decomposed into several product functions (PF) using LMD to extract the intrinsic features in the signal. Scintillation noise was removed by the DFA algorithm by selecting a suitable threshold. It was observed that the performance of the proposed LMD–DFA was better than that of empirical mode decomposition (EMD)–DFA.     

Multi-objective optimal design for flood risk management with resilience objectives

In flood risk management, the divergent concept of resilience of a flood defense system cannot be fully defined quantitatively by one indicator and multiple indicators need to be considered simultaneously. In this paper, a multi-objective optimization (MOO) design framework is developed to determine the optimal protection level of a levee system based on different resilience indicators that depend on the probabilistic features of the flood damage cost arising under the uncertain nature of rainfalls. An evolutionary-based MOO algorithm is used to find a set of non-dominated solutions, known as Pareto optimal solutions for the optimal protection level. The objective functions, specifically resilience indicators of severity, variability and graduality, that account for the uncertainty of rainfall can be evaluated by stochastic sampling of rainfall amount together with the model simulations of incurred flood damage estimation for the levee system. However, these model simulations which usually require detailed flood inundation simulation are computationally demanding. This hinders the wide application of MOO in flood risk management and is circumvented here via a surrogate flood damage modeling technique that is integrated into the MOO algorithm. The proposed optimal design framework is applied to a levee system in a central basin of flood-prone Jakarta, Indonesia. The results suggest that the proposed framework enables the application of MOO with resilience objectives for flood defense system design under uncertainty and solves the decision making problems efficiently by drastically reducing the required computational time.     

多年平均气温数据空间化误差的尺度效应

属性数据空间化是利用矢量数据生成栅格数据产品的有效方法,它有助于不同来源、不同格式之间的数据的综合分析。空间化是一种必然有误差伴随的过程,为探讨空间化误差与数据源密度、空间化模型方法,以及空间化分辨率之间的关系,本文利用7种水平的气象站点密度、5种空间化模型方法和19种栅格分辨率分析多年平均气温数据空间化误差与这3类影响因子之间的关系。分析发现：（1）气象站点密度的降低导致多年平均气温数据的空间化误差增加;（2）在IDW、Kriging、Adjusted IDW、Regression和Anusplin 5种空间化模型方法中,Adjusted IDW、Regression、Anusplin比IDW、Kriging的精度高;（3）随着栅格分辨率的变粗,多年平均气温数据空间化误差增大;（4）在影响空间化精度的3类因子中,空间化模型方法对空间化精度的影响最大,栅格分辨率次之,气象站点密度的影响最小。通过多元回归分析,建立了多年平均气温数据空间化误差与这3类影响因子之间的定量模型,可为空间化技术方案的制定提供参考和依据。     

Geological evaluation of EIGEN-6C4 and GOCE derived gravity models in and around Karakoram shear zone,Leh, India

New generation high resolution gravity models derived from space-borne gravity data, integrated with land based surveys, have enabled understanding of regional gravity field over regions, which are till date considered to be inaccessible for land surveys, especially in mountainous terrains. In this study, we evaluate two high resolution gravity models EIGEN-6C4 and GO_CONS_GCF_2_TIM_R5 in order to understand its usability in identification of meso-scale regional geological features and lithological boundaries around the Karakoram shear zone, in Leh, India. The EIGEN-6C4 is a “hybrid” model integrating data from space-borne sensors and terrestrial data, whereas GO_CONS_GCF_2_TIM_R5 is a model derived from the latest space-borne GOCE sensor. Bouguer gravity anomaly has been derived for both the models and compared. It is seen that, the GOCE derived model pertains to the regional gravity field of the region and compares well with the regional derivative of the EIGEN-6C4 model. Further, the EIGEN-6C4 has been analyzed using horizontal derivatives (dx, dy), analytical signal (ANS) and tilt derivative (TDR) techniques. These, derived maps are then overlain on published geological map of the area to understand the correlation between sub-surface geology vis a vis gravitational signal. The major and distinct geological signatures as derived from the various derivative maps correlate well with the existing geological map. The source boundaries derived from the TDR map agrees reasonably well with the lithological boundaries. Further, the anomaly and derivative maps from EIGEN-6C4 indicates towards a possible continuation of the Shyok suture zone in the region. Therefore, for the given spatial extent of the area under consideration, the GOCE derived model represents the regional field, whereas the EIGEN-6C4 data and derivatives are of sufficient resolution for understanding the geological variability in and around the Karakoram shear zone.     

Analysis and prediction of a catastrophic Indian coastal heat wave of 2015

Heat wave of 2015 over India, a natural disaster with 2500 human deaths, was studied to understand the characteristics, associated atmospheric circulation patterns and to evaluate its predictability. Although temperatures are highest in May over India, occurrence of heat wave conditions over southeast coastal parts of India in May 2015 had been unanticipated. Analyses revealed that isolated region of Andhra Pradesh (AP) had experienced severe heat wave conditions during May 23–27, 2015, with temperatures above 42 °C and the sudden escalation by 7–10 °C within a short span of 2–3 days. Short-range weather predictions with Advanced Research Weather Research and Forecasting model at 3-km resolution, up to 72-h lead time, have been found accurate with statistical metrics of small mean absolute error and root-mean-square error and high index of agreement confirming the predictability of the heat wave evolution. Analyses have indicated that regional atmospheric pressure disparities within the Eurasia region, i.e., increased pressure gradient between the Middle East and India, had been responsible for increased northwest wind flow over to northwest India and to southeast India which have advected higher temperatures. Estimates of warm air advection have shown heat accumulation over AP region, due to sea breeze effect. The study led to the conclusion that changing pressure gradients between Middle East and India, enhancement of northwest wind flow with warm air advection and sea breeze effect along southeast coast blocking the free flow have contributed to the observed heat wave episode over coastal Andhra Pradesh.     

Managing migration in the Greater Mekong Subregion: Regulation,extra‐legal relation and extortion

One major aim of the Greater Mekong Subregion (GMS) integration programme, supported by the Asian Development Bank (ADB), is to foster regional ‘community’ for sharing resources, people and financial flows. This ‘community’ is the target of both economic growth and poverty reduction. The emphasis on ‘community’ in the ADB's mushrooming quantity of documents raises important questions about what kinds of people are included, in what roles and with what kinds of support and protection. This paper explores these questions in relation to the political economy of regulating ethnic migrants from Myanmar working in Thailand. This paper argues that extra‐legal relations between migrants and state/para‐state agents constitute a crucial part of regulation. In transferring the regulation of migration to the national scale, the ADB inadvertently reinforces national differences between Thais and cross‐border people. Additionally, the complicated and fluctuating implementation of national regulations in both countries leaves migrants subject to violence and extortion from state and quasi‐state agents in Thailand. This paper shows that the dynamics of global capitalism require ‘deportable labour’ supplied by ethnic migrants who are included in the GMS community as the most invisible, vulnerable and exploited members.     

Processes of India’s offshore summer intraseasonal sea surface temperature variability

Active and break phases of the Indian summer monsoon are associated with sea surface temperature (SST) fluctuations at 30–90 days timescale in the Arabian Sea and Bay of Bengal. Mechanisms responsible for basin-scale intraseasonal SST variations have previously been discussed, but the maxima of SST variability are actually located in three specific offshore regions: the South-Eastern Arabian Sea (SEAS), the Southern Tip of India (STI) and the North-Western Bay of Bengal (NWBoB). In the present study, we use an eddy-permitting 0.25° regional ocean model to investigate mechanisms of this offshore intraseasonal SST variability. Modelled climatological mixed layer and upper thermocline depth are in very good agreement with estimates from three repeated expendable bathythermograph transects perpendicular to the Indian Coast. The model intraseasonal forcing and SST variability agree well with observed estimates, although modelled intraseasonal offshore SST amplitude is undere-stimated by 20–30 %. Our analysis reveals that surface heat flux variations drive a large part of the intraseasonal SST variations along the Indian coastline while oceanic processes have contrasted contributions depending of the region considered. In the SEAS, this contribution is very small because intraseasonal wind variations are essentially cross-shore, and thus not associated with significant upwelling intraseasonal fluctuations. In the STI, vertical advection associated with Ekman pumping contributes to ～30 % of the SST fluctuations. In the NWBoB, vertical mixing diminishes the SST variations driven by the atmospheric heat flux perturbations by 40 %. Simple slab ocean model integrations show that the amplitude of these intraseasonal SST signals is not very sensitive to the heat flux dataset used, but more sensitive to mixed layer depth.