New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

This study presents daily high-resolution (5 km × 5 km) grids of mean, minimum, and maximum temperature and relative humidity for Germany and its catchment areas, from 1951 to 2015. These observational datasets (HYRAS) are based upon measurements gathered for Germany and its neighbouring countries, in total more than 1300 stations, gridded in two steps: first, the generation of a background field, using non-linear vertical temperature profiles, and then an inverse distance weighting scheme to interpolate the residuals, subsequently added onto the background field. The modified Euclidian distances used integrate elevation, distance to the coast, and urban heat island (UHI) effect. A direct station-grid comparison and cross-validation yield low errors for the temperature grids over most of the domain and greater deviations in more complex terrain. The interpolation of relative humidity is more uncertain due to its inherent spatial inhomogeneity and indirect derivation using dew point temperature. Compared with other gridded observational datasets, HYRAS benefits from its high resolution and captures complex topographic effects. HYRAS improves upon its predecessor by providing datasets for additional variables (minimum and maximum temperature), integrating temperature inversions, maritime influence and UHI effect, and representing a larger area. With a long-term observational dataset of multiple meteorological variables also including precipitation, various climatological analyses are possible. We present long-term historical climate trends and relevant indices of climate extremes, pointing towards a significantly warming climate over Germany, with no significant change in total precipitation. We also evaluate extreme events, specifically the summer heat waves of 2003 and 2015.

Development of Climate Data Bias Corrector (CDBC) Tool and Its Application over the Agro-Ecological Zones of India

The use of global and regional climate models has been increasing in the past few decades, in order to analyze the future of natural resources and the socio-economic aspects of climate change. However, these climate model outputs can be quite biased, which makes it challenging to use them directly for analysis purpose. Therefore, a tool named Climate Data Bias Corrector was developed to correct the bias in climatic projections of historical and future periods for three primary climatic variables—rainfall, temperature (maximum and minimum), and solar radiation. It uses the quantile mapping approach, known for its efficiency and low computational cost for bias correction. Its Graphical User Interface (GUI) was made to be feasible to take input and give output in commonly used file formats—comma and tab delimited file formats. It also generates month-wise cumulative density function (CDF) plot of a random station/grid to allow the user to investigate the effectiveness of correction statistically. The tool was verified with a case study on several agro-ecological zones of India and found to be efficient.

Adaptive mobile Web server framework for Mist computing in the Internet of Things

Purpose The distant data centre-centric Internet of Things (IoT) systems face the latency issue especially in the real-time-based applications, such as augmented reality, traffic analytics and ambient assisted living. Recently, Fog computing models have been introduced to overcome the latency issue by using the proximity-based computational resources, such as the computers co-located with the cellular base station, grid router devices or computers in local business. However, the increasing users of Fog computing servers cause bottleneck issues and consequently the latency issue arises again. This paper aims to introduce the utilisation of Mist computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of the IoT networks. Design/methodology/approach This paper proposes a service-oriented mobile-embedded Platform as a Service (mePaaS) framework that allows the mobile device to provide a flexible platform for proximal users to offload their computational or networking program to mePaaS-based Mist computing node. Findings The prototype has been tested and performance has been evaluated on the real-world devices. The evaluation results have shown the promising nature of mePaaS. Originality/value The proposed framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. In addition, the framework also supports task distribution among a group of Mist nodes.

Hydrography, Circulation, and Mixing at the Calypso Deep (the Deepest Mediterranean Trough) during 2006–09

The mass and flow fields from June 2006 to May 2009 in the Calypso Deep (bottom depth ~5.2 km) are investigated using eddy-resolving surface-to-bottom hydrography (station grid spacing ~0.2°) and two tall moorings yielding current-meter records at depths from 700 m to near bottom. A salty warm lens (excess core salinity and temperature are ~0.01 and 0.025°C relative to the surrounding water) of Cretan Deep Water with a core at ~3000 m and a horizontal (vertical) scale of ~50 km (1.5 km) is identified in June 2006 to be locked over the trough. The lens coincides with local maxima in dissolved oxygen. In October 2006 the salinity content of the lens and of all deeper layers is increased; the oxygen maxima are shifted to the bottom layers, indicating an episodic intrusion of higher-density ventilated Adriatic water. The circulation changes from anticyclonic at all depths in June 2006 to cyclonic below ~2.5 km in October 2006, whereas after January 2007 it is cyclonic at all instrumented depths. The measured currents are weak (mean speeds < 5 cm s−1) and persistent in direction, being mostly along the bottom topography at all current-meter depths. After October 2006, the lens erodes due to salt/heat loss caused predominantly by lateral (intrusive) mixing, which works from the outside toward the lens center. The horizontal diffusivity is on the order of ~10 m2 s−1, near the center of the lens, and ~102 to 103 m2 s−1, at its periphery, with an average error ~15 times the diffusivity value. In the deepest part of the trough and in periods of predominance of vertical mixing the vertical diffusivity at 4400 m is ~(4 ± 3) × 10−3 m2 s−1.