Climate Data Processing Made Easy

2008 ◽  
Author(s):  
Scott Mindock
Keyword(s):  
Data Processing ◽  
Climate Data

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Climate Data Processing for Needs of Energy Analysis

2014 ◽  
Vol 1041 ◽  
pp. 129-134 ◽  
Author(s):  
Daniela Štaffenová ◽  
Radoslav Ponechal ◽  
Pavol Ďurica ◽  
Marek Cangár
Keyword(s):  
Data Processing ◽  
Experimental Measurement ◽  
Energy Analysis ◽  
Measured Data ◽  
Meteorological Station ◽  
Weather Station ◽  
Climate Data ◽  
Technical Parameters

This article describes a part of experimental measurement with assistance of independent weather station. Later on, after the data processing from chosen period of time (1 week in March 2014) measurements are described, graphic processed, interpreted and compared to the results of real meteorological station from SHMÚ. Convenient climatic units are elaborated. The influence of differences in measured data on results of calculations for thermal technical parameters of covering constructions is analysed.

scholarly journals AREAdata: a worldwide climate dataset averaged across spatial units at different scales through time

2021 ◽  
Author(s):  
Thomas P. Smith ◽  
Michael Stemkovski ◽  
Austin Koontz ◽  
William D. Pearse
Keyword(s):  
Data Processing ◽  
Global Climate ◽  
Climate Variables ◽  
Climate Data ◽  
Temporal Scales ◽  
Processing Methods ◽  
Spatial And Temporal Scales ◽  
Gis Data ◽  
Administrative Units ◽  
Data Processing Methods

ABSTRACTIn an era of increasingly cross-discipline collaborative science, it is imperative to produce data resources which can be quickly and easily utilised by non-specialists. In particular, climate data often require heavy processing before they can be used for analyses. Here we describe AREAdata, a free-to-use online global climate dataset, pre-processed to provide the averages of various climate variables across differing administrative units (e.g., countries, states). These are daily estimates, based on the Copernicus Climate Data Store’s ERA-5 data, regularly updated to the near-present and provided as direct downloads from our website (https://pearselab.github.io/areadata/). The daily climate estimates from AREAdata are consistent with other openly available data, but at much finer-grained spatial and temporal scales than available elsewhere. AREAdata complements the existing suite of climate resources by providing these data in a form more readily usable by researchers unfamiliar with GIS data-processing methods, and we anticipate these resources being of particular use to environmental and epidemiological researchers.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Web Services ◽  
2019 ◽  
pp. 490-509 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

scholarly journals Integrating e-infrastructures for remote climate data processing

2020 ◽  
Author(s):  
Christian Pagé ◽  
Wim Som de Cerff ◽  
Maarten Plieger ◽  
Alessandro Spinuso ◽  
Iraklis Klampanos ◽  
...  
Keyword(s):  
Climate Change ◽  
Data Processing ◽  
Climate Science ◽  
End Users ◽  
Climate Indices ◽  
Climate Data ◽  
Data Infrastructure ◽  
Front End ◽  
Science Domain ◽  
Almost All

<p>Accessing and processing large climate data has nowadays become a particularly challenging task for end users, due to the rapidly increasing volumes being produced and made available. Access to climate data is crucial for sustaining research and performing climate change impact assessments. These activities have strong societal impact as climate change affects and requires that almost all economic and social sectors need adapting.</p><p>The whole climate data archive is expected to reach a volume of 30 PB in 2020 and up to 2000 PB in 2024 (estimated), evolving from 0.03 PB (30 TB) in 2007 and 2 PB in 2014. Data processing and analysis must now take place remotely for the users: users typically have to rely on heterogeneous infrastructures and services between the data and their physical location. Developers of Research Infrastructures have to provide services to those users, hence having to define standards and generic services to fulfil those requirements.</p><p>It will be shown how the DARE eScience Platform (http://project-dare.eu) will help developers to develop needed services more quickly and transparently for a large range of scientific researchers. The platform is designed for efficient and traceable development of complex experiments and domain-specific services. Most importantly, the DARE Platform integrates the following e-infrastructure services: the climate IS-ENES (https://is.enes.org) Research Infrastructure front-end climate4impact (C4I: https://climate4impact.eu), the EUDAT CDI (https://www.eudat.eu/eudat-collaborative-data-infrastructure-cdi) B2DROP Service, as well as the ESGF (https://esgf.llnl.gov). The DARE Platform itself can be deployed by research communities on local, public or commercial clouds, thanks to its containerized architecture.</p><p>More specifically, two distinct Use Cases for the climate science domain will be presented. The first will show how an open source software to compute climate indices and indicators (icclim: https://github.com/cerfacs-globc/icclim) is leveraged using the DARE Platform to enable users to build their own workflows. The second Use Case will demonstrate how more complex tools, such as an extra-tropical and tropical cyclone tracking software (https://github.com/cerfacs-globc/cyclone_tracking), can be easily made available to end users by infrastructure and front-end software developers.</p>

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2018 ◽  
pp. 427-446 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Scientists struggle to access Africa's historical climate data

Nature ◽  
2019 ◽  
Vol 574 (7780) ◽  
pp. 605-606 ◽  
Author(s):  
Linda Nordling
Keyword(s):  
Climate Data ◽  
Historical Climate
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