scholarly journals Capturing and Sharing Our Collective Expertise on Climate Data: The CHARMe Project

2016 ◽  
Vol 97 (4) ◽  
pp. 531-539 ◽  
Author(s):  
Debbie Clifford ◽  
Raquel Alegre ◽  
Victoria Bennett ◽  
Jon Blower ◽  
Cecelia Deluca ◽  
...  
Keyword(s):  
Climate Science ◽  
Volcanic Eruptions ◽  
Climate Data ◽  
Journal Articles ◽  
Climate Services ◽  
Web Standards ◽  
Interactive Data ◽  
Climate Time Series ◽  
Community Access

Abstract For users of climate services, the ability to quickly determine the datasets that best fit one’s needs would be invaluable. The volume, variety, and complexity of climate data makes this judgment difficult. The ambition of CHARMe (Characterization of metadata to enable high-quality climate services) is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports, or feedback on previous applications of the data. The capture and discovery of this “commentary” information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search, and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems already in wide use for discovering climate data. In addition, the project has developed advanced tools for exploring data and commentary in innovative ways, including an interactive data explorer and comparator (“CHARMe Maps”), and a tool for correlating climate time series with external “significant events” (e.g., instrument failures or large volcanic eruptions) that affect the data quality. Although the project focuses on climate science, the concepts are general and could be applied to other fields. All CHARMe system software is open-source and released under a liberal license, permitting future projects to reuse the source code as they wish.

The overwhelming jungle of climate information and the role of climate services.

2020 ◽  
Author(s):  
Carlo Buontempo
Keyword(s):  
Climate Adaptation ◽  
Climate Science ◽  
Sources Of Information ◽  
Climate Data ◽  
Climate Services ◽  
National Programmes ◽  
Relevant Variables ◽  
Engineering Standards ◽  
Climate Service ◽  
Different Sources

<div>Climate adaptation often requires high resolution information about the expected changes in the statistical distribution of user-relevant variables. Thanks to targeted national programmes, research projects and international climate service initiatives  this kind of information is not only becoming more easily available but it is also making its way into building codes, engineering standards as well as the risk assessments for financial products.  If such an increase in the use of climate data can be seen as a positive step towards the construction of a climate resilient society, it is also true that the inconsistencies that exist between the information derived from different sources of information, have the potential to reduce the user uptake, increase the costs of adaptation and even undermine the credibility of both climate services and the underpinning climate science.</div><div>This paper offers a personal reflection on the emerging user requirements in this field. The presenation also aims at suggesting  some prelimimary ideas in support of the development of appropriate methodologies for extracting robust evidence from different sources in a scalable way.</div>

scholarly journals Trials, Errors, and Improvements in Coproduction of Climate Services

2019 ◽  
Vol 100 (8) ◽  
pp. 1419-1428 ◽  
Author(s):  
Erik W. Kolstad ◽  
Oda N. Sofienlund ◽  
Hanna Kvamsås ◽  
Mathew A. Stiller-Reeve ◽  
Simon Neby ◽  
...  
Keyword(s):  
Climate Science ◽  
Lessons Learned ◽  
Decision Makers ◽  
Climate Information ◽  
Climate Data ◽  
Climate Services ◽  
Political Landscape ◽  
Challenges And Opportunities ◽  
Service Projects ◽  
Climate Service

AbstractClimate change yields both challenges and opportunities. In both cases, costly adaptations and transformations are necessary and desirable, and these must be based on realistic and relevant climate information. However, it is often difficult for climate scientists to communicate this information to decision-makers and stakeholders, and it can be equally difficult for such actors to interpret and put the information to use. In this essay, we discuss experiences and present recommendations for scientists producing climate services. The basis is our work in several climate service projects. One of them aimed to provide local-scale climate data for municipalities in western Norway and to explore how the data were interpreted and implemented. The project was first based solely on climate science expertise, and the participants did not have sufficient competence on coproduction and knowledge about the regulatory and political landscape in which municipalities operate. Initially, we also subscribed to an outdated idea of climate services, where knowledge providers (climate scientists) “deliver” their information to knowledge users (e.g., municipal planners). Increasingly, as stressed in the literature on coproduction of knowledge, we learned that climate service should be an iterative process where actionable information is coproduced through two-way dialogue. On the basis of these and other lessons learned the hard way, we provide a set of concrete recommendations on how to embed the idea of coproduction from the preproposal stage to beyond the end of climate service projects.

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

scholarly journals Translational Science for Climate Services: Mapping and Understanding Users’ Climate Service Needs in CSSP China

2021 ◽  
Vol 35 (1) ◽  
pp. 64-76
Author(s):  
Sarah Opitz-Stapleton ◽  
Roger Street ◽  
Qian Ye ◽  
Jiarui Han ◽  
Chris D. Hewitt
Keyword(s):  
Climate Science ◽  
Service Needs ◽  
User Engagement ◽  
Translational Science ◽  
Regular User ◽  
Climate Services ◽  
Weather Information ◽  
The United Kingdom ◽  
Climate Service ◽  
User Groups

AbstractThe Climate Science for Service Partnership China (CSSP China) is a joint program between China and the United Kingdom to build the basis for climate services to support the weather and climate resilient economic development and welfare in China. Work Package 5 (WP5) provides the translational science on identification of: different users and providers, and their mandates; factors contributing to communication gaps and capacities between various users and providers; and mechanisms to work through such issues to develop and/or evolve a range of climate services. Key findings to emerge include that users from different sectors have varying capacities, requirements, and needs for information in their decision contexts, with a current strong preference for weather information. Separating climate and weather services when engaging users is often not constructive. Furthermore, there is a need to move to a service delivery model that is more user-driven and science informed; having sound climate science is not enough to develop services that are credible, salient, reliable, or timely for diverse user groups. Greater investment in building the capacity of the research community supporting and providing climate services to conduct translational sciences and develop regular user engagement processes is much needed. Such a move would help support the China Meteorological Administration’s (CMA) ongoing efforts to improve climate services. It would also assist in potentially linking a broader group of “super” users who currently act as providers and purveyors of climate services because they find the existing offerings are not relevant to their needs or cannot access CMA’s services.

scholarly journals Spatially enabling the Global Framework for Climate Services: Reviewing geospatial solutions to efficiently share and integrate climate data & information

2017 ◽  
Vol 8 ◽  
pp. 44-58 ◽  
Author(s):  
Gregory Giuliani ◽  
Stefano Nativi ◽  
Andre Obregon ◽  
Martin Beniston ◽  
Anthony Lehmann
Keyword(s):  
Climate Data ◽  
Climate Services

scholarly journals Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1459
Author(s):  
Edouard Pignède ◽  
Philippe Roudier ◽  
Arona Diedhiou ◽  
Vami Hermann N’Guessan Bi ◽  
Arsène T. Kobea ◽  
...  
Keyword(s):  
West Africa ◽  
Ivory Coast ◽  
Vegetation Index ◽  
Cultural Practices ◽  
Machine Learning Algorithms ◽  
Climate Data ◽  
Climate Services ◽  
Climate Risks ◽  
The North ◽  
Sugarcane Yield

One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.

scholarly journals The Causal Interaction between Complex Subsystems

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 3
Author(s):  
X. San Liang
Keyword(s):  
Fluid Dynamics ◽  
Maximum Likelihood ◽  
Information Flow ◽  
Maximum Likelihood Estimators ◽  
Climate Science ◽  
Causal Interaction ◽  
Natural Measure ◽  
Analytical Formulas ◽  
Real World Problems

Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system. Analytical formulas have been obtained in a closed form. Under a Gaussian assumption, their maximum likelihood estimators have also been obtained. These formulas have been validated using different subsystems with preset relations, and they yield causalities just as expected. On the contrary, the commonly used proxies for the characterization of subsystems, such as averages and principal components, generally do not work correctly. This study can help diagnose the emergence of patterns in complex systems and is expected to have applications in many real world problems in different disciplines such as climate science, fluid dynamics, neuroscience, financial economics, etc.

scholarly journals Experiences with virtual schools on “Climate data for impact assessments”

2021 ◽  
Author(s):  
Janette Bessembinder ◽  
Judith Klostermann ◽  
Rutger Dankers ◽  
Vladimir Djurdjevic ◽  
Tomas Halenka
Keyword(s):  
Summer School ◽  
Climate Modeling ◽  
Virtual Schools ◽  
Virtual School ◽  
Climate Data ◽  
Climate Services ◽  
Impact Modelling ◽  
Set Up ◽  
Impact Models

<p>The provision of climate services to users is a fast developing field. In support of this development, the IS-ENES3 project, funded within the EC Horizon2020 program, organized three schools on “Climate data for impact assessments” in 2020 and 2021. In an Autumn school, a Spring school and a Summer school, climate scientists and impact scientists were brought together. An important aim of the schools was to enhance interaction between Vulnerability-Impact-Adaptation (VIA) researchers, climate services providers and climate researchers. Another aim was to provide an overview of information on climate modeling, climate data, impact modelling and climate services based on the work of the IS-ENE3 project.</p><p>In the first three weeks a series of lectures was given, covering topics such as climate data and modelling, impact models, portals for accessing and processing climate data, setting-up impact assessments, and communication of results to stakeholders. In the last three weeks the participants worked in small groups of one climate scientist with one impact scientist on a case study under the guidance of the course lecturers. Impact and climate researchers were combined on purpose to let them experience how they could help each other.</p><p>Originally the schools were planned to take place on-site (e.g. in Prague) during one week; however, due to COVID-19 the schools had to be transformed to virtual schools with two weekly sessions during six weeks. Although the virtual set-up had some disadvantages (e.g. less possibilities for networking), there were also some advantages (e.g. the possibility to record the lectures and make them available to a broader audience; more time to explore and work with climate data in between the sessions, no CO<sub>2</sub> emissions for travelling). During this presentation we will present the set-up of the schools and the conversion to a virtual school. We will focus on the lessons learnt and the evaluation of the virtual schools by the participants and give some recommendations for similar schools and how to link the climate and VIA research communities .</p>

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