scholarly journals Advances in Climate Analysis and Monitoring: Reflections on 40 Years of Climate Diagnostics and Prediction Workshops

2017 ◽  
Vol 98 (3) ◽  
pp. 461-471 ◽  
Author(s):  
C. F. Ropelewski ◽  
P. A. Arkin
Keyword(s):  
Data Analysis ◽  
Climate Variability ◽  
Numerical Models ◽  
Climate Science ◽  
Data Access ◽  
Climate Data ◽  
Physical Mechanisms ◽  
Single Station ◽  
Climate Analysis ◽  
Sophisticated Analysis

Abstract We examine the progress in the analysis of climate variability through the lens of a 40-year series of annual Climate Diagnostics and Prediction Workshops initiated by the National Oceanic and Atmospheric Administration (NOAA) in 1976. The evolution of climate data and data access, data analysis and display, and our understanding of the physical mechanisms associated with climate variability, as well as the evolution in the character of the workshops, are documented by reference to the series of workshop proceedings. This retrospective essay chronicles the transition from the mid-1970s, when individual investigators or their organizations held much of the climate data suitable for research, to the present day, where many of the key climate datasets are freely accessible on the Internet. In parallel we also chart the evolution in data analysis and display tools from hand-drawn line graphs of single-station data to color animations of regional and global fields based on satellite data, numerical models, and sophisticated analysis tools. Discussion of these two themes is augmented by documentation of the increasing understanding of the physical climate system as climate science moved away from the “bones of bare statistics” that characterized climate analysis in the mid–twentieth century toward the “flesh of physical understanding.”

scholarly journals Web-Based Reanalysis Intercomparison Tools (WRIT) for Analysis and Comparison of Reanalyses and Other Datasets

2014 ◽  
Vol 95 (11) ◽  
pp. 1671-1678 ◽  
Author(s):  
Catherine A. Smith ◽  
Gilbert P. Compo ◽  
Don K. Hooper
Keyword(s):  
Cross Sections ◽  
Climate Science ◽  
Data Access ◽  
Physical Sciences ◽  
Web Based ◽  
University Of Colorado ◽  
Standard Pressure ◽  
Climate Analysis ◽  
The University ◽  
Reanalysis Intercomparison

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions. Their metadata, such as variable name and units, can also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, regrid if needed, and convert units. Even if a dataset can be read via the Open-Source Project for a Network Data Access Protocol (commonly known as OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. Our group at the Cooperative Institute for Research in the Environmental Sciences at the University of Colorado and affiliated colleagues at the NOAA's Earth System Research Laboratory Physical Sciences Division have expertise both in making reanalysis datasets available and in creating web-based climate analysis tools that have been widely used throughout the meteorological community. To overcome some of the obstacles in reanalysis intercomparison, we have created a set of web-based Reanalysis Intercomparison Tools (WRIT) at www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis datasets, and to test hypotheses. For standard pressure-level and surface variables there are tools to plot trajectories, monthly mean maps and vertical cross sections, and monthly mean time series. Some observational datasets are also included. Users can refine date, statistics, and plotting options. WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.

scholarly journals Analysis of livestock breeders’ perceptions and their adaptation measures to climate change in Morocco‘s arid rangelands

2020 ◽  
Vol 6 (1) ◽  
pp. 1-25
Author(s):  
Wadii Snaibi
Keyword(s):  
Climate Change ◽  
Local Level ◽  
Small Scale ◽  
Climate Data ◽  
Chi Square ◽  
Adaptation Measures ◽  
Significant Difference ◽  
The Difference ◽  
Eastern Morocco ◽  
Climate Analysis

AbstractThe high plateaus of eastern Morocco are already suffering from the adverse impacts of climate change (CC), as the local populations’ livelihoods depend mainly on extensive sheep farming and therefore on natural resources. This research identifies breeders’ perceptions about CC, examines whether they correspond to the recorded climate data and analyses endogenous adaptation practices taking into account the agroecological characteristics of the studied sites and the difference between breeders’ categories based on the size of owned sheep herd. Data on perceptions and adaptation were analyzed using the Chi-square independence and Kruskal-Wallis tests. Climate data were investigated through Mann-Kendall, Pettitt and Buishand tests.Herders’ perceptions are in line with the climate analysis in term of nature and direction of observed climate variations (downward trend in rainfall and upward in temperature). In addition, there is a significant difference in the adoption frequency of adaptive strategies between the studied agroecological sub-zones (χ2 = 14.525, p <.05) due to their contrasting biophysical and socioeconomic conditions, as well as among breeders’ categories (χ2 = 10.568, p < .05) which attributed mainly to the size of sheep flock. Policy options aimed to enhance local-level adaptation should formulate site-specific adaptation programs and prioritise the small-scale herders.

scholarly journals Striped Data Analysis Framework

2020 ◽  
Vol 245 ◽  
pp. 06042
Author(s):  
Oliver Gutsche ◽  
Igor Mandrichenko
Keyword(s):  
Data Analysis ◽  
Data Storage ◽  
High Energy Physics ◽  
Data Access ◽  
High Energy ◽  
Data Representation ◽  
General Idea ◽  
Common Data Model ◽  
Local File ◽  
Energy Physics

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

scholarly journals Enabling Confirmatory Secondary Data Analysis by Logging Data ‘Checkout’

2018 ◽  
Author(s):  
Kimberly Megan Scott ◽  
Melissa Kline
Keyword(s):  
Data Analysis ◽  
Standard Form ◽  
Secondary Data ◽  
Data Access ◽  
Secondary Data Analysis ◽  
Exploratory Research ◽  
Critical Elements ◽  
Library Book ◽  
Confirmatory Analysis

As more researchers make their datasets openly available, the potential of secondary data analysis to address new questions increases. However, the distinction between primary and secondary data analysis is unnecessarily confounded with the distinction between confirmatory and exploratory research. We propose a framework, akin to library book checkout records, for logging access to datasets in order to support confirmatory analysis where appropriate. This would support a standard form of preregistration for secondary data analysis, allowing authors to demonstrate that their plans were registered prior to data access. We discuss the critical elements of such a system, its strengths and limitations, and potential extensions.

scholarly journals Evaluating the Yield Response of Maize (Zea mays L.) and Rice (Oryza sativa L.) to Future Climate Variability in The Gambia

2019 ◽  
Vol 7 (2) ◽  
pp. 11
Author(s):  
Ebrima Sonko ◽  
Sampson K. Agodzo ◽  
Philip Antwi-Agyei
Keyword(s):  
Climate Variability ◽  
Rice Yield ◽  
Oryza Sativa L ◽  
Maize Yield ◽  
Future Climate ◽  
Yield Response ◽  
Climate Data ◽  
Negative Effects ◽  
Yield Data ◽  
Maize Cultivars

Climate change and variability impact on staple food crops present a daunting challenge in the 21st century. The study assesses future climate variability on maize and rice yield over a 30-year period by comparing the outcomes under two GCM models, namely, CSIRO_RCP4.5 and NOAA_RCP4.5 of Australia’s Commonwealth Scientific and National Oceanic and Atmospheric Administration respectively. Historical climate data and yield data were used to establish correlations and then subsequently used to project future yields between 2021 and 2050. Using the average yield data for the period 1987-2016 as baseline yield data, future yield predictions for 2021-2030, 2031-2040 and 2041-2050 were then compared with the baseline data. The results showed that the future maize and rice yield would be vulnerable to climate variability with CSIRO_RCP4.5 showing increase in maize yield whilst CSIRO_RCP4.5 gives a better projection for rice yield. Furthermore, the results estimated the percentage mean yield gain for maize under CSIRO_RCP4.5 and NOAA_ RCP4.5 by about 17 %, 31 % and 48 % for the period 2021-2030, 2031-2040 and 2041-2050 respectively. Mean rice yield lossess of -23 %, -19 % and -23 % were expected for the same period respectively. The study recommended the use of improved rice and maize cultivars to offset the negative effects of climate variability in future.

scholarly journals Hydrological Hazard: Analysis and Prevention

Geosciences ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 389 ◽  
Author(s):  
Tommaso Caloiero
Keyword(s):  
Climate Variability ◽  
Safety Climate ◽  
Hazard Analysis ◽  
Health And Safety ◽  
Natural Environments ◽  
Flood Hazards ◽  
Climate Data ◽  
Special Issue ◽  
Drought Analysis ◽  
Hydrological Extreme

As a result of the considerable impacts of hydrological hazard on water resources, on natural environments and human activities, as well as on human health and safety, climate variability and climate change have become key issues for the research community. In fact, a warmer climate, with its heightened climate variability, will increase the risk of hydrological extreme phenomena, such as droughts and floods. The Special Issue “Hydrological Hazard: Analysis and Prevention” presents a collection of scientific contributions that provides a sample of the state-of-the-art and forefront research in this field. In particular, innovative modelling methods for flood hazards, regional flood and drought analysis, and the use of satellite and climate data for drought analysis were the main topics and practice targets that the papers published in this Special Issue aimed to address.

scholarly journals Enabling Confirmatory Secondary Data Analysis by Logging Data Checkout

2019 ◽  
Vol 2 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Kimberly M. Scott ◽  
Melissa Kline
Keyword(s):  
Data Analysis ◽  
Standard Form ◽  
Secondary Data ◽  
Data Access ◽  
Secondary Data Analysis ◽  
Data Sets ◽  
Exploratory Research ◽  
Critical Elements ◽  
Library Book ◽  
Access To Data

As more researchers make their data sets openly available, the potential of secondary data analysis to address new questions increases. However, the distinction between primary and secondary data analysis is unnecessarily confounded with the distinction between confirmatory and exploratory research. We propose a framework, akin to library-book checkout records, for logging access to data sets in order to support confirmatory analysis when appropriate. This system would support a standard form of preregistration for secondary data analysis, allowing authors to demonstrate that their plans were registered prior to data access. We discuss the critical elements of such a system, its strengths and limitations, and potential extensions.

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