Bibliografías temáticas / Climate data

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Literatura académica sobre el tema "Climate data"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 14 de junio de 2025

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Artículos de revistas sobre el tema "Climate data"

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Schipper, Janus Willem, Julia Hackenbruch, Hilke Simone Lentink, and Katrin Sedlmeier. "Integrating Adaptation Expertise into Regional Climate Data Analyses through Tailored Climate Parameters." Meteorologische Zeitschrift 28, no. 1 (2019): 41–57. http://dx.doi.org/10.1127/metz/2019/0878.

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Mrozewski, Tomasz. "Climate change data." Bulletin - Association of Canadian Map Libraries and Archives (ACMLA), no. 162 (July 26, 2019): 20–24. http://dx.doi.org/10.15353/acmla.n162.1528.

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Böhm, Reinhard. "Instrumental Climate Data." PAGES news 11, no. 2-3 (2003): 9–10. http://dx.doi.org/10.22498/pages.11.2-3.9.

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Koenig, R. "Missing: Climate Data." Science 314, no. 5801 (2006): 907c. http://dx.doi.org/10.1126/science.314.5801.907c.

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Espinoza, Maria I., and Melissa Aronczyk. "Big data for climate action or climate action for big data?" Big Data & Society 8, no. 1 (2021): 205395172098203. http://dx.doi.org/10.1177/2053951720982032.

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Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational reports, and media publicity, we evaluate the logic driving Data for Climate Action initiatives, examining the implications of applying commercial datasets and expertise to environmental problems. Despite the increasing adoption of Data for Climate Action paradigms in government and public sector efforts to address climate change, we argue Data for Climate Action is better seen as a strategy to legitimate extractive, profit-oriented data practices by companies than a means to achieve global goals for environmental sustainability.
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P. Mott, T. W. Sammis, and G. M. Southward. "Climate Data Estimation Using Climate Information From Surrounding Climate Stations." Applied Engineering in Agriculture 10, no. 1 (1994): 41–44. http://dx.doi.org/10.13031/2013.25825.

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Heffernan, Olive. "Climate data spat intensifies." Nature 460, no. 7257 (2009): 787. http://dx.doi.org/10.1038/460787a.

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Godøy, Øystein, and Bard Saadatnejad. "ACCESS climate data management." Ambio 46, S3 (2017): 464–74. http://dx.doi.org/10.1007/s13280-017-0963-1.

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Delvaux, C., M. Journée, and C. Bertrand. "The FORBIO Climate data set for climate analyses." Advances in Science and Research 12, no. 1 (2015): 103–9. http://dx.doi.org/10.5194/asr-12-103-2015.

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Abstract. In the framework of the interdisciplinary FORBIO Climate research project, the Royal Meteorological Institute of Belgium is in charge of providing high resolution gridded past climate data (i.e. temperature and precipitation). This climate data set will be linked to the measurements on seedlings, saplings and mature trees to assess the effects of climate variation on tree performance. This paper explains how the gridded daily temperature (minimum and maximum) data set was generated from a consistent station network between 1980 and 2013. After station selection, data quality control procedures were developed and applied to the station records to ensure that only valid measurements will be involved in the gridding process. Thereafter, the set of unevenly distributed validated temperature data was interpolated on a 4 km × 4 km regular grid over Belgium. The performance of different interpolation methods has been assessed. The method of kriging with external drift using correlation between temperature and altitude gave the most relevant results.
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Liu, Songbin, Xiaomeng Huang, Haohuan Fu, Guangwen Yang, and Zhenya Song. "Data Reduction Analysis for Climate Data Sets." International Journal of Parallel Programming 43, no. 3 (2013): 508–27. http://dx.doi.org/10.1007/s10766-013-0287-0.

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Tesis sobre el tema "Climate data"

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Metaferia, Gohe Amhayesus. "Daily Climate Change Data Generation and Dissemination." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32457.

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The worldwide challenges to achieve cost effective protection against global warming impacts and to acquire reliable decision making tools continually force new developments in the area of climate change research. Climate change impacts projections involve several steps: emission scenarios generation, Global Circulation Models and Regional Climate Models (GCM/RCM) runs, downscaling, impact model running, analysis of results and decision making. Unfortunately, GCM/RCMs outputs are often biased and need to be processed before being fed into impact models. This thesis describes the effort carried out to alleviate the burden of downscaling coarse hydro-climatology data outputs from GCM/RCM and making results readily available for climate change impact analysis for specific regions, particularly in the African continent. GCM/RCM outputs are highly unreliable at the sub-grid scale to be used for region specific impact analysis (Wilby, Hay, & Leavesly, 1999). Furthermore, raw GCM/RCM outputs are often downscaled under the premises that the latter offer very coarse spatial resolution. The Internet is a common resource for users of climate change data to access relevant information. Web-based interfaces offer users the capability to retrieve such data. This thesis involves the development of a new web-portal, which addresses the demand for climate change data at the daily scale. It is a user-friendly interactive web-based interface with multiple functionalities including: capacity to process information, capacity to search, sort, retrieve and filter data and download features. Six climate variables are considered in this project: precipitation, maximum temperature, minimum temperature, wind speed, relative humidity and solar radiation. The aforementioned climate variables have been downscaled to specific geographical locations and results have been made available at a fine temporal resolution – the daily scale. The data portal currently hosts climate change data for nine stations in western Africa: Agadez, Brini N’Konni, Gaya, Maine Soroa, Maradi Airport, Niamey Airport, Tahoua, Tillabery and Zinder Airport. The above mentioned climate stations are all located in Niger. Nonetheless, the project aims to expand and cover further ground in Africa. Quantile - Quantile downscaling, also known as Quantile-Quantile mapping, matching or transformation is a statistical procedure used in this project to downscale raw GCM/RCM outputs. GCM/RCM outputs from the AMMA-Ensemble sets under the SRES A1B scenario were used as raw data.
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Monahan, Adam Hugh. "Nonlinear principal component analysis of climate data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ48678.pdf.

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Ross, Ian. "Nonlinear dimensionality reduction methods in climate data analysis." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492479.

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Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These hnear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three such techniques to the study of El Niño/Southern Oscillation variability in tropical Pacific sea surface temperatures and thermocline depth, comparing observational data with simulations from coupled atmosphere-ocean general circulation models from the CMIP3 multi-model ensemble.
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Nicolai, Andreas. "DELPHIN 6 Climate Data File Specification, Version 1.0." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-221222.

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This paper describes the file format of the climate data container used by the DELPHIN, THERAKLES and NANDRAD simulation programs. The climate data container format holds a binary representation of annual and continuous climatic data needed for hygrothermal transport and building energy simulation models. The content of the C6B-Format is roughly equivalent to the epw-climate data format.
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Rojo, Juan. "URBAN CLIMATE RESILIENCE AND THE PROMISE OF BIG DATA SOLUTIONS : ASSESSING BIG DATA APPLICATION INTO MADRID’S URBAN CLIMATE CHANGE RESILIENCE SCENARIO." Thesis, KTH, Urbana och regionala studier, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286117.

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In the midst of a climate crisis like the one the world is facing right now, it is essential to try to find new tools that allow better decision-making both to mitigate climate change and to adapt to it. To this day, data science has yet to develop the necessary knowledge to tackle climate change, even though there are large databases with climate data available. With the technological revolution that society is experiencing, and the large amounts of data generated every moment, it is inevitable to think that the necessary responses will inevitably require greater collection and use of data, along with the tools, knowledge, and infrastructure needed. Cities, as great centers of knowledge, population density and innovation, must take the lead to promote data science and Big Data and incorporate them into building urban resilience. For the combination to be productive, both concepts must also be understood in a holistic and complemented way, resilience and Big Data. Both dynamic and relatively new concepts must find the point of union and scientists investigating adaptation must reach out to data scientists to find the skills necessary to clean the data as well as organize, analyze and manage it. Pairing Big Data insights with a well-established and localized urban resilience context can reveal deeper understanding of climate vulnerability, leading to the adaptation of better early-warning systems, more rigorous monitoring and evaluation and ultimately more robust adaptation response based on more accurately defined problems. This study analyzes both concepts, fully understanding what Big Data is, and studying urban climate resilience in a specific setting: the city of Madrid. In this way, the results of this study allow the clear identification of the varied applications of Big Data for a given environment of climate change threats, such as heatwaves, loss of biodiversity and flooding, describing their main data sources, methods, and standing criteria. In addition, the major characteristics of the Big Data use process are explained in the decision-making mechanism, describing the barriers and key drivers of data access, assessment, and application. Such considerations include the correct integration of the different stakeholders in the data collection, cleaning and application processes, ethical considerations of privacy, use and ownership, as well as good governance issues such as fostering citizen participation, encouraging innovation and urging the creation of a solid and robust management infrastructure that promotes the proper operation of the data conditions. The use of Big Data can be a fundamental tool for the development of more robust, flexible and reflexive resilience strategies, which keep climate threats projections updated, allowing adaptation measures to be more relevant and suited for a system’s shocks and stresses. This study broadens the knowledge on which are the correct data sources, the relevance of these data on their application in urban climate resilience and specific Big Data considerations for the city of Madrid.
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Pretis, Felix. "Econometric methods and applications in modelling non-stationary climate data." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:f4c9122b-5270-4b55-a292-2cdf10ad7f2a.

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Understanding of climate change and policy responses thereto rely on accurate measurements as well as models of both socio-economic and physical processes. However, data to assess impacts and establish historical climate records are non-stationary: distributions shift over time due to shocks, measurement changes, and stochastic trends - all of which invalidate standard statistical inference. This thesis establishes econometric methods to model non-stationary climate data consistent with known physical laws, enabling joint estimation and testing, develops techniques for the automatic detection of structural breaks, and evaluates socio-economic scenarios used in long-run climate projections. Econometric cointegration analysis can be used to overcome inferential difficulties stemming from stochastic trends in time series, however, cointegration has been criticised in climate research for lacking a physical justification for its use. I show that physical two-component energy balance models of global mean climate can be mapped to a cointegrated system, making them directly testable, and thereby provide a physical justification for econometric methods in climate research. Automatic model selection with more variables than observations is introduced in modelling concentrations of atmospheric CO<sub>2</sub>, while controlling for outliers and breaks at any point in the sample using impulse indicator saturation. Without imposing the inclusion of variables a-priori, model selection results find that vegetation, temperature and other natural factors alone cannot explain the trend or the variation in CO<sub>2</sub> growth. Industrial production components, driven by business cycles and economic shocks, are highly significant contributors. Generalizing the principle of indicator saturation, I present a methodology to detect structural breaks at any point in a time series using designed functions. Selecting over these break functions at every point in time using a general-to-specific algorithm, yields unbiased estimates of the break date and magnitude. Analytical derivations for the split-sample approach are provided under the null of no breaks and the alternative of one or more breaks. The methodology is demonstrated by detecting volcanic eruptions in a time series of Northern Hemisphere mean temperature derived from a coupled climate simulation spanning close to 1200 years. All climate models require socio-economic projections to make statements about future climate change. The large span of projected temperature changes then originates predominantly from the wide range of scenarios, rather than uncertainty in climate models themselves. For the first time, observations over two decades are available against which the first sets of socio-economic scenarios used in the Intergovernmental Panel on Climate Change reports can be assessed. The results show that the growth rate in fossil fuel CO<sub>2</sub> emission intensity (fossil fuel CO2 emissions per GDP) over the 2000s exceeds all main scenario values, with the discrepancy being driven by underprediction of high growth rates in Asia. This underestimation of emission intensity raises concerns about achieving a world of economic prosperity in an environmentally sustainable fashion.
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Izumi, Kenji. "Application of Paleoenvironmental Data for Testing Climate Models and Understanding Past and Future Climate Variations." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18510.

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Paleo data-model comparison is the process of comparing output from model simulations of past periods with paleoenvironmental data. It enables us to understand both the paleoclimate mechanism and responses of the earth environment to the climate and to evaluate how models work. This dissertation has two parts that each involve the development and application of approaches for data-model comparisons. In part 1, which is focused on the understanding of both past and future climatic changes/variations, I compare paleoclimate and historical simulations with future climate projections exploiting the fact that climate-model configurations are exactly the same in the paleo and future simulations in the Coupled Model Intercomparison Project Phase 5. In practice, I investigated large-scale temperature responses (land-ocean contrast, high-latitude amplification, and change in temperature seasonality) in paleo and future simulations, found broadly consistent relationships across the climate states, and validated the responses using modern observations and paleoclimate reconstructions. Furthermore, I examined the possibility that a small set of common mechanisms controls the large-scale temperature responses using a simple energy-balance model to decompose the temperature changes shown in warm and cold climate simulations and found that the clear-sky longwave downward radiation is a key control of the robust responses. In part 2, I applied the equilibrium terrestrial biosphere models, BIOME4 and BIOME5 (developed from BIOME4 herein), for reconstructing paleoclimate. I applied inverse modeling through the iterative forward-modeling (IMIFM) approach that uses the North American vegetation data to infer the mid-Holocene (MH, 6000 years ago) and the Last Glacial Maximum (LGM, 21,000 years ago) climates that control vegetation distributions. The IMIFM approach has the potential to provide more accurate quantitative climate estimates from pollen records than statistical approaches. Reconstructed North American MH and LGM climate anomaly patterns are coherent and consistent between variables and between BIOME4 and BIOME5, and these patterns are also consistent with previous data synthesis. This dissertation includes previously published and unpublished coauthored material.
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Polcer, James. "Generalized Bathtub Hazard Models for Binary-Transformed Climate Data." TopSCHOLAR®, 2011. http://digitalcommons.wku.edu/theses/1060.

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In this study, we use a hazard-based modeling as an alternative statistical framework to time series methods as applied to climate data. Data collected from the Kentucky Mesonet will be used to study the distributional properties of the duration of high and low-energy wind events relative to an arbitrary threshold. Our objectiveswere to fit bathtub models proposed in literature, propose a generalized bathtub model, apply these models to Kentucky Mesonet data, and make recommendations as to feasibility of wind power generation. Using two different thresholds (1.8 and 10 mph respectively), results show that the Hjorth bathtub model consistently performed better than all other models considered with coefficient of R-squared values at 0.95 or higher. However, fewer sites and months could be included in the analysis when we increased our threshold to 10 mph. Based on a 10 mph threshold, Bowling Green (FARM), Hopkinsville (PGHL), and Columbia (CMBA) posted the top 3 wind duration times in February of 2009. Further studies needed to establish long-term trends.
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Evans, Jason Peter, and jason evans@yale edu. "Modelling Climate - Surface Hydrology Interactions in Data Sparse Areas." The Australian National University. Centre for Resource and Environmental Studies, 2000. http://thesis.anu.edu.au./public/adt-ANU20020313.032142.

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The interaction between climate and land-surface hydrology is extremely important in relation to long term water resource planning. This is especially so in the presence of global warming and massive land use change, issues which seem likely to have a disproportionate impact on developing countries. This thesis develops tools aimed at the study and prediction of climate effects on land-surface hydrology (in particular streamflow), which require a minimum amount of site specific data. This minimum data requirement allows studies to be performed in areas that are data sparse, such as the developing world. ¶ A simple lumped dynamics-encapsulating conceptual rainfall-runoff model, which explicitly calculates the evaporative feedback to the atmosphere, was developed. It uses the linear streamflow routing module of the rainfall-runoff model IHACRES, with a new non-linear loss module based on the Catchment Moisture Deficit accounting scheme, and is referred to as CMD-IHACRES. In this model, evaporation can be calculated using a number of techniques depending on the data available, as a minimum, one to two years of precipitation, temperature and streamflow data are required. The model was tested on catchments covering a large range of hydroclimatologies and shown to estimate streamflow well. When tested against evaporation data the simplest technique was found to capture the medium to long term average well but had difficulty reproducing the short-term variations. ¶ A comparison of the performance of three limited area climate models (MM5/BATS, MM5/SHEELS and RegCM2) was conducted in order to quantify their ability to reproduce near surface variables. Components of the energy and water balance over the land surface display considerable variation among the models, with no model performing consistently better than the other two. However, several conclusions can be made. The MM5 longwave radiation scheme performed worse than the scheme implemented in RegCM2. Estimates of runoff displayed the largest variations and differed from observations by as much as 100%. The climate models exhibited greater variance than the observations for almost all the energy and water related fluxes investigated. ¶ An investigation into improving these streamflow predictions by utilizing CMD-IHACRES was conducted. Using CMD-IHACRES in an 'offline' mode greatly improved the streamflow estimates while the simplest evaporation technique reproduced the evaporative time series to an accuracy comparable to that obtained from the limited area models alone. The ability to conduct a climate change impact study using CMD-IHACRES and a stochastic weather generator is also demonstrated. These results warrant further investigation into incorporating the rainfall-runoff model CMD-IHACRES in a fully coupled 'online' approach.
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Ruan, Tao. "The climate of Mars from assimilations of spacecraft data." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:15d6f785-b17b-4328-a18f-21e7e0ebeb86.

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The Mars climate has been explored using two reanalysis datasets based on combining spacecraft observations of temperature and dust with the UK version of the LMD Mars GCM. The semiannual oscillation (SAO) of zonal-mean zonal wind was studied using the existing Mars Analysis Correction Data Assimilation reanalysis during Mars Years (MYs) 24-27. The SAO of zonal-mean zonal wind was shown to exist and extend over a wide range of latitudes. The dynamical driving processes of the SAO in the tropics were investigated, and the forcing due to meridional advection appeared to be the main contributor to the SAO. The study also highlighted some phenomena associated with perturbations of the global circulation during the MY 25 global dust storm (GDS). The meridional advection term was shown to be weaker in the first half of GDS year MY 25 than in the following year, but the forcing due to meridional advection and westward thermal tides both appeared to intensify during the MY 25 GDS. The capabilities of the Mars data assimilation system were also extended in this thesis, 1) to represent dynamic dust lifting and dust transport during the assimilation and 2) to assimilate measurements of the dust vertical distribution. The updated reanalysis was then used to study several major dust events during MY28-29. It proved able to reproduce a southward-moving regional dust storm without the overwhelming assistance of the assimilation. Dust devil lifting was found to at least partly provide the initial pattern of dust of this moving dust storm. The cold anomaly of the cooling zone beneath this dust storm could be as large as &Tilde; 2 K similar to the magnitude of what was found during the MY 25 GDS. Using the reanalysis, the life cycle of the planet-encircling global dust storm in MY28 was also studied. The Noachis dust storm that occurred just before the MY 28 GDS was found to be the joint result of a travelling Chryse storm, enhanced by dust lifting along its path and local dust lifting in Noachis itself. The adiabatic heating associated with the north polar warming that occurred during MY 28 GDS was up to &Tilde; 3 times as large as that found during the non-GDS year MY 29. The wind stress dust lifting was shown to in strong correlation with the global average dust loadings, and significantly decreased when the GDS decayed.
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Libros sobre el tema "Climate data"

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Dobesch, Hartwig, Pierre Dumolard, and Izabela Dyras, eds. Spatial Interpolation for Climate Data. ISTE, 2007. http://dx.doi.org/10.1002/9780470612262.

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Closs, James W. NASA's climate data system primer. National Space Science Data Center, National Aeronautics and Space Administration, Goddard Space Flight Center, 1989.

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Brewer, Michael J. Estimating natural vegetation from climatic data. C.W. Thornthwaite Associates, 2001.

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National Research Council (U.S.). Committee on Climate Data Records from NOAA Operational Satellites., ed. Climate data records from environmental satellites. National Academies Press, 2004.

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1952-, Bourges Bernard, and Commission of the European Communities., eds. Climatic data handbook for Europe: Climatic data for the design of solar energy systems. Kluwer Academic Publishers, 1992.

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Gruber, Arnold. Reviews of modern climate diagnostic techniques: Satellite data in climate diagnostics. International Council of Scientific Unions, World Meteorological Organization, 1992.

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Climate data and resources: A reference and guide. Routledge, 1992.

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Mauco, G. M. Inventory for climate data from synoptic stations. Botswana Meteorological Services, 1992.

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Sharan, Girja. Fourier representation of climatic data of Kothara-Kutch. Indian Institute of Management, 2004.

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Redmond, Kelly T. An inventory of climate data for the State of Oregon. Office of the State Climatologist, Climate Research Institute, Oregon State University, 1985.

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Capítulos de libros sobre el tema "Climate data"

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Simon, J. Mason, Ceccato Pietro, and D. Hewitt Chris. "Climate Data." In Climate Information For Public Health Action. Routledge, 2018. http://dx.doi.org/10.4324/9781315115603-6.

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De Larminat, Philippe. "Climatic Data." In Climate Change. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119053989.ch2.

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Korevaar, C. G. "Climatological data." In North Sea Climate. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-1982-2_3.

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Wood, Eric F. "Climate Data Records." In Encyclopedia of Remote Sensing. Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_15.

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Visconti, Guido. "Experimental Data and Climate." In Springer Climate. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65669-4_5.

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Lloyd, Elisabeth A. "Satellite Data and Climate Models." In Climate Modelling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_3.

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Jenne, Roy L. "Data Management Methods; Data for Europe." In Climate and Geo-Sciences. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2446-8_30.

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Lindau, Ralf. "Data and Data Treatment." In Climate Atlas of the Atlantic Ocean. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59526-4_2.

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Cracknell, Arthur P., and Costas Varotsos. "Climate data, analysis, modelling." In Understanding Global Climate Change, 2nd ed. CRC Press, 2021. http://dx.doi.org/10.1201/9780429203329-6.

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Stuiver, M. "Dating Proxy Data." In Climate and Geo-Sciences. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2446-8_3.

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Actas de conferencias sobre el tema "Climate data"

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Lee, Seungwon, Lei Pan, Chengxing Zhai, et al. "Climate model diagnostic analyzer." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363973.

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Jadoul, Nathan, and Erick Stattner. "Climate change data analysis." In the 23rd International Database Applications & Engineering Symposium. ACM Press, 2019. http://dx.doi.org/10.1145/3331076.3331122.

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DaPonte, John S., Thomas Sadowski, and Paul Thomas. "Animating climate model data." In Defense and Security Symposium, edited by Zia-ur Rahman, Stephen E. Reichenbach, and Mark A. Neifeld. SPIE, 2006. http://dx.doi.org/10.1117/12.661309.

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Kumar, Vipin. "Big Data in Climate." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2016. http://dx.doi.org/10.1145/2911451.2911550.

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Karpatne, Anuj, and Vipin Kumar. "Big Data in Climate." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. http://dx.doi.org/10.1145/3097983.3105810.

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SOON, WILLIE. "SUN-CLIMATE CONNECTION: FROM BOUNDARY DATA TO CLIMATIC RESPONSES." In International Seminar on Nuclear War and Planetary Emergencies — 48th Session. World Scientific, 2016. http://dx.doi.org/10.1142/9789813148994_0027.

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Williams, Dean N., Charles M. Doutriaux, Robert S. Drach, and Renata B. McCoy. "The Flexible Climate Data Analysis Tools (CDAT) for Multi-model Climate Simulation Data." In 2009 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2009. http://dx.doi.org/10.1109/icdmw.2009.64.

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McGuire, Michael P., and Nam P. Nguyen. "Community structure analysis in big climate data." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004442.

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"Consistent Climate Scenarios: projecting representative future daily climate from global climate models based on historical climate data." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l11.ricketts.

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Wang, Hengyi, and Zhendong Niu. "Climate Event Detection Algorithm Based on Climate Category Word Embedding." In ICBDC '18: 2018 International Conference on Big Data and Computing. ACM, 2018. http://dx.doi.org/10.1145/3220199.3220203.

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Informes sobre el tema "Climate data"

1

Yurivilca, Rossemary. 2016 IDBG Climate Finance Data. Inter-American Development Bank, 2019. http://dx.doi.org/10.18235/0001954.

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Yurivilca, Rossemary. 2018 IDBG Climate Finance Data. Inter-American Development Bank, 2019. http://dx.doi.org/10.18235/0001955.

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Yurivilca, Rossemary. 2019 IDBG Climate Finance Data. Inter-American Development Bank, 2020. http://dx.doi.org/10.18235/0002841.

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Hamilton, Lawrence. Data Snapshot: Millennials and Climate Change. University of New Hampshire Libraries, 2018. http://dx.doi.org/10.34051/p/2020.326.

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Wentz, Katherine. The Microwave Climate Data Center Repository. Remote Sensing Systems, 2022. http://dx.doi.org/10.56236/rss-bh.

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Auffhammer, Maximilian, Solomon Hsiang, Wolfram Schlenker, and Adam Sobel. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19087.

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Kukla, G., and J. Gavin. Global climate change model natural climate variation: Paleoclimate data base, probabilities and astronomic predictors. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/145219.

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Cheng, Nancy. Data-Driven Illustrations for Climate Smart Communities Scenarios. Portland State University Library, 2013. http://dx.doi.org/10.15760/trec.57.

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Ma, Kwan-Liu. Interactive Correlation Analysis and Visualization of Climate Data. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1325752.

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Marlais, S. M. Development of a climate data analysis tool (CDAT). Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/641114.

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