100+ datasets found
  1. W

    jdc_it_asim_cosmo_ng_st: non-GTS surface station data from the Servizio...

    • wdc-climate.de
    Updated Apr 6, 2009
    + more versions
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    Dorninger, Manfred (2009). jdc_it_asim_cosmo_ng_st: non-GTS surface station data from the Servizio Idro-Meteo-Clima of the province of Emilia-Romagna, ARPA-SIMC, Italy [Dataset]. https://www.wdc-climate.de/ui/entry?acronym=jdc_it_asim_cosmo_ng_st
    Explore at:
    Dataset updated
    Apr 6, 2009
    Dataset provided by
    World Data Center for Climate (WDCC) at DKRZ
    Authors
    Dorninger, Manfred
    Time period covered
    Jan 1, 2007 - Dec 31, 2007
    Area covered
    Variables measured
    wind_speed, air_temperature, present_weather, total_cloud_cover, visibility_in_air, precipitation_rate, wind_speed_of_gust, cloud_base_altitude, wind_from_direction, dew_point_temperature, and 13 more
    Description

    non-GTS data (COSMO data set) from the province of Emilia-Romagna from January to November 2007,
    data are provided by ARPA-SIMC,
    data have been processed at the Department of Meteorology and Geophysics,
    no data quality control at the Department of Meteorology and Geophysics, University of Vienna at all,
    for further details see file: jdc_data_description.pdf in entry "jdc_obsdata_info_1".

  2. e

    Environmental Climatology Information Subsystem (CLIMA). Andalusia

    • data.europa.eu
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    Environmental Climatology Information Subsystem (CLIMA). Andalusia [Dataset]. https://data.europa.eu/data/datasets/30e7a030-a7e6-469e-bee2-dc70cfd132d5
    Explore at:
    Description

    Database that integrates data from different networks of weather stations in Andalusia and its environment. This database has been launched by the Ministry of Agriculture, Fisheries and the Environment in order to respond to the need for climate and meteorological information in the framework of multiple studies.

  3. E

    DEAR-Clima

    • ecoedatahub.eratosthenes.org.cy
    html
    Updated May 24, 2018
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    PILOT 1: Adaptation to Climate Change (ACC) (2018). DEAR-Clima [Dataset]. https://ecoedatahub.eratosthenes.org.cy/dataset/dear-clima
    Explore at:
    htmlAvailable download formats
    Dataset updated
    May 24, 2018
    Dataset provided by
    PILOT 1: Adaptation to Climate Change (ACC)
    Description

    The DEAR-Clima (Data Extraction Application for Regional Climate) is a user friendly interactive web application tool that visualizes and provides time series of essential climate variables and climate indices based on high horizontal resolution Regional Climate Model (RCM) simulations from the Coordinated Regional Downscaling Experiment ( CORDEX ) research program. Reliable and user friendly open access of future climate change data from high resolution RCM projections is essential to support decision makers, stakeholders, intermediary users and end-users for climate change impacts, mitigation and adaptation. The RCM data processed in this web application tool have a high spatial resolution (0.11°) over the european doamin and cover a time period from 1950 to 2100. The historical period of each experiment refers to 1950-2004, while the future period is 2006-2100 under the influence of three Representative Concentration Pathways (RCPs) adopted by the IPCC for its fifth Assessment Report (AR5); rcp26, rcp45 and rcp85. The simulation experiments are a product of various RCMs driven by several Global Climate Models (GCMs).

  4. d

    Hydro-Climatic Data Network (HCDN) -- A USGS Streamflow Data Set for the...

    • catalog.data.gov
    • data.usgs.gov
    • +4more
    Updated Oct 5, 2024
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    U.S. Geological Survey (2024). Hydro-Climatic Data Network (HCDN) -- A USGS Streamflow Data Set for the U.S. for the Study of Climate Fluctuations [Dataset]. https://catalog.data.gov/dataset/hydro-climatic-data-network-hcdn-a-usgs-streamflow-data-set-for-the-u-s-for-the-study-of-c
    Explore at:
    Dataset updated
    Oct 5, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    United States
    Description

    A streamflow data set, which is specifically suitable for the study of surface-water conditions throughout the United States under fluctuations in the prevailing climatic conditions, has been developed. This data set, called the Hydro-Climatic Data Network, or HCDN, consists of streamflow records for 1,659 sites throughout United States and its Territories.

  5. b

    temperatures-hist-bcn

    • opendata-ajuntament.barcelona.cat
    csv
    Updated Mar 20, 2025
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    (2025). temperatures-hist-bcn [Dataset]. https://opendata-ajuntament.barcelona.cat/data/dataset/temperatures-hist-bcn
    Explore at:
    csvAvailable download formats
    Dataset updated
    Mar 20, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Temperatures mitjanes mensuals de l’aire de la ciutat de Barcelona des de 1780. La temperatura es proporciona en graus centígrads (ºC).

  6. A

    ‘Valores climatológicos normales’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 1, 2001
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2001). ‘Valores climatológicos normales’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-europa-eu-valores-climatologicos-normales-537f/662f4ee1/?iid=000-159&v=presentation
    Explore at:
    Dataset updated
    Feb 1, 2001
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Valores climatológicos normales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-datosabiertos-dipucadiz-es-catalogo-clima-normales on 19 January 2022.

    --- Dataset description provided by original source is as follows ---

    Periodo: 1981-2010 - Altitud (m): 2
    Latitud: 36° 29' 59'' N - Longitud: 6° 15' 28'' O
    Leyenda
    T Temperatura media mensual/anual (°C)
    TM Media mensual/anual de las temperaturas máximas diarias (°C)
    Tm Media mensual/anual de las temperaturas mínimas diarias (°C)
    R Precipitación mensual/anual media (mm)
    H Humedad relativa media (%)
    DR Número medio mensual/anual de días de precipitación superior o igual a 1 mm
    DN Número medio mensual/anual de días de nieve
    DT Número medio mensual/anual de días de tormenta
    DF Número medio mensual/anual de días de niebla
    DH Número medio mensual/anual de días de helada
    DD Número medio mensual/anual de días despejados
    I Número medio mensual/anual de horas de sol

    --- Original source retains full ownership of the source dataset ---

  7. U

    Spreadsheet of best models for each downscaled climate dataset and for all...

    • data.usgs.gov
    • catalog.data.gov
    Updated Apr 1, 2022
    + more versions
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    Michelle Irizarry-Ortiz; John Stamm (2022). Spreadsheet of best models for each downscaled climate dataset and for all downscaled climate datasets considered together (Best_model_lists.xlsx) [Dataset]. http://doi.org/10.5066/P935WRTG
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    Dataset updated
    Apr 1, 2022
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Michelle Irizarry-Ortiz; John Stamm
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Time period covered
    1981 - 2005
    Description

    The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period.
    A Microsoft Excel workbook is provided that tabulates best models for each downscaled climate dataset and for all downscaled climate datasets considered together. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Clima ...

  8. e

    Weather stations integrated into the Environmental Climatology Information...

    • data.europa.eu
    Updated Nov 19, 2022
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    (2022). Weather stations integrated into the Environmental Climatology Information Subsystem (CLIMA). Andalusia [Dataset]. https://data.europa.eu/88u/dataset/cc0f94a1-2456-4c2e-8bad-7569b86a73f1~~1
    Explore at:
    Dataset updated
    Nov 19, 2022
    Description

    Vectorial and alphanumeric information related to the characteristics of the meteorological stations integrated in the Environmental Climatology Information Subsystem (CLIMA). CLIMA integrates data from meteorological and related variables such as stratospheric ozone and pollen levels. It does not integrate data on variables related to environmental quality, although meteorological variables measured at stations belonging to these measurement networks are included.

  9. Extended data 2: Tipos de clima con su traducción en Shawi y su definción en...

    • figshare.com
    pdf
    Updated May 11, 2025
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    Junior Chanchari Huiñapi; Nerita Inuma Tangoa; Guillermo Lancha-Rucoba; Manuel Pizango Tangoa; Rosa Silvera-Ccallo; Carol Zavaleta-Cortijo; Shawi Indigenous people (2025). Extended data 2: Tipos de clima con su traducción en Shawi y su definción en español / Types of weather with its translation in Shawi and its definition in Spanish [Dataset]. http://doi.org/10.6084/m9.figshare.23290304.v4
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 11, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Junior Chanchari Huiñapi; Nerita Inuma Tangoa; Guillermo Lancha-Rucoba; Manuel Pizango Tangoa; Rosa Silvera-Ccallo; Carol Zavaleta-Cortijo; Shawi Indigenous people
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This table has key definitions about weather characteristics based on Shawi Indigenous language and knowledge that were prepared by Shawi researchers to inform the protocol : "Does food biodiversity protect against malnutrition and favour the resilience to climate change-related events in Amazon Indigenous communities? A protocol for a mixed methods study"

  10. H

    Data from: Mapas de Zonas Homogéneas de Clima (ZHC) para aguacate y plátano...

    • dataverse.harvard.edu
    • search.dataone.org
    pdf, zip
    Updated Mar 15, 2019
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    Harvard Dataverse (2019). Mapas de Zonas Homogéneas de Clima (ZHC) para aguacate y plátano en Colombia [Dataset]. http://doi.org/10.7910/DVN/YBR4YE
    Explore at:
    zip(1295109), pdf(1252455)Available download formats
    Dataset updated
    Mar 15, 2019
    Dataset provided by
    Harvard Dataverse
    Time period covered
    2011 - 2013
    Area covered
    Colombia
    Dataset funded by
    Fondo Nacional de Fomento Hortifrutícola
    Description

    Los mapas de zonas homogéneas de clima para aguacate y plátano en Colombia fueron desarrolladas con base más de 4000 registros de cosechas reales de todo el país, y la información climática de la base de datos de Worldclim (Hijmans et al. 2005). Un análisis clustering permitió identificar las zonas del país con condiciones de clima similares para los cultivos estudiados. Estos mapas fueron usados en el proyecto de Agricultura Especifica por Sitio Compartiendo Experiencias (AESCE) para transferir conocimientos y practicas exitosas entre productores de una misma zona. Los mapas están en el sistema de coordenadas GCS_WGS_1984. La metodología detallada esta descrita en el archivo Protocolo_Clustering_de_clima_v1.pdf

  11. c

    Datos climáticos y de modelos digitales de clima. Andalucía

    • portalrediam.cica.es
    • data.europa.eu
    Updated Feb 19, 2024
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    Consejería de Sostenibilidad y Medio Ambiente. Junta de Andalucía (2024). Datos climáticos y de modelos digitales de clima. Andalucía [Dataset]. https://portalrediam.cica.es/geonetwork/static/api/records/c2820e86-3605-4c72-bf4a-09d0f8395a0e
    Explore at:
    www:link-1.0-http--relatedAvailable download formats
    Dataset updated
    Feb 19, 2024
    Dataset authored and provided by
    Consejería de Sostenibilidad y Medio Ambiente. Junta de Andalucía
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    Modelos digitales de variables climáticas elaborados a partir de los datos procedentes de las estaciones meteorológicas andaluzas para el periodo 1970-2004.

  12. c

    TIPOS DE CLIMA (GADPEO)

    • geonode.ciifen.org
    • cloud.csiss.gmu.edu
    • +1more
    Updated Mar 28, 2017
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    http://geonode.ciifen.org/people/profile/eflores/ (2017). TIPOS DE CLIMA (GADPEO) [Dataset]. http://geonode.ciifen.org/layers/geonode%3Atipos_clima_eloro
    Explore at:
    Dataset updated
    Mar 28, 2017
    Authors
    http://geonode.ciifen.org/people/profile/eflores/
    Description

    Estudio de vulnerabilidad y desarrollo del sistema de información on-line sobre vulnerabilidad frente al cambio climático de la provincia de el oro

  13. W

    jdc_it_asim_syn_ng_st_ac24: 24h-accumulated non-GTS precipitation data from...

    • wdc-climate.de
    Updated Apr 6, 2009
    + more versions
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    Dorninger, Manfred (2009). jdc_it_asim_syn_ng_st_ac24: 24h-accumulated non-GTS precipitation data from the Servizio Idro-Meteo-Clima of the province of Emilia-Romagna, ARPA-SIMC, Italy [Dataset]. https://www.wdc-climate.de/ui/entry?acronym=jdc_it_asim_syn_ng_st_ac24
    Explore at:
    Dataset updated
    Apr 6, 2009
    Dataset provided by
    World Data Center for Climate (WDCC) at DKRZ
    Authors
    Dorninger, Manfred
    Time period covered
    Jan 1, 2007 - Dec 31, 2007
    Area covered
    Variables measured
    wind_speed, air_temperature, present_weather, total_cloud_cover, visibility_in_air, precipitation_rate, wind_speed_of_gust, cloud_base_altitude, wind_from_direction, dew_point_temperature, and 13 more
    Description

    non-GTS data from the province of Emilia-Romagna for 2007,
    data are provided by ARPA-SIMC,
    data have been processed at the Department of Meteorology and Geophysics,
    no data quality control at the Department of Meteorology and Geophysics, University of Vienna at all,
    24h accumulation of precipitation data performed at University of Hohenheim, for further details see file: jdc_data_description.pdf in entry "jdc_obsdata_info_1".

  14. Agregados diários do ERA5: a mais recente reanálise climática produzida pelo...

    • developers.google.com
    + more versions
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    ECMWF / Copernicus Climate Change Service, Agregados diários do ERA5: a mais recente reanálise climática produzida pelo ECMWF / Copernicus Climate Change Service [Dataset]. https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY?hl=pt-br
    Explore at:
    Dataset provided by
    European Centre for Medium-Range Weather Forecastshttp://ecmwf.int/
    Time period covered
    Jan 2, 1979 - Jul 9, 2020
    Area covered
    Earth
    Description

    O ERA5 é a quinta geração da reanálise atmosférica do ECMWF do clima global. A reanálise combina dados do modelo com observações de todo o mundo em um conjunto de dados globalmente completo e consistente. O ERA5 substitui o ERA-Interim, que é a reanálise anterior. O ERA5 DAILY fornece valores agregados para cada dia de sete parâmetros de reanálise climática do ERA5: …

  15. e

    Medias mensuales y anual de datos de precipitación, temperatura máxima,...

    • data.europa.eu
    Updated Sep 12, 2023
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    (2023). Medias mensuales y anual de datos de precipitación, temperatura máxima, media y mínima del año 1963 en Andalucía [Dataset]. https://data.europa.eu/data/datasets/1d2e7fec-613a-4c57-8f8f-4eda60013e21?locale=en
    Explore at:
    Dataset updated
    Sep 12, 2023
    Area covered
    Andalusia
    Description

    Proyecto de clima donde se recogen las medias mensuales y anual de datos de precipitación, temperatura máxima, media y mínima del año 1963 en Andalucía. Los datos climáticos han sido elaborados a partir de los datos obtenidos por el Instituto Nacional de Metereología. Se ha utilizado como base cartográfica el mapa Topográfico del Instituto Cartográfico de Andalucía 1:100.000 de 1999.

  16. d

    Documentation of the Perspectives and Experiences of Partners with the South...

    • catalog.data.gov
    • s.cnmilf.com
    Updated Jun 15, 2024
    + more versions
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    Climate Adaptation Science Centers (2024). Documentation of the Perspectives and Experiences of Partners with the South Central Climate Adaptation Science Center from Two 2017 Focus Groups [Dataset]. https://catalog.data.gov/dataset/documentation-of-the-perspectives-and-experiences-of-partners-with-the-south-central-clima
    Explore at:
    Dataset updated
    Jun 15, 2024
    Dataset provided by
    Climate Adaptation Science Centers
    Description

    This dataset consists of transcripts from two focus groups with science users (1st group) and science producers (2nd group) who were partners of the South Central Climate Adaptation Science Center (CASC). The participants in the focus groups were science users and science producers identified by the South Central CASC and recruited in collaboration with Cornell's Center for Conservation Social Sciences. A total of 11 individuals participated in the science users focus group, and 16 participated in the science producers focus group. The purpose of the focus groups was to understand the range of perspectives and experiences of CASC partners in relation to their work with the CASC. We attempted to include participants that represented a diversity of organizations and regions working with the CASC. Participants in the science users groups included representatives of agencies intended to benefit from the science produced by the CASC: Landscape Conservation Cooperatives, federal natural resource agencies, state fish and wildlife agencies, tribal organizations, and nongovernmental conservation organizations. Participants in the science producers focus group included researchers that had received research funding from the CASC. The focus groups consisted of semi-structured conversations guided by a series of open-ended questions and lasted approximately two hours. The questions were designed to explore how partners contributed to the work of the CASC and the factors that influenced the ability of the CASC to work with their partners. The specific question topics focused on: how participants have worked with the CASC, reasons for becoming involved with the CASC, benefits of involvement with the CASC, challenges to involvement, and what the CASC could do to promote even more benefits from involvement. Additionally, we specifically explored how the CASC contributed to the coproduction of science and the generation of actionable science, with questions about interactions between science producers and science users and the role of the CASC in connecting them.

  17. u

    Data from: Tipo de Clima Anual para Campina Grande e Areia: Variabilidade e...

    • repositorio.ufpb.br
    Updated Sep 26, 2019
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    (2019). Tipo de Clima Anual para Campina Grande e Areia: Variabilidade e Tipologia [Dataset]. https://repositorio.ufpb.br/jspui/handle/123456789/24704?locale=en
    Explore at:
    Dataset updated
    Sep 26, 2019
    Area covered
    Campina Grande
    Description

    Entender o clima de uma região é fundamental para subsidiar processos de tomada de decisão pela gestão pública e providenciar ferramentas de desenvolvimento regional e de atividades econômicas ligadas ao campo como, por exemplo, a agropecuária e a agricultura familiar, importantes fatores que viabilizam, com algum sucesso, a sobrevivência na região mais seca do país: o Semiárido. O Semiárido Brasileiro apresenta características climáticas marcantes e apresenta bastante variabilidade tanto temporal quanto espacial, principalmente devido a sua extensão que abrange dez estados brasileiros. A característica estática dos métodos clássicos de classificação climática sofre duras críticas quanto aos resultados mais abrangentes, gerais, e, menos detalhados. Contudo, os métodos mais recentes que promovem uma climatologia mais dinâmica são alvos da falta de dados acessíveis, concisos, em séries prolongadas e em escalas, no máximo, diárias. Diante do exposto, o objetivo principal deste trabalho foi analisar, no período de 1994 a 2018, para os municípios de Areia e Campina Grande, ambos localizados no estado da Paraíba, o Tipo de Clima Anual (TCA), que é uma aplicação de uma técnica que utiliza métodos clássicos de classificação climatológica, contudo, de forma um pouco mais dinâmica para entender as características climáticas em escala anual. Além disso, foi analisada a variabilidade interanual das variáveis precipitação e temperatura para os 25 anos da série e para a Normal Climatológica de 1981 a 2010. Ademais, foram avaliados os critérios da inserção de municípios na nova delimitação do semiárido relacionados à quantidade de precipitação e índice de aridez. A base de Sistemas de Classificação Climática utilizada foi o sitema de Köppen-Geiger e o de Thornthwaite. Os resultados encontrados apontaram que, apesar da proximidade entre eles, os municípios de Areia e Campina Grande têm índices pluviométricos muito distintos, todavia suas distribuições têm comportamento semelhante. O município de Areia tem precipitação anual média de 1317,6 mm, o que a torna 70% mais úmida que Campina Grande (774,0 mm), porém com amplitude térmica semelhantes e médias de 22,5°C e 23,5°C, respectivamente. O TCA para Areia apresenta mais variações de clima que para Campina Grande, isso para os dois tipos de Classificações Climáticas utilizadas, e não correspondem necessariamente ao clima habitual para as localidades, sendo mais visível no método de Thornthwaite. O tipo climático habitual por Thornthwaite para Areia é B1rB’4b’4 e para Campina C1dA’a’, sendo por Köppen As’ para as duas localidades. Por fim, a análise dos critérios de inserção na delimitação mostrou que Campina Grande está apta a compor o semiárido, ao contrário de Areia. Ainda se levanta uma reflexão quanto ao índice de aridez utilizado para a classificação.

  18. b

    mesures-estacions-meteorologiques

    • opendata-ajuntament.barcelona.cat
    csv
    Updated Mar 20, 2025
    + more versions
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    (2025). mesures-estacions-meteorologiques [Dataset]. https://opendata-ajuntament.barcelona.cat/data/dataset/mesures-estacions-meteorologiques
    Explore at:
    csvAvailable download formats
    Dataset updated
    Mar 20, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Detall dels recursos estadístics de les estacions meteorològiques de la ciutat de Barcelona. Cada estació mostra la informació captada per els diferents sensors.

  19. e

    Atlante climatico 1961-2015 - Edizione 2017 - Temperatura minima - Media...

    • data.europa.eu
    unknown
    Updated Jul 20, 2017
    + more versions
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    (2017). Atlante climatico 1961-2015 - Edizione 2017 - Temperatura minima - Media invernale - Clima 1961-1990 - Ed.2017 [Dataset]. https://data.europa.eu/data/datasets/arpa-2017-07-20t141900?locale=fi
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    unknownAvailable download formats
    Dataset updated
    Jul 20, 2017
    Description

    Media sul periodo 1961-1990 della media invernale della temperatura minima giornaliera (°C)

  20. e

    Atlante climatico 1961-2015 - Edizione 2017 - Temperatura media - Media...

    • data.europa.eu
    unknown
    Updated Jul 20, 2017
    + more versions
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    (2017). Atlante climatico 1961-2015 - Edizione 2017 - Temperatura media - Media annuale - Clima 1961-1990 - Ed.2017 [Dataset]. https://data.europa.eu/data/datasets/arpa-2017-07-20t122801?locale=en
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    Dataset updated
    Jul 20, 2017
    Description

    Media sul periodo 1961-1990 della media annuale della temperatura media giornaliera (°C)

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Dorninger, Manfred (2009). jdc_it_asim_cosmo_ng_st: non-GTS surface station data from the Servizio Idro-Meteo-Clima of the province of Emilia-Romagna, ARPA-SIMC, Italy [Dataset]. https://www.wdc-climate.de/ui/entry?acronym=jdc_it_asim_cosmo_ng_st

jdc_it_asim_cosmo_ng_st: non-GTS surface station data from the Servizio Idro-Meteo-Clima of the province of Emilia-Romagna, ARPA-SIMC, Italy

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190 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Apr 6, 2009
Dataset provided by
World Data Center for Climate (WDCC) at DKRZ
Authors
Dorninger, Manfred
Time period covered
Jan 1, 2007 - Dec 31, 2007
Area covered
Variables measured
wind_speed, air_temperature, present_weather, total_cloud_cover, visibility_in_air, precipitation_rate, wind_speed_of_gust, cloud_base_altitude, wind_from_direction, dew_point_temperature, and 13 more
Description

non-GTS data (COSMO data set) from the province of Emilia-Romagna from January to November 2007,
data are provided by ARPA-SIMC,
data have been processed at the Department of Meteorology and Geophysics,
no data quality control at the Department of Meteorology and Geophysics, University of Vienna at all,
for further details see file: jdc_data_description.pdf in entry "jdc_obsdata_info_1".

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