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Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format
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Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format Example of International Journal of Climatology format
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International Journal of Climatology — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Atmospheric Science #14 of 124 -
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1766 Published Papers | 13801 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 15/06/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

3.928

9% from 2018

Impact factor for International Journal of Climatology from 2016 - 2019
Year Value
2019 3.928
2018 3.601
2017 3.1
2016 3.76
graph view Graph view
table view Table view

7.8

15% from 2019

CiteRatio for International Journal of Climatology from 2016 - 2020
Year Value
2020 7.8
2019 6.8
2018 5.6
2017 6.5
2016 6.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 9% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 15% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.58

6% from 2019

SJR for International Journal of Climatology from 2016 - 2020
Year Value
2020 1.58
2019 1.685
2018 1.587
2017 1.797
2016 1.916
graph view Graph view
table view Table view

1.658

5% from 2019

SNIP for International Journal of Climatology from 2016 - 2020
Year Value
2020 1.658
2019 1.586
2018 1.455
2017 1.621
2016 1.646
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 6% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 5% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

International Journal of Climatology

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Wiley

International Journal of Climatology

A Journal of the Royal Meteorological Society The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, major reviews of progress and reviews of new books and reports in the ...... Read More

Atmospheric Science

Earth and Planetary Sciences

i
Last updated on
15 Jun 2020
i
ISSN
0899-8418
i
Impact Factor
High - 1.647
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
apa
i
Citation Type
Author Year
(Blonder et al., 1982)
i
Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. 1982. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7):4515–4532.

Top papers written in this journal

Journal Article DOI: 10.1002/JOC.1276
Very high resolution interpolated climate surfaces for global land areas.
Robert J. Hijmans1, Susan E. Cameron2, Susan E. Cameron1, Juan L. Parra1, Peter G. Jones3, Andy Jarvis3

Abstract:

We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources a... We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright  2005 Royal Meteorological Society. read more read less

Topics:

Spatial variability (54%)54% related to the paper, Elevation (53%)53% related to the paper, Weather station (53%)53% related to the paper, Spatial ecology (51%)51% related to the paper
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16,411 Citations
open accessOpen access Journal Article DOI: 10.1002/JOC.3711
Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset
Ian Harris1, Philip Jones1, Philip Jones2, Timothy J. Osborn1, David Lister1

Abstract:

This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (exc... This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society read more read less
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4,840 Citations
Journal Article DOI: 10.1002/JOC.5086
WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas
Stephen E. Fick1, Robert J. Hijmans1

Abstract:

We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 197... We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15% (0.07–0.17 °C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data. read more read less

Topics:

Weather station (57%)57% related to the paper, Wind speed (52%)52% related to the paper, Cloud cover (51%)51% related to the paper
4,104 Citations
Journal Article DOI: 10.1002/JOC.1181
An improved method of constructing a database of monthly climate observations and associated high-resolution grids
Timothy D. Mitchell1, Philip Jones1

Abstract:

A database of monthly climate observations from meteorological stations is constructed. The database includes six climate elements and extends over the global land surface. The database is checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partial... A database of monthly climate observations from meteorological stations is constructed. The database includes six climate elements and extends over the global land surface. The database is checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partially overlapping records and by detecting inhomogeneities with opposite signs in different seasons. The method includes the development of reference series using neighbouring stations. Information from different sources about a single station may be combined, even without an overlapping period, using a reference series. Thus, a longer station record may be obtained and fragmentation of records reduced. The reference series also enables 1961–90 normals to be calculated for a larger proportion of stations. The station anomalies are interpolated onto a 0.5° grid covering the global land surface (excluding Antarctica) and combined with a published normal from 1961–90. Thus, climate grids are constructed for nine climate variables (temperature, diurnal temperature range, daily minimum and maximum temperatures, precipitation, wet-day frequency, frost-day frequency, vapour pressure, and cloud cover) for the period 1901–2002. This dataset is known as CRU TS 2.1 and is publicly available (http://www.cru.uea.ac.uk/). Copyright  2005 Royal Meteorological Society. read more read less

Topics:

Homogenization (climate) (62%)62% related to the paper
3,906 Citations
Journal Article DOI: 10.1002/JOC.859
Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island
A. John Arnfield1

Abstract:

Progress in urban climatology over the two decades since the first publication of the International Journal of Climatology is reviewed. It is emphasized that urban climatology during this period has benefited from conceptual advances made in microclimatology and boundary-layer climatology in general. The role of scale, hetero... Progress in urban climatology over the two decades since the first publication of the International Journal of Climatology is reviewed. It is emphasized that urban climatology during this period has benefited from conceptual advances made in microclimatology and boundary-layer climatology in general. The role of scale, heterogeneity, dynamic source areas for turbulent fluxes and the complexity introduced by the roughness sublayer over the tall, rigid roughness elements of cities is described. The diversity of urban heat islands, depending on the medium sensed and the sensing technique, is explained. The review focuses on two areas within urban climatology. First, it assesses advances in the study of selected urban climatic processes relating to urban atmospheric turbulence (including surface roughness) and exchange processes for energy and water, at scales of consideration ranging from individual facets of the urban environment, through streets and city blocks to neighbourhoods. Second, it explores the literature on the urban temperature field. The state of knowledge about urban heat islands around 1980 is described and work since then is assessed in terms of similarities to and contrasts with that situation. Finally, the main advances are summarized and recommendations for urban climate work in the future are made. Copyright © 2003 Royal Meteorological Society. read more read less

Topics:

Urban climatology (76%)76% related to the paper, Urban climate (68%)68% related to the paper, Urban heat island (58%)58% related to the paper, Urban area (57%)57% related to the paper
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2,355 Citations
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Frequently asked questions

1. Can I write International Journal of Climatology in LaTeX?

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Yes, the template is compliant with the International Journal of Climatology guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in International Journal of Climatology?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the International Journal of Climatology citation style.

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5. Can I use a manuscript in International Journal of Climatology that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper International Journal of Climatology that you can download at the end.

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To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for International Journal of Climatology?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for International Journal of Climatology. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In International Journal of Climatology?

The 5 most common citation types in order of usage for International Journal of Climatology are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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16. Can I download International Journal of Climatology in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in International Journal of Climatology Endnote style according to Elsevier guidelines.

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