Geoscientific Model Development

雜志介紹

Geoscientific Model Development雜志介紹

《Geoscientific Model Development》是一本以English為主的開(kāi)放獲取國(guó)際優(yōu)秀期刊，中文名稱地球科學(xué)模型開(kāi)發(fā)，本刊主要出版、報(bào)道地球科學(xué)-GEOSCIENCES, MULTIDISCIPLINARY領(lǐng)域的研究動(dòng)態(tài)以及在該領(lǐng)域取得的各方面的經(jīng)驗(yàn)和科研成果，介紹該領(lǐng)域有關(guān)本專業(yè)的最新進(jìn)展，探討行業(yè)發(fā)展的思路和方法，以促進(jìn)學(xué)術(shù)信息交流，提高行業(yè)發(fā)展。該刊已被國(guó)際權(quán)威數(shù)據(jù)庫(kù)SCIE收錄，為該領(lǐng)域相關(guān)學(xué)科的發(fā)展起到了良好的推動(dòng)作用，也得到了本專業(yè)人員的廣泛認(rèn)可。該刊最新影響因子為4，最新CiteScore 指數(shù)為8.6。

本刊近期中國(guó)學(xué)者發(fā)表的論文主要有：

• Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model

  Author: Wang, Liang; Wan, Bingcheng; Zhou, Shaohui; Sun, Haofei; Gao, Zhiqiu

• Climate impacts of parameterizing subgrid variation and partitioning of landsurface heat fluxes to the atmosphere with the NCAR CESM1.2

  Author: Yin, Ming; Han, Yilun; Wang, Yong; Sun, Wenqi; Deng, Jianbo; Wei, Daoming; Kong, Ying; Wang, Bin

• A nonhydrostatic oceanic regional model, ORCTM v1, for internal solitary wavesimulation

  Author: Huang, Hao; Song, Pengyang; Qiu, Shi; Guo, Jiaqi; Chen, Xueen

• WRF-ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer

  Author: Zhong, Xiaohui; Ma, Zhijian; Yao, Yichen; Xu, Lifei; Wu, Yuan; Wang, Zhibin

英文介紹

Geoscientific Model Development雜志英文介紹

Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication:

* geoscientific model descriptions, from statistical models to box models to GCMs;

* development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results;

* new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data;

* papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data;

* model experiment descriptions, including experimental details and project protocols;

* full evaluations of previously published models.