Journal of the Meteorological Society of Japan. Ser. II
Journal of the Meteorological Society of Japan (JMSJ) is an international, peer-reviewed, and open-access journal for the publication of research in areas of meteorology.

Aims and Scope
JMSJ publishes Articles and Notes and Correspondence that report novel scientific discoveries or technical developments that advance understanding in meteorology and related sciences. The journal’s broad scope includes meteorological observations, modeling, data assimilation, analyses, global and regional climate research, satellite remote sensing, chemistry and transport, and dynamic meteorology including geophysical fluid dynamics. In particular, JMSJ welcomes papers related to Asian monsoons, climate and mesoscale models, and numerical weather forecasts. Insightful and well-structured original Review Articles that describe the advances and challenges in meteorology and related sciences are also welcome.

JMSJ encourages authors to include the data underlying their work as supplementary material. These data, which must be under 50MB, may describe observations, experiments, modeling or analyses and may take the form of databases, simulations, movies, large figures or as otherwise appropriate.

As an international journal in the meteorological science community, JMSJ maintains a high standard of peer review and offers readers worldwide the benefit of articles being freely available online.

Instructions for Authors
The journal's Instructions for Authors document contains important details about the editorial policies, ethics, copyright, fees and other information.

Other information
More information about the journal is available at the society’s journal website This includes plain language summaries, graphical abstracts, access and citation statistics, and JMSJ awards. We have also complied ten great features of JMSJ and encourage you to submit to the journal.

The journal has an active social media presence at Facebook and Twitter. JMSJ is indexed in many services, including Web of Science/SCIE, Scopus, DOAJ and more.
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7,031 registered articles
(updated on October 03, 2023)
Online ISSN : 2186-9057
Print ISSN : 0026-1165
ISSN-L : 0026-1165
2022 Journal Impact Factor (JIF)
DOAJ Scopus J-STAGE Data
Editor's Highlight
The 30-year (1987-2016) Trend of Strong Typhoons and Genesis Locations Found in the Japan Meteorological Agency's Dvorak Reanalysis Data Read more
Particle Filtering and Gaussian Mixtures―On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP Read more
Editor's pick

This paper describes a particle filter in the global NWP at Deutscher Wetterdienst (DWD).  A particle filter (PF) in the global NWP at DWD is proposed and evaluated its skills in comparison with the operational system. To alleviate the degeneration, which is the largest issue in PFs with high-dimensional systems, several approaches are effectively incorporated such as localization, Gaussian mixture approximation in the prior distribution, adaptive resampling, and so on (See Section 2.3). Since comprehensive formulations in this system are described, the readers can totally understand its theoretical aspects.

Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System Version 3 (JMA/MRI-CPS3) Read more
Editor's pick

This paper describe a newly developed operational seasonal forecast system, JMA/MRI-CPS3. Ocean 4D-Var and sea ice 3D-Var data assimilation methods are newly introduced. The errors in the ocean analysis are now represented in the initial perturbations. Updated physical processes and increased resolution of the atmospheric model contribute to the improved climate reproducibility of the MJO and North Atlantic blocking highs. The introduction of a 0.25-degree-resolution ocean model provides a realistic representation of tropical instability waves and contributes to improved ENSO pattern.

Effects of Dry Vegetation Coverage Estimated from the MODIS Soil Tillage Index on Dust Occurrence: Verification by Surface Synoptic Observations Read more
Editor's pick

This paper proposed a machine learning method as an observation operator for satellite radiances within a data assimilation system. Model forecast and satellite microwave radiance observations are used to train machine learning models to obtain the observation operator for satellite data assimilation.Data assimilation experiments using the machine learning-based observation operator show promising results without a separate bias correction procedure.The machine learning-based observation operator can potentially accelerate the development of using new satellite observations in numerical weather prediction.

A Machine Learning Approach to the Observation Operator for Satellite Radiance Data Assimilation Read more
Editor's pick

This paper proposed a new method calculating the threshold wind speed for dust occurrence. A new method to obtain threshold wind speed that takes account of the interannual variations of dry vegetation cover is proposed. Dry vegetation coverage is a key factor determining interannual variations in the April dust occurrence. Other land surface factors such as soil freeze-thaw and snow cover should be considered to explain dust occurrence variations in March.

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