Special Issue "Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere and Oceans"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 30 November 2023 | Viewed by 1280

Special Issue Editors

IMSG at NOAA/NWS/NCEP Environmental Modeling Center, College Park, MD 20740, USA
Interests: numerical methods; modeling; data assimilation; machine learning/artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Institute of Meteorology School of Physics, University of Belgrade, Belgrade, Serbia
Interests: extreme temperature events; precipitation; large-scale circulation and climate variability

Special Issue Information

Dear Colleagues,

This Special Issue explores “advanced” or “novel” numerical modeling and data assimilation techniques to assess weather, specifically the climate of the atmosphere and oceans.  The aim is to provide a platform for presenting and testing new ideas and methods where authors will be able to express their creativity without restrictions and verifications so necessary for establishing scientific rigor. The regular process of creating, testing, and transitioning into operations of new ideas is often connected with practical limitations that can obstruct and discourage such creative efforts. The objective of this Special Issue is, therefore, to strongly encourage creative endeavors. We are looking for techniques that may bring challenges, but can potentially lead to fundamental breakthroughs, i.e., methods which are still in a relatively early experimental stage, but promise major advancements, and even paradigm shifts. The examples may include, but are not limited to:

  • new approaches to quasi-uniform gridding of the sphere;
  • unstructured and moving meshes;
  • grid adaptation techniques;
  • parallelization in time;
  • exponential time integration;
  • discontinuous Galerkin methods;
  • nonlinear data assimilation;
  • data assimilation techniques based on the non-Gaussian statistics;
  • methods for improving preconditioning in variational data assimilation;
  • ML/AI as emulation for standard techniques in weather forecasting and data assimilation;
  • application of recent advancements in ML/AI, such as “next generation” of reservoir computing or deep learning clustering for modeling and data assimilation;
  • multigrid techniques;
  • evolutionary programing;
  • application of quantum computing.

Dr. Miodrag Rancic
Dr. Ivana Tosic
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • numerical methods
  • weather and climate prediction
  • data assimilation
  • novel techniques and approaches
  • emulations by machine learning and artificial intelligence

Published Papers (2 papers)

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Research

Article
The Impacts of Assimilating Fengyun-4A Atmospheric Motion Vectors on Typhoon Forecasts
Atmosphere 2023, 14(2), 375; https://doi.org/10.3390/atmos14020375 - 14 Feb 2023
Cited by 1 | Viewed by 442
Abstract
Atmospheric motion vectors (AMVs), known as cloud track winds, have positive impacts on global numerical weather forecasts (NWP). In this study, AMVs that were retrieved from Fengyun-2G and Fengyun-4A were compared in their data quality and impacts on the typhoon forecasts in order [...] Read more.
Atmospheric motion vectors (AMVs), known as cloud track winds, have positive impacts on global numerical weather forecasts (NWP). In this study, AMVs that were retrieved from Fengyun-2G and Fengyun-4A were compared in their data quality and impacts on the typhoon forecasts in order to investigate the differences between the first and second generation of the geostationary meteorological satellites of China. This report conducted data evaluation and assimilation-forecasting experiments on FY-2G and FY-4A atmospheric motion vectors (AMVs), respectively. The results showed that the AMVs data of FY-4A are of better quality than those of FY-2G and assimilating the AMVs of FY-2G and FY-4A have a neutral to slightly positive impacts on typhoon forecasts, which is quite encouraging for their operational use in the future. Full article
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Article
A Simple and Effective Random Forest Refit to Map the Spatial Distribution of NO2 Concentrations
Atmosphere 2022, 13(11), 1832; https://doi.org/10.3390/atmos13111832 - 03 Nov 2022
Viewed by 578
Abstract
This study proposes a random forest–random pixel ID (RF–RID) method, which could reduce local anomalies in the simulation of NO2 spatial distribution and significantly improve prediction accuracy in rural areas. First, the 470 nm MAIAC AOD and OMI NO2 total and [...] Read more.
This study proposes a random forest–random pixel ID (RF–RID) method, which could reduce local anomalies in the simulation of NO2 spatial distribution and significantly improve prediction accuracy in rural areas. First, the 470 nm MAIAC AOD and OMI NO2 total and tropospheric vertical column were packed using the two-step method (TWS). Second, using RID, the filled data and auxiliary variables were combined with random forest (RF) to build an RF–RID model to predict the 1 km/d NO2 spatial distribution in southwestern Fujian (SWFJ) in 2018. The results show that the RF–RID achieves enhanced performance in the CV of the observed sample (R = 0.9117, RMSE = 3.895). Meanwhile, RF–RID has a higher correlation with the road length (RL) in remote areas, and the proposed method solves the issue related to strips or patches of NO2 spatial distribution. This model offers insights into the related research on air pollutants in large areas. Full article
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