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Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere...
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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: closed (30 November 2023) | Viewed by 6908

Special Issue Editors

Dr. Miodrag Rancic

E-Mail Website
Guest Editor
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
Dr. Ivana Tosic

E-Mail Website
Guest Editor
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 2400 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 (6 papers)

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Research

17 pages, 5172 KiB  
Article
Comparative Analysis of Neural Network Models for Predicting Ammonia Concentrations in a Mechanically Ventilated Sow Gestation Facility in Korea
by Junsu Park, Gwanggon Jo, Minwoong Jung and Youngmin Oh
Atmosphere 2023, 14(8), 1248; https://doi.org/10.3390/atmos14081248 - 05 Aug 2023
Viewed by 807
Abstract
Conventional methods for monitoring ammonia (NH3) emissions from livestock farms have several challenges, such as a poor environment for measurement, difficulty in accessing livestock, and problems with long-term measurement. To address these issues, we applied various neural network models for the [...] Read more.
Conventional methods for monitoring ammonia (NH3) emissions from livestock farms have several challenges, such as a poor environment for measurement, difficulty in accessing livestock, and problems with long-term measurement. To address these issues, we applied various neural network models for the long-term prediction of NH3 concentrations from sow farms in this study. Environmental parameters, including temperature, humidity, ventilation rate, and past records of NH3 concentrations, were given as inputs to the models. These neural network models took the encoder or the feature extracting parts from the representative deep learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer, to encode temporal patterns of time series. However, all of these models adopted dense layers for the decoder to format the task of long-term prediction as a regression problem. Due to their regression nature, all models showed a robust performance in predicting long-term NH3 concentrations at a scale of weeks or even months despite there being a relatively short period of input signals (a few days to a week). Given one week of input, LSTM showed the minimum mean absolute errors (MAE) of 1.83, 1.78, and 1.87 ppm for the prediction of one, two, and three weeks, respectively, whereas Transformer performed best with a MAE of 1.73 ppm for a four-week prediction. In the long-term estimation of spanning months, LSTM showed the minimum MAEs of 1.95 and 1.90 ppm when trained on predicting two and three weeks of windows. At the same condition, Transformer gave the minimum MAEs of 1.87 and 1.83 when trained on predicting one and four weeks of windows. Overall, the neural network models can facilitate the prediction of national-level NH3 emissions, the development of mitigation strategies for NH3-derived air pollutants, odor management, and the monitoring of animal-rearing environments. Further, their integration of real-time measurement devices can significantly prolong device longevity and offer substantial cost savings. Full article
(This article belongs to the Special Issue Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere and Oceans)
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17 pages, 12451 KiB  
Article
Can the Assimilation of the Ascending and Descending Sections’ Data from Round-Trip Drifting Soundings Improve the Forecasting of Rainstorms in Eastern China?
by Xupeng Zhang, Lu Sun, Xulin Ma, Huan Guo, Zerui Gong and Xiaohan Yan
Atmosphere 2023, 14(7), 1127; https://doi.org/10.3390/atmos14071127 - 07 Jul 2023
Viewed by 840
Abstract
To further examine the effectiveness of the ascending and descending sections of the new round-trip drifting sounding system in forecasting high-impact weather such as heavy rainfall, in this study, three assimilation experiments were designed for a rainstorm in eastern China using WRF (Weather [...] Read more.
To further examine the effectiveness of the ascending and descending sections of the new round-trip drifting sounding system in forecasting high-impact weather such as heavy rainfall, in this study, three assimilation experiments were designed for a rainstorm in eastern China using WRF (Weather Research and Forecast) and WRFDA-3DVAR. Then, the reasons for the improvement in the effects of forecasting rainfall after assimilation were analyzed from various perspectives. The results showed that the assimilation of round-trip drifting sounding data can increase the accuracy of regions of heavy precipitation and improve the effectiveness of rainstorm forecasting. The quality of the wind field improved after the assimilation of the round-trip drifting sounding data, which improved the conditions of moisture transport, resulting in increased humidity in the lower layer, which accumulated more unstable energy for the development of storms. In addition, the enhanced low-level convergence after the assimilation of the new sounding data created a strong upward motion, which was more conducive to triggering heavy rainfall, thus improving the ability to forecast such heavy rainfall. Full article
(This article belongs to the Special Issue Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere and Oceans)
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30 pages, 2893 KiB  
Article
Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin
by Venelin Todorov and Ivan Dimov
Atmosphere 2023, 14(7), 1078; https://doi.org/10.3390/atmos14071078 - 26 Jun 2023
Cited by 9 | Viewed by 1291
Abstract
Thorough examination of various aspects related to the distribution of air pollutants in a specific region and the factors contributing to high concentrations is essential, as these elevated levels can be detrimental. To accomplish this, the development and improvement of a digital twin [...] Read more.
Thorough examination of various aspects related to the distribution of air pollutants in a specific region and the factors contributing to high concentrations is essential, as these elevated levels can be detrimental. To accomplish this, the development and improvement of a digital twin that encompasses all relevant physical processes in the atmosphere is necessary. This tool, known as DIGITAL AIR, has been created, and it is now necessary to extend it with precise sensitivity analysis. DIGITAL AIR is gaining popularity due to its effectiveness in addressing complex problems that arise in intricate environments; this motivates our further investigations. In this paper, we focus on the preparation and further investigation of DIGITAL AIR through sensitivity analysis with improved stochastic approaches for investigating high-level air pollutants. We discuss and test the utilization of this digital tool in tackling the issue. The unified Danish Eulerian model (UNI-DEM) plays a crucial role within DIGITAL AIR. This mathematical model, UNI-DEM, is highly versatile and can be applied to various studies concerning the adverse effects caused by elevated air pollution levels. Full article
(This article belongs to the Special Issue Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere and Oceans)
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17 pages, 2091 KiB  
Article
Spherical Grid Creation and Modeling Using the Galerkin Compiler GC_Sphere
by Jürgen Steppeler
Atmosphere 2023, 14(6), 966; https://doi.org/10.3390/atmos14060966 - 31 May 2023
Viewed by 856
Abstract
The construction of spherical grids is, to a large extent, a question of organized programming. Such grids come in the form of rhomboidal/triangular grids and hexagonal grids. We are here mainly interested in Local-Galerkin high-order schemes and consider the classical fourth-order o4 method [...] Read more.
The construction of spherical grids is, to a large extent, a question of organized programming. Such grids come in the form of rhomboidal/triangular grids and hexagonal grids. We are here mainly interested in Local-Galerkin high-order schemes and consider the classical fourth-order o4 method for comparison. High-order Local-Galerkin schemes imply sparse grids in a natural way, with an expected saving of computer runtime. Sparse grids on the sphere are described for rhomboidal and hexagonal cells. They are obtained by not using some of the full grid points. Technical problems and grid organization will be discussed with the purpose of reaching fully realistic applications. We present the description of a programming concept allowing people, using different programming styles at different locations, to work together. The concept of geometric files is introduced. Such geometric files can be offered for downloading and are supposed to allow Local-Galerkin methods to be introduced into an existing model with little effort. When the geometric files are known, the solution on a spherical grid is equivalent to the limited-area Galerkin solutions on the (irregular) plane grids on the patches. The proposed grids can be used with spectral elements (SE) and the Local-Galerkin methods o2o3 and o3o3. The latter offer an increased numerical efficiency which, in a toy model test, resulted in a ten-times-reduced computer run time. Full article
(This article belongs to the Special Issue Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere and Oceans)
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15 pages, 6928 KiB  
Article
The Impacts of Assimilating Fengyun-4A Atmospheric Motion Vectors on Typhoon Forecasts
by Keyi Chen and Peigen Guan
Atmosphere 2023, 14(2), 375; https://doi.org/10.3390/atmos14020375 - 14 Feb 2023
Cited by 2 | Viewed by 1162
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
(This article belongs to the Special Issue Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere and Oceans)
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20 pages, 10751 KiB  
Article
A Simple and Effective Random Forest Refit to Map the Spatial Distribution of NO2 Concentrations
by Yufeng Chi and Yu Zhan
Atmosphere 2022, 13(11), 1832; https://doi.org/10.3390/atmos13111832 - 03 Nov 2022
Cited by 2 | Viewed by 1260
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
(This article belongs to the Special Issue Advanced Numerical Techniques for Modeling and Data Assimilation of Atmosphere and Oceans)
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