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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 (31 December 2022) | Viewed by 20090

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Special Issue Editors

Dr. Seung-Jae Lee

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Guest Editor

National Center for AgroMeteorology (NCAM), Seoul 08826, Republic of Korea
Interests: earth system modeling; numerical weather prediction; regional climate simulation; land-air-water-life interaction; user-customized data processing
Special Issues, Collections and Topics in MDPI journals

Dr. Ji-Sun Kang

E-Mail Website
Guest Editor

Korea Institute of Science and Technology Information (KISTI), Daejeon 34141, Korea
Interests: numerical modeling; ensemble data assimilation; predictability of uncertainty; prediction systems of natural disasters; computational skills for HPC utilization

Special Issue Information

Dear Colleagues,

With the continued growth and development of computing resources comes the user expectation that we will be able to obtain increasingly more detailed and specific weather and climate information. The aim of this Special Issue is to gather and share recent advances in the field of high-resolution weather and climate modeling, data assimilation, predictability, and industrial applications. These topics have become more important than ever and are always a top priority in many industries, including the agriculture, forestry, fishery, aviation, and health sectors. However, they are still challenging to us for both normal and abnormal (extreme) phenomena of nature and essentially deal with the state-of-the-art sciences, technologies, and multidisciplinary approaches in software and hardware. This topic encompasses various dynamical, physical, biogeochemical, probabilistic, and statistical aspects including artificial intelligence in research institutes and operational centers. The topic is also relevant to coupling or linkage between high-resolution weather (climate) models and diverse applied models such as crop, animals, fire, landslide, drought, flood, and pollution modules and schemes. Here, we cordially invite scientists to submit articles regarding the above subjects so that both nations and people can understand, predict, and wisely manage our Earth system better than before.

Dr. Seung-Jae Lee
Dr. Ji-Sun Kang
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 weather prediction (NWP)
climate simulation and projection
data assimilation and data fusion
physical parameterization and chemical modules
dynamical core
horizontal and vertical grid
adaptive and staggered grids
spatiotemporal resolutions and grey zone
downscaling, upscaling, and scale awareness
large eddy simulation
cloud resolving model
computational fluid dynamics (CFD)
urban modeling
coupled modeling
unified modeling and seamless forecasting
ensemble modeling and statistical modeling
high performance computing (HPC) and cloud computing
supercomputers and Linux clusters
CPU, GPU and APU
high-resolution 3-D visualization

Published Papers (11 papers)

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Research

14 pages, 3396 KiB

Open AccessArticle

Fine Particulate Matter Concentration and Early Deaths Related to Thermal Power Plants and National Industrial Complexes in South Korea

by Jongsik Ha, Nankyoung Moon and Jihyun Seo

Atmosphere 2023, 14(2), 344; https://doi.org/10.3390/atmos14020344 - 09 Feb 2023

Viewed by 1213

Abstract

Thermal power plants (TPPs) and national industrial complexes (NICs) are widely known as being among the major causes of changes in the concentrations of fine particulate matter (PM_2.5). However, little is known about the changes in PM_2.5 concentration caused by the operation of these facilities in South Korea and the health burden attributable to them, including early death. There were two purposes to this study. The first was to quantitatively evaluate the changes in PM_2.5 concentration caused by TPPs and NICs in Korea. The second was to estimate the number of early deaths as a health burden attributable to such changes in PM_2.5 concentration. The changes in PM_2.5 concentration caused by the operation of TPPs and NICs were investigated within TPPs in 2013 and within NICs in 2015. The number of early deaths in 2015 caused by changes in PM_2.5 concentration was estimated using the Environmental Benefits Mapping and Analysis Program (BenMAP). Nationwide, the annual average concentration of PM_2.5 caused by the operation of TPPs and NICs was estimated to increase by 0.611 μg/m³ and 1.245 μg/m³, respectively, suggesting that NICs contributed about twice as much to this concentration as TPPs. The same trend was also observed regarding the number of early deaths, with TPPs and NICs accounting for 1017 and 2091 early deaths per year, respectively, indicating that the operation of NICs causes a health burden about twice as high as that caused by TPPs. However, the changes in PM_2.5 concentration were found to be high near TPPs and NICs, while the health burden caused by exposure to PM_2.5 varied according to the level of population distribution and mortality in each air (quality) control zone (ACZ) to which one is exposed. The findings of this study are expected to be utilized as reference data when setting goals to strengthen air quality management (AQM) in each ACZ in Korea. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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19 pages, 13490 KiB

Open AccessArticle

A Case Study of Bias Correction and the Dynamical Downscaling of CFSv2 S2S Forecasts Using a WRF Model: Heatwave in 2018 over South Korea

by Jiwon Oh, Jaiho Oh and Morang Huh

Atmosphere 2022, 13(12), 2086; https://doi.org/10.3390/atmos13122086 - 11 Dec 2022

Cited by 1 | Viewed by 1401

Abstract

Extreme weather events caused by climate change affect the growth of crops, requiring reliable weather forecasts. In order to provide day-to-season seamless forecasting data for the agricultural sector, improving the forecasting performance of the S2S period is necessary. A number of studies have been conducted to improve prediction performance based on the bias correction of systematic errors in GCM or by producing high-resolution data via dynamic detailing. In this study, a daily simple mean bias correction technique is applied on CFSv2 (∼100 km) data. We then use case studies to evaluate how beneficial the precision of the high-resolution RCM simulation is in improving S2S prediction performance using the bias-corrected lateral boundary. Based on our examination of 45-day sequences of WRF simulations with 27–9–3 km resolution, it can be concluded that a higher resolution is correlated with better prediction in the case of the extreme heatwave in Korea in 2018. However, the effect of bias correction in improving predictive performances is not significant, suggesting that further studies on more cases are necessary to obtain more solid conclusions in the future. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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17 pages, 15523 KiB

Open AccessArticle

Impact Analysis of Variable Resolution of MPAS on Intrinsic Predictability Using Bred Vectors

by Ji-Sun Kang, Seoleun Shin and Hunjoo Myung

Atmosphere 2022, 13(12), 2070; https://doi.org/10.3390/atmos13122070 - 09 Dec 2022

Viewed by 947

Abstract

Variable resolution configuration is a defining feature of the NCAR MPAS (Model for Prediction Across Scales) model, which allows us to smoothly vary the horizontal resolution for taking a closer look at an area of interest. In this study, we aimed to analyze the impact of variable resolution on intrinsic predictability using bred vectors. Thus, the breeding cycles of the MPAS model with and without variable resolution configuration were implemented and tested with two different rescaling intervals of 6 h and 1 day. Rescaling within our breeding cycles were centered by the nature run, thus we could deal with the intrinsic predictability limited only by the initial error growth. We confirmed reasonable estimates of fast-growing errors by bred vectors at two different scales of convective and synoptic systems. We then found that the variable resolution configuration gave consistent improvement of intrinsic predictability not only over the high-resolution area but also outside. A quantitative analysis showed that an improvement with the variable resolution could be found in general for most vertical levels for both rescaling interval experiments. Additionally, we present the computational cost and experience of performing the variable resolution model which would help users in their decisions on this setting. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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17 pages, 4424 KiB

Open AccessArticle

Monthly Agricultural Reservoir Storage Forecasting Using Machine Learning

by Soo-Jin Kim, Seung-Jong Bae, Seung-Jae Lee and Min-Won Jang

Atmosphere 2022, 13(11), 1887; https://doi.org/10.3390/atmos13111887 - 11 Nov 2022

Cited by 2 | Viewed by 1351

Abstract

Storage rate forecasting for the agricultural reservoir is helpful for preemptive responses to disasters such as agricultural drought and planning so as to maintain a stable agricultural water supply. In this study, SVM, RF, and ANN machine learning algorithms were tested to forecast the monthly storage rate of agricultural reservoirs. The storage rate observed over 30 years (1991–2022) was set as a label, and nine datasets for a one- to three-month storage rate forecast were constructed using precipitation and evapotranspiration as features. In all, 70% of the total data was used for training and validation, and the remaining 30% was used as a test. The one-month storage rate forecasting showed that all SVM, RF, and ANN algorithms were highly reliable, with R² values ≥ 0.8. As a result of the storage rate forecast for two and three months, the ANN and SVM algorithms showed relatively reasonable explanatory power with an average R² of 0.64 to 0.69, but the RF algorithm showed a large generalization error. The results of comparing the learning time showed that the learning speed was the fastest in the order of SVM, RF, and ANN algorithms in all of the one to three months. Overall, the learning performance of SVM and ANN algorithms was better than RF. The SVM algorithm is the most credible, with the lowest error rates and the shortest training time. The results of this study are expected to provide the scientific information necessary for the decision-making regarding on-site water managers, which is expected to be possible through the connection with weather forecast data. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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15 pages, 6129 KiB

Open AccessArticle

Impact of IMO Sulfur Regulations on Air Quality in Busan, Republic of Korea

by Yumi Kim, Nankyoung Moon, Yoonbae Chung and Jihyun Seo

Atmosphere 2022, 13(10), 1631; https://doi.org/10.3390/atmos13101631 - 07 Oct 2022

Cited by 3 | Viewed by 1589

Abstract

In this study, we investigate the air quality improvement effect in Busan, the largest port city in South Korea, caused by the implementation of International Maritime Organization (IMO) sulfur regulations. Currently, the Korean government is struggling with problems related to PM_2.5, and ships are one of the major sources of PM_2.5 generation in South Korea. Therefore, we tried to estimate how much the PM_2.5 levels in South Korea could be improved via low-sulfur regulation. According to the Clean Air Quality Policy Support System (CAPSS; National Emission Inventory) in 2016, ship emissions in Busan accounted for 39%, 71%, and 39% of PM_2.5, SO₂, and NO₂ emissions, respectively. To simulate the effect of the IMO’s 0.5 percent sulfur regulation, SOx and PM_2.5 emissions from oil-fueled cargo ships were reduced. Via ship fuel regulation, the PM_2.5 concentration was improved by up to 19% at a site near the port in 2020. In addition, in the case of sulfate, the reduction rate was higher on the downwind side of the Busan port and not near the port, which can be considered as the cause of advection and secondary formation. The PM_2.5 contributions from ships to each of the sub-regions in Busan also decreased by an average of 47% because of IMO sulfur regulation. Although there were limitations in terms of emission estimations because of the application of low-sulfur regulation, we expect that the results of this paper can be used for additional PM_2.5 improvement plans developed by the Korean government and by the local government as well. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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29 pages, 9656 KiB

Open AccessArticle

An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea

by Hyunsu Hong, IlHwan Choi, Hyungjin Jeon, Yumi Kim, Jae-Bum Lee, Cheong Hee Park and Hyeon Soo Kim

Atmosphere 2022, 13(9), 1462; https://doi.org/10.3390/atmos13091462 - 09 Sep 2022

Cited by 5 | Viewed by 2475

Abstract

Exposure to air pollutants, such as PM_2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m³ compared to the case of using only CMAQ. This means that when PM_2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM_2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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19 pages, 5282 KiB

Open AccessArticle

Forecasts of MJO during DYNAMO in a Coupled Tropical Channel Model: Impact of Planetary Boundary Layer Schemes

by Yun Hu, Xiaochun Wang, Jing-Jia Luo, Dongxiao Wang, Huiping Yan, Chaoxia Yuan and Xia Lin

Atmosphere 2022, 13(5), 666; https://doi.org/10.3390/atmos13050666 - 22 Apr 2022

Cited by 1 | Viewed by 1572

Abstract

It is challenging to predict the eastward-propagating Madden–Julian Oscillation (MJO) events across the Maritime Continent (MC) in models. We constructed an air–sea coupled numerical weather prediction model—a tropical channel model—to investigate the role of the planetary boundary layer (PBL) scheme on eastward-propagating and non-propagating MJO precipitation events during the Dynamics of the MJO (DYNAMO) campaign period. Analysis of three hindcast experiments with different PBL schemes illustrates that the PBL scheme is crucial to simulating the eastward-propagating MJO events. The experiment with the University of Washington (UW) PBL scheme can predict the convection activity over the MC due to a good representation of moist static energy (MSE) tendency relatively well. The horizontal advection and the upward transport of moisture from the PBL to the free atmosphere play a major role in the MSE tendency ahead of MJO convection. The difference in the meridional component of MSE advection accounts for the different MSE budgets in the three hindcast experiments. A well-simulated meridional advection can transport the meridional water vapor to moisten the MC. Our results suggest that a proper PBL scheme with better simulated meridional water vapor distribution is crucial to predicting the eastward propagation of MJO events across the MC in the tropical channel model. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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17 pages, 6613 KiB

Open AccessArticle

Applicability Study of a Global Numerical Weather Prediction Model MPAS to Storm Surges and Waves in the South Coast of Korea

by Jin-Hee Yuk, Ji-Sun Kang and Hunjoo Myung

Atmosphere 2022, 13(4), 591; https://doi.org/10.3390/atmos13040591 - 06 Apr 2022

Cited by 3 | Viewed by 1800

Abstract

The south coast of Korea is vulnerable to coastal disasters, such as storm surges, high waves, wave overtopping, and coastal flooding caused by typhoons. It is imperative to predict such disastrous events accurately in advance, which requires accurate meteorological forcing for coastal ocean modeling. In this study, to acquire accurate meteorological data as important forcing variables for the prediction of storm surges and waves, we examined the forecast performance and applicability of a next-generation global weather/climate prediction model, the Model for Prediction Across Scales (MPAS). We compared the modeled surface pressure and wind with observations on the south coast of Korea for three typhoons that damaged Korea in 2020—Bavi, Maysak, and Haishen—and investigated the accuracy of these observations with the MPAS prediction. Those meteorological forcing variables were then used in the tightly coupled tide-surge-wave model, Advanced CIRCulation (ADCIRC) and the Simulating Waves Nearshore (SWAN) for the simulation of a typhoon-induced storm surge and wave. We also performed the hindcast of the wave and storm surges using a parametric tropical cyclone model, the best-track-based Generalized Asymmetric Holland Model (GAHM) embedded in ADCIRC+SWAN, to better understand the forecast performance and applicability of MPAS. We compared the forecast results of the typhoon-induced wave and storm surges with their hindcast in terms of the time-series and statistical indices for both significant wave height and storm surge height and found that wave and storm surge prediction forced by MPAS forecast provides a comparable accuracy with the hindcast. Comparable results of MPAS forcing with that of hindcast using best track information are encouraging because ADCIRC+SWAN forced by MPAS forecast with an at most four-day lead time still provides a reasonable prediction of wave and storm surges. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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7 pages, 2221 KiB

Open AccessCommunication

Monitoring Temperature Variation in Rising Small Defunct Volcano on Jeju Island, Republic of Korea, Using High-Resolution Sentinel-2 Images

by Seong Uk Yoon, Jinhyun Ahn, Yoon Seok Kim, Gyung Deok Han, Yong Suk Chung and Seung-Jae Lee

Atmosphere 2022, 13(4), 576; https://doi.org/10.3390/atmos13040576 - 03 Apr 2022

Cited by 1 | Viewed by 1624

Abstract

Global warming is not an expectation but a reality in the “oreums” (common local name for rising, small defunct volcanoes on Jeju Island, Republic of Korea). The oreums exhibit wide biodiversity. However, their ecology is threatened by its associated climate change and their ecological changes have rarely been monitored or recorded. We used three years of Sentinel-2 image data to generate a normalized difference vegetation index (NDVI) map of the Geum-oreum area. We found that the NDVI was highly associated with temperature, implying that Sentinel-2 images could be utilized to monitor the temperature variation in the oreums to assist in planning and preparation to conserve their ecosystems before they are jeopardized. The results indicated that the NDVI maps derived from Sentinel-2 images were highly associated with temperature in Geum-oreum. We expect this method could be applied in other regions to detect temperature variation for ecological management planning in large areas (such as forests). Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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21 pages, 28201 KiB

Open AccessArticle

A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling

by Li Xiang, Jie Xiang, Jiping Guan, Fuhan Zhang, Yanling Zhao and Lifeng Zhang

Atmosphere 2022, 13(4), 511; https://doi.org/10.3390/atmos13040511 - 24 Mar 2022

Cited by 4 | Viewed by 2103

Abstract

The spatial resolution of precipitation predicted by general circulation models is too coarse to meet current research and operational needs. Downscaling is one way to provide finer resolution data at local scales. The single-image super-resolution method in the computer vision field has made great strides lately and has been applied in various fields. In this article, we propose a novel reference-based and gradient-guided deep learning model (RBGGM) to downscale daily precipitation considering the discontinuity of precipitation and ill-posed nature of downscaling. Global Precipitation Measurement Mission (GPM) precipitation data, variables in ERA5 re-analysis data, and topographic data are selected to perform the downscaling, and a residual dense attention block is constructed to extract features of them. By exploring the discontinuous feature of precipitation, we introduce gradient feature to reconstruct precipitation distribution. We also extract the feature of high-resolution monthly precipitation as a reference feature to resolve the ill-posed nature of downscaling. Extensive experimental results on benchmark data sets demonstrate that our proposed model performs better than other baseline methods. Furthermore, we construct a daily precipitation downscaling data set based on GPM precipitation data, ERA5 re-analysis data and topographic data. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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14 pages, 5540 KiB

Open AccessEditor’s ChoiceArticle

A High-Resolution (20 m) Simulation of Nighttime Low Temperature Inducing Agricultural Crop Damage with the WRF–LES Modeling System

by Ilseok Noh, Seung-Jae Lee, Seoyeon Lee, Sun-Jae Kim and Sung-Don Yang

Atmosphere 2021, 12(12), 1562; https://doi.org/10.3390/atmos12121562 - 26 Nov 2021

Cited by 3 | Viewed by 2333

Abstract

In Korea, sudden cold weather in spring occurs repeatedly every year and causes severe damage to field crops and fruit trees. Detailed forecasting of the daily minimum or suddenly decreasing temperature, closely related to the local topography, has been required in the farmer community. High-resolution temperature models based on empirical formulas or statistical downscaling have fundamental limitations, making it difficult to perform biophysical application and mechanism explanation on small-scale complex terrains. Weather Research and Forecasting–Large Eddy Simulation (WRF–LES) can provide a dynamically and physically scientific tool to be easily applied for farm-scale numerical weather predictions. However, it has been applied mainly for urban areas and in convective boundary layer studies until now. In this study, 20 m resolution WRF–LES simulation of nighttime near-surface temperature and wind was performed for two cold spring weather events that induced significant crop damages in the apple production area and the results were verified with automatic weather station observation data. The study showed that the maximum mean bias of temperature was −1.75 °C and the minimum was −0.68 °C in the spring, while the root mean square error varied between 2.13 and 3.00 °C. The minimum temperature and its duration significantly affected the crop damage, and the WRF–LES could accurately simulate both features. This implies that the application of WRF–LES, with proper nest-domain configuration and harmonized physical options, to the prediction of nighttime frost in rural areas has promising feasibility for orchard- or farm-scale frost prevention and low-temperature management. Full article

(This article belongs to the Special Issue High-Resolution Weather and Climate Modeling with Industrial Applications)

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