Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.3
Journals
Atmosphere
Special Issues
ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System...
share announcement

ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 6493

Special Issue Editors

Dr. Seung-Jae Lee

E-Mail Website
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. Byong-Lyol Lee

E-Mail Website
Guest Editor
National Center for AgroMeteorology (NCAM), Suwon 16200, Republic of Korea
Interests: agronomy; field crop science; agricultural ecology

Special Issue Information

Dear Colleagues,

Meteorological and climatological disasters such as heavy rainfall, floods, droughts, and typhoons threaten the stable supply of agricultural products and the income of farmers. Overall, the frequency and intensity of such disasters are increasing under the changing climate. We aim to reduce the damage caused by natural hazards through adopting preventative measures including greenhouse cultivation. A more complete system with efficient disaster-reduction capability is required to mitigate the impact of extreme weather. The development of climate-smart agriculture can help reduce the impact of natural hazards on food production systems and improve the livelihoods of farming communities. Specifically, the further development of disaster prevention technology is required in important crop-production areas. Such technology should provide pre-disaster warnings and facilitate the identification of disaster-affected areas and aid in conducting post-disaster surveys for the rehabilitation of crops. Disaster prevention technology can also be used to clarify the critical conditions of different crops and growth periods, estimate the probability of crop losses, and establish a complete knowledge database of crop disasters.

In this Special Issue, we will emphasize concepts and practices related to reducing agricultural weather disaster risks through systematic efforts to analyze and reduce the causal factors of disasters. This Special Issue will include articles on the sensible management of crops and the environment using innovative disaster prevention technologies, setup of the early-warning systems which send SNS messages to farmers, and evaluation of vulnerability to cultivation and economic losses as a result of disasters. In particular, we invite information and communication technology vendors to demonstrate the latest products for environmental monitoring and crop disaster (or cultivation) warning systems. We hope this collection of articles will aid in expanding climate smart agriculture and reducing the impact of natural hazards on food production systems through a multidimensional discussion.

Dr. Seung-Jae Lee
Dr. Byong-Lyol Lee
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

  • agrometeorological disaster
  • early-warning system
  • weather risk index
  • farmstead-specific weather data
  • high-resolution weather and climate model
  • downscaling and upscaling
  • agrometeorological service
  • crop phenology
  • diseases and insects
  • frost
  • hail
  • wind gusts
  • heavy rainfall
  • drought

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

14 pages, 2846 KB  
Article
Evaluation of Phenology Models for Predicting Full Bloom Dates of ‘Niitaka’ Pear Using Orchard Image-Based Observations in South Korea
by Jin-Hee Kim, Eun-Jeong Yun, Dae Gyoon Kang, Jeom-Hwa Han, Kyo-Moon Shim and Dae-Jun Kim
Atmosphere 2025, 16(9), 996; https://doi.org/10.3390/atmos16090996 - 22 Aug 2025
Viewed by 648
Abstract
Abnormally warm winters in recent years have accelerated flowering in fruit trees, increasing their vulnerability to late frost damage. To address this challenge, this study aimed to evaluate and compare the performance of three phenology models—the development rate (DVR), modified DVR (mDVR), and [...] Read more.
Abnormally warm winters in recent years have accelerated flowering in fruit trees, increasing their vulnerability to late frost damage. To address this challenge, this study aimed to evaluate and compare the performance of three phenology models—the development rate (DVR), modified DVR (mDVR), and Chill Days (CD) models—for predicting full bloom dates of ‘Niitaka’ pear, using image-derived phenological observations. The goal was to identify the most reliable and regionally transferable model for nationwide application in South Korea. A key strength of this study lies in the integration of real-time orchard imagery with automated weather station (AWS) data, enabling standardized and objective phenological monitoring across multiple regions. Using five years of temperature data from seven orchard sites, chill and heat unit accumulations were calculated and compared with observed full bloom dates obtained from orchard imagery and field records. Correlation analysis revealed a strong negative relationship between cumulative heat units and bloom timing, with correlation coefficients ranging from –0.88 (DVR) to –0.94 (mDVR). Among the models, the mDVR model demonstrated the highest stability in chill unit estimation (CV = 6.3%), the lowest root-mean-square error (RMSE = 2.9 days), and the highest model efficiency (EF = 0.74), indicating superior predictive performance across diverse climatic conditions. In contrast, the DVR model showed limited generalizability beyond its original calibration zone. These findings suggest that the mDVR model, when supported by image-based phenological data, provides a robust and scalable tool for forecasting full bloom dates of temperate fruit trees and enhancing grower preparedness against late frost risks under changing climate conditions. Full article
(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)
►▼ Show Figures

Figure 1

16 pages, 9567 KB  
Article
Using the Multiple-Sensor-Based Frost Observation System (MFOS) for Image Object Analysis and Model Prediction Evaluation in an Orchard
by Su Hyun Kim, Seung-Min Lee and Seung-Jae Lee
Atmosphere 2024, 15(8), 906; https://doi.org/10.3390/atmos15080906 - 29 Jul 2024
Cited by 1 | Viewed by 1364
Abstract
Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to [...] Read more.
Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to replace the naked-eye observation of frost. The MFOS, herein installed and operated in an apple orchard, provides temporally high-resolution frost observations that show the onset, end, duration, persistence, and discontinuity of frost more clearly than conventional naked-eye observations. This study introduces recent additions to the MFOS and presents the results of its application to frost weather analysis and forecast evaluation in an orchard in South Korea. The NCAM’s Weather Research and Forecasting (WRF) model was employed as a weather forecast model. The main findings of this study are as follows: (1) The newly added image-based object detection capabilities of the MFOS helped with the extraction and quantitative comparison of surface temperature data for apples, leaves, and the LWS. (2) The resolution matching of the RGB and thermal infrared images was made successful by resizing the images, matching them according to horizontal movement, and conducting apple-centered averaging. (3) When applied to evaluate the frost-point predictions of the numerical weather model at one-hour intervals, the results showed that the MFOS could be used as a much more objective tool to verify the accuracy and characteristics of frost predictions compared to the naked-eye view. (4) Higher-resolution and realistic land-cover and vegetation representation are necessary to improve frost forecasts using numerical grid models based on land–atmosphere physics. Full article
(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

15 pages, 5307 KB  
Review
Farmstead-Specific Weather Risk Prediction Technique Based on High-Resolution Weather Grid Distribution
by Dae-Jun Kim, Jin-Hee Kim, Eun-Jeong Yun, Dae Gyoon Kang and Eunhye Ban
Atmosphere 2024, 15(1), 116; https://doi.org/10.3390/atmos15010116 - 18 Jan 2024
Cited by 1 | Viewed by 1632
Abstract
In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage [...] Read more.
In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage caused by natural disasters) to risk management (e.g., prediction and preparation) while concentrating on early warning, thanks to the development of media and communication conditions. The Rural Development Administration (Korea) has developed the “early warning service for weather risk management in the agricultural sector” that detects weather risks for crops from high-resolution weather information in advance and provides customized information to respond to possible disaster risks in advance in response to the increasing number of extreme weather events. The core technology of this service is damage prediction technology that determines the overall agricultural weather risk level by quantifying the current growth stage of cultivated crops and the probability of possible weather disasters according to the weather conditions of the farm. Agrometeorological disasters are damages caused by weather conditions that can affect crops and can be predicted by estimating the probability of damage that may occur from the interaction between hazardous weather and crop characteristics. This review introduces the classification of possible weather risks by their occurrence mechanisms, based on the developmental stage of crops and prediction techniques that have been developed or applied to date. The accumulated crop growth and weather risk information is expected to be utilized as support material for farming decision-making, which helps farmers proactively respond to crop damage due to extreme weather events by providing highly reliable disaster forecasts through the advancement of prediction technology. Full article
(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

21 pages, 3996 KB  
Technical Note
Design of a Standards-Based Cloud Platform to Enhance the Practicality of Agrometeorological Countermeasures
by Sejin Han, Minju Baek, Jin-Ho Lee, Sang-Hyun Park, Seung-Gil Hong, Yong-Kyu Han and Yong-Soon Shin
Atmosphere 2025, 16(8), 924; https://doi.org/10.3390/atmos16080924 - 30 Jul 2025
Viewed by 383
Abstract
The need for systems that forecast and respond proactively to meteorological disasters is growing amid climate variability. Although the early warning system in South Korea includes countermeasure information, it remains limited in terms of data recency, granularity, and regional adaptability. Additionally, its closed [...] Read more.
The need for systems that forecast and respond proactively to meteorological disasters is growing amid climate variability. Although the early warning system in South Korea includes countermeasure information, it remains limited in terms of data recency, granularity, and regional adaptability. Additionally, its closed architecture hinders interoperability with external systems. This study aims to redesign the countermeasure function as an independent cloud-based platform grounded in the common standard terminology framework in South Korea. A multi-dimensional data model was developed using attributes such as crop type, cultivation characteristics, growth stage, disaster type, and risk level. The platform incorporates user-specific customization features and history tracking capabilities, and it is structured using a microservices architecture to ensure modularity and scalability. The proposed system enables real-time management and dissemination of localized countermeasure suggestions tailored to various user types, including central and local governments and farmers. This study offers a practical model for enhancing the precision and applicability of agrometeorological response information. It is expected to serve as a scalable reference platform for future integration with external agricultural information systems. Full article
(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)
►▼ Show Figures

Figure 1

15 pages, 15327 KB  
Technical Note
Establishment and Operation of an Early Warning Service for Agrometeorological Disasters Customized for Farmers and Extension Workers at Metropolitan-Scale
by Yong-Soon Shin, Hee-Ae Lee, Sang-Hyun Park, Yong-Kyu Han, Kyo-Moon Shim and Se-Jin Han
Atmosphere 2025, 16(3), 291; https://doi.org/10.3390/atmos16030291 - 28 Feb 2025
Cited by 1 | Viewed by 1034
Abstract
A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For [...] Read more.
A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For effective implementation, local governments must lead operating early warning services that reflect regional agricultural characteristics and farmers’ needs, while the central government provides foundational data. The system connects data from each region to the cloud, enabling the establishment of a nationwide integrated service operation framework that includes the central government, metropolitan cities, municipalities, and farmers. Full article
(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop