Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.9
Journals
Atmosphere
Special Issues
Precipitation Monitoring and Databases
share announcement

Precipitation Monitoring and Databases

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

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 1923

Special Issue Editor

Prof. Dr. Akiyo Yatagai

E-Mail Website
Guest Editor
Graduate School of Science and Technology, Hirosaki University, 3 Bunkyocho, Hirosaki 036-8561, Aomori, Japan
Interests: climate; hydrological cycle; precipitation; solar activity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To improve disaster-causing precipitation forecasts, accurate observation as well as high-resolution numerical models are needed. Many satellite-derived precipitation products exist; however, “final products” often involve rain-gauge based observation. Namely, in order to monitor accurate precipitation, both satellite estimates and numerical forecasts require rain-gauge-based observations. Machine learning techniques, such as neural networks, have been used for meteorological forecasts. For these techniques, accurate “teacher” data are of course necessary.

Accurate monthly and/or daily precipitation products are also key for monitoring agricultural/hydrological early warming purposes. Recently, meteorological databases have been developed not only on a national scale but also for individual river basins.

For this Special Issue, we welcome studies on developing precipitation databases from all aspects, with no limit on regions, periods, or time scales. Our scope includes all precipitation monitoring techniques and products. As for methodology, case studies, statistical analyses, numerical modeling, observational methods, and validating precipitation products are all welcome.

Prof. Dr. Akiyo Yatagai
Guest Editor

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

  • precipitation
  • rain
  • snow
  • moisture
  • rain gauges
  • satellites
  • merged products
  • reanalysis
  • forecast

Published Papers (3 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

14 pages, 4681 KiB  
Article
Monitoring Snow Cover in Typical Forested Areas Using a Multi-Spectral Feature Fusion Approach
by Yunlong Wang and Jianshun Wang
Atmosphere 2024, 15(4), 513; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15040513 - 22 Apr 2024
Viewed by 323
Abstract
Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging [...] Read more.
Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging with either in situ or remote sensing observations. The global SNOWMAP algorithm pertinent to the fixed normalized difference snow index (NDSI) threshold is, therefore, no longer applicable in a typical forested region of Northeast China. In order to achieve the goal of improving the accuracy of monitoring snow cover in areas with forest, utilizing MOD09GA and MOD13A1 products, a new approach of snow mapping was developed in this study, and it exploits the fusion and coupling of spectral features by integrating and analyzing the normalized difference forest snow index (NDFSI), the normalized difference vegetation index (NDVI), and the NDSI index. Then, Landsat 8 OLI images of high resolution were used to evaluate snow cover mapping precision. The experimental results indicated that the NDFSI index combined with the NDVI index showed great potential for extracting the snow cover distribution in forested regions. Compared with the snow distribution obtained from the Landsat 8 images, the average bias and FAR (false alarm ratio) values of snow cover mapping obtained by this algorithm were 1.23 and 13.54%, which were reduced by 1.98 and 29.36%, respectively. The overall accuracy of 81.31% was reached, which is improved by 20.19%. Thus, the snow classification scheme combining multiple spectral features from MODIS data works effectively in improving the precision of automatic snow cover mapping in typical forested areas of Northeast China, which provides essential support and significant perspectives for the next step of establishing a runoff model and rationally regulating forest water resources. Full article
(This article belongs to the Special Issue Precipitation Monitoring and Databases)
►▼ Show Figures

Figure 1

13 pages, 6971 KiB  
Article
Connection between Barents Sea Ice in May and Early Summer Monsoon Rainfall in the South China Sea and Its Possible Mechanism
by Fangyu Li, Gang Zeng, Shiyue Zhang and Monzer Hamadlnel
Atmosphere 2024, 15(4), 433; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15040433 - 30 Mar 2024
Viewed by 501
Abstract
The impacts of Arctic sea ice on climate in middle and high latitudes have been extensively studied. However, its effects on climate in low latitudes, particularly on summer monsoon rainfall in the South China Sea (SCS), have received limited attention. Thus, this study [...] Read more.
The impacts of Arctic sea ice on climate in middle and high latitudes have been extensively studied. However, its effects on climate in low latitudes, particularly on summer monsoon rainfall in the South China Sea (SCS), have received limited attention. Thus, this study investigates the connection between the Arctic sea ice concentration (SIC) anomaly and the early summer monsoon rainfall (ESMR) in the SCS and its underlying physical mechanism. The results reveal a significant positive correlation between the Barents Sea (BS) SIC in May and the ESMR in the SCS. When there is more (less) SIC in the Barents Sea (BS) during May, this results in a positive (negative) anomaly of the local turbulent heat flux, which lasts until June. This, in turn, excites an upward (downward) air motion anomaly in the vicinity of the BS, causing a corresponding downward (upward) motion anomaly over the Black Sea. Consequently, this triggers a wave train similar to the Eurasian (SEU) teleconnection, propagating eastward towards East Asia. The SEU further leads to an (a) upward (downward) motion anomaly and weakens (strengthens) the western Pacific subtropical high (WPSH) over the SCS, which is accompanied by a southwest adequate (scarce) water vapor anomaly transporting from the Indian Ocean, resulting in more (less) precipitation in the SCS. Furthermore, the response of ESMR in the SCS to the SIC in the BS is further verified by using the Community Atmosphere Model version 5.3 (CAM5.3). This study introduces novel precursor factors that influence the South China Sea summer monsoon (SCSSM), presenting a new insight for climate prediction in this region, which holds significant implications. Full article
(This article belongs to the Special Issue Precipitation Monitoring and Databases)
►▼ Show Figures

Figure 1

15 pages, 5137 KiB  
Communication
An Ensemble-Based Model for Specific Humidity Retrieval from Landsat-8 Satellite Data for South Korea
by Sungwon Choi, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Jongho Woo, Nayeon Kim, Sungwoo Park and Kyung-soo Han
Atmosphere 2024, 15(2), 218; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15020218 - 11 Feb 2024
Viewed by 613
Abstract
Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface. SH is typically computed using microwave satellites. However, the spatial resolution of data [...] Read more.
Specific humidity (SH) which means the amount of water vapor in 1 kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface. SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016–2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination (R) of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%. We applied this model to compute land surface SH using data from 2018 to 2019 and found that it had high accuracy (R = 0.927, RMSE = 0.002, bias = 0, RRMSE = 28.35%). Although the data used in this study were limited, the model was able to accurately represent a small region based on an ensemble of satellite and model data, demonstrating its potential to address important issues related to SH measurements from satellites. Full article
(This article belongs to the Special Issue Precipitation Monitoring and Databases)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop