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Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 11998

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Special Issue Editor

Dr. Cheng-Ku Yu

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Guest Editor

Department of Atmospheric Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan
Interests: orographic precipitation; severe weather and mesoscale atmospheric phenomena;structure and dynamics of tropical cyclone (frontal) rainbands

Special Issue Information

Dear Colleagues,

As revealed by advances in observing technology such as Doppler radar and satellite remote sensing and in numerical modeling, it has been recognized that most of the hazardous weather events (e.g., flash floods and severe storms) occurring in the real atmosphere are closely related to mesoscale phenomena. Because of the inherent complexity and rapidly evolving characteristics of these scenarios, the theoretical principles of synoptic meteorology cannot usually be applied to explain the causes of these high-impact weather conditions. Temporally and spatially high-resolution remote sensing observations and numerical simulations have thus represented vital resources and tools for a broad range of mesoscale research and operational applications. Improving our understanding of mesoscale processes over various environmental and terrain configurations is critically important for better prediction of hazardous events and their impacts on weather, climate, and hydrology. The primary objective of this Special Issue is to provide a scientific forum for students, scientists, and forecasters to share recent research findings relating to mesoscale meteorology from observational and modeling perspectives. This Special Issue welcomes submissions from all aspects of mesoscale phenomena and their precipitation monitoring and processes using remote sensing observations and relevant technologies. Mesoscale phenomena of particular interest in this Special Issue include but are not limited to mesoscale convective systems, thunderstorms, frontal precipitation, tropical cyclones, landfalling weather systems, rainbands, orographic precipitation, and diurnally generated circulations and precipitation.

Dr. Cheng-Ku Yu
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

mesoscale meteorology
mesoscale phenomena
hazardous weather
flash floods
severe storms
precipitation
Doppler radar
satellite
remote sensing observations
frontal precipitation
tropical cyclones
rainbands
orographic precipitation
diurnally generated circulations and precipitation

Published Papers (6 papers)

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Research

19 pages, 5691 KiB

Open AccessArticle

A Multi-Source Data Fusion Method to Improve the Accuracy of Precipitation Products: A Machine Learning Algorithm

by Mazen E. Assiri and Salman Qureshi

Remote Sens. 2022, 14(24), 6389; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246389 - 17 Dec 2022

Cited by 3 | Viewed by 2111

Abstract

In recent decades, several products have been proposed for estimating precipitation amounts. However, due to the complexity of climatic conditions, topography, etc., providing more accurate and stable precipitation products is of great importance. Therefore, the purpose of this study was to develop a multi-source data fusion method to improve the accuracy of precipitation products. In this study, data from 14 existing precipitation products, a digital elevation model (DEM), land surface temperature (LST) and soil water index (SWI) and precipitation data recorded at 256 gauge stations in Saudi Arabia were used. In the first step, the accuracy of existing precipitation products was assessed. In the second step, the importance degree of various independent variables, such as precipitation interpolation maps obtained from gauge stations, elevation, LST and SWI in improving the accuracy of precipitation modelling, was evaluated. Finally, to produce a precipitation product with higher accuracy, information obtained from independent variables were combined using a machine learning algorithm. Random forest regression with 150 trees was used as a machine learning algorithm. The highest and lowest degree of importance in the production of precipitation maps based on the proposed method was for existing precipitation products and surface characteristics, respectively. The importance degree of surface properties including SWI, DEM and LST were 65%, 22% and 13%, respectively. The products of IMERGFinal (9.7), TRMM3B43 (10.6), PRECL (11.5), GSMaP-Gauge (12.5), and CHIRPS (13.0 mm/mo) had the lowest RMSE values. The KGE values of these products in precipitation estimation were 0.56, 0.48, 0.52, 0.44 and 0.37, respectively. The RMSE and KGE values of the proposed precipitation product were 6.6 mm/mo and 0.75, respectively, which indicated the higher accuracy of this product compared to existing precipitation products. The results of this study showed that the fusion of information obtained from different existing precipitation products improved the accuracy of precipitation estimation. Full article

(This article belongs to the Special Issue Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies)

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18 pages, 7615 KiB

Open AccessArticle

Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes

by Febryanto Simanjuntak, Ilham Jamaluddin, Tang-Huang Lin, Hary Aprianto Wijaya Siahaan and Ying-Nong Chen

Remote Sens. 2022, 14(23), 5950; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14235950 - 24 Nov 2022

Cited by 4 | Viewed by 2747

Abstract

Increasing the accuracy of rainfall forecasts is crucial as an effort to prevent hydrometeorological disasters. Weather changes that can occur suddenly and in a local scope make fast and precise weather forecasts increasingly difficult to inform. Additionally, the results of the numerical weather model used by the Indonesia Agency for Meteorology, Climatology, and Geophysics are only able to predict the rainfall with a temporal resolution of 1–3 h and cannot yet address the need for rainfall information with high spatial and temporal resolution. Therefore, this study aims to provide the rainfall forecast in high spatiotemporal resolution using Himawari-8 and GPM IMERG (Global Precipitation Measurement: The Integrated Multi-satellite Retrievals) data. The multivariate LSTM (long short-term memory) forecasting is employed to predict the cloud brightness temperature by using the selected Himawari-8 bands as the input and training data. For the rain rate regression, we used the random forest technique to identify the rainfall and non-rainfall pixels from GPM IMERG data as the input in advance. The results of the rainfall forecast showed low values of mean error and root mean square error of 0.71 and 1.54 mm/3 h, respectively, compared to the observation data, indicating that the proposed study may help meteorological stations provide the weather information for aviation purposes. Full article

(This article belongs to the Special Issue Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies)

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Graphical abstract

19 pages, 7790 KiB

Open AccessArticle

Contrasting Mesoscale Convective System Features of Two Successive Warm-Sector Rainfall Episodes in Southeastern China: A Satellite Perspective

by Yipeng Huang and Murong Zhang

Remote Sens. 2022, 14(21), 5434; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215434 - 28 Oct 2022

Cited by 1 | Viewed by 1384

Abstract

Based on Himawari-8 satellite observations, the mesoscale convective system (MCS) behaviors of two successive but distinct warm-sector rainfall episodes (EP1 and EP2) on 6–7 May 2018 over southeastern China were compared, with the latter episode being a record-breaking rainfall event. Results showed that MCSs played a dominant role in EP2, but not in EP1, by contributing over 80% of the extreme rainfall total and all the 10-min rainfalls over 20 mm. MCS occurrences were more frequent in EP2 than EP1, especially in the coastal rainfall hotspots, along with more frequent merging processes. Overall, the MCS samples in EP2 were larger in size, more intense, and moved slower and more in parallel to their orientation, which facilitated local rainfall accumulation. Two new indices are proposed—the overlap index (OLI) and merging potential index (MPI)—to evaluate two MCS processes vital for rainfall production: the repeated passage of an individual MCS over given areas and the merging between MCSs, respectively. Both OLI and MPI in EP2 were significantly larger than in EP1, which tended to produce larger maximum rainfall amount and stronger 10-min rain rates in the following hour. These results demonstrate the potential value of satellite-based MCS information for heavy rainfall nowcasting, which is particularly significant for warm-sector rainfall with its limited predictability. Full article

(This article belongs to the Special Issue Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies)

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22 pages, 8032 KiB

Open AccessArticle

Detecting the Greatest Changes in Global Satellite-Based Precipitation Observations

by Majid Kazemzadeh, Hossein Hashemi, Sadegh Jamali, Cintia B. Uvo, Ronny Berndtsson and George J. Huffman

Remote Sens. 2022, 14(21), 5433; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215433 - 28 Oct 2022

Cited by 3 | Viewed by 1461

Abstract

In recent years, the analysis of abrupt and non-abrupt changes in precipitation has received much attention due to the importance of climate change-related issues (e.g., extreme climate events). In this study, we used a novel segmentation algorithm, DBEST (Detecting Breakpoints and Estimating Segments in Trend), to analyze the greatest changes in precipitation using a monthly pixel-based satellite precipitation dataset (TRMM 3B43) at three different scales: (i) global, (ii) continental, and (iii) climate zone, during the 1998–2019 period. We found significant breakpoints, 14.1%, both in the form of abrupt and non-abrupt changes, in the global scale precipitation at the 0.05 significance level. Most of the abrupt changes were observed near the Equator in the Pacific Ocean and Asian continent, relative to the rest of the globe. Most detected breakpoints occurred during the 1998–1999 and 2009–2011 periods on the global scale. The average precipitation change for the detected breakpoint was ±100 mm, with some regions reaching ±3000 mm. For instance, most portions of northern Africa and Asia experienced major changes of approximately +100 mm. In contrast, most of the South Pacific and South Atlantic Ocean experienced changes of −100 mm during the studied period. Our findings indicated that the larger areas of Africa (23.9%), Asia (22.9%), and Australia (15.4%) experienced significant precipitation breakpoints compared to North America (11.6%), South America (9.3%), Europe (8.3%), and Oceania (9.6%). Furthermore, we found that the majority of detected significant breakpoints occurred in the arid (31.6%) and polar (24.1%) climate zones, while the least significant breakpoints were found for snow-covered (11.5%), equatorial (7.5%), and warm temperate (7.7%) climate zones. Positive breakpoints’ temporal coverage in the arid (54.0%) and equatorial (51.9%) climates were more than those in other climates zones. Here, the findings indicated that large areas of Africa and Asia experienced significant changes in precipitation (−250 to +250 mm). Compared to the average state (trend during a specific period), the greatest changes in precipitation were more abrupt and unpredictable, which might impose a severe threat to the ecology, environment, and natural resources. Full article

(This article belongs to the Special Issue Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies)

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16 pages, 9806 KiB

Open AccessArticle

Analysis of Diurnal Evolution of Cloud Properties and Convection Tracking over the South China Coastal Area

by Xinyue Wang, Hironobu Iwabuchi and Jean-Baptiste Courbot

Remote Sens. 2022, 14(19), 5039; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14195039 - 09 Oct 2022

Viewed by 1245

Abstract

Different diurnal rainfall cycles occur over the offshore and inland regions of the South China coastal area (SCCA). Inspired by these findings, in this study, we investigated the diurnal evolution features of cloud systems and cloud properties inside such systems for both the SCCA offshore and inland regions, using cloud data retrieved from a recently developed deep neural network model. Rainy day data for June 2017 revealed that the ice cloud optical thickness and top height reached their peak intensities at noon (~12 local standard time (LST)) over the offshore region, approximately 2 h later than the rainfall peak. Over the inland region, cloud and rainfall peaks simultaneously appeared from ~18 to 20 LST. When further examining the cloud-amount variation of different ice-cloud types, we found a clear diurnal oscillation in the medium-thick cloud amount over the offshore region, while for the inland region, this cloud type had no obvious diurnal peak, showing a low cloud amount throughout the day. This phenomenon suggests different inner structures and intensities between offshore and inland convections. To better elucidate the convection features over different regions, a tracking algorithm was applied to obtain various parameters, such as size, number, and duration of mesoscale convective systems. The strongest convections, which lasted over 12 h, tended to be abundant over the offshore region from ~03 to 12 LST, and an inland to offshore migration at ~03 LST was facilitated by the beneficial meteorological conditions observed at 113–116˚E, 20.5–22.5˚N. Full article

(This article belongs to the Special Issue Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies)

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20 pages, 8249 KiB

Open AccessArticle

Is an NWP-Based Nowcasting System Suitable for Aviation Operations?

by Vincenzo Mazzarella, Massimo Milelli, Martina Lagasio, Stefano Federico, Rosa Claudia Torcasio, Riccardo Biondi, Eugenio Realini, Maria Carmen Llasat, Tomeu Rigo, Laura Esbrí, Markus Kerschbaum, Marco-Michael Temme, Olga Gluchshenko and Antonio Parodi

Remote Sens. 2022, 14(18), 4440; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184440 - 06 Sep 2022

Cited by 6 | Viewed by 2034

Abstract

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories. Full article

(This article belongs to the Special Issue Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies)

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