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Atmosphere
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Advances on Remote Sensing of Precipitation
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Advances on Remote Sensing of Precipitation

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

Deadline for manuscript submissions: closed (17 June 2022) | Viewed by 7843

Special Issue Editors

Dr. Khalil Ur Rahman

E-Mail Website
Guest Editor
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Interests: meteorology; remote sensing precipitation; merging remote sensing precipitation; hydrological applications of remote sensing precipitation; hydrological modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Shahid

E-Mail Website
Guest Editor
NICE, SCEE, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Interests: climate change; remote sensing; hydrometeorology; watershed modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Abdul Moiz

E-Mail Website
Guest Editor
Center for Global Commons, Institute for Future Initiatives, The University of Tokyo, Tokyo 113-8654, Japan
Interests: decision support systems; distributed hydrological modeling; snow hydrology; precipitation phase partitioning; real-time hydrological forecasting

Special Issue Information

Dear Colleagues,

Precipitation, an imperative component of the global water and energy cycle, is an indispensable meteorological variable for hydrological modeling. Climate change has a critical role in defining the behavior and occurrence of precipitation across different regions around the world. Therefore, to comprehend the complex mechanism of the Earth–Atmosphere interaction, it is extremely important to understand the spatial and temporal characteristics of precipitation at local, regional, and global scale. Freely available satellite precipitation datasets covering the entire globe are the alternative sources for conventional rain gauge data.

On the broad perspective, this Special Issue aims to publish articles on all perspectives of Remote Sensing of Precipitation. Researchers are invited to publish articles on the precipitation estimation, evaluation, and accuracy assessment of satellite precipitation products (TMPA, IMERG, PERSIANN, CHIRPS, CMORPH, GSMaP, etc.) over complex topography, merging of satellite precipitation datasets, hydrological applications of remote sensing precipitation, calibration and validation of precipitation (precipitation modelling), comprehending the microphysical properties of clouds, integration of remote sensing precipitation into numerical weather prediction models, etc. The topics of research include (but are not limited to) those listed below:

  • Quantitative estimation of precipitation;
  • Spatial and temporal evaluation of remote sensing precipitation;
  • Assessing the role of complex topography on precipitation;
  • Merging satellite precipitation datasets and its application;
  • Hydrological applications of remote sensing precipitation;
  • Precipitation modeling;
  • Extreme precipitation events;
  • Precipitation phase partitioning in cold regions;
  • Real-time hydrological modeling using remotely sensed precipitation;
  • Modeling of Atmospheric-Hydrologic Processes.

Dr. Khalil Ur Rahman
Dr. Muhammad Shahid
Dr. Abdul Moiz
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

  • remote sensing of precipitation
  • evaluation and accuracy assessment
  • correction methods for remotely sensed precipitation
  • merging remote sensing precipitation
  • hydrological applications
  • precipitation in cold regions
  • meteorology
  • climate change
  • model evaluation

Published Papers (4 papers)

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Research

22 pages, 7202 KiB  
Article
Analysis of Pre-Monsoon Convective Systems over a Tropical Coastal Region Using C-Band Polarimetric Radar, Satellite and Numerical Simulation
by Dharmadas Jash, Eruthiparambil Ayyappan Resmi, Chirikandath Kalath Unnikrishnan, Ramesh Kala Sumesh, Sumit Kumar and Nita Sukumar
Atmosphere 2022, 13(9), 1349; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13091349 - 24 Aug 2022
Cited by 2 | Viewed by 1576
Abstract
Analysis of pre-monsoon convective systems over the southern peninsular India has been performed using C-band radar and numerical simulation. Statistics on the radar polarimetric measurements show that the distribution of differential reflectivity (Zdr) and specific differential phase (Kdp) have [...] Read more.
Analysis of pre-monsoon convective systems over the southern peninsular India has been performed using C-band radar and numerical simulation. Statistics on the radar polarimetric measurements show that the distribution of differential reflectivity (Zdr) and specific differential phase (Kdp) have much higher spread over convective regions. The distribution of Kdp is almost uniform across the vertical over the stratiform regions. The mean profile of Zdr over stratiform regions shows a distinct local maxima near melting level. A comprehensive analysis has been done on an isolated deep convective system on 13 May 2018. Plan position indicator (PPI) diagrams and satellite measured cloud top temperature demonstrate that pre-monsoon deep convective systems can develop very rapidly within a very short span of time over the region. Heavy precipitation near the surface is reflected in the high value of Kdp (>5° km−1). High values of Zdr (>3 dB) were measured at lower levels indicating the oblate shape of bigger raindrops. A fuzzy logic-based hydrometeor identification algorithm has been applied with five variables (Zh, Zdr, ρhv, Kdp, and T) to understand the bulk microphysical properties at different heights within the storm. The presence of bigger graupel particles near the melting layer indicates strong updrafts within the convective core regions. The vertical ice hydrometeor signifies the existence of a strong electric field causing them to align vertically. Numerical simulation with the spectral bin microphysics (SBM) scheme could produce most of the features of the storm reasonably well. In particular, the simulated reflectivity, graupel mixing ratio and rainfall were in good agreement with the observed values. Full article
(This article belongs to the Special Issue Advances on Remote Sensing of Precipitation)
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21 pages, 2504 KiB  
Article
Assessment of Drought Severity and Their Spatio-Temporal Variations in the Hyper Arid Regions of Kingdom of Saudi Arabia: A Case Study from Al-Lith and Khafji Watersheds
by Nuaman Ejaz and Jarbou Bahrawi
Atmosphere 2022, 13(8), 1264; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13081264 - 10 Aug 2022
Cited by 3 | Viewed by 1597
Abstract
The goal of this study is to calculate meteorological drought using the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) for the Al-Lith and Khafji basins of the Kingdom of Saudi Arabia (KSA) from 2001 to 2020. The in situ (rain [...] Read more.
The goal of this study is to calculate meteorological drought using the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) for the Al-Lith and Khafji basins of the Kingdom of Saudi Arabia (KSA) from 2001 to 2020. The in situ (rain gauges, RGs) and Integrated Multi-satellite Retrievals for GPM (IMERG) data are used in the current study. The meteorological drought is monitored across the AL-Lith and Khafji watersheds. The climate of the Khafji watershed is like the climate of Al-Lith to some extent. Still, due to complex terrain, Al-Lith receives relatively high precipitation and has a higher average temperature than the Khafji watershed. Results show that the total drought periods observed are 166 and 139 months based on SPEI and SPI on a multiple time scale (1, 3, 6, and 12 months) in the Al-Lith watershed, respectively. While, based on SPEI and SPI, the Khafji watershed experienced a drought of 129 and 72 months, respectively. This finding indicates that the SPEI-calculated drought is more severe and persistent in both watersheds than the SPI-calculated drought. Additionally, the correlation coefficient (CC) between SPI and SPEI is investigated; a very low correlation is observed at a smaller scale. CC values of 0.86 and 0.93 for Al-Lith and 0.61 and 0.79 for the Khafji watershed are observed between SPEI-1/SPI-1 and SPEI-3/SPI-3. However, the correlation is significant at high temporal scales, i.e., 6 and 12 months, with CC values of 0.95 and 0.98 for Al-Lith and 0.86 to 0.94 for the Khafji watershed. Overall, the study compared the performance of IMERG with RGs to monitor meteorological drought, and IMERG performed well across both watersheds during the study period. Therefore, the current study recommends the application of IMERG for drought monitoring across data-scarce regions of KSA. Furthermore, SPEI estimates a more severe and long-lasting drought than SPI because of the temperature factor it considers. Full article
(This article belongs to the Special Issue Advances on Remote Sensing of Precipitation)
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18 pages, 5893 KiB  
Article
Preliminary Statistical Characterizations of the Lowest Kilometer Time–Height Profiles of Rainfall Rate Using a Vertically Pointing Radar
by Arthur R. Jameson and Michael L. Larsen
Atmosphere 2022, 13(4), 635; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13040635 - 17 Apr 2022
Cited by 1 | Viewed by 1541
Abstract
A realistic approach for gathering high-resolution observations of the rainfall rate, R, in the vertical plane is to use data from vertically pointing Doppler radars. After accounting for the vertical air velocity and attenuation, it is possible to determine the fine, spatially [...] Read more.
A realistic approach for gathering high-resolution observations of the rainfall rate, R, in the vertical plane is to use data from vertically pointing Doppler radars. After accounting for the vertical air velocity and attenuation, it is possible to determine the fine, spatially resolved drop size spectra and to calculate R for further statistical analyses. The first such results in a vertical plane are reported here. Specifically, we present results using MRR-Pro Doppler radar observations at resolutions of ten meters in height over the lowest 1.28 km, as well as ten seconds in time, over four sets of observations using two different radars at different locations. Both the correlation functions and power spectra are useful for translating observations and numerical model outputs of R from one scale down to other scales that may be more appropriate for particular applications, such as flood warnings and soil erosion, for example. However, it was found in all cases that, while locally applicable radial power spectra could be calculated, because of statistical heterogeneity most of the power spectra lost all generality, and proper correlation functions could not be computed in general except for one 17-min interval. Nevertheless, these results are still useful since they can be combined to develop catalogs of power spectra over different meteorological conditions and in different climatological settings and locations. Furthermore, even with the limitations of these data, this approach is being used to gain a deeper understanding of rainfall to be reported in a forthcoming paper. Full article
(This article belongs to the Special Issue Advances on Remote Sensing of Precipitation)
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21 pages, 6868 KiB  
Article
Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China
by Xiaona Sun, Jingcheng Wang, Lunwu Zhang, Chenjia Ji, Wei Zhang and Wenkai Li
Atmosphere 2022, 13(3), 476; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13030476 - 15 Mar 2022
Cited by 10 | Viewed by 2174
Abstract
High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) [...] Read more.
High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work’s results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field’s overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications. Full article
(This article belongs to the Special Issue Advances on Remote Sensing of Precipitation)
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