Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Microwave Remote Sensing of the Atmosphere: Current Progress and Future...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Microwave Remote Sensing of the Atmosphere: Current Progress and Future Vision

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

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 7045

Special Issue Editors

Dr. Robbie Iacovazzi

E-Mail Website
Guest Editor
National Oceanic and Atmospheric Administration (NOAA), Washington, DC, USA
Interests: microwave remote sensing; validation; atmospheric physics; precipitation
Dr. Quanhua Liu

E-Mail Website
Guest Editor
National Oceanic and Atmospheric Administration (NOAA), Washington D.C., WA, USA
Interests: radiative transfer models; satellite radiance assimilation; sensor calibration and climate studies
Special Issues, Collections and Topics in MDPI journals
Dr. William J. Blackwell

E-Mail Website
Guest Editor
Associate Leader of the Applied Space Systems Group, Massachusetts Institute of Technology Lincoln Laboratory, 244 Wood Street, Lexington, MA 02421-6426, USA
Interests: atmospheric remote sensing; airborne and space-borne microwave sensors; radiometry; signal processing; tropical cyclone
Prof. Dr. V. Chandrasekar

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523, USA
Interests: radar meteorology; radar system and networking; polarimetric analysis and signal processing; wave propagation and remote sensing; machine learning
Dr. Rachael Kroodsma

E-Mail Website
Guest Editor
NASA GSFC Global Change Data Center, NASA Goddard Space Flight Center, University of Maryland, Greenbelt, MD 20771, USA
Interests: microwave radiometer calibration; remote sensing of the atmosphere
Dr. Qifeng Lu

E-Mail Website
Guest Editor
National Satellite Meteorological Center of the China Meteorological Agency (CMA), Beijing, China
Interests: data assimilation; weather forecasting; radiative transfer; aerosol property; remote sensing
Prof. Dr. Steven C. Reising

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523-1373, USA
Interests: microwave remote sensing of the earth's atmosphere and oceans; earth science measurements from nanosatellites and CubeSats; radiometer and radar systems from GHz to THz frequencies; low-noise monolithic microwave IC design and packaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microwave remote sensing of the atmosphere is critical to weather forecasting and environmental and climate monitoring. This special issue focuses on the progress and future vision of ground-, aircraft-, and satellite-based active and passive atmospheric microwave remote sensing. Progress relates to improvements to calibration, and product validation and quality monitoring, of current microwave observations. It also includes current pre-launch sensor hardware and calibration algorithm design, fabrication, and testing updates. The future vision is an imagined state of atmospheric microwave sensing out to 2050 that expands data volume, reduces data latency and improves integration of observations from all missions. It communicates possible new instrument technologies and platforms, SI-traceable microwave standards, calibration approaches, data sharing strategies, and methods to harmonize and integrate data in space and time. This MDPI Remote Sensing special issue call for papers is an opportunity to globally share your progress and future vision on this topic along with international colleagues from public and private institutions alike.

Dr. Robbie Iacovazzi
Dr. Quanhua Liu
Dr. William J. Blackwell
Prof. Dr. V. Chandrasekar
Dr. Rachael Kroodsma
Dr. Qifeng Lu
Dr. Steven C. Reising
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. 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

  • microwave radiometers
  • microwave radar
  • atmospheric remote sensing
  • microwave engineering innovation
  • microwave instrument calibration
  • remote sensing product validation
  • atmospheric measurement constellation planning

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

21 pages, 14745 KiB  
Article
Improved Gridded Precipitation Data Derived from Microwave Link Attenuation
by Micha Silver, Arnon Karnieli and Erick Fredj
Remote Sens. 2021, 13(15), 2953; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152953 - 27 Jul 2021
Cited by 4 | Viewed by 1879
Abstract
The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. [...] Read more.
The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. CML networks offer a unique advantage for precipitation measurements due to their high density. However, these data experience uncertainty from several sources as reported in earlier research. This current work determines the reliability of rainfall measurements for each link by comparing CML-derived rain rates to adjusted weather radar rainfall at the link location, over three months. Dynamic Time Warping (DTW) is applied to the pair of CML/radar time-series data in two study areas, Israel and Netherlands. Based on the DTW amplitude and temporal distance, unreliable links are identified and flagged, and interpolated gridded precipitation data are derived in each country after filtering out those unreliable links. Correlations between CML-derived grids and rain observations from an independent set of gauges, tested over several rain events in both study areas, are higher for the reliable subset of CML than the full set. For certain storm events, the Kendall rank correlation for the set of reliable CML is almost double that of the complete set, demonstrating that improved gridded precipitation data can be obtained by removing unreliable links. Full article
(This article belongs to the Special Issue Microwave Remote Sensing of the Atmosphere: Current Progress and Future Vision)
►▼ Show Figures

Figure 1

20 pages, 4237 KiB  
Article
Effect Analysis of the Digital Spectrometer FFT Algorithm on THz Atmospheric Limb Sounder (TALIS) System Sensitivity
by Haowen Xu, Hao Lu, Zhenzhan Wang, Wenming He and Wenyu Wang
Remote Sens. 2021, 13(15), 2921; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152921 - 25 Jul 2021
Cited by 3 | Viewed by 1929
Abstract
THz Atmospheric Limb Sounder (TALIS) is a microwave radiometer designed by the National Space Science Center of the Chinese Academy of Sciences (NSSC, CAS) for measuring the vertical distribution of temperature and chemical constituents in the middle and upper atmosphere. The digital spectrometer [...] Read more.
THz Atmospheric Limb Sounder (TALIS) is a microwave radiometer designed by the National Space Science Center of the Chinese Academy of Sciences (NSSC, CAS) for measuring the vertical distribution of temperature and chemical constituents in the middle and upper atmosphere. The digital spectrometer is an important part of TALIS’ back end, which mainly realizes the function of spectral analysis. The radix 16 real-time complex fast Fourier transform (FFT) algorithm used in the digital spectrometer was obtained by improving and combining the parallel processing and complex processing of the FFT algorithm. In this study, the digital spectrometer parameter selection is systematically presented, and the effect of the digital spectrometer FFT algorithm on the TALIS system sensitivity is analyzed based on the TALIS system simulation model we established in the previous stage of this research. The results show that the actual full-band sensitivity obtained after using the FFT algorithm is consistent with the ideal full-band sensitivity of TALIS for different integration time, spectral resolutions, and quantization bits. However, the results of the comparison of the actual sub-band sensitivity after using the FFT algorithm with the ideal sub-band sensitivity show that the deterioration of the sub-band sensitivity will be caused by the FFT algorithm. The mean value of the deterioration ratio was found to be approximately 18%, and the maximum value of the deterioration ratio was approximately 33%. Full article
(This article belongs to the Special Issue Microwave Remote Sensing of the Atmosphere: Current Progress and Future Vision)
►▼ Show Figures

Figure 1

11 pages, 4389 KiB  
Communication
Estimating Water Vapor Using Signals from Microwave Links below 25 GHz
by Kun Song, Xichuan Liu, Taichang Gao and Peng Zhang
Remote Sens. 2021, 13(8), 1409; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081409 - 07 Apr 2021
Cited by 5 | Viewed by 1868
Abstract
Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links [...] Read more.
Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting. Full article
(This article belongs to the Special Issue Microwave Remote Sensing of the Atmosphere: Current Progress and Future Vision)
►▼ Show Figures

Graphical abstract

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