Special Issue "Satellite Remote Sensing for Tropical Meteorology and Climatology"

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

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editor

Dr. Corene Matyas
Website
Guest Editor
Department of Geography, University of Florida, Gainesville, FL 32611-7315, USA
Interests: spatial analysis of precipitation; tropical cyclones; geographic information systems; spatial metrics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Data obtained from a variety of remote sensing platforms provide invaluable information about atmospheric processes as well as the interaction between the atmosphere, land, and water-covered areas. These data have increased our understanding of human interactions with the biophysical environment. Along multiple temporal scales, remote sensing is used to detect and monitor extreme meteorological events and datasets are becoming extensive enough to provide information on longer-term changes. The spatial resolution of remotely-sensed datasets has dramatically improved over time, allowing fine-scale atmospheric processes to be monitored globally. The tropics are home to a wide range of landforms that host the habitats of a vast quantity of species that are vulnerable to extreme weather events and the changing climate. In the tropics, remotely-sensed data have facilitated the tracking of cloud clusters that have improved our ability to predict extreme weather events such as tropical cyclones, and monitor atmospheric teleconnection-based activity associated with the El-Nino Southern Oscillation, Madden Julian Oscillation, Indian Ocean Dipole, and others. They also facilitate the monitoring of the spatial extent and severity of floods and droughts.

This Special Issue focuses on remotely-sensed datasets and the information they have revealed that has advanced the fields of tropical meteorology and climatology. A key focus is on processes that contribute to precipitation in the tropics across scales ranging from cloud microphysical properties and the distribution of water vapor, dust, and aerosols to well-organized precipitation systems such as tropical cyclones and the intertropical convergence zone. Other areas of emphasis include studies that improve research and forecast models including techniques to downscale precipitation or assimilation remotely sensed precipitation into numerical weather prediction models. Results from field campaigns undertaken in the tropics to collect data about the atmosphere and interactions with the sea and land surfaces can be included. Studies may compare observations across different platforms as well as use remotely-sensed datasets for model validation. Explorations of the impacts of extreme meteorological events on the biophysical environment are also welcome.

Dr. Corene Matyas
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 papers will be 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 2000 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

  • Atmospheric processes occurring in the tropics
  • Spatio-temporal analysis of rainfall
  • Identification and tracking of cloud clusters
  • Analysis of atmospheric particulates in the tropics
  • Tropical cyclones
  • Teleconnections
  • Comparisons between observations and model output
  • Comparisons of weather-related variables among different sensors and/or blended datasets
  • Impacts of extreme weather events on the biophysical environment

Published Papers (2 papers)

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Research

Open AccessArticle
Evaluation of Gridded Precipitation Datasets in Malaysia
Remote Sens. 2020, 12(4), 613; https://doi.org/10.3390/rs12040613 - 12 Feb 2020
Abstract
This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and [...] Read more.
This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)
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Open AccessArticle
A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment
Remote Sens. 2020, 12(1), 78; https://doi.org/10.3390/rs12010078 - 24 Dec 2019
Abstract
Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible [...] Read more.
Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. The model, well-trained by S-NPP data, was used to generate both S-NPP and NOAA-20 CSMs for the independent data, and the results were validated against the biases between the sensor observations and Community Radiative Transfer Model (CRTM) calculations (O-M). The preliminary result shows that the FCDN-CSM model works well for identifying clear-sky pixels. Both O-M mean biases and standard deviations were comparable with the Advance Clear-Sky Processor over Ocean (ACSPO) and were significantly better than a prototype cloud mask (PCM) and the case without a clear-sky check. In addition, by replacing CRTM brightness temperatures (BTs) with the atmosphere air temperature and water vapor contents as input features, the FCDN-CSM exhibits its potential to generate fast and accurate VIIRS CSM onboard follow-up Joint Polar Satellite System (JPSS) satellites for sensor calibration and validation before the physics-based CSM is available. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)
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