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Satellite Remote Sensing for Tropical Meteorology and Climatology

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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 2020) | Viewed by 20516

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Special Issue Editor

Prof. Dr. Corene Matyas

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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, Collections and Topics 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 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

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 (6 papers)

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Research

25 pages, 6023 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa

by Isaac Kwesi Nooni, Daniel Fiifi T. Hagan, Guojie Wang, Waheed Ullah, Shijie Li, Jiao Lu, Asher Samuel Bhatti, Xiao Shi, Dan Lou, Nana Agyemang Prempeh, Kenny T. C. Lim Kam Sian, Mawuli Dzakpasu, Solomon Obiri Yeboah Amankwah and Chenxia Zhu

Remote Sens. 2021, 13(3), 533; https://doi.org/10.3390/rs13030533 - 02 Feb 2021

Cited by 10 | Viewed by 3157

Abstract

Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSI_PM) and Thornthwaite (scPDSI_TH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSI_PM and scPDSI_TH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSI_PM) and 0.2 mm/decade (scPDSI_TH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)

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22 pages, 3784 KiB

Open AccessArticle

NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology

by Zhong Liu, Chung-Lin Shie, Angela Li and David Meyer

Remote Sens. 2020, 12(17), 2821; https://doi.org/10.3390/rs12172821 - 31 Aug 2020

Cited by 7 | Viewed by 3834

Abstract

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)

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17 pages, 8400 KiB

Open AccessArticle

Intraseasonal Vertical Cloud Regimes Based on CloudSat Observations over the Tropics

by Meng-Pai Hung, Wei-Ting Chen, Chien-Ming Wu, Peng-Jen Chen and Pei-Ning Feng

Remote Sens. 2020, 12(14), 2273; https://doi.org/10.3390/rs12142273 - 15 Jul 2020

Cited by 7 | Viewed by 2688

Abstract

This study identifies the evolution of tropical vertical cloud regimes (CRs) and their associated heating structures on the intraseasonal time scales. Using the cloud classification retrievals of CloudSat during boreal winter between 2006 and 2017, the CR index is defined as the leading pair of the combined multivariate empirical orthogonal functions of the daily mean frequency of deep, high, and low clouds over the tropical Indian Ocean, Maritime Continents, and the Western Pacific. The principal components of the CR index exhibit robust temporal variance in the 30 to 80 day intraseasonal band. Based on the propagation stages of the CRs, the coherent vertical structures of cloud composition and large-scale moisture and vertical motion exhibit a westward-tilted structure. The associated Q1-QR diabatic heating and cloud radiative forcing are consistent with the key characteristics of the Madden Julian Oscillation (MJO) documented in the previous studies. Lastly, an MJO case study showcases that the presented approach characteristically captures the propagation of moisture, cloud vertical structure, and precipitation activity across spatial and temporal scales. The current results suggest that the CR index can potentially serve as an evaluation metric to cloud-associated processes in the simulated tropical intraseasonal variability in global climate models. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)

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16 pages, 5809 KiB

Open AccessArticle

Atmospheric Forcing of the High and Low Extremes in the Sea Surface Temperature over the Red Sea and Associated Chlorophyll-a Concentration

by Kamal A. Alawad, Abdullah M. Al-Subhi, Mohammed A. Alsaafani and Turki M. Alraddadi

Remote Sens. 2020, 12(14), 2227; https://doi.org/10.3390/rs12142227 - 11 Jul 2020

Cited by 7 | Viewed by 2228

Abstract

Taking advantage of 37-year-long (1982–2018) of high-quality satellite datasets, we examined the role of direct atmospheric forcing on the high and low sea surface temperature (SST) extremes over the Red Sea (RS). Considering the importance of SST in regulating ocean physics and biology, the associated impacts on chlorophyll (Chl-a) concentration were also explored, since a small change in SST can cause a significant impact in the ocean. After describing the climate features, we classified the top 5% of SST values (≥31.5 °C) as extreme high events (EHEs) during the boreal summer period and the lowest SST values (≤22.8 °C) as extreme low events (ELEs) during the boreal winter period. The spatiotemporal analysis showed that the EHEs (ELEs) were observed over the southern (northern) basin, with a significant warming trend of 0.027 (0.021) °C year⁻¹, respectively. The EHEs were observed when there was widespread less than average sea level pressure (SLP) over southern Europe, northeast Africa, and Middle East, including in the RS, leading to the cold wind stress from Europe being relatively less than usual and the intrusion of stronger than usual relatively warm air mass from central Sudan throughout the Tokar Gap. Conversely, EHEs were observed when above average SLP prevailed over southern Europe and the Mediterranean Sea as a result of the Azores high and westward extension of the Siberian anticyclone, which led to above average transfer of cold and dry wind stress from higher latitudes. At the same time, notably less wind stress due to southerlies that transfer warm and humid air masses northward was observed. Furthermore, physical and biological responses related to extreme stress showed distinct ocean patterns associated with each event. It was found that the Chl-a concentration anomalies over the northern basin caused by vertical nutrient transport through deep upwelling processes are the manifestation of the superimposition of ELEs. The situation was the opposite for EHEs due to the stably stratified ocean boundary layer, which is a well-known consequence of global warming. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)

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22 pages, 7278 KiB

Open AccessArticle

Evaluation of Gridded Precipitation Datasets in Malaysia

by Afiqah Bahirah Ayoub, Fredolin Tangang, Liew Juneng, Mou Leong Tan and Jing Xiang Chung

Remote Sens. 2020, 12(4), 613; https://doi.org/10.3390/rs12040613 - 12 Feb 2020

Cited by 41 | Viewed by 4974

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 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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15 pages, 2271 KiB

Open AccessArticle

A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment

by Xingming Liang, Quanhua Liu, Banghua Yan and Ninghai Sun

Remote Sens. 2020, 12(1), 78; https://doi.org/10.3390/rs12010078 - 24 Dec 2019

Cited by 7 | Viewed by 2663

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 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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