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Remote Sensing of Clouds and Precipitation at Multiple Scales

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 23792

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Dr. Min Min

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Guest Editor

School of Atmospheric Sciences, Sun Yat-Sen University (Zhuhai Campus), Haiqin Building #2, A271, Xiangzhou District, Zhuhai 519082, China
Interests: cloud; precipitation; convection; aerosol remote sensing using LEO and GEO satellites
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chao Liu

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Guest Editor

School of Atmospheric Physics, Nanjing University of Information Science and Technology, Ningliu Road 219, Nanjing 210044, China
Interests: atmospheric radiative transfer and remote sensing of clouds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clouds and precipitation play an essential role in global weather and climate systems because of their impact on the distribution of atmospheric energy. Many well developed remote sensing techniques have greatly assisted our ability to characterize the inter-decadal, inter-annual, and diurnal variability of clouds and precipitation, connect clouds and precipitation to large-scale circulation patterns, and understand the impacts of clouds and precipitation on the Earth’s atmosphere. However, some recent advances and innovations in terms of active/passive sensors for detecting clouds and precipitation have successfully been launched. We invite studies using these or other new observational data to help further understand the internal processes and dynamics of clouds and precipitation, spanning global to regional scales.

This Special Issue aims at collecting new developments and methodologies, best practices, and applications of remote sensing for clouds and precipitation at multiple scales. We welcome submissions that provide the community with the most recent advancements on all aspects of cloud and precipitation remote sensing, including, but not limited to, the following:

Active and passive detection of cloud and precipitation
Cloud remote sensing
Precipitation remote sensing
Convection remote sensing
Multi-instruments
Cloud and precipitation detections for weather, climatic, and environment studies

Authors are required to check and follow the specific Instructions to Authors; please see: https://www.mdpi.com/journal/remotesensing/instructions

Dr. Min Min
Prof. Dr. Chao Liu
Guest Editors

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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

Active and passive detection of cloud and precipitation
Cloud remote sensing
Precipitation remote sensing
Convection remote sensing
Multi-instruments
Cloud and precipitation detections for weather, climatic, and environment studies

Published Papers (11 papers)

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Research

26 pages, 15428 KiB

Open AccessArticle

Inter-Zone Differences of Convective Development in a Convection Outbreak Event over Southeastern Coast of China: An Observational Analysis

by Yipeng Huang, Murong Zhang, Yuchun Zhao, Ben Jong-Dao Jou, Hui Zheng, Changrong Luo and Dehua Chen

Remote Sens. 2022, 14(1), 131; https://doi.org/10.3390/rs14010131 - 29 Dec 2021

Cited by 4 | Viewed by 1534

Abstract

Among the densely-populated coastal areas of China, the southeastern coast has received less attention in convective development despite having been suffering from significantly increasing thunderstorm activities. The convective complexity under such a region with extremely complex underlying and convective conditions deserves in-depth observational surveys. This present study examined a high-impact convection outbreak event with over 40 hail reports in the southeastern coast of China on 6 May 2020 by focusing on contrasting the convective development (from convective initiation to supercell occurrences) among three adjacent convection-active zones (north (N), middle (M), and south (S)). The areas from N to S featured overall flatter terrain, higher levels of free convection, lower relative humidity, larger convective inhibition, more convective available potential energy, and greater vertical wind shears. With these mesoscale environmental variations, distinct inter-zone differences in the convective development were observed with the region’s surveillance radar network and the Himawari-8 geostationary satellite. Convection initiated in succession from N to S and began with more warm-rain processes in N and M and more ice-phase processes in S. The subsequent convection underwent more vigorous vertical growth from N to S. The extremely deep convection in S was characterized by the considerably strong precipitation above the freezing level, echo tops of up to 18 km, and a great amount of deep (even overshooting) and thick convective clouds with significant cloud-top glaciation. Horizontal anvil expansion in convective clouds was uniquely apparent over S. From N to S, more pronounced mesocyclone and weak-echo region signatures indicated high risks of severe supercell hailstorms. These results demonstrate the strong linkage between the occurrence likelihood of severe convection and associated weather (such as supercells and hailstones) and the early-stage convective development that can be well-captured by high-resolution observations and may facilitate fine-scale convection nowcasting. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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15 pages, 45512 KiB

Open AccessArticle

Quality Scoring of the Fengyun 4A Clear Sky Radiance Product

by Tianlei Yu, Gang Ma, Feng Lu, Xiaohu Zhang and Peng Zhang

Remote Sens. 2021, 13(18), 3658; https://doi.org/10.3390/rs13183658 - 13 Sep 2021

Cited by 2 | Viewed by 1427

Abstract

The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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23 pages, 12172 KiB

Open AccessArticle

Applications of the Advanced Radiative Transfer Modeling System (ARMS) to Characterize the Performance of Fengyun–4A/AGRI

by Fei Tang, Xiaoyong Zhuge, Mingjian Zeng, Xin Li, Peiming Dong and Yang Han

Remote Sens. 2021, 13(16), 3120; https://doi.org/10.3390/rs13163120 - 06 Aug 2021

Cited by 7 | Viewed by 1961

Abstract

This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere). Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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17 pages, 9807 KiB

Open AccessArticle

Projection of Air Pollution in Northern China in the Two RCPs Scenarios

by Chengrong Dou, Zhenming Ji, Yukun Xiao, Zhiyuan Hu, Xian Zhu and Wenjie Dong

Remote Sens. 2021, 13(16), 3064; https://doi.org/10.3390/rs13163064 - 04 Aug 2021

Cited by 4 | Viewed by 1638

Abstract

Air pollution in North China (NC) is an important issue affecting the economy and health. In this study, we used a regional climate model, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) to project air pollution in NC and investigate the variations of air pollutions response to future climate changes, which probably has an implication to strategy and control policy for air quality in NC. A comprehensive model evaluation was conducted to verify the simulated aerosol optical depth (AOD) based on MODIS and MISR datasets, and the model also showed reasonable results in aerosol concentrations. Future changes of air pollution in the middle of the 21st century (2031–2050) were projected in the two Representative Concentration Pathways (RCP4.5 and RCP8.5) and compared with the situation in the historical period (1986–2005). In the two RCPs, the simulated averaged PM_2.5 concentration was projected with the highest values of 50–250

μ g \cdot

m⁻³ over the Bohai Rim Economic Circle (BREC) in winter. The maximum AOD is in the Beijing–Tianjin–Hebei (BTH) region in summer, with an average value of 0.68. In winter, in the RCP4.5 scenario, PM_2.5 concentration and AOD obviously declined in BTH and Shandong province. However, in the RCP8.5 scenario, PM_2.5 concentration and AOD increased. Results indicated that air pollution would be reduced in winter if society developed in the low emission pathway. Precipitation was projected to increase both in the two RCPs scenarios in spring, summer, and winter, but it was projected to decrease in autumn. The planetary boundary layer height decreased in the two RCPs scenarios in the central region of NC in the summer and winter. The results indicated that changes of meteorological conditions have great impact on air pollution in future scenarios. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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19 pages, 3073 KiB

Open AccessArticle

Morphology of Rain Clusters Influencing Rainfall Intensity over Hainan Island

by Tingting Huang, Chenghui Ding, Weibiao Li and Yilun Chen

Remote Sens. 2021, 13(15), 2920; https://doi.org/10.3390/rs13152920 - 25 Jul 2021

Cited by 1 | Viewed by 2637

Abstract

Continuous observations from geostationary satellites can show the morphology of precipitation cloud systems in quasi-real-time, but there are still large deviations in the inversion of precipitation. We used binary-connected area recognition technology to identify meso-β-scale rain clusters over Hainan Island from 1 June 2000 to 31 December 2018, based on Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM data. We defined and statistically analyzed the parameters of rain clusters to reveal the typical morphological and precipitation characteristics of rain clusters, and to explore the relationship between the parameters and rainfall intensity of rain clusters. We found that the area and long axis of rain clusters over land were larger than those over the ocean, and that continental rain clusters were usually square in shape. Rain clusters with a larger area and longer axis were concentrated on the northern side of the mountains on Hainan Island and the intensity of rain was larger on the northern and eastern sides of the mountains. The variation of continental rain clusters over time was more dramatic than the variation of oceanic clusters. The area and long axis of rain clusters was larger between 14:00 and 21:00 from April to September and the long axis of the oceanic rain clusters increased in winter. There were clear positive correlations between the area, long axis and shape of the rain clusters and the maximum rain rate. The area and long axis of continental rain clusters had a higher correlation with the rain rate than those of oceanic clusters. The establishment of a relationship between the morphology of rain clusters and precipitation helps us to understand the laws of precipitation and improve the prediction of precipitation in this region. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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17 pages, 7880 KiB

Open AccessArticle

Evaluation of an Air Pollution Forecasting System Based on Micro-Pulse Lidar Cruising Measurements in the South China Sea

by Yuzhang Tang, Zhenming Ji, Yuan Li, Zhiyuan Hu, Xian Zhu and Wenjie Dong

Remote Sens. 2021, 13(15), 2855; https://doi.org/10.3390/rs13152855 - 21 Jul 2021

Cited by 2 | Viewed by 1698

Abstract

In this study, we evaluated the performance of an air pollution forecasting system during a scientific cruise in the South China Sea (SCS) from 9 August to 7 September 2016. The air pollution forecasting system consisted of a Lagrangian transport and dispersion model, the flexible particle dispersion model (FLEXPART), coupled with a high-resolution Weather Research and Forecasting model (WRF). The model system generally reproduced the meteorological variability and reasonably simulated the distribution of aerosols both vertically and horizontally along the cruise path. The forecasting system was further used to study the regional transport of non-local aerosols over the SCS and track its sources during the cruise. The model results showed that Southeast Asia contributed to more than 90% of the non-local aerosols over the northern region of the SCS due to the southwesterly prevailing winds. Specifically, the largest mean contribution was from Vietnam (39.6%), followed by Thailand (25.1%). This study indicates that the model system can be applied to study regional aerosols transport and provide air pollution forecasts in the SCS. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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23 pages, 7978 KiB

Open AccessArticle

Sensitivity of Mixed-Phase Cloud Optical Properties to Cloud Particle Model and Microphysical Factors at Wavelengths from 0.2 to 100 µm

by Qing Luo, Bingqi Yi and Lei Bi

Remote Sens. 2021, 13(12), 2330; https://doi.org/10.3390/rs13122330 - 14 Jun 2021

Viewed by 2433

Abstract

The representation of mixed-phase cloud optical properties in models is a critical problem in cloud modeling studies. Ice and liquid water co-existing in a cloud layer result in significantly different cloud optical properties from those of liquid water and ice clouds. However, it is not clear as to how mixed-phase cloud optical properties are affected by various microphysical factors, including the effective particle size, ice volume fraction, and ice particle shape. In this paper, the optical properties (extinction efficiency, scattering efficiency, single scattering albedo, and asymmetry factor) of mixed-phase cloud were calculated assuming externally and internally mixed cloud particle models in a broad spectral range of 0.2–100 μm at various effective particle diameters and ice volume fraction conditions. The influences of various microphysical factors on optical properties were comprehensively examined. For the externally mixed cloud particles, the shapes of ice crystals were found to become more important as the ice volume fraction increases. Compared with the mixed-phase cloud with larger effective diameter, the shape of ice crystals has a greater impact on the optical properties of the mixed-phase cloud with a smaller effective diameter (<20 μm). The optical properties calculated by internally and externally mixed models are similar in the longwave spectrum, while the optical properties of the externally mixed model are more sensitive to variations in ice volume fraction in the solar spectral region. The bulk scattering phase functions were also examined and compared. The results indicate that more in-depth analysis is needed to explore the radiative properties and impacts of mixed-phase clouds. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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18 pages, 4088 KiB

Open AccessArticle

Fengyun-3D/MERSI-II Cloud Thermodynamic Phase Determination Using a Machine-Learning Approach

by Dexin Zhao, Lin Zhu, Hongfu Sun, Jun Li and Weishi Wang

Remote Sens. 2021, 13(12), 2251; https://doi.org/10.3390/rs13122251 - 09 Jun 2021

Cited by 4 | Viewed by 2214

Abstract

Global cloud thermodynamic phase (CP) is normally derived from polar-orbiting satellite imaging data with high spatial resolution. However, constraining conditions and empirical thresholds used in the MODIS (Moderate Resolution Imaging Spectroradiometer) CP algorithm are closely associated with spectral properties of the MODIS infrared (IR) spectral bands, with obvious deviations and incompatibility induced when the algorithm is applied to data from other similar space-based sensors. To reduce the algorithm dependence on spectral properties and empirical thresholds for CP retrieval, a machine learning (ML)-based methodology was developed for retrieving CP data from China’s new-generation polar-orbiting satellite, FY-3D/MERSI-II (Fengyun-3D/Moderate Resolution Spectral Imager-II). Five machine learning algorithms were used, namely, k-nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), Stacking and gradient boosting decision tree (GBDT). The RF algorithm gave the best performance. One year of EOS (Earth Observation System) MODIS CP products (July 2018 to June 2019) were used as reference labels to train the relationship between MODIS CP (MYD06 IR) and six IR bands of MERSI-II. CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), MODIS, and FY-3D/MERSI-II CP products were used together for cross-validation. Results indicate strong spatial consistency between ML-based MERSI-II and MODIS CP products. The hit rate (HR) of random forest (RF) CP product could reach 0.85 compared with MYD06 IR CP products. In addition, when compared with the operational FY-3D/MERSI CP product, the RF-based CP product had higher HRs. Using the CALIOP cloud product as an independent reference, the liquid-phase accuracy of the RF CP product was higher than that of operational FY-3D/MERSI-II and MYD06 IR CP products. This study aimed to establish a robust algorithm for deriving FY-3D/MERSI-II CP climate data record (CDR) for research and applications. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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20 pages, 7767 KiB

Open AccessArticle

Seasonal and Diurnal Variations in Cloud-Top Phase over the Western North Pacific during 2017–2019

by Xiaoyong Zhuge, Xiaolei Zou, Xin Li, Fei Tang, Bin Yao and Lu Yu

Remote Sens. 2021, 13(9), 1687; https://doi.org/10.3390/rs13091687 - 27 Apr 2021

Cited by 3 | Viewed by 1682

Abstract

The cloud-top-phase climatology over the western North Pacific (WNP) has received little attention. Using 3 years (2017–2019) of cloud-top-phase products from the Advanced Himawari Imager onboard the Japanese Himawari-8 satellite, this study examines the seasonal and diurnal variations in the cloud-top phase over the WNP. Results show that over the low- and mid-latitude maritime regions, ice (water) clouds occur more (less) frequently during boreal winter than summer. Water clouds are more likely to be related to moisture conditions in the lower troposphere than to the underlying sea surface temperature. Owing to the combined effects of moist air mass transport and ocean currents (topography), the WNP region east of Hokkaido (the Sichuan Basin) has a high frequency of water clouds in summer (winter). Furthermore, supercooled water cloud populations have a clear seasonal cycle. The fraction of water clouds that are supercooled appears to be modulated by the near-surface air temperature. A diurnal cycle is seen in ice-cloud populations, which are highest in the late afternoon over both ocean and land except for the Sichuan Basin where summer nocturnal precipitation is typical. The occurrences of continental water clouds peak at noon in summer but early morning (around sunrise) in winter. An increase in the frequency of continental summer water clouds around noon is found to be associated with variations in both the cloud-top elevation of already-existing water clouds and new formations of boundary-layer clouds. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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18 pages, 7020 KiB

Open AccessArticle

Statistical Characteristics of Mesoscale Convective Systems Initiated over the Tibetan Plateau in Summer by Fengyun Satellite and Precipitation Estimates

by Xidi Zhang, Wenqiang Shen, Xiaoyong Zhuge, Shunan Yang, Yun Chen, Yuan Wang, Tao Chen and Shushi Zhang

Remote Sens. 2021, 13(9), 1652; https://doi.org/10.3390/rs13091652 - 23 Apr 2021

Cited by 10 | Viewed by 2021

Abstract

In order to investigate the key characteristics of mesoscale convective systems (MCSs) initiated over the Tibetan Plateau (TP) in recent years and the main differences in circulation and environmental factors between different types of MCSs, an automatic MCS identification and tracking method was applied based on the data from China’s Fengyun satellite and precipitation estimates. In total, 8820 MCSs were found to have been initiated over the TP during the summers from 2013 to 2019, and a total of 9.3% of them were able to move eastward out of the TP (EO). The number of MCSs showed a monthly variation, with a maximum in July and a minimum in June, while most EOs occurred in June. Compared with other types of MCSs, EOs usually had a lower cloud-top temperature, a greater rainfall intensity, a longer life duration, more rapid development, larger areas of rainfall and convective clouds, longer tracks and a wider influence range, indicating that EOs are more vigorous than the other types of MCSs. The movement of MCSs is mainly due to the mid- to high-level dynamic conditions, and moisture is an essential factor in their development and maintenance. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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20 pages, 2681 KiB

Open AccessArticle

Potential Driving Factors on Surface Solar Radiation Trends over China in Recent Years

by Qiuyan Wang, Hua Zhang, Su Yang, Qi Chen, Xixun Zhou, Guangyu Shi, Yueming Cheng and Martin Wild

Remote Sens. 2021, 13(4), 704; https://doi.org/10.3390/rs13040704 - 14 Feb 2021

Cited by 13 | Viewed by 2550

Abstract

The annual mean surface solar radiation (SSR) trends under all-sky, clear-sky, all-sky-no-aerosol, and clear-sky-no-aerosol conditions as well as their possible causes are analyzed during 2005–2018 across China based on different satellite-retrieved datasets to determine the major drivers of the trends. The results confirm clouds and aerosols as the major contributors to such all-sky SSR trends over China but play differing roles over sub-regions. Aerosol variations during this period result in a widespread brightening, while cloud effects show opposite trends from south to north. Moreover, aerosols contribute more to the increasing all-sky SSR trends over northern China, while clouds dominate the SSR decline over southern China. A radiative transfer model is used to explore the relative contributions of cloud cover from different cloud types to the all-types-of-cloud-cover-induced (ACC-induced) SSR trends during this period in four typical sub-regions over China. The simulations point out that the decreases in low-cloud-cover (LCC) over the North China Plain are the largest positive contributor of all cloud types to the marked annual and seasonal ACC-induced SSR increases, and the positive contributions from both high-cloud-cover (HCC) and LCC declines in summer and winter greatly contribute to the ACC-induced SSR increases over East China. The contributions from medium-low-cloud-cover (mid-LCC) and LCC variations dominate the ACC-caused SSR trends over southwestern and South China all year round, except for the larger HCC contribution in summer. Full article

(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales)

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