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Synergetic Remote Sensing of Clouds and Precipitation
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Synergetic Remote Sensing of Clouds and Precipitation

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

Deadline for manuscript submissions: closed (1 January 2023) | Viewed by 25522

Special Issue Editors

Dr. Haoran Li

E-Mail Website
Guest Editor
Chinese Academy of Meteorological Sciences, Beijing, China
Interests: radar meteorology; cloud and precipitation physics
Special Issues, Collections and Topics in MDPI journals
Dr. Haonan Chen

E-Mail Website
Guest Editor
NOAA Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305, USA
Interests: radar remote sensing; radar polarimetry; radar and satellite data fusion; precipitation microphysics; precipitation classification and quantification using multiparameter weather radar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Observations of clouds and precipitation are essential for understanding the global hydrological cycle, assessing the Earth’s radiation budgets, and monitoring high-impact events. Remote sensing technologies provide in-depth insights into the formation and development of clouds and precipitation thanks to the development of a wide variety of observing instruments, such as radars, lidars, spectrometers, microwave radiometers (MWRs), etc. These instruments as carried by multiple platforms, e.g., vehicles, satellites, and ships, bring an unprecedented opportunity to observe clouds and precipitation with a synergy of observations. In recent years, a vast variety of algorithms have been proposed and developed for synergetic retrievals, such as remote sensing and in situ, active and passive remote sensing, multifrequency radars, radar and lidar, and radar and lidar and MWR, to disentangle the complex of clouds and precipitation. The emerging artificial intelligence (AI) techniques further extend the capability of remote sensing measurements for scientific research and operational applications.

This Special Issue will be focused on recent advances in synergetic remote sensing of clouds and precipitation, including algorithm development, comparison and evaluation of multisource remote sensing data, as well as applications of AI in remote sensing. The topics include but are not limited to research on cloud/precipitation physics, nowcasting, high-impact weather monitoring using weather/cloud/phased-array radars, lidars, spectrometers, microwave radiometers, etc.

Research articles, review articles, and short communications are welcome.

Dr. Haoran Li
Dr. Haonan Chen
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

  • clouds and precipitation
  • artificial intelligence
  • synergetic remote sensing
  • radars
  • lidars
  • spectrometers
  • microwave radiometers
  • severe weather

Published Papers (15 papers)

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Research

23 pages, 190879 KiB  
Article
Weather Radar Super-Resolution Reconstruction Based on Residual Attention Back-Projection Network
by Qiu Yu, Ming Zhu, Qiangyu Zeng, Hao Wang, Qingqing Chen, Xiangyu Fu and Zhipeng Qing
Remote Sens. 2023, 15(8), 1999; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15081999 - 10 Apr 2023
Cited by 2 | Viewed by 1618
Abstract
Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead [...] Read more.
Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead to the loss of some valid information by using image processing methods for super-resolution reconstruction. To solve this problem, a weather radar that echoes the super-resolution reconstruction algorithm—based on residual attention back-projection network (RABPN)—is proposed to improve the the radar base data resolution. RABPN consists of multiple Residual Attention Groups (RAGs) connected with long skip connections to form a deep network; each RAG is composed of some residual attention blocks (RABs) connected with short skip connections. The residual attention block mined the mutual relationship between low-resolution radar echoes and high-resolution radar echoes by adding a channel attention mechanism to the deep back-projection network (DBPN). Experimental results demonstrate that RABPN outperforms the algorithms compared in this paper in visual evaluation aspects and quantitative analysis, allowing a more refined radar echo structure, especially in terms of echo details and edge structure features. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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24 pages, 7424 KiB  
Article
Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region
by Faisal Baig, Muhammad Abrar, Haonan Chen and Mohsen Sherif
Remote Sens. 2023, 15(4), 1078; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041078 - 16 Feb 2023
Cited by 5 | Viewed by 1516
Abstract
Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity [...] Read more.
Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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20 pages, 8876 KiB  
Article
Dynamic Field Retrieval and Analysis of Structural Evolution in Offshore Core Area of Typhoon Higos Based on Ground-Based Radar Observation
by Ruiyi Li, Qifeng Lu, Ming Wei, Lei Wu, Ruifeng Li, Shudong Wang and Hua Liu
Remote Sens. 2023, 15(3), 809; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030809 - 31 Jan 2023
Viewed by 1025
Abstract
Three ground-based radars in the Pearl River Delta successfully observed Typhoon Higos (2020), which traveled over the offshore area in the South China Sea. During the observation period, the stratiform region of the outer rainband of HIGOS became active while swirling inward, merging [...] Read more.
Three ground-based radars in the Pearl River Delta successfully observed Typhoon Higos (2020), which traveled over the offshore area in the South China Sea. During the observation period, the stratiform region of the outer rainband of HIGOS became active while swirling inward, merging into an unclosed eyewall and spreading outward, but its structure was asymmetric between upwind and downwind. To understand the dynamic mechanism of the asymmetry of the stratiform region in detail, the refined wind speed distributions in the inner core of Higos was retrieved by using the radar observation data and a three-dimensional, variational, direct, data assimilation, Dual-Doppler analysis (DDA). In addition, an Observing System Simulation Experiment (OSSE) was conducted with the numerical simulations by the Weather Research and Forecasting (WRF) model and numerical emulations by Cloud Resolving Model Radar SIMulator (CR-SIM) software to validate the retrieved data. From the OSSE, the emulated retrieved data were comparable with the WRF-out data. The analysis shows that the dynamic mechanisms are different between upwind and downwind in the stratiform rainband. In the former, the inflow sinks in the middle troposphere. In addition, there is an inflow in the lower troposphere, with an outflow aloft the inflow. In the latter, however, the stratiform rainband is primarily influenced by outflow from inside the rainband and inflow from outside the monsoon-related southwesterly winds. The vertical velocity characteristics in the stratiform rainband downwind also differ from those upwind. The upwind updraft was distinct in the middle troposphere, whereas the downwind updraft was caused by the convergence of the outflow from inside the stratiform rainband and the monsoon-related southwesterly inflow in the lower troposphere. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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18 pages, 5756 KiB  
Article
Snowfall Microphysics Characterized by PARSIVEL Disdrometer Observations in Beijing from 2020 to 2022
by Yonghai Shen, Yichen Chen, Yongheng Bi, Daren Lyu, Hongbin Chen and Shu Duan
Remote Sens. 2022, 14(23), 6025; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236025 - 28 Nov 2022
Viewed by 1450
Abstract
Accurate snowfall forecasting and quantitative snowfall estimation remain challenging due to the complexity and variability of snow microphysical properties. In this paper, the microphysical characteristics of snowfall in the Yanqing mountainous area of Beijing are investigated by using a Particle Size and Velocity [...] Read more.
Accurate snowfall forecasting and quantitative snowfall estimation remain challenging due to the complexity and variability of snow microphysical properties. In this paper, the microphysical characteristics of snowfall in the Yanqing mountainous area of Beijing are investigated by using a Particle Size and Velocity (PARSIVEL) disdrometer. Results show that the high snowfall intensity process has large particle-size distribution (PSD) peak concentration, but the distribution of its spectrum width is much smaller than that of moderate or low snowfall intensity. When the snowfall intensity is high, the corresponding Dm value is smaller and the Nw value is larger. Comparison between the fitted μ−Λ relationship and the relationships of different locations show that there are regional differences. Based on dry snow samples, the Ze−SR relationship fitted in this paper is more consistent with the Ze−SR relationship of dry snow in Nanjing, China. The fitted ρs−Dm relationship of dry snow is close to the relationship in Pyeongchang, Republic of Korea, but the relationship of wet snow shows greatly difference. At last, the paper analyzes the statistics on velocity and diameter distribution of snow particles according to different snowfall intensities. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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20 pages, 13526 KiB  
Article
Raindrop Size Distribution Prediction by an Improved Long Short-Term Memory Network
by Yongjie Zhu, Zhiqun Hu, Shujie Yuan, Jiafeng Zheng, Dejin Lu and Fujiang Huang
Remote Sens. 2022, 14(19), 4994; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194994 - 07 Oct 2022
Viewed by 1375
Abstract
The observation of and research on raindrop size distribution (DSD) is important for mastering and understanding the mutual restriction relationship between cloud dynamics and cloud microphysics in a process of precipitation; it also plays an irreplaceable role in many fields, such as radar [...] Read more.
The observation of and research on raindrop size distribution (DSD) is important for mastering and understanding the mutual restriction relationship between cloud dynamics and cloud microphysics in a process of precipitation; it also plays an irreplaceable role in many fields, such as radar meteorology, weather modification, boundary layer land surface processes, aerosols, etc. Using more than 1.7 million minutes of raindrop data observed with 17 laser disdrometers at 17 stations in Anhui Province, China, from 7 August 2009 to 30 April 2020, a DSD training dataset was constructed. Furthermore, the data are fitted to a normalized Gamma function and used to obtain its three parameters, i.e., the normalized intercept Nw, the mass weighted average diameter Dm, and the shape factor μ. Based on the long short-term memory network (LSTM), a DSD Gamma distribution prediction network (DSDnet) was designed. In the process of modeling based on DSDnet, a self-defined loss function (SLF) was proposed in order to improve the DSD prediction by increasing the weight values in the poor fitting regions according to the common mean square error loss function (MLF). By means of the training dataset, a DSDnet-based model was trained to realize the prediction of Nw, Dm, and μ minute-to-minute over the course of 30 min, and then was evaluated by the test dataset according to three indicators, namely, mean relative error (MRE), mean absolute error (MAE), and correlation coefficient (CC). The CC of lgNw, Dm, and μ can reach 0.93403, 0.90934, and 0.89741 for 12-min predictions, and 0.87559, 0.85261, and 0.84564 for 30-min predictions, respectively, which means that the DSD prediction accuracy within 30 min can basically reach the application level. Furthermore, the 12- and 30-min predictions of 3 precipitation processes were taken as examples to fully demonstrate the application effect of model. The prediction effects of Nw and Dm are better than that of μ, and the stratiform precipitation is better than the convective and convective-stratiform mixed cloud precipitation. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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14 pages, 33620 KiB  
Communication
Reconstruction of Rainfall Field Using Earth–Space Links Network: A Compressed Sensing Approach
by Yingcheng Zhao, Xichuan Liu, Lei Liu, Kang Pu and Kun Song
Remote Sens. 2022, 14(19), 4966; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194966 - 06 Oct 2022
Viewed by 1175
Abstract
High-precision rainfall information is of great importance for the improvement of the accuracy of numerical weather prediction and the monitoring of floods and mudslides that affect human life. With the rapid development of satellite constellation networks, there is great potential for reconstructing high-precision [...] Read more.
High-precision rainfall information is of great importance for the improvement of the accuracy of numerical weather prediction and the monitoring of floods and mudslides that affect human life. With the rapid development of satellite constellation networks, there is great potential for reconstructing high-precision rainfall fields in large areas by using widely distributed Earth–space link (ESL) networks. In this paper, we have carried out research on reconstructing high-precision rainfall fields using an ESL network with the compressed sensing (CS) method in the case of a sparse distribution of the ESLs. Firstly, ESL networks with different densities are designed using the K-means clustering algorithm. The real rainfall fields are then reconstructed using the designed ESL networks with CS, and the reconstructed results are compared with that of the inverse distance weighting (IDW) algorithm. The results show that the root mean square error (RMSE) and correlation coefficient (CC) of the reconstructed rainfall fields using the ESL network with CS are lower than 0.15 mm/h and higher than 0.999, respectively, when the density is 0.05 links per square kilometer, indicating that the ESL network with CS is capable of reconstructing the high-precision rainfall fields under sparse sampling. Additionally, the performance of reconstructing the rainfall fields using the ESL networks with CS is superior compared to the reconstructed results of the IDW algorithm. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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22 pages, 50323 KiB  
Article
Application of Random Forest Algorithm on Tornado Detection
by Qiangyu Zeng, Zhipeng Qing, Ming Zhu, Fugui Zhang, Hao Wang, Yin Liu, Zhao Shi and Qiu Yu
Remote Sens. 2022, 14(19), 4909; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194909 - 01 Oct 2022
Cited by 1 | Viewed by 1995
Abstract
Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and [...] Read more.
Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and have strict multi-altitude constraints, such as the tornado detection algorithm based on tornado vortex signatures (TDA-TVS), which may lead to high false alarm rates, and the performance of the detection algorithm is greatly affected by the radar data quality control algorithm. A novel TDA-RF algorithm based on the random forest (RF) classification algorithm is proposed for real-time tornado identification of the S-band China new generation of Doppler weather radar (CINRAD-SA). The TDA-RF algorithm uses velocity features to identify tornadoes and adds features related to reflectivity and velocity spectrum width in radar level-II data. Historical CINRAD-SA tornado data from 2006–2015 are used to construct the tornado dataset and train the TDA-RF model. The performance of TDA-RF is evaluated using CINRAD-SA data from five tornadoes of 2016–2020 with enhanced Fujita(EF) scale ratings ranging from EF0 to EF4 and distances from 10 to 130 km to the radar. TDA-RF performs well overall with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 71%, 29%, and 55%, respectively. Moreover, the TDA-RF improves POD and CSI, and reduces FAR compared to the TDA-TVS. The maximum tornado early-warning time of TDA-RF is 17 min, and the average is 6 min; TDA-RF can provide classification probability according to the tornado generation and development process to facilitate tracking ability. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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22 pages, 5802 KiB  
Article
Cloud Macro- and Microphysical Properties in Extreme Rainfall Induced by Landfalling Typhoons over China
by Dajun Zhao, Yubin Yu, Ying Li, Hongxiong Xu and Lianshou Chen
Remote Sens. 2022, 14(17), 4200; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174200 - 26 Aug 2022
Cited by 1 | Viewed by 1343
Abstract
Extreme rainfall induced by landfalling typhoon (ERLTC) can cause destructive natural disasters throughout China. Cloud properties in ERLTC are not yet well understood and parameterized, which limits the forecast accuracy of ERLTC to some extent. The 99th percentile intensity of daily rainfall associated [...] Read more.
Extreme rainfall induced by landfalling typhoon (ERLTC) can cause destructive natural disasters throughout China. Cloud properties in ERLTC are not yet well understood and parameterized, which limits the forecast accuracy of ERLTC to some extent. The 99th percentile intensity of daily rainfall associated with LTC is objectively defined as ERLTC and using the CloudSat tropical cyclone (CSTC) dataset from 2006 to 2018, cloud macro- and microphysical characteristics are statistically investigated. Results show that the proportion of single-layer (double-layered) clouds increases (decreases) significantly on the occurrence day of ERLTC. In the TC inner core region, the proportion of deep convective cloud at 2–10 km is the highest, reaching 50%. In the TC envelop region, deep convective cloud at the height of 3–8 km and cirrus at the height of 12–14 km account for the highest proportions. For the TC outer region, cirrus around 13 km has the highest proportion. During the ERLTC period, the ice-water content is mainly distributed in 5–18 km, and is mostly distributed in the TC inner core, followed by the envelop region. A large number of smaller ice particles are gathering in the upper troposphere at 13–18 km, while a small number of larger ones is gathering in the middle levels around 8–10 km. These results are useful for evaluating the ERLTC simulations and are expected to provide new forecasting factors for ERLTC in cloud macro- and microphysical perspectives. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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18 pages, 16561 KiB  
Article
Three-Dimensional Structure Analysis and Droplet Spectrum Characteristics of Southwest Vortex Precipitation System Based on GPM-DPR
by Hao Wang, Linyin Tan, Fugui Zhang, Jiafeng Zheng, Yanxia Liu, Qiangyu Zeng, Yilin Yan, Xinyue Ren and Jie Xiang
Remote Sens. 2022, 14(16), 4063; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164063 - 19 Aug 2022
Cited by 5 | Viewed by 1245
Abstract
This study is the first in the region to use Global Precipitation Mission Dual-Frequency Precipitation Radar (GPM-DPR) and Fengyun-2G (FY-2G) observations to qualitatively and quantitatively study the Southwest Vortex evolution characteristics during the flood season from 2019 to 2021. Furthermore, vertical characteristics of [...] Read more.
This study is the first in the region to use Global Precipitation Mission Dual-Frequency Precipitation Radar (GPM-DPR) and Fengyun-2G (FY-2G) observations to qualitatively and quantitatively study the Southwest Vortex evolution characteristics during the flood season from 2019 to 2021. Furthermore, vertical characteristics of the two main precipitation types in the Southwest Vortex, stratiform and convective, were statistically analyzed at different life stages, including horizontal and vertical distribution of precipitation particles, droplet spectrum characteristics, and vertically layered precipitation contribution. The results showed that: (1) The typical convective precipitation (CP) in the developing and mature stages has strong reflectivity distribution centers in the upper and lower layers, showing characteristics related to terrain. Additionally, the high-level hydrometeor particles are mainly solid precipitation particles, and particles in the lower layers collide and coalesce in the violent vertical motion of the airflow. (2) For the three stages of CP, the reflectivity below melting layer (ML) first showed a rapid weakening trend toward the surface and then remained unchanged, significantly changing its vertical structure. The main rainfall type of the Southwest Vortex system was stratiform precipitation (SP) in the three stages. (3) In the two types of cloud precipitation, the developing stage is generally composed of large and sparse precipitation particles, the mature stage of large and dense precipitation particles, and the dissipating stage of small and sparse precipitation particles. The findings of this study reveal the three-dimensional refined structure and vertical variation characteristics of different life stages of the Southwest Vortex precipitation cloud system and provide important tools and references for improving the accuracy of numerical models and the forecast level of short-term heavy precipitation under complex terrain. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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21 pages, 13565 KiB  
Article
Evolution and Structure of a Dry Microburst Line Observed by Multiple Remote Sensors in a Plateau Airport
by Xuan Huang, Jiafeng Zheng, Yuzhang Che, Gaili Wang, Tao Ren, Zhiqiang Hua, Weidong Tian, Zhikun Su and Lianxia Su
Remote Sens. 2022, 14(15), 3841; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153841 - 08 Aug 2022
Cited by 2 | Viewed by 1733
Abstract
The civilian airplane is a common transportation mode for the local people in the Qinghai-Tibet Plateau (QTP). Due to the profound dynamic and thermal effects, the QTP can trigger strong windstorms during the warm season, during which downbursts can cause severe low-level wind [...] Read more.
The civilian airplane is a common transportation mode for the local people in the Qinghai-Tibet Plateau (QTP). Due to the profound dynamic and thermal effects, the QTP can trigger strong windstorms during the warm season, during which downbursts can cause severe low-level wind shear and threaten aviation safety. However, the study of downbursts over QTP has not been given much attention. This study analyzes and interprets a typical traveling dry microburst line that happened at the Xining Caojiapu International Airport (ZLXN) on 14 May 2020, intending to show a better understanding of the dry downbursts over QTP and explore the synergetic usage of different remote sensing technologies for downburst detection and warning in plateau airports. Specifically, the characteristics of synoptic conditions, the convective system formation process, and the structure and evolution of downbursts and relevant low-level winds are comprehensively investigated. The results show that, under the control of an upstream shallow trough, features of the local atmosphere state, including a dry-adiabatic stratification, a shallow temperature inversion, increases in solar radiation heating, and strong vertical shears of horizontal winds, can be favorable atmospheric prerequisites for the formation and development of dry storms and downbursts. Low-reflectivity storm cells of the Mesoscale Convective System (MCS) organize to form narrow bow echoes, and downbursts show features of radial wind convergences and rapid descending reflectivity cores with hanging virga as observed by a Doppler weather radar. Moreover, details of gales, gust fronts, convergences, turbulences, wind collisions, and outflow interactions triggered by the downburst line are also detected and interpreted by a scanning Doppler wind lidar from different perspectives. In addition, the findings in this work have been compared with the results observed in Denver, U.S., and some simulation studies. Finally, a few conceptual models of low-level wind evolutions influenced by the dry downburst line are given. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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19 pages, 7364 KiB  
Article
Intercomparison of Cloud Vertical Structures over Four Different Sites of the Eastern Slope of the Tibetan Plateau in Summer Using Ka-Band Millimeter-Wave Radar Measurements
by Xia Wan, Jiafeng Zheng, Rong Wan, Guirong Xu, Jianfeng Qin and Lan Yi
Remote Sens. 2022, 14(15), 3702; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153702 - 02 Aug 2022
Cited by 3 | Viewed by 1348
Abstract
The eastern slope of the Tibetan Plateau is a crucial corridor of water-vapor transport from the Tibetan Plateau to Eastern China. This is also a region with active cloud initiation, and the locally hatched cloud systems have a profound impact on the radiation [...] Read more.
The eastern slope of the Tibetan Plateau is a crucial corridor of water-vapor transport from the Tibetan Plateau to Eastern China. This is also a region with active cloud initiation, and the locally hatched cloud systems have a profound impact on the radiation budget and hydrological cycle over the downstream Sichuan Basin and the middle reach of the Yangtze River. It is noteworthy that there is a strong diversification in the characteristics and evolution of the ESTP cloud systems due to the complex terrain. Therefore, in this study, ground-based Ka-band millimeter-wave cloud radar measurements collected at the Ganzi (GZ), Litang (LT), Daocheng (DC), and Jiulong (JL) sites of the ESTP in 2019 were analyzed to compare the vertical structures of summer nonprecipitating clouds, including cloud occurrence frequency, radar reflectivity factor, cloud base height, cloud top height, and cloud thickness. The occurrence frequency exhibits two peaks on the ESTP with maximum values of ~20% (2–4 km) and 15% (7–9 km), respectively. The greatest (smallest) occurrence frequency occurs in the JL (GZ). The cloud occurrence frequency of all sites increases rapidly in the afternoon, and the occurrence frequency of the DC presents larger values at 2–4 km. In contrast, the occurrence frequency in the JL shows another increase from 2000 LT to midnight at 7–11 km. Stronger radar echoes occur most frequently in the LT at 5–7 km, and hydrometeor sizes and phase states vary dramatically in mixed-phase clouds. A small number of radar echoes occur at midnight in the JL. A characteristic bimodality of the cloud base height and top height for single-layer, double-layer, and triple-layer clouds was observed. Clouds show a higher base height in the GZ and higher top height in the JL. The ESTP is dominated by thin clouds with thicknesses of 200–400 m. The cloud base height, top height, and thickness exhibit an increase in the afternoon, and higher top height occurs more frequently from midnight to the next early morning in the JL because of its mountain-valley terrain. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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23 pages, 11503 KiB  
Article
A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements
by Spandan Das, Yiding Wang, Jie Gong, Leah Ding, Stephen J. Munchak, Chenxi Wang, Dong L. Wu, Liang Liao, William S. Olson and Donifan O. Barahona
Remote Sens. 2022, 14(15), 3631; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153631 - 29 Jul 2022
Cited by 3 | Viewed by 2589
Abstract
Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote [...] Read more.
Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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23 pages, 16222 KiB  
Article
Analysis of the Characteristics and Evolution Mechanisms of a Bow-Shaped Squall Line in East China Observed with Dual-Polarization Doppler Radars
by Bin Wu, Ming Wei, Yanfang Li, Zhangwei Wang, Shuang Du and Chen Zhao
Remote Sens. 2022, 14(15), 3531; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153531 - 23 Jul 2022
Cited by 1 | Viewed by 1472
Abstract
To gain a deeper understanding of the formation and evolutionary mechanisms of a bow-shaped squall line (BSL) that occurred in East China on 10 May 2021, observations from S-band dual-polarization radars, a disdrometer and other instruments are used to investigate the characteristics and [...] Read more.
To gain a deeper understanding of the formation and evolutionary mechanisms of a bow-shaped squall line (BSL) that occurred in East China on 10 May 2021, observations from S-band dual-polarization radars, a disdrometer and other instruments are used to investigate the characteristics and evolution of the kinematic, microphysical and radar echo structure within the squall line during its formative and mature stages. The results are as follows. The updraft induced by upper-level divergence and vertical thermal instability induced by the cold source at the middle and top of the troposphere provided environmental conditions suitable for the formation and strengthening of a squall line. The characteristics of the vertical vorticity at the leading edge of the squall line provided a good indication of its echo structure and evolutionary trend. The mechanism behind a new echo phenomenon—double high-differential reflectivity (ZDR) bands—observed in plan position indicator scans produced by the dual-polarization radar is investigated from the kinematic and microphysical structural perspectives. The evolutionary characteristics of the microphysical structure of the bulk of the squall line and its trailing stratiform cloud region are analyzed based on the quasi-vertical profiles retrieved from the S-band dual-polarization radar in Quzhou. Moreover, a conceptual model describing this type of BSL with a trailing region of stratiform rain in the warm sector is developed to provide technical support for the monitoring and early warning of BSLs. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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19 pages, 5263 KiB  
Article
Multiscale Perspectives on an Extreme Warm-Sector Rainfall Event over Coastal South China
by Yiliang Pu, Sheng Hu, Yali Luo, Xiantong Liu, Lihua Hu, Langming Ye, Huiqi Li, Feng Xia and Lingyu Gao
Remote Sens. 2022, 14(13), 3110; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133110 - 28 Jun 2022
Cited by 8 | Viewed by 2035
Abstract
On 22 June 2017, an extreme warm-sector rainfall event hit the western coastal area of South China, with maximum hourly and 12-h rainfall accumulations of 189.4 and 464.8 mm, respectively, which broke local historical records. Multisource observations were used to reveal multiscale processes [...] Read more.
On 22 June 2017, an extreme warm-sector rainfall event hit the western coastal area of South China, with maximum hourly and 12-h rainfall accumulations of 189.4 and 464.8 mm, respectively, which broke local historical records. Multisource observations were used to reveal multiscale processes contributing to the extreme rainfall. The results showed that a marine boundary layer jet (BLJ) coupled with a synoptic low-level jet (LLJ) inland played an important role in the formation of an extremely humid environment with a very low lifting condensation level of near-surface air. Under the favorable pre-convective conditions, convection was initialized at a mesoscale convergence line, aided by topographic lifting in the evening. During the nocturnal hours, the rainstorm developed and was maintained by a quasi-stationary mesoscale outflow boundary, which continuously lifted warm, moist air transported by the enhanced BLJ. When producing the extreme rainfall rates, the storm possessed relatively weak convection, with the 40 dBZ echo top hardly reaching 6 km. The extreme rainfall was produced mainly by the warm rain microphysical processes, mainly because the humid environment and the deep warm cloud layer facilitated the clouds’ condensational growth and collision–coalescence, and also reduced rain evaporation. As the storm evolved, the raindrop concentration increased rapidly from its initial stage and remained high until its weakening stage, but the mean raindrop size changed little. The extreme rain was characterized by the highest concentration of raindrops during the storm’s lifetime with a mean size of raindrops slightly larger than the maritime regime. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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14 pages, 5227 KiB  
Article
A Dataset of Overshooting Cloud Top from 12-Year CloudSat/CALIOP Joint Observations
by Haoyang Li, Xiaocheng Wei, Min Min, Bo Li, Ziqi Nong and Lin Chen
Remote Sens. 2022, 14(10), 2417; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102417 - 18 May 2022
Cited by 3 | Viewed by 1796
Abstract
A strong convective storm is a disastrous weather system with a small spatio-temporal scale. It often occurs suddenly and can cause huge disasters. Thus, it is necessary to improve the forecast accuracy of strong convective storms. Overshooting cloud top (OT) is the product [...] Read more.
A strong convective storm is a disastrous weather system with a small spatio-temporal scale. It often occurs suddenly and can cause huge disasters. Thus, it is necessary to improve the forecast accuracy of strong convective storms. Overshooting cloud top (OT) is the product of strong updrafts in convective storms, which can penetrate the tropopause and enter the lower stratosphere. OT is closely related to severe weather and can influence water vapor transport and the material exchange between the troposphere and stratosphere. Therefore, the timely detection of OT can help improve the accuracy of forecasting. In this study, we develop a new objective OT detection algorithm based on geostationary satellite observations from 2006 to 2017. The accuracy of the new algorithm in identifying OT is verified by manually comparing it with the radar echo images and the cloud images of MODIS 250 m. Then, the OT is statistically analyzed in a long time series. It is found that OT events are mainly concentrated in equatorial and low latitude regions, with higher frequency in summer. There are obvious differences between OT events on land and sea. Additionally, this dataset also reveals the close connection between the seasonal shift of OT and the seasonal average precipitation distribution around the globe. This study provides a scientific basis for determining the geographical characteristics of OT frequency and explores the application of this OT objective detection algorithm in the operational forecast of strong convective weather. We hope this study can benefit OT monitoring in operational weather forecasting. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
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